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you will use the copy only for the purposes of research or private study you will recognise the author's right to be identified as the author of the thesis and due acknowledgement will be made to the author where appropriate you will obtain the author's permission before publishing any material from the thesis. Novel markers for drought resistance in white clover
A thesis by manuscript
submitted in partial fulfilment
of the requirements for the Degree of
Doctor of Philosophy
at
Lincoln University
by
Wouter Leonard Ballizany
Lincoln University
2011
© Wouter L. Ballizany 2012
Thesis by Manuscript
This thesis by manuscript is built around a set of peer-reviewed published
manuscripts, or manuscripts prepared for submission.
Abstract
Abstract of a thesis by manuscript submitted in partial fulfilment of the
requirements for the Degree of Doctor of Philosophy.
Novel markers for drought resistance in white clover
by
Wouter Leonard Ballizany
Introduction. White clover (Trifolium repens L.) is an out-crossing allotetraploid (2n=4x=32) that acts
like a diploid, also called amphidiploid. White clover is a forage crop with a high level of genetic
heterogeneity, which allows it to adapt to a wide range of environments. However, growth of white
clover is often impaired by summer drought. New cultivars need to adapt to higher levels of abiotic
stress to survive and persist. Recent research in white clover populations demonstrated that levels of
a secondary metabolite, glycosides of the phenolic compound quercetin (Q), were positively
associated with abiotic stress resistance, and inversely related to dry matter (DM) production.
Molecular plant breeding methods using DNA markers and quantitative trait loci (QTL) analysis can
increase breeding efficiency using information on genotype to complement phenotype, and identify
genes controlling complex traits.
Aims. The objectives of this study were (i) to investigate genetic control of the complex traits Q
glycosides and DM production in contrasting environments, (ii) to test the inverse correlation
between Q glycosides and DM production, and (iii) to identify DNA markers associated with
quantitative trait loci (QTL) for use in marker-assisted selection (MAS).
Materials and methods. An F1 mapping population (n = 190) created from a paired cross between
two highly heterozygous white clover genotypes sampled from New Zealand cultivar “Kopu II” and
Chinese ecotype “Tienshan” was used for phenotyping and genotyping. Phenotyping consisted of
three parts: 1) in pots under outside conditions in separate pilot and phenotyping experiments, 2) in
pots outside under imposed water deficit, and 3) in the field in two environments contrasting in soil
moisture. Important traits were measured, including morphological, e.g. root DM, shoot DM, total
DM, root-to-shoot DM ratio, stolon density, leaf size; biochemical, glycoside levels of the flavonols
quercetin and kaempferol (K), Q:K glycoside ratio (QKR); as well as physiological, e.g. water potential,
iii
stomatal conductance and carbon isotope discrimination. Genotyping was investigated by means of
single sequence repeat (SSR) markers and diversity array technology (DArT) markers from the
genomes of Trifolium repens, Trifolium occidentale and Medicago truncatula.
Results. Phenotyping in pot studies revealed sufficient genetic variation in the population, and only a
weak albeit significant (P< 0.001) correlation between dry matter yield and Q, with levels of one
explaining less than 10% of variation in the other. Imposed water deficit decreased leaf water
potential by more than half overall, Q glycosides increased more than twofold and the ratio of
quercetin to kaempferol glycosides increased preferentially towards Q. Furthermore, the most
productive genotypes in the controls showed the greatest proportional reduction, the root:shoot
ratio increased by half and individuals with high QKR levels reduced their biomass least under water
deficit, and in turn increased their Q glycosides and QKR most. In the field studies, water deficit
significantly reduced carbon isotope discrimination, Q glycosides were preferentially synthesised
over K glycosides, accumulation of Q glycosides was related to retaining biomass, and stolon density
was inversely related to stolon numbers. Genotyping resulted in a large number of DNA markers
associated with genome regions influencing traits in the population. QTL effects were consistent over
pot- and field experiments in some cases. Suitable markers were used to build a partial linkage map
to better test for chromosomal locations containing QTLs associated with the traits of interest.
Interaction between Q glycosides and DM QTLs was analysed.
Discussion and conclusions. The data show, for the first time, that at the individual genotype level
increased Q glycoside accumulation in response to water deficit is associated with retaining higher
levels of biomass production. These findings are the first indication that forage populations that are
both high yielding and show high Q glycoside levels are possible. The single locus discoveries of
marker-trait associations provide a basis for enhanced plant breeding efficiency for these traits, and
specific sources of new variation for economically significant traits. Overall, the findings can be used
to better understand the physiological underpinnings of water stress relations in forage plants, and
improve gain from selection in white clover by increasing the precision with which improved plants
and populations are identified.
Keywords: white clover, Trifolium repens, biomass, dry matter, herbage yield, quercetin, kaempferol,
flavonoids, abiotic stress, drought resistance, molecular markers, linkage & QTL analysis, plant
breeding, genetic variation, heritability
iv
Acknowledgements
I want to express my gratitude to the following people who helped me realising this Ph.D. study:
Dr. Rainer Hofmann (Lincoln University) as supervisor, Dr. Chris Winefield (LU) as associate supervisor
and Dr. Richard Sedcole (LU) for biometric advice; Brent Barrett M.Sc. (AgResearch) and Dr. Alan
Stewart (PGG Wrightson Seeds) as external advisors;
Dr. Zulfi Jahufer, Dr. Andrew Griffiths and Dr. Marty Faville (all AgResearch) for intellectual support;
Dr. Benjamin Franzmayr, Greig Cousins, Jana Schmidt, Brandi-Lee Carey, Keith Widdup, Ben Harvey
and Shirley Nichols (all AgResearch), Brent Richards, Stephen Stilwell, Rosy Tung and Jenny Zhao (all
LU), Margreet Gutker, Sarena Che Omar, Lydia-Beth Disseveld, Amber Parker, Jackie Sammonds and
Shravani Mudumbai for technical support.
I specifically want to thank my partner of 20 years, Margreet Gutker, for supporting my choice to
pursue a Ph.D., for her encouragement, her sacrifice in her own career, her kindness and her
patience.
This research was made possible with a fellowship from the New Zealand Foundation for Research
Science and Technology (contract number PGGS0801) and funds made available by AgResearch
Grasslands Research Centre, Palmerston North, NZ.
v
vi
Table of Contents
Abstract ...................................................................................................................................... iii
Acknowledgements ...................................................................................................................... v
Table of Contents ........................................................................................................................vii
List of Tables ............................................................................................................................... xi
List of Figures ..............................................................................................................................xii
List of Equations ......................................................................................................................... xiv
Chapter 1 Introduction ................................................................................................................. 1
1.1 Background and knowledge gap .................................................................................................... 2
1.1.1 White clover and pastoral systems ................................................................................... 2
1.1.2 Flavonoids ......................................................................................................................... 2
1.1.3 Marker-assisted selection ................................................................................................. 3
1.1.4 Knowledge gap and aims .................................................................................................. 3
1.1.5 Resources .......................................................................................................................... 4
1.2 Hypotheses and objectives ............................................................................................................ 4
1.2.1 Hypotheses tests ............................................................................................................... 4
1.2.2 Experimental objectives .................................................................................................... 5
Soil-mix test under drought ....................................................................................... 7
Pilot experiment ........................................................................................................ 7
Phenotyping experiment in pots................................................................................ 7
Wilting experiment .................................................................................................... 7
Drought experiment in pots....................................................................................... 7
Phenotyping experiment in the field ......................................................................... 8
Genotyping and linkage analysis ............................................................................... 8
1.3 Thesis structure.............................................................................................................................. 8
Chapter 2 Review of the literature .............................................................................................. 11
2.1 White clover (Trifolium repens L)................................................................................................. 12
2.1.1 Morphology ..................................................................................................................... 12
2.1.2 Ecology ............................................................................................................................ 13
2.1.3 Physiology ....................................................................................................................... 14
2.1.4 Plant breeding ................................................................................................................. 14
Breeding Programs .................................................................................................. 15
Kopu II Synthetic Cultivar......................................................................................... 15
Tienshan Ecotype ..................................................................................................... 16
2.2 Abiotic Stress................................................................................................................................ 17
2.2.1 Types of abiotic environmental stress ............................................................................ 18
2.2.2 Water deficit stress and soil water availability ............................................................... 18
Stress conditions in the root zone............................................................................ 21
Crop water use and evapotranspiration.................................................................. 23
2.3 Physiology of Drought .................................................................................................................. 24
2.3.1 Water productivity and yield ..........................................................................................25
vii
2.3.2
2.3.3
2.3.4
2.3.5
2.4
2.5
Drought-induced stress and drought resistance terms .................................................. 26
Water use efficiency ....................................................................................................... 28
Crop water productivity versus drought resistance........................................................ 29
Plant responses to water deficit and effects on growth ................................................. 32
Effects of water deficit in plants over time .............................................................. 33
2.3.6 Whole crop responses to water deficit ........................................................................... 34
2.3.7 Plant adaptation to dry environments ............................................................................ 35
Flavonoid biochemistry ................................................................................................................ 35
2.4.1 Secondary metabolites ................................................................................................... 35
2.4.2 Flavonoids ....................................................................................................................... 36
2.4.3 Flavonols ......................................................................................................................... 38
Quercetin as a biochemical response to UV-B stress............................................... 38
Quercetin and its association with yield .................................................................. 39
2.4.4 Flavonoids in white clover ecotypes ............................................................................... 40
2.4.5 Flavonoids and drought-induced stress .......................................................................... 40
Plant breeding .............................................................................................................................. 41
2.5.1 Traditional plant breeding .............................................................................................. 41
2.5.2 Quantitative genetics ...................................................................................................... 43
Heritability ............................................................................................................... 43
Crossing and Mendelian genetics ............................................................................ 44
2.5.3 Breeding for abiotic stress resistance ............................................................................. 45
2.5.4 Molecular plant breeding................................................................................................ 47
Molecular markers .................................................................................................. 47
Quantitative Trait Loci ............................................................................................. 48
Genetic mapping ..................................................................................................... 49
Marker-assisted breeding........................................................................................ 50
Chapter 3 Pilot experiment: Phenotypic trait variation in novel white clover F1 progeny............... 54
3.1 Introduction ................................................................................................................................. 55
3.2 Materials and methods ................................................................................................................ 55
3.2.1 Plant material .................................................................................................................. 55
A New White Clover Hybrid ..................................................................................... 55
3.2.2 Experimental design ........................................................................................................ 58
3.2.3 Soil mix test under drought............................................................................................. 58
3.2.4 Measurements ................................................................................................................ 58
3.2.5 HPLC Analysis .................................................................................................................. 59
3.2.6 Statistical Analysis ........................................................................................................... 60
3.3 Results .......................................................................................................................................... 61
3.4 Discussion..................................................................................................................................... 68
Chapter 4 Phenotyping experiment: Phenotypic trait variation in novel white clover F1 progeny .. 72
4.1 Introduction ................................................................................................................................. 73
4.2 Materials and methods ................................................................................................................ 74
4.2.1 Plant material .................................................................................................................. 74
4.2.2 Experimental design ........................................................................................................ 74
4.2.3 Measurements ................................................................................................................ 74
4.2.4 Quantitative flavonoid analysis with HPLC ..................................................................... 75
4.2.5 Statistical analyses .......................................................................................................... 75
4.3 Results .......................................................................................................................................... 76
4.4 Discussion..................................................................................................................................... 78
viii
Chapter 5 Journal article: “Multivariate associations of flavonoid and biomass accumulation in
white clover (Trifolium repens) under drought” ........................................................................... 80
5.1 Abstract ........................................................................................................................................ 81
5.2 Additional keywords .................................................................................................................... 81
5.3 Introduction ................................................................................................................................. 81
5.4 Materials and methods ................................................................................................................ 83
5.4.1 Plant material, experimental design and treatment conditions ..................................... 83
5.4.2 Measurements ................................................................................................................ 85
5.4.3 Analysis of flavonoids by high performance liquid chromatography (HPLC).................. 86
5.4.4 Statistical analyses .......................................................................................................... 86
5.5 Results .......................................................................................................................................... 89
5.5.1 Results soil dehydration test ........................................................................................... 89
5.5.2 White clover population germplasm source effects ....................................................... 89
5.5.3 Physiological effects of drought ...................................................................................... 91
5.5.4 Variance and heritability ................................................................................................. 92
5.5.5 Percent change trait correlations ................................................................................... 94
5.5.6 Multivariate trait responses to drought across treatments ........................................... 95
5.5.7 Multivariate trait responses to drought percent changes between treatments............ 96
5.5.8 Plant traits linked to the principal water deficit measure Δ% PC1 ................................. 97
5.6 Discussion................................................................................................................................... 101
5.6.1 Responses to drought ................................................................................................... 101
5.6.2 Heritability of traits ....................................................................................................... 102
5.6.3 Trait correlations and plant breeding perspective........................................................ 102
5.6.4 Multivariate analyses .................................................................................................... 102
5.6.5 Cluster analyses............................................................................................................. 103
5.6.6 Plant traits linked to the principal water deficit measure Δ% PC1 ............................... 103
5.6.7 Metabolic implications of flavonol activity ................................................................... 103
5.6.8 Conclusions ................................................................................................................... 104
Chapter 6 Journal article: “Genotype × Environment analysis of flavonoid accumulation and
morphology in white clover under contrasting field conditions” .................................................106
6.1 Abstract ...................................................................................................................................... 107
6.1.1 Keywords ....................................................................................................................... 107
6.2 Introduction ............................................................................................................................... 107
6.3 Materials & methods ................................................................................................................. 110
6.3.1 Field preparation ........................................................................................................... 110
6.3.2 Plant Material................................................................................................................ 112
6.3.3 Experimental design ...................................................................................................... 113
6.3.4 Measurements .............................................................................................................. 114
Soil moisture content ............................................................................................. 114
Carbon isotope discrimination............................................................................... 114
Morphology ........................................................................................................... 117
6.3.5 Analysis of flavonols by high performance liquid chromatography (HPLC) .................. 117
6.3.6 Statistical analysis ......................................................................................................... 118
6.4 Results ........................................................................................................................................ 119
6.4.1 Physiological traits ........................................................................................................ 120
6.4.2 Morphological and biochemical traits .......................................................................... 120
6.4.3 Heritability..................................................................................................................... 121
6.4.4 Univariate analyses and correlations ............................................................................ 123
6.4.5 Multivariate analyses .................................................................................................... 127
6.4.6 Cluster analyses............................................................................................................. 130
ix
6.5
6.4.7 Trait correlations with across-environment PC1 .......................................................... 131
Discussion................................................................................................................................... 132
6.5.1 Physiology ..................................................................................................................... 132
6.5.2 White clover morphology ............................................................................................. 132
6.5.3 Biochemistry ................................................................................................................. 133
6.5.4 Heritability..................................................................................................................... 133
6.5.5 Univariate analyses and correlations ............................................................................ 134
6.5.6 Multivariate analyses .................................................................................................... 134
6.5.7 Cluster analyses............................................................................................................. 135
6.5.8 Plant traits associated with to the across-environment PC1 ........................................ 135
6.5.9 Conclusions ................................................................................................................... 135
Chapter 7 Genetic mapping of flavonoids, biomass production and morphological attributes
under water deficit in white clover (Trifolium repens L.) .............................................................138
7.1 Abstract ...................................................................................................................................... 139
7.2 Introduction ............................................................................................................................... 139
7.3 Materials and methods .............................................................................................................. 144
7.3.1 Plant material and DNA extraction ............................................................................... 144
7.3.2 Genotyping and genetic linkage mapping..................................................................... 144
Overview of DArT technology ................................................................................ 146
7.3.3 Statistical analyses ........................................................................................................ 147
7.4 Results ........................................................................................................................................ 147
7.4.1 Selective DNA pooling ................................................................................................... 147
7.4.2 Genetic linkage mapping............................................................................................... 149
7.4.3 Marker-trait associations .............................................................................................. 152
7.4.4 Common markers for multiple traits and parents ........................................................ 161
7.5 Discussion................................................................................................................................... 162
7.5.1 Quantitative genetic analysis ........................................................................................ 163
7.5.2 QTL effects and interactions ......................................................................................... 164
7.5.3 Breeding perspectives ................................................................................................... 167
7.5.4 Conclusion ..................................................................................................................... 168
Chapter 8 Overall Discussion ......................................................................................................170
8.1 Summary of findings relative to research objectives ................................................................ 171
8.2 Similarities and contrasts between experiments ...................................................................... 173
8.3 Context and limitations of the findings ..................................................................................... 174
8.4 Recommendations for further investigation ............................................................................. 175
References ................................................................................................................................178
Appendix A Terms of reference ..................................................................................................202
A.1 Stress tolerance in forage legumes - white clover quercetin genetics investigation (Hofmann
& Barrett) ................................................................................................................................... 202
Appendix B Evapotranspiration ..................................................................................................204
B.1 Potential versus reference evapotranspiration ......................................................................... 205
B.2 FAO crop evapotranspiration concepts ..................................................................................... 209
Appendix C Silt loam with gypsum and wetting agent .................................................................212
x
C.1
C.2
C.3
C.4
Potting mix ................................................................................................................................. 212
Soil texture triangle.................................................................................................................... 213
Gypsum addition to silt loam ..................................................................................................... 213
Hydraflo addition to the soil mix ............................................................................................... 214
C.4.1 (Scotts Australia Pty Ltd © 2003)Wetting agent benefits ............................................ 214
Appendix D The flavonoid biochemical pathway.........................................................................215
Appendix E Metabolon formation ..............................................................................................217
Appendix F Experimental designs ...............................................................................................218
F.1 Design pilot experiment ............................................................................................................. 218
F.2 Design phenotyping experiment................................................................................................ 219
F.3 Design drought experiment ....................................................................................................... 220
F.3.1 Plant material ................................................................................................................ 220
F.4 Design field experiment ............................................................................................................. 222
Appendix G Soil fertility analysis reports ....................................................................................225
Appendix H Crossing genotypes .................................................................................................226
H.1 Introduction ............................................................................................................................... 227
H.2 Materials & methods ................................................................................................................. 227
H.2.1 Plant material ................................................................................................................ 227
H.2.2 Crossing method ........................................................................................................... 227
H.3 Results ........................................................................................................................................ 231
H.4 Discussion................................................................................................................................... 232
Appendix I Genetic analysis .......................................................................................................237
I.1
Selective DNA pooling, PCR and genotyping protocols ............................................................. 237
I.1.1 DNAzol Clover DNA extraction ...................................................................................... 237
I.1.2 Selective DNA pooling ................................................................................................... 237
I.1.3 Polymerase Chain Reaction (PCR) in thermal cycler ..................................................... 238
I.1.4 Genetic analyser or genotyper...................................................................................... 239
I.1.5 Markers and primers ..................................................................................................... 239
Appendix J Published papers......................................................................................................241
List of Tables
Table 1.1
Table 3.1
Table 4.1
Table 5.1
Table 5.2
Table 5.3
Presentation of the defence of the research. ..................................................................... 8
BLUP estimates for the trait means in the pilot experiment............................................. 62
BLUP means for the traits in the phenotyping experiment............................................... 76
Trait means across 32 white clover full-sibling F1 progeny (Pr) and the means of their
parents Kopu II (K) and Tienshan (T).................................................................................. 90
Trait means and water deficit-induced percent change. .................................................. 92
Within-treatments genotypic (𝝈𝒈²), and experimental error (𝝈𝜺²) variance
components, standard errors (±SE) and broad sense heritability (Hb). ............................. 93
xi
Table 5.4
Across-treatments genotypic (𝝈𝒈²), genotype-by-treatment interaction (𝝈𝒈. 𝒕²), and
experimental error (𝝈𝜺²) components of variance. .......................................................... 94
Table 5.5 Correlation coefficients (r) and probabilities P for the drought-induced percent
change (Δ%) ....................................................................................................................... 98
Table 6.1 Trait means of 190 white clover full-siblings and two parent genotypes across two
environments, and environment-induced percent changes (Δ%) in 10 traits. ............... 121
Table 6.2 Within individual environment genotypic (𝝈𝒈²), and experimental error (𝝈𝜺²)
components of variance. ................................................................................................. 122
Table 6.3 Across environment genotypic (𝝈𝒈²), genotype × environment (G×E) interaction
(𝝈𝒈. 𝒆²), and experimental error (𝝈𝜺²) components of variance. ................................. 123
Table 6.4 Correlation coefficients (r) and probabilities (P) for nine plant traits linked to the first
two principal components (PC1, PC2) of the across-environment (G×E) PCA. ............... 132
Table 7.1 Trait means in the high and low bulk DNA pools used in selective DNA pooling............ 148
Table 7.2 Markers that showed polymorphism (allelic variation) in both parents and in progeny
bulks. ................................................................................................................................ 148
Table 7.3 Marker alleles associated with the field experiment biochemical traits. ....................... 155
Table 7.4 Marker alleles associated with field experiment dry matter traits. ................................ 156
Table 7.5 Marker alleles associated with the field experiment stolon traits (1/2). ........................ 157
Table 7.6 Marker alleles associated with the field experiment stolon traits (2/2). ........................ 158
Table 7.7 Marker alleles associated with the field experiment leaf size traits. .............................. 159
Table 7.8 Marker alleles associated with the field experiment canopy traits. ............................... 160
Table 7.9 Marker alleles associated with the phenotyping pot experiment traits. ........................ 161
Table 7.10 Alleles associated with more than one trait across two harvests, averaged across two
environments in the field experiment. ............................................................................ 162
List of Figures
Structure of the mature plant of white clover (Trifolium repens L.) ................................. 13
Yaks grazing on the Tian Shan, Xinjiang, China. [ Photo by Doron / CC BY-SA 3.0 ] ......... 16
The hydrologic cycle (Hillel 2004). ..................................................................................... 19
The water balance of a root zone (Hillel 2004) ................................................................. 20
Soil water availability depending on soil texture (Ehlers and Goss 2003)......................... 21
Relationship of soil moisture to transpiration rate, plant productivity, and capillary
potential. ........................................................................................................................... 23
Figure 2.7 The subdividing of evapotranspiration into evaporation and transpiration over the
growth cycle of an annual field crop (Allen et al. 1998). ................................................... 24
Figure 2.8 Plant drought resistance mechanisms. (Adapted from Levitt (1980)). ............................. 28
Figure 2.9 A diagram for water productivity terms and components (Kijne et al. 2003). ................. 30
Figure 2.10 Structure, biosynthesis and oxidation of flavonols (Pourcel et al. 2007). ......................... 37
Figure 2.11 Marker development ‘pipeline’ (Collard and Mackill 2008). ............................................. 52
Figure 3.1 Examples of phenotypes of Kopu II, Tienshan and their pair-crossed F1 progeny. .......... 56
Figure 3.2 Two phenotypes of two TxK F1 progeny showing large variation for vigour. .................... 56
Figure 3.3 The distinctive phenotypic differences in roots and shoots between F1 progeny and
parents. (The white label measures 128 mm in length). ................................................... 57
Figure 3.4 Comparison of phenotypic variation of quercetin glycosides (Q) and shoot dry matter
(SDM) between parental genotypes Tienshan and Kopu II, and their F1 progeny
genotypes KxT. Error bars are SEM, n=5. .......................................................................... 63
Figure 3.5 Phenotypic variation in root:shoot ratio (RSR). ................................................................. 64
Figure 3.6 Phenotypic variation for root- (RDM) and shoot dry matter (SDM) together making up
total DM (TDM). ................................................................................................................. 65
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
xii
Figure 3.7
Figure 3.8
Figure 3.9
Figure 4.1
Figure 4.2
Figure 4.3
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Figure 5.10
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12
Figure 7.1
Figure 7.2
Figure 8.1
Phenotypic variation for the flavonol glycosides quercetin (Q), kaempferol (K) and the
Q:K ratio. Error bars are SEM, n=5..................................................................................... 66
Phenotypic variation for petiole length and primary stolon length. Error bars are SEM,
n=5. .................................................................................................................................... 67
PCA biplot summarising correlations between nine traits for the 10 F1 progeny
genotypes. ......................................................................................................................... 68
Traits for the F1 progeny and parents. .............................................................................. 76
Correlation between the traits quercetin glycosides (Q) and shoot dry matter (SDM)
for the F1 progeny and parent (not indicated) genotypes. ................................................ 77
Biplot from PCA of seven traits for the F1 progeny including parental (not indicated)
genotypes. ......................................................................................................................... 78
Close-up of a well-watered sub-plot in the drought experiment. ..................................... 84
A water-limited sub-plot in the drought experiment. ....................................................... 85
Soil moisture content per pot (averaged over 10 pots) over nine days of pot-soil
drying. ................................................................................................................................ 89
Root:shoot ratio (RSR) means of the white clover parents Kopu II and Tienshan, and
their full sib F1 progeny. ..................................................................................................... 91
White clover dry matter response under water deficit. .................................................... 95
Biplot representing 34 white clover genotypes evaluated across the well watered and
water deficit treatments.................................................................................................... 96
Delta biplot representing 34 white clover genotypes, evaluated for the percent
change (Δ%) between the well watered and water deficit treatments. ........................... 97
Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and total dry matter (TDM) production. ........................................................................... 99
Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and QKR in the well watered treatment.......................................................................... 100
Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and QKR in the water deficit treatment. ......................................................................... 101
The experimental site at the AgResearch farm in January 2010. .................................... 110
The experimental site at Ashley Dene dryland farm in January 2010. ............................ 111
Selecting cloned white clover genotypes for planting in the field. ................................. 112
Weeding was an important part of experiment maintenance. ....................................... 114
Volumetric soil moisture (%) measured by TDR in the water-limited (TDRWL) and
water-sufficient (TDRWS) environments. ........................................................................ 120
Phenotypic correlation between quercetin glycosides (Q) accumulation and shoot dry
matter (SDM). .................................................................................................................. 124
Phenotypic correlation between quercetin glycosides (Q) accumulation and shoot dry
matter (SDM). .................................................................................................................. 125
Phenotypic correlation between water deficit growth response (shoot dry matter
(SDM) production in the water-limited (WL) environment. ............................................ 126
Phenotypic correlation between water deficit growth response (shoot dry matter
(SDM) production in the water-limited (WL) environment. ............................................ 127
Water-sufficient environment biplot generated from principal component analysis. ... 128
Water-limited environment biplot generated from principal component analysis. ....... 129
Across-environment biplot generated from principal component analysis.................... 130
Kopu II (KII) maternal partial linkage map with four linkage groups. .............................. 150
Tienshan (T) paternal partial linkage map with six linkage groups. ................................ 151
Conceptual model linking morphological, physiological, biochemical and genetical
responses in white clover to water deficit stress. ........................................................... 173
xiii
List of Equations
Equation 2.1 De Wit’s equation ............................................................................................................. 29
Equation 2.2 Transpiration water productivity. ..................................................................................... 30
Equation 2.3 Evapotranspiration water productivity............................................................................. 30
Equation 2.4 Irrigation water productivity............................................................................................. 30
Equation 2.5 Passioura’s yield model. ................................................................................................... 31
Equation 2.6 Leaf Transpiration and Water Vapor Gradients (Taiz and Zeiger 2006). .......................... 31
Equation 5.1 The linear model used in the analysis across environments. ........................................... 87
Equation 5.2 Broad-sense heritability within treatments...................................................................... 88
Equation 5.3 Broad-sense heritability across treatments...................................................................... 88
Equation 6.1 Molar ratio 13C/12C in CO2. .............................................................................................. 115
Equation 6.2 Carbon isotope ratio δ13C deviation from a standard. ................................................... 115
Equation 6.3 Isotope effect, also called fractionation factor............................................................... 115
Equation 6.4 The measure of the carbon isotope discrimination by the plant. .................................. 115
Equation 6.5 Deviation of the isotopic composition of the air from a standard. ................................ 116
Equation 6.6 Deviation of the isotopic composition of the plant from a standard. ............................ 116
Equation 6.7 Carbon isotope discrimination in the plant sample........................................................ 116
Equation 6.8 Water use efficiency as calculated from ∆. .................................................................... 116
Equation 6.9 Broad-sense heritability within environments ............................................................... 118
Equation 6.10 Broad-sense heritability across environments ............................................................. 119
xiv
Chapter 1
Introduction
Lovely helpers at harvest time for dry matter at the nursery, 8 April 2009.
1
1.1 Background and knowledge gap
1.1.1 White clover and pastoral systems
Productivity of pastoral systems depends on persistence of legumes in grasslands, particularly under
adverse summer conditions (Woodfield et al. 1996; Humphreys 2005; 2007). White clover (Trifolium
repens L.) is an important temperate perennial forage crop with outstanding nutritional value
(Woodfield and Easton 2004) and ability to fix atmospheric nitrogen (Andrews et al. 2007). Since it is
an out-crossing allotetraploid species (2n=4x=32) with high levels of genetic heterogeneity both
within and among populations, breeding for complex traits such as abiotic stress resistance and yield
is slow and costly (Woodfield and Caradus 1994; Jahufer et al. 2002).
1.1.2 Flavonoids
Flavonoids have been associated with multiple stress tolerance in plants.. VThese secondary
metabolites display several functions in planta, such as UV-screening, energy dissipation, antioxidant
and herbivore-deterring capacities (Izaguirre et al. 2007; Kuhlmann and Muller 2009; O'Neill et al.
2010). Hofmann et al. (2000) found that certain flavonoid derivatives were involved in the response
of nine populations of white clover to UV-B radiation. They found a converse association between
plant productivity and quercetin glycoside (Q) accumulation. Furthermore, higher quercetinglycoside accumulation under UV-B radiation was correlated with tolerance against UV-B-induced
biomass reduction. In other studies (Hofmann et al. 2003a; Hofmann and Campbell 2011) where UVB was combined with drought, it was shown that, under well-watered conditions, a high level of Q
glycosides was associated with both lower plant production and higher UV-B tolerance. Exposure to
higher levels of UV-B radiation or drought increased the synthesis of flavonones and flavonols.
Smaller T. repens ecotypes primed by other stresses, had a higher capacity for physiological
adaptation to UV-B, and these traits also added to a diminished UV-B sensitivity under water-deficit
induced stress.
On the basis of these population studies, high levels of Q glycosides appeared to be associated with
high levels of abiotic stress tolerance, and low levels of productivity. It was shown in a later white
clover population experiment that this observed relationship was associative (Hofmann and Jahufer
2011), a trade-off between biomass and flavonoid accumulation, which reflects contrasting plant
strategies (Grime 2001). This is an unintended and deleterious by-product of natural and artificial
selection processes. Plant breeders may be able to reverse these relationships and develop forages with
high levels of Q-mediated abiotic stress resistance and high plant biomass production. Formulating an
ideotype with detailed morphological, physiological and developmental characteristics for a target
environment would be a challenging but rewarding task for plant breeders. Complementing ecophysiological modelling with a marker-aided breeding strategy to develop ideotypes tailored for
2
specific environmental parameters is seen as the approach that most likely meets that challenge for
the future (Yin et al. 2003; Abberton and Marshall 2005; Clark et al. 2007; Williams et al. 2007).
1.1.3 Marker-assisted selection
Molecular markers are used to choose desired traits based on genotype rather than phenotype and
can complement and accelerate breeding programs (Barrett et al. 2001; Forster et al. 2001). Barrett
et al. (2004a) developed simple sequence repeat (SSR) markers by mining an expressed sequence tag
(EST) database and by isolation from enriched genomic libraries. They detected 493 loci in the white
clover genome from 335 polymorphic EST-SSR markers and 30 genomic SSRs in 92 F1 progeny that
originated from a pair-cross between two highly heterozygous genotypes (2-way pseudo-testcross F1
population). The total map length was 1,144 centiMorgan (cM) and the SSRs spanned all 16
homoeologues. This microsatellite map will help resolving quantitative traits into Mendelian
characters (Barrett et al. 2004a); elucidate chromosomal positions of key loci controlling yield and
other important traits through marker-trait associations, and accelerate white clover improvement
programmes (Faville et al. 2003).
1.1.4 Knowledge gap and aims
While the role of flavonoids in UV-B protection is well established, less is known about the impact of
these compounds on drought response. No information is available on testing white clover
genotypes under drought-induced stress for both dry matter (DM) production and flavonoids. There
is also no literature on markers and QTLs for these traits that could inform a marker-assisted
selection (MAS) breeding strategy.
This project aimed to discover molecular markers for the polygenic trait DM production (yield) and
Q-glycoside accumulation (Mehrtens et al. 2005), and to investigate associative relationships of
flavonoids with plant DM production and related traits. The work was first undertaken under
normative conditions, then under drought treatment and was subsequently carried out under
limiting field conditions at two sites. A plant family with known genetic structure was used to
observe the inheritance of flavonoids and biomass production from a parental to a progeny
generation, as well as use DNA markers to define regions of the white clover genome regulating
flavonoids and biomass production. In order to examine the potential for breeding white clover for
these traits, a program of experiments, tests and analyses in a full-sibling population have been
developed and are presented here.
A mapping population consisting of full sib F1 progeny was created from a pair cross between two
morphologically contrasting genotypes sampled from: 1) the productive New Zealand synthetic
cultivar of Trifolium repens ‘Kopu II’ accession C20133 (Woodfield et al. 2001); and 2) the stress3
resistant T. repens ‘Tienshan’ accession C8749, a Chinese high altitude ecotype originating in the Tien
Shan Mountain range (Hofmann et al. 2000). Accession numbers are from the Margot Forde
Germplasm Centre in Palmerston North, New Zealand.
A series of phenotyping and genotyping experiments was designed using three main criteria to
characterise genotypes: 1) levels of genetic variation for flavonoids, DM yield and related traits, with
high levels preferred; 2) evidence for or against breakage of a negative correlation between
flavonoids and DM, with breakage being preferred; 3) identifying plants that harbour QTL for
flavonoids and DM, as identified by linked molecular markers.
1.1.5 Resources
Resources required for this project were available at the Lincoln University (LU) Nursery, LU Field
Service Centre (FSC), LU Plant Science Lab, LU HPLC Lab and the Plant Molecular Genetics Labs at
Grasslands Research Centre, AgResearch, Palmerston North.
1.2 Hypotheses and objectives
We hypothesised that flavonoids would be associated with biomass drought response in a novel
white clover population, analogous to its association with UV-B plant response. Furthermore, we
hypothesised that the capacity of white clover plants to withstand periods of drought may possibly
be related to its genetic background, specifically in stress-resistant ecotypes originating from remote
areas.
1.2.1 Hypotheses tests
Hypothesis 1: Genetic variation for flavonoids among white clover genotypes.
The null hypothesis (H0) is that the genetic variation for flavonoids among genotypes is zero.
The alternative hypothesis (Ha) is that the genetic variation for flavonoids among genotypes is
significantly different from zero.
Hypothesis 2: Genetic variation for dry matter (DM) among white clover genotypes.
H0 is that the genetic variation for DM among genotypes is zero.
Ha is that the genetic variation for DM among genotypes is significantly different from zero.
Hypothesis 3: Correlation between flavonoids and DM.
4
Assumptions are normality and homogeneity of variance in the range of possible values of one
variable at each value of the other variable (Snedecor and Cochran 1989).
H0 is that the coefficient of correlation (r) between flavonoids and DM is not significantly (alpha = .05)
different from zero (no correlation);
Ha is that r between flavonoids and DM is significantly different from zero (two-tailed test):
H0 is that r between flavonoids and DM is not significantly positive*;
Ha is that r between flavonoids and DM is significantly positive.
H0 is that r between flavonoids and DM is not significantly negative;
Ha is that r between flavonoids and DM is significantly negative*.
H0 is that r2 does not explain more than 75 % (P = 0.01) of the co-variation between flavonoids and
DM (flavonoids account for 75 % of the variability in DM scores in Hofmann et al. (2000));
Ha is that r2 explains 75 % (P = 0.01) or more of the co-variation between flavonoids and DM*.
*Together, these hypotheses would confirm the findings of Hofmann et al. (2000) among
populations. Acceptance of the other hypotheses would mean there is evidence for breakage of the
negative correlation between flavonoids and DM.
Hypothesis 4: Genetic markers linked to the major genetic factors (QTLs) that determine levels of
flavonoids and DM.
H0 is that QTLs that determine levels of flavonoids and DM cannot be identified by genetic markers.
Ha is that QTLs that determine levels of flavonoids and DM can be identified by genetic markers.
1.2.2 Experimental objectives
This Ph.D. project, funded by a FRST Technology Fellowship, targets delivery of three commercially
relevant outcomes: Verification of a new breeding target leading to forage cultivars combining high
yield and high Q glycoside levels, including demonstrated impacts of resilience to drought; utilisation
of the trait-linked DNA marker resource to increase speed to market and precision of selection; and
development of new plant populations suitable for integration into a range of breeding programmes.
The project will combine the strengths of genetic resources, plant biochemistry, physiology and gene
mapping to characterise the genetic control of biomass production and stress tolerance in a white
clover population / family. Whilst the results will be specific to this studied family, the contrasting
5
provenances of the parents (stress-resistant ecotype vs productive cultivar) enable a wide spectrum
of plant responses in the hybrid offspring and can therefore be used to extrapolate beyond the
family level. A pilot experiment will use two criteria to identify a population that is most suited to this
use. The first criterion is levels of phenotypic variation for the flavonoid quercetin (Q) glycoside and
biomass production, with high levels preferred. The second criterion is evidence (or lack thereof) for
breakage of the correlation between flavonoids and biomass production, with breakage being
preferred.
In the primary experiment, the entire sampled research population will be grown in replicated
experiments, preferably under standard and inductive (i.e. stress) conditions, wherein plant biomass
production and Q glycoside levels will be monitored. Concurrently, individuals from either extreme of
the plant performance spectrum for flavonoids or biomass production will be surveyed with genetic
markers to identify regions of the white clover genome that are putatively linked to changes in plant
performance potential for flavonoids, biomass production, or adaptive response to stress factors.
There are five project objectives consistent with the purposes of this Ph.D. programme:
Objective 1 is the compilation and presentation of a research proposal and a literature review.
Objective 2, a pilot study aiming to identify white clover germplasm suitable for full scale genetic and
biochemical analysis. This will be undertaken in a replicated pot experiment under water-limited and
water-sufficient conditions and will measure morphological (plant biomass production and leaf
characteristics), physiological (e.g. plant water status with a pressure bomb, photosynthesis with the
LICOR 6400 Gas exchange system) and biochemical parameters. The latter will use high-performance
liquid chromatography (HPLC) to identify and quantify Q glycosides.
Objective 3 will apply the key measures of Objective 2 among a larger number of genotypes of the
sampled white clover population under water-limited and water-sufficient conditions in pots and in
contrasting field sites.
Objective 4 will focus on plants traits identified and observed in Objective 3 to identify genome
regions influencing these traits. This objective will be performed utilising expertise and equipment
from the AgResearch Plant Breeding group. A genome-wide scan of DNA markers for allelic
associations will be used to identify markers unique to either high or low- selected bulk.
Objective 5 will result in the submission of a Ph.D. thesis and a report for PGG Wrightson Seeds Ltd.
on the improvement of targets and precision in the cultivar selection process related to drought
tolerance in white clover.
6
Project outcomes will thus include knowledge of the genetic and biochemical factors controlling
plant biomass production, flavonoid levels, and adaptive responses to stress; and new tools for
applied breeding programmes to develop and characterise superior white clover varieties through
selective breeding. It is important to note that the results will be specific to the studied family,
although the contrasting backgrounds of the parents allows for extrapolation beyond the family
level.
Soil-mix test under drought
The purpose of this experiment was to find the best available multi-purpose soil-mix for white clover
moisture stress testing in pots outside, with the following qualities: reduced shrinking or clotting of
the local silt loam, a fine, crumby structure that washes well from the roots, and structure allowing
the establishment of roots throughout the soil-mix.
Pilot experiment
The aims for the pilot study were to expose a population of F1 progeny genotypes to water-sufficient
Canterbury summer conditions in pots, and to determine significant key attributes of the progeny
population: morphology, physiology (DM) & biochemistry (Q) relevant for the phenotyping
experiment, as well as to estimate a range of phenotypic variation in clonal copies of an F1 subset for
flavonoids and DM.
Phenotyping experiment in pots
The phenotyping experiment used information gained from the pilot study, to expose a population of
progeny genotypes to non-stress conditions in pots, to test genotype variation in a replicated
random sample of 130 F1 genotypes for flavonoids and DM, and to select genotypes at a threshold of
around 10 % in each trait of highest and lowest flavonoid and DM levels for DNA bulk samples in
selective genotyping.
Wilting experiment
A number of pots with the same soil mix used in the drought experiment was used to determine field
capacity, wilting point and dry weight of the soil, so that the amount of irrigation water needed in
the drought treatment to maintain a soil moisture level of just above wilting point, could be
established.
Drought experiment in pots
The drought experiment had the purpose of exposing a clonally replicated population of full-sibling
progeny and their parents to drought-induced stress conditions in pots, and to investigate repeatable
phenotypic variation for a range of traits related to abiotic stress.
7
Phenotyping experiment in the field
An experiment in the field was needed as environmental effects are very different from those in pot
experiments. Main aims of this phenotyping experiment were to search for repeatable phenotypic
variation for a range of traits, and associations between the quantitative traits biomass production
and quercetin under adverse climate and soil moisture conditions typical for alluvial soils in a
Canterbury summer.
Genotyping and linkage analysis
In the first instance, DNA analysis focussed on white clover genotypes identified as high and low in
flavonoid accumulation; and genotypes with high and low biomass production. A set of DNA markers
for the genome scan was used to identify marker regions associated with changes in trait means.
These provided starting points for QTL analysis. The aim was to identify potential DNA markers in
bulk DNA samples that were polymorphic in both parents and F1 progeny for flavonoids and biomass
production. After screening nearly 400 molecular markers on all the white clover individuals in the
mapping population, a set of selected SSR markers was used in linkage mapping to identify genome
regions associated with changes (polymorphisms /allelic variation) in trait values in the parents.
These provided selected markers for segregation screening in the progeny, and the marker data was
used for linkage mapping and QTL detection.
This is a step towards understanding the genetic basis for these complex traits. DNA analysis
focussed on the entire available white clover mapping population of 190 F1 for flavonoid
accumulation and biomass production. The ultimate objective is to use the information from this
project for the development of forages with high levels of Q-mediated abiotic stress tolerance and
high plant productivity, thus strongly improving targets and precision in the cultivar selection
process.
1.3 Thesis structure
Table 1.1
Presentation of the defence of the research.
Chapter Title
Abstract
1 Overall
Introduction
2 Literature Review
3-7 Manuscriptbased chapters
8
Purpose
To summarise the research programme and its findings.
It is used for library cataloguing and literature search.
To give an overview of the background and objectives of the thesis.
Summarises what existed prior to the research, the specific contributions of
the project and what existed after the research was completed.
Summarises the mechanisms by which the key researchers and the major
forums for publications of work were identified. Shows the development of a
research methodology and experimentation system based upon the work of
peers.
All manuscript-based chapters have similar structure:
Abstract (for the published chapters 5 and 6)
Chapter Title
3 Pilot Experiment
4 Phenotyping
5 Drought
6 Field
7 Genotyping
8 Overall Discussion
& Conclusion
References
Appendices
Purpose
Introduction; contains a brief background, the problem statement,
hypothesis, goals, objectives, study approach and chapter outline.
Materials & Methods; justifies the chosen methodology and analyses and
outlines the methods and materials used.
Experimental Results; describing findings, i.e., the empirical component.
Discussion; evaluates the findings and their implications in terms of the
literature, theories, etc.
Set up to investigate the possible variance of morphological, physical and
biochemical traits between 10 F1 progeny of the mapping population and
parents, replicated 5 times.
An up-scaled experiment with a sample size of 130 F1 progeny and parents to
investigate variance in the important morphological, physical and
biochemical traits, replicated three times.
To study the variance and heritability of morphological, physical and
biochemical traits in a random selection of 32 F1 progeny under induced
water deficit stress plus control, replicated three times.
To research the variance and heritability of morphological, physiological and
biochemical traits of the whole mapping population of 190 F1 progeny and
their parents in the field in contrasting soil-moisture environments,
replicated three times per location.
Marker genotyping, linkage and QTL analyses carried out to identify markertrait associations and inform marker-assisted selection in white clover
breeding projects.
Integrates the entire thesis, provides a broader context of the experimental
results, and assesses the contribution the research has made to the sum of
knowledge. Summarises the findings relative to the stated research
objectives, highlights any deficiencies in the research, and makes
recommendations how these can be remedied through further investigation.
A detailed listing of all the sources from which knowledge and significant
information was acquired, including the references from all the manuscripts
for submission.
Storage of information which is important to the arguments raised in the
thesis, but because of its detail, length or complexity would otherwise
interrupt the flow of arguments in the thesis. Includes published papers in
full.
After Dr. D.J. Toncich - Key Factors in Postgraduate Research - A Guide for Students (http://www.doctortee.net/files/PHDBK2006-10p.pdf)
9
10
Chapter 2
Review of the literature
What’s on a man’s mind?
11
2.1 White clover (Trifolium repens L)
White clover is New Zealand’s most important forage crop (Woodfield et al. 1996; Stewart et al.
2006). It fixes nitrogen in symbiosis with Rhizobium bacteria in root nodules that later benefits grass
in the pasture, and provides high-quality herbage as a protein-rich feed for grazing animals. Animal
performance improves with increasing clover content in the diet (Hyslop et al. 2000). White clover
performs well in pastoral systems on fertile, moist soils, but persistence of most cultivars is less
satisfactory under dry summer conditions in New Zealand (McManus et al. 2000; Woodward and
Caradus 2000).
2.1.1 Morphology
White clover in a pasture sward has two morphologically dissimilar growth stages, starting with a
tap-rooted phase with out-spreading stolon structures that persist up to two years, after which
follows a clonal growth phase (Caradus and Woodfield 1998). The transition from tap-rooted to
clonal phase ensues when the tap root and main stem die approximately one year after sowing, and
fragmentation is initiated to form several stolon structures as autonomous clonal plants (Figure 2.1).
This is when the shorter secondary root systems on the stolon fragments take over (Brock and
Tilbrook 2000). Because the clonal phase roots are not as deep as the original taproot, white clover
has difficulty persisting under dry summer conditions, and can be lost from pastures (Erith 1924;
Taylor 1985; Baker and Williams 1987). In the field, white clover shows large phenotypic plasticity
due to the ability to adapt to different environmental conditions (Collins et al. 2002).
Morphological attributes such as plant height and -spread, stolon thickness,-branching and -density
and leaf length, often express strong genotypic association with average vegetative yield (Jahufer et
al. 1994). The genotypic variation estimated for vegatative production and other plant traits can
potentially be used to identify sources of variation for the genetic improvement of white clover for
adverse environments (Jahufer et al. 1997).
12
Figure removed because of copy-right issues.
The original image/graph/figure can be found in:
Figure 1.1 by R.G. Thomas, 1987 in Baker and Williams (1987).
Figure 2.1
Structure of the mature plant of white clover (Trifolium repens L.)
Drawing of a main stolon (MS) of white clover, showing axillary buds (AB), lateral branches (LB), and
a lateral stolon (LS). S = stipule; Pe = petiole; RT = nodal root primordium; I = inflorescence; P =
peduncle. Emerged leaves on the main stolon, and the nodes bearing them, are numbered 1 to 8.
(Figure 1.1 by R.G. Thomas, 1987 in Baker and Williams (1987).)
Lane et al. (2000b) found that populations of white clover showed significant difference in
morphological traits such as leaf size and stolon characters conveyed in different environments, and
that significant genetic variation affecting stolon and flowering traits occurred, with consequences
for agronomic value. For persistence breeding in white clover aiming at dry environments, a small
group of populations was found to have useful characteristics (Lane et al. 2000b). Sanderson et al.
(2003) found that biotic and abiotic stresses break plants up into smaller, less competitive
individuals, reducing white clover production in pastures. . Weather and herbivore stresses were the
major aspects influencing fluctuations of white clover stolon density in pastures of the north-eastern
USA (Sanderson et al. 2003).
2.1.2 Ecology
Locally evolved ecological genotypes (ecotypes) of white clover are often found naturalised in
pastures, often persevering in the face of hostile environmental and cultural conditions (Bouton et al.
2005). Because of natural selection for adaptation into a wide range of marginal environments,
ecotypes of white clover can present a suitable source of genetic material for breeding programs and
cultivar development for persistence (Brink et al. 1999; Frankow-Lindberg 1999; Collins et al. 2002).
13
2.1.3 Physiology
The way the white clover plant utilises available soil water to convert nutrients into biomass depends
on the available root mass, DM partitioning and radiation use efficiency (RUE) of photosynthetically
active radiation (PAR) with a wavelength between 400 and 700 nm. White clover in Australia has a
typical RUE of 1.65 with a PAR utilisation of 2.9 % (Monteith 1977; Sinclair and Muchow 1999). Root
and shoot dry weights, and root-to-shoot ratio relating to biomass are important parameters to get
an understanding of partitioning of assimilates and yield levels (Hay and Porter 2006), water uptake
and water use efficiency (WUE) (Lucero et al. 2000; Siddique et al. 2001). White clover individuals
that demonstrate an advantageous root-to-shoot ratio could play an important role in breeding
programs by improving yield stability in less favourable environments.
2.1.4 Plant breeding
White clover (Trifolium repens L.) is an out-crossing allotetraploid (2n=4x=32), and at meiosis
bivalents (n=16) (a pair of associated homologous chromosomes) are formed, i.e. white clover is an
allotetraploid that acts like a diploid, also called an amphidiploid species with disomic inheritance
(Marshall et al. 1995; Majumdar et al. 2004). Populations and cultivars are a heterogeneous mixture
of heterozygous individuals, with high levels of genetic variation both within and among populations
(Taylor 1985; Woodfield et al. 1996). This variability contributes to the broad adaptation to
environmental conditions, dedifferentiation and extensive phenotypic plasticity of white clover.
Compared to the breeding of self-pollinated species, breeding allogamous, self-sterile species like
white clover tends to focus on population improvement rather than the improvement of individual
plants, to prevent inbreeding depression. A viable selection population of white clover is a group of
interbreeding individuals. Each individual plant in an open-pollinated population is a different
genotype. Genotypes may be propagated indefinitely as vegetative clones (Woodfield and Caradus
1994).
There are specific methods that are suited to population improvement (Sleper and Poehlman 2006;
Acquaah 2007). Mass selection comprises selecting and bulking superior genotypes from a
population without progeny evaluation (Banziger et al. 2006). Recurrent selection entails a repeated
cycle during which desirable individuals are selected from a population and randomly inter-mated to
form a new population (Chloupek et al. 2003). Half-sib and full-sib selection are family selection
methods, where half-sib selection is widely used for perennial forage grasses and legumes
(McLaughlin and Windham 1996). A poly-cross mating system is used to generate half-sib families
from selected vegetatively maintained clones (Caradus and Chapman 1996). A range of breeding
system modifications include among- and within- family selection strategies (Casler and Brummer
14
2008). Selecting traits of high heritability is generally more effective than traits of low heritability in
that method. Development of synthetic cultivars represents an advanced generation of polycrossfertilised (random mating in all combinations) seed mixture of parental stock that may be strains,
clones or hybrids (Reed et al. 2004). Application of the topcross and rolling backcross technique is
used because white clover as a normally cross-pollinated species is sensitive to increased
homozygosis linked with inbreeding, which results in inbreeding depression (loss of vigor, yield
capacity decreases in advanced generations of hybrids) (Marshall et al. 2001). It is important for the
breeder to maintain genetic diversity by using two or three backcrosses using plants from within a
large segregating population (Chrispeels et al. 2003).
First Filial (F1) generation progeny frequently show heterosis or hybrid vigour, which results in
improved traits in the progeny over its parents, including growth rate, reproductive success and yield
(Lippman and Zamir 2007). Capturing heterosis in seed available for sowing on farms is desirable
(Brummer 1999).
Breeding Programs
Naturalised populations of white clover growing in the wild hold potential, sometimes hidden, for
cultivar development. The importance of the use of ecotype-derived cultivars is emphasised by the
increased persistence and capability to sustain a higher clover share in the total available dry matter
of pastures (Bouton et al. 2005), together with the reduced need for expensive nitrogen fertiliser, for
the development of more persistent white clover cultivars, such as Grasslands “Crusader” (Woodfield
and Caradus 1994; Brink et al. 1999; Lane et al. 2000b; Woodfield et al. 2001; Collins et al. 2002;
Jahufer et al. 2002). Breeding projects have been developed to create new synthetic white clover
cultivars, such as Grasslands Trophy (Ayres et al. 2007), that have resistance to summer moisture
stress in pasture areas prone to drought, exhibit wide adaptation, express active summer and winter
development, and are dependably persistent for at least four years (Woodfield and Caradus 1994;
Jahufer et al. 2002).
Kopu II Synthetic Cultivar
Woodfield et al. (2001) described the development of the synthetic cultivar ‘Grasslands Kopu II’,
which was based on high clover yields, high stolon density, large-leaf size, and persistence. Kopu II
has characteristics such as large leaf size, low stolon density, and under normative conditions (e.g.
sufficient soil moisture), is persistent and shows strong agronomic performance and high yields
(Woodward and Caradus 2000). However, it does not perform well under summer drought, which
coincides with high temperatures and often high UV-B levels in the Eastern regions of New Zealand,
and has lower flavonoid levels under these circumstances (Hofmann et al. 2000).
15
Tienshan Ecotype
The white clover ecotype ‘Tienshan’ was discovered in the Tien Shan Mountains, which are located at
the border between China and Kyrgyzstan and also extend into Kazakhstan, Tajikistan, and
Uzbekistan. The Tien Shan (in Chinese, the name 天山 means "celestial mountains"), one of the
largest mountain systems in Central Asia, stretches over 1980 km east to west (Concise Encyclopædia
Britannica
http://concise.britannica.com/dday/print?articleId=110526&fullArticle=true&tocId=47937).
According to Ann Piersall’s weblog, the Tien Shan are part of the Himalayan orogenic belt which was
formed 540 million years ago by the collision of the Indian and Eurasian plates (Ann Piersall, 2008
http://tienshanglaciers.blogspot.com/p/tien-shan.html). Located thousands of miles from the
nearest ocean, the huge relief draws snow and rain on what would otherwise be a vast desert (Aizen
et al. 1995). With peaks up to 7000 meters tall, the complex mountain terrain creates significant
differences in microclimates (Han et al. 2007), but in general most of the region is very arid. These
dry conditions combined with strong seasonal and elevational temperature variations are
characteristic of Central Asia’s continental climate (Figure 2.2).
Figure 2.2
Yaks grazing on the Tian Shan, Xinjiang, China. [ Photo by Doron / CC BY-SA 3.0 ]
The ecotype was collected in the wild from a habitat that was exposed to multiple stress conditions
at a latitude of 43.0º N and an altitude of 2000 m, including UV-B irradiance of 7.44 kJ m-2 day-1,
heavy winter snow, and extreme cold (Hofmann et al. 2000). It was stated that the site
16
characteristics were a hay meadow with low annual rainfall of 600 mm, and Tienshan has high
resistance against abiotic stresses, including coldness (Hofmann et al. 2000). However, this ecotype is
slow growing and shows low productivity even under normative conditions (Lindroth et al. 2000).
Tienshan has small leaf size, high stolon density and produces high levels of flavonoids when stressed
(Hofmann et al. 2003a). In a comparison between ecotypes and cultivars, Tienshan showed a high
correlation between UV-B and drought-induced flavonoid levels and biomass production (Hofmann
et al. 2003b). However, biomass production was significantly lower than a range of cultivars tested
(Hofmann et al. 2001; Hofmann et al. 2003a).
2.2 Abiotic Stress
Most external adverse conditions result in the creation of reactive oxygen species (ROS) within
different plant cellular compartments and this creates osmotic and oxidative stress (Slater et al.
2003; Vinocur and Altman 2005). Variable environmental conditions influencing abiotic stress have
an impact on species adaptation including plant development, production, persistence and
reproductive fitness. Respiration and photosynthesis in plants, being aerobic metabolic processes,
lead to the production of ROS in leaf cell organelles, such as chloroplasts, mitochondria and
peroxisomes. Anti-oxidative compounds are present in high concentrations inside these cell
organelles and they act as scavengers of excess ROS that will otherwise damage macromolecules,
such as DNA, proteins and lipids (Noctor and Foyer 1998; Xiang and Oliver 1999). Oxidative stress is
caused by conditions unbalancing this oxidative / anti-oxidative cycle and thus promoting the
formation of ROS that damage or kill cells. These abiotic conditions include air pollution by increased
ozone or sulphur dioxide, wounding, herbicides, senescence, drought, heat, cold, heavy metals, high
salinity, flooding, high air CO2 concentrations, intense light and high UV-B radiation (Mittler 2002).
A general feature of stress conditions in plants is an increased production of toxic oxygen species.
Most organisms, including plants, have evolved a number of systems to deal with such reactive
molecules (e.g. hydroxyl radicals).These are very destructive to lipids, nucleic acids and proteins;
some ROS, like superoxide anion and hydrogen peroxide, are necessary for lignification (Kuzniak and
Urbanek 2000), and function as signals of environmental stresses (Mittler et al. 2004; Hung et al.
2005). The antioxidant defence systems of plants involve a variety of antioxidant molecules and
enzymes to scavenge and dispose of ROS in several sub-cellular compartments. When these defences
fail to halt the self-propagating auto-oxidation reactions associated with ROS, cell death ultimately
results (del Rio et al. 2006; Pandhair and Sekhon 2006), with consequent impacts on plant
performance.
17
2.2.1 Types of abiotic environmental stress
Plants respond to stress physiologically either by permanent changes in the phenotype, or by
temporary changes, after which the plant returns in its normal state (Ashraf and Harris 2005). Most
external stresses result in the production of ROS in the plant and this creates osmotic and oxidative
stress (Slater et al. 2003; Vinocur and Altman 2005). Common abiotic stresses relative to water loss
include drought, caused by lack of rainfall, radiation which causes heat stress by prolonged high
temperatures causing irreparable damage, ultraviolet-B radiation inducing production of “sunscreen”
flavonols, chilling or freezing by exposure to low temperatures, high salinity caused by dissolved salts
accumulation in the soil solution and mineral toxicity by a high concentration of a mineral in the soil
solution. Other stresses include oxidation causing degenerative conditions in plants by free radicals
of oxygen or reactive oxygen species (Pandhair and Sekhon 2006), water-logging by prolonged
rainfall which can cause anoxic soil conditions which are harmful for the plant’s roots and mineral
deficiency caused by missing essential minerals or trace elements which impairs plant growth
(Acquaah 2007; Gao et al. 2007). In the next paragraphs the focus will be on the abiotic stress
relevant to this thesis: drought-induced or water deficit stress caused by plant placement in waterlimited environments.
2.2.2 Water deficit stress and soil water availability
Water deficit stress occurs in plants when water shortage is triggered by an imbalance in water
uptake compared to water loss. Water shortage in tissues causes water deficit stress and
physiological responses resulting in a lower CO2 assimilation rate and reduced production of dry
matter (Bray 1997). Another sign of water deficit stress in plants is when the induction of turgor
pressure is below the maximal potential pressure, determined by the extent and duration of water
deficit (Ehlers and Goss 2003). Water imbalance in plants is caused by a mismatch between the
continuous evaporative demand of the atmosphere, radiation, leaf area transpiration and soil water
availability in the hydrologic cycle (Figure 2.3) (Jamieson 1999).
18
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Hillel 2004.
Figure 2.3
The hydrologic cycle (Hillel 2004).
Soil water availability is directly related to the amount of precipitation, or irrigation. Precipitation is
rainwater coming down out of the atmosphere and is used in rain-fed agriculture, while irrigation is
the artificial water supply by man, used in irrigated agriculture. Irrigation is normally applied when
soil moisture deficit occurs in areas with an irregular pattern of precipitation, or a lack of
precipitation during the crop development stages when it is most needed. The soil water extractable
by plant roots is held in the soil in an available form (Hillel 2004).
19
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Hillel 2004.
Figure 2.4
The water balance of a root zone (Hillel 2004)
The amount of soil water changes during and after rain. When rain falls, water infiltrates the soil until
the soil pores are saturated with water. The soil is saturated and the rest of the water runs off (Figure
2.4). After a few days of drainage to the deeper water table where the soil is always saturated, the
soil is considered to be at soil water capacity or “field capacity”. Free draining soils with field capacity
have about half of the pores filled with water. After weeks of drought, soil water will be lost by
evaporation from the soil and transpiration from the crop canopy. When the pores in the soil are
empty and the crop cannot extract the remaining water the plants wilt. This is called the wilting
point. The water available to plants is soil water at field capacity minus that at wilting point, for a
particular soil texture (Figure 2.5) (Ehlers and Goss 2003; Decoteau 2005).
20
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Ehlers and Goss 2003.
Figure 2.5
Soil water availability depending on soil texture (Ehlers and Goss 2003).
Stress conditions in the root zone
When the soil is wet, water is spontaneously absorbed by the plant’s roots. Water in dry soils has a
lower potential energy and is more strongly bound to the soil by capillary and absorptive forces.
Therefore, water is less available to be extracted by crops. The crop is said “to be water stressed,
when the potential energy of the soil water drops below a certain critical threshold value” (Ehlers
and Goss 2003).
Soil water availability (SWA) (Figure 2.5) and critical soil water content (Figure 2.6) vary for different
species and different soil management and plant growth stages. Fallow land with bare soil has a
higher evaporation loss than cultivated land, which depletes the soil water reserve for the following
growing season of annuals. SWA is determined by the rooting density characteristics of the species,
evaporation rate, and soil type (Allen et al. 1998). Perennial pastures develop longer roots to reach
deeper soil water and increase soil water deficit compared with annual-only pastures (Sandral et al.
2006). SWA depends on the soil moisture reserve (SMR): if there is low SMR, a soil moisture deficit
(SMD) can occur.
Although neither the concept of soil water availability nor the term “field capacity (FC)” were ever
defined in physical terms, three main classic hypotheses regarding the availability of soil water to
plants have been postulated (Figure 2.7) (Harrold et al. 1986). The most commonly accepted of these
21
is the Pierce curve, which shows equal availability at maximum rates when crop water requirement
equals reference evapotranspiration (ETc = ET0) from FC down to a critical or threshold value, beyond
which availability decreases. The Veimeyer model shows equal availability from FC to the “wilting
point” (also called “stress point” when the plant starts wilting) and then it drops sharply. The rarely
used Thornthwaite model shows that availability decreases gradually as soil-moisture content
decreases. None of these hypotheses was based on a sound theoretical framework that would take
into account all of the factors possibly influencing the soil-plant-atmosphere continuum (SPAC)
((Hillel 2004), pp. 367, 438 / (Ehlers and Goss 2003), pp. 85). In fact, they had a propensity to draw
general conclusions from a limited set of experiments conducted under detailed and often poorly
defined circumstances (Hillel 2004). The point of no return for plants where there is no more water
left is the “permanent wilting point”. When the plant reaches this point, soil water has almost
reached 0% and the plant cannot recover, even after soil moisture increases again thereafter. For
white clover, the ideal level to maintain soil moisture is from the stress or wilting point up to half
field capacity (Clifford 1986; Thomas et al. 2009).
The Soil-Plant-Atmosphere Continuum hypothesis explains the movement of water by differences in
pressure potential alone - that is, water concentrations in the air, leaf, root, and soil are such that the
atmosphere literally sucks water from the soil through the plant tissues. The steepest fraction of the
water potential curve (from soil to air) is from the leaf to the air. This is why stomatal regulation is so
important in conserving water (Davies et al. 2002). According to the Cohesion-Tension Theory
(Angeles et al. 2004), water is drawn up and out of the plant by transpiration. Because of the
cohesive / adhesive properties of water, as soon as one water molecule evaporates at the stoma it
pulls the other molecules up and sends this pull all the way down the tracheids and vessel elements
in the plant. This pull is strong enough to transport water to the top of even the tallest trees (Taiz and
Zeiger 2006).
22
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Ward and Trimble 2004.
Figure 2.6
Relationship of soil moisture to transpiration rate, plant productivity, and capillary
potential.
By ((Ward and Trimble 2004). Note: AET = crop ET (ETc), PET = reference ET (ET0), PAW = Total
Available Water (TAW) (Allen et al. 1998))
Crop water use and evapotranspiration
Evapotranspiration (ET) is the evaporation of water molecules from open water, vegetation and soil,
combined with the transpiration of water molecules from the roots of plants trough the plant into
the leaves and out of the stomata into the atmosphere (Allen et al. 1998). Evapotranspiration
completes the hydrologic cycle (Figure 2.3).
Evaporation and transpiration occur simultaneously and it is difficult to distinguish between the two
processes. Evaporation from soil is largely determined by the fraction of the solar radiation reaching
the soil surface. This fraction decreases as the crop develops over the growing season and the canopy
casts more shade on the soil. Figure 2.7 shows the subdividing of evapotranspiration into
evaporation and transpiration, and the development of the leaf area index (LAI), the ratio between
total leaf area of the crop and the soil surface it covers. At sowing time ET is nearly all evaporation,
but a crop at full cover transpires for around 90% of ET (Allen et al. 1998).
23
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Allen et al. 1998.
Figure 2.7
The subdividing of evapotranspiration into evaporation and transpiration over the
growth cycle of an annual field crop (Allen et al. 1998).
The adapted Penman-Monteith equation (Allen et al. 1998) was accepted by the Food and
Agricultural Organization of the United Nations (FAO) as the preferred method to calculate water
requirements for irrigated crops (Smith et al. 1996). The term reference crop evapotranspiration
(ET0) represents the evaporative power of the atmosphere under standardised conditions. This
method is now widely accepted and the use of the earlier terms potential ET and actual ET is strongly
discouraged because of ambiguities in the definitions (Allen et al. 1998). For further information on a
dual crop coefficient method for estimating of crop ET in periods of naked soil and partial and full
vegetation cover, see Allen et al. (2005) and Appendix B.
2.3 Physiology of Drought
Drought and the ensuing plant water deficit is the greatest environmental cause of abiotic plant
stress in worldwide agriculture (Bartels and Sunkar 2005). For example, photosynthetic efficiency and
consequently biomass production can be significantly reduced by water deficit stress (Jaleel et al.
2009). Climate change and drought continues to impact global crop production resulting in food
shortages, and is reliably forecast to increase (Sansom and Renwick 2007). Additionally, the world
population continues growing, while the area of fertile arable land is stable or declining, and the
potential for increased irrigation is limited due to water shortages (Witcombe et al. 2008). Improved
24
drought tolerance in economic species including white clover is needed to mitigate against these
factors.
Osmotic stresses caused by drought makes up the most serious limitation to plant growth,
production and distribution, and causes serious physiological damage or even total crop loss (Munns
2011). Breeding for drought in pasture crops is directed at short-term drought under rain-fed
conditions, improving physiological activity during the drought periods, which results in better quality
and higher yields (Passioura 1996; Reed et al. 2001; Tester and Langridge 2010). Plant hormones like
abscisic acid (ABA) accumulate in plant tissues which experience water deficit, and are believed to be
a key factor in water deficit stress response (Verslues et al. 2006; Ren et al. 2007; Shao et al. 2007;
Neumann 2008).
Several water deficit stress-responsive genes have been recognized and described (Kizis et al. 2001;
Bartels and Sunkar 2005; Vij and Tyagi 2007; Hadiarto and Tran 2011). A higher biomass production
per unit of land and water is needed to satisfy increasing food demand. More efficient use of rainand irrigation water is important to increase production. New breeding advances to improve drought
resistance of crops, such as genetics- and genomics-based techniques, can only be successful if they
are joined with traditional breeding methods and water management practises (Turner 1997; Mittler
and Blumwald 2010).
2.3.1 Water productivity and yield
Water deficit symptoms appear in a variety of ways: changes in phenology, phasic development,
growth, carbon accumulation, assimilate partitioning and reproductive processes and fitness. Water
deficit causes reduced cell expansion, reduced plant water use and reduced plant productivity or
yield (Chaves and Oliveira 2003). To conserve soil moisture, soil and water saving practices can be
implemented. Zhang et al. (2007b) explained that to conserve soil moisture, soil and water saving
practices can be implemented, such as bio-water-saving, managing cropping systems and watersaving irrigation, and breeding new varieties with good drought resistance, high yielding (high water
use efficiency (WUE)), and improved quality traits (Zhang et al. 2007b).
Stress resistance is an integrated part of many cultivar development programs. Before releasing a
cultivar, prospective genotypes are evaluated at different sites and over several years to determine
environmental adaptation. Under conditions of severe stress, high-yielding plant genotypes perform
inadequately. Cultivars with higher stress tolerance have very low yield potential. It would be
challenging to combine desirable stress tolerance traits with high yield potential (Blum 2001; Herve
and Serraj 2009).
25
Water is essential for plant growth and development. Water deficit stress has more limiting effects
on the growth, distribution and performance of crop plants than do any other environmental
variables (Shao et al. 2008). The occurrence of drought can be separated in atmospheric drought and
soil drought. The duration of drought is variable, lasting from a short time with no lasting effects to
the crop plants, to the entire growth season of a crop, which causes serious physiological damage or
even total crop failure (Cattivelli et al. 2008). Higher plants react to drought stress with physiological
responses resulting in reduced growth rates, biochemical responses which result in reduced ROS
accumulation and molecular responses resulting in drought stress tolerance (Bray 1997; Reddy et al.
2004).
One of the most important water management issues is to prevent crop water deficit stress from
occurring in the field. In areas where water is in short supply, water deficit management practices
such as optimising rainwater-use efficiency of crops in dryland farming systems, and deficit irrigation,
which keeps the crop at a light stress level, can be used to improve the harvest index, for suitable
drought resistant varieties of crops (Turner 2004; Passioura 2006). The relationship between crop
yield and soil water deficit, and the concepts of evapotranspiration and crop water productivity is
coined in the term “more crop per drop” by Kijne et al. (2003). Similarly, Blum (2009) stated that
often high water use efficiency (WUE) is achieved at the expense of reduced “efficient use of water”
(EUW), via maximal soil moisture capture for transpiration and sustained partitioning of assimilate.
2.3.2 Drought-induced stress and drought resistance terms
Drought-induced stress is a physiological concept based on dehydration imposed on plant cells by
water deficit. Both tolerance and resistance are used as terms in literature to describe plant response
to stress. They are used as if they are exchangeable. From a physiological point of view, plant
response to stress can be characterised as stress avoidance (where the environmental factor is
excluded from plant tissue), or stress tolerance (the factor enters the tissue but the tissue survives).
The term “resistance” from a physiological point of view is mechanism-neutral (implying neither
tolerance nor exclusion). Resistance seems to be used as a generic term for describing a number of
mechanisms of which tolerance is one. Plant breeders refer to cultivars as being resistant to a stress
when, after transferring desirable anti-stress traits, a cultivar performs well in that stress
environment (Levitt 1980; Turner and Kramer 1980).
Drought resistance involves the way plants cope with drought-induced stress, and is conditioned by
two major strategies: dehydration avoidance and dehydration tolerance. Dehydration avoidance is
the capacity of plants to avoid dehydration penetrating the plant tissues and cells, reducing water
loss by for example having a waxy surface or the capacity to efficiently remove soil moisture.
Dehydration tolerance occurs where dehydration enters the plant tissues but the plant does not
26
suffer injury and sustains function (Levitt 1980; Ludlow and Muchow 1990; Blum 2011a) (Figure 2.8).
Dehydration tolerance enables certain plants like e.g. cereals to extract stored carbohydrate for grain
filling, which makes them more tolerant of drought after the flowering period. White clover
maintains seed development during dry periods (Martin et al. 2003) and some water stress is
essential for reproductive growth and balanced leaf production (Blum 2011b). Another characteristic
of dehydration tolerant plants is the way they maintain turgor by osmotic adjustment in the plant
cells. Dehydration tolerance is also known as “hardening” to function at low plant water potential,
and can only be measured between genotypes when they are at the same plant water status (Blum
2011a).
Dehydration avoidance is desirable in modern agriculture, where drought resistance requires the
maintenance of economically viable plant production under stress. The role of dehydration
avoidance is maintaining water supply and sustaining leaf hydration and turgidity with the purpose of
maintaining stomatal opening as long as possible under water deficit, which is essential for leaf gas
exchange, photosynthesis and plant production through carbon assimilation (Izanloo et al. 2008;
Farooq et al. 2009b). There are two types of dehydration avoidant plants: the “water savers” that
reduce water loss by reducing transpiration, e.g. by a thick layer of leaf epicuticular wax, leaf
pubescence, leaf rolling or leaf posture; and the “water spenders” that maintain water uptake, for
example by reaching for available soil water with deeper roots. Dehydration escape involves early
maturing, during which the plant uses the previously optimal conditions to develop vigor.
Dehydration - or drought recovery means that some plant species are able to recuperate after brief
drought periods (Price et al. 2002; Chaves and Oliveira 2004; Jenks and Hasegawa 2005; Acquaah
2007). Drought resistance is specific to the environmental factors in a certain location, and new
drought-resistant genotypes need to be evaluated under specific environmental conditions (Denby
and Gehring 2005).
27
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Levitt 1980.
Figure 2.8
Plant drought resistance mechanisms. (Adapted from Levitt (1980)).
In dryland farming systems, drought resistance is the major breeding objective (Araus et al. 2007),
whereas in more advanced agricultural production systems, the objective is to obtain economic plant
production under drought conditions (Passioura 2007). Previous breeding objectives in grain crops
for drought resistance were aimed at developing a genotype that had water-saving capacity.
However, the relation between crop yield and water use shows that the focus for drought resistance
should be on efficient water use when water is limited by drought, rather than on water-saving
capacity (Blum 2005; Passioura 2006).
2.3.3 Water use efficiency
The relationship between water use and crop yield can be expressed in a variety of ways. Water use
efficiency (WUE), which can also be defined as transpiration efficiency, has historically been used in
two different contexts, hydrological and physiological (Stanhill 1986; Bacon 2004). The hydrological
concept of WUE is used by engineers to assess the performance of irrigation projects (Jensen et al.
1990), and is useful for determining water components delivered to the field, but do not reach the
crop. However, it does not give information about the amount of produce yielded with an amount of
available water.
WUE in the physiological context is often used by agronomists and refers to the biomass or grain
yield per unit volume of water evapotranspired (de Wit 1958) (Equation 2.1). Tanner (1983) and
28
Sinclair(1983) reviewed several methods to estimate WUE, and showed that plant biomass
accumulation, and consequently yield, was inextricably linked to transpiration, and that only a few
variables were available for manipulating WUE, which had already been exploited to a large degree.
According to Hillel (2004), WUE is ‘The measure of crop produced (in terms of vegetative growth or preferably - in terms of the weight or value of the marketable product), either per unit amount of
water applied or per unit of water consumed (evaporated and transpired). Unlike other measures of
efficiency, this index is not expressed in dimensionless terms as percentage, but as crop mass or
value per unit volume of water.’
Equation 2.1
P=
P
m
T
ETp
n=1
n=0
De Wit’s equation
mT
ETpn
biomass or DM (Mg ha-1)
crop-specific factor
transpiration (mm)
potential evapotranspiration (= ET0)
for semi-arid to arid zones with high radiation regimes
for humid, temperate zones
Equation 2.1 shows there is a direct relationship between cumulative transpiration (T) and dry
matter yield in crop stands.
2.3.4 Crop water productivity versus drought resistance
Passioura (2004) and Richards et al. (2010) have pointed out that plant breeders need to focus
onimproving water productivity (predominantly in cereal crops), rather than on drought resistance.
In their opinion, drought resistance is a plant survival strategy under drought, but it is not useful for
economical production with available water (Morison et al. 2008). Blum (2005) noted that there is
usually a negative relation between yield potential and drought resistance, but that there is still room
for drought resistance improvement in marginal, drought-prone areas (Morison et al. 2008).
29
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Kijne et al. 2003.
Figure 2.9
A diagram for water productivity terms and components (Kijne et al. 2003).
Transpiration (T), evaporation (E), irrigation (I), evapotranspiration (ET), drainage (D) (deep
percolation) and runoff (R).
Water productivity (WP) is the term mostly used in later publications, and refers to WUE in the
physiological sense, relating to the weight of biomass or yield produced per unit weight of water
used. WP per unit evapotranspiration (ET) will therefore be equivalent to the physiological context of
WUE mentioned above (Kijne et al. 2003; Passioura 2006).
From Figure 2.9, transpiration WP (WPt) (Equation 2.2), evapotranspiration (WPET) (Equation 2.3) and
irrigation water productivity (WPI) (Equation 2.4) can be defined as follows (Kijne et al. 2003):
Equation 2.2
Transpiration water productivity.
WPt = Y / T
Equation 2.3
Evapotranspiration water productivity.
WPET = Y / (T + E)
This is similar to the physiological WUE mentioned earlier.
Equation 2.4
WPI = ∆Y / ∆ET
30
Irrigation water productivity.
Water productivity under irrigation in which ∆Y and ∆ET are the increase in yield and
evapotranspiration due to irrigation, respectively.
In the farmer’s field, where numerous environmental parameters influence crop yield, it is not
always easy to find out if water is the main limited source. A simple model framework was developed
by John Passioura (1977) for the components of crop yield, with a focus on crop production under
water limitation (Morison et al. 2008):
Equation 2.5
Passioura’s yield model.
Y = WU x WUE x HI
where Y is the yield or total dry matter (DM); WU is water use or crop transpiration (T); and HI is the
harvest index. Any large differences that come to light when comparing actual yield with projected or
potential yield with the aid of Equation 2.5 can give a decisive answer. Water use efficiency (WUE) or
water productivity (WP) is often expressed as the ratio of yield to water supply or total ET.
Transpiration efficiency (TE) is also called the net exchange of leaves between water and CO2,
resulting in biomass production (Passioura et al. 1993). Thus T × WP equals TE. Harvest index (HI) is a
dimensionless quantity that is usually defined as “the ratio of grain yield to above ground biomass”
(Passioura et al. 1993; Passioura 2004, 2006). To use this equation with white clover, in the HI term
one could substitute the grain DM in cereals with the grazed DM of white clover.
Equation 2.6
𝐸=
Leaf Transpiration and Water Vapor Gradients (Taiz and Zeiger 2006).
𝑐𝑤𝑣(𝑙𝑒𝑎𝑓) − 𝑐𝑤𝑣(𝑎𝑖𝑟)
𝑟𝑠 + 𝑟𝑏
E, transpiration rate (mol m–2 s–1)
cwv (leaf) – cwv (air) , the difference in water vapor concentration; where cwv (leaf) is the
concentration of water vapor inside the leaf, and cwv (air) is the concentration of water vapor of the
air outside the leaf (mol m–3).
rs, resistance of the stomatal pore (s m–1)
rb, resistance due to the layer of unstirred air at the surface of the leaf (the so-called boundary layer)
(s m–1)
Resistance (r) is the inverse of conductance, i.e. resistance = 1/conductance (high resistance is equal
to low conductance) (Taiz and Zeiger 2006).
31
Monteith (1993) and Stanhill (1986) have pointed out that although the usual terms ‘water use’ and
‘water use efficiency’ are inaccurate, they persist in common use. Monteith’s (1993) terminology of
”water loss” and “biomass water ratio” (BWR) have not yet been accepted widely. The latter
represents the amount of biomass accumulated by a crop stand per unit of water transpired (= DM /
T or sometimes DM / ETc, where DM is dry biomass, depending on changes in humidity, wind speed
and solar radiation). Accordingly, the use of BWR or “biomass water use efficiency” are
recommended instead of the term WUE.
When water is the main limiting factor, there are several ways to improve crop yields by agronomic
management or plant breeding. The most effective use of water according to Passioura (2006) is:
•
capturing as much as possible of it: deminishing losses from soil evaporation, deep drainage and
runoff;
•
transpire more of the limited water supply: improve TE by breeding;
•
using captured water as effectively as possible when trading it at the stomata for CO2 to form
photo-assimilate;
•
converting as much of this assimilate as possible into a harvestable form, for instance grain;
improving timing of flowering closer to the optimum.
Agronomic management can be used to improve WP of rain-fed crops. For example, in grain crops it
is essential to avoid water deficit stress at critical crop growth stages, such as the period before
anthesis (flowering) and the period of seed setting. A balance between water used during canopy
development and during grain filling contributes to an optimal flowering time (Thomas and Fukai
1995). Mishra et al. (2001) showed that yields of winter maize in India were maximised by water
application at tasseling, flowering, silking and grain-filling growth stages.
2.3.5 Plant responses to water deficit and effects on growth
Plants develops water deficit when the demand for water exceeds supply. This balance is determined
by the crop evapotranspiration, a combination of plant transpiration and soil evaporation (Chiew et
al. 1995; Farooq et al. 2009b). The total leaf area is the most important determinant of plant
transpiration. The extractable soil moisture is the amount of water a certain crop can extract from
the soil at a given water potential and depth (Araus et al. 2007). During periods of water shortage,
plants lose water from their tissues. Plants experience a state of stress or tension during soil water
deficit. Water deficit stress causes stomatal closure which stops gas exchange with the atmosphere
so that the plant cannot assimilate properly and stops producing dry matter. More efficient use of
water by crops increases the assimilation rate and improves yields (Ehlers and Goss 2003).
32
Water deficit stress-induced changes in plants include physiological responses resulting in loss of
turgor and osmotic adjustment, reduced leaf water potential, wilting, decreased stomatal
conductance, reduced internal CO2 concentration, decline in net photosynthesis, reduced growth
rates and leaf area, and increased root-to-shoot ratio; biochemical responses which result in reduced
ROS accumulation; and molecular (genetic) responses resulting in drought stress resistance (Bray
1997; Reddy et al. 2004; Ashraf 2010). The main source of water for plants is the absorption of soil
water through the root system of the plant. Under less favourable soil characteristics and climatic
conditions, the availability of water for plant uptake is limited because precipitation is less than
evapotranspiration, and plants suffer from water deficit stress (Decoteau 2005).
Turner (1997) stated the effects of water deficit stress on plants as follows:
•
Water shortage in tissues; loss of turgor pressure (= water potential).
•
Shoots with transpiring leaves sense a reduction in water potential.
•
Opening and closing of the stomata is controlled by the turgor of the guard cells. When the
inflow of water by osmosis is decreased, turgor in the guard cells is decreased and the stoma
closes, and reduces assimilation of CO2.
•
Physiological responses such as leaf wilting, rolling and folding; either permanent changes in the
phenotype, or temporary changes, after which the plant returns in its normal state (Ashraf and
Harris 2005).
•
Biochemical responses:
•
Water deficits trigger the production of free radicals or reactive oxygen species (ROS) which
cause damage to cell organs and enzymes and can result in oxidative stress, low water potential
and ultimately cell death. The Mehler reaction initiates the glutathione-ascorbate cycle, a
pathway for removal of excess electrochemical energy caused by drought and other abiotic
stresses (Smirnoff 1993; Slater et al. 2003; Vinocur and Altman 2005).
•
Insufficient transpiration by closing stomata causes reduced cooling effect of leaves and a smaller
difference between leaf temperature and ambient air temperature.
Effects of water deficit in plants over time
When temperature, radiation and other essential growth factors are not limited, water is the most
limiting factor for plant growth rates and productivity. Water is necessary for metabolism, which
explains why water deficit has serious effects on photosynthesis, solute transport and accumulation.
Effects of water deficit on plants over time can be classified into (Pugnaire 1999):
•
Short-term changes: physiological changes
33
o
Stomatal aperture regulation by abscisic acid, reducing water loss by transpiration and
maximising CO2 intake, which has an optimal efficiency when there is a constant ratio
between transpiration and photosynthesis
o
•
•
Reduction of water potential pressure (turgor)
Medium-term changes: acclimation to a certain level of water availability
o
Adjustment of osmotic potential by solute accumulation
o
Changes in the cell wall elasticity
o
Morphological changes
o
Reduction of cell division and elongation
Long-term changes: adaptations to drought
o
Genetically fixed patterns of biomass allocation
o
Specific anatomical modifications
o
Overall growth reduction to balance resource acquisition
2.3.6 Whole crop responses to water deficit
To improve yield under water-limited conditions, and thus increase crop water productivity, the
negative effects of crop responses to water deficit have to be addressed. The relationship between
yield loss and soil moisture deficit can be explained as a collection of crop responses (Jordan 1983;
Turner 1997; Jamieson 1999; Ehlers and Goss 2003; Wery 2005):
•
Lower crop transpiration by closing stomates
•
Lower assimilation rate
•
Decreasing growth rate
•
Leaf development (tillering, number, extension, expansion and senescence)
•
Morphological effects, such as leaf wilting, rolling, folding and abortion
•
Lower LAI
•
Reduced light interception by changes in leaf canopy
•
Lower PAR
•
Higher soil evaporation
•
Less negative canopy temperature difference (TLeaf - TAir)
•
Change in biomass partitioning and harvest index
•
Higher crop water stress index (CWSI), which is negatively related to crop yield over defined
growth periods
To increase WP and maximise soil moisture use when water resources are scarce, not only crop
parameters such as yield potential and drought resistance traits need to be considered (Blum 2005),
but also radiation use efficiency (RUE).
34
During water shortage, the level of tension can be measured with suitable equipment. However, it
may be sufficient to evaluate the level of stress or tension by scoring the degree of leaf wilting. In
some members of the grass family, leaf rolling and folding can be used for the same purpose (Ehlers
and Goss 2003).
2.3.7 Plant adaptation to dry environments
Over time, plants have adapted to different environments. For example, hydrophytes grow in water,
have low oxygen requirements and poorly developed root systems. In contrast, xerophytes have
adapted to dry, desert-like regions by developing xeromorphic features: thick epidermis, waxy
surface cuticle, recessed stomata, reduced leaf area, all of which enhances water storage or
minimises water loss. Certain succulent plants, which utilise the phosphoenolpyruvate carboxylase
enzyme, which has a strong affinity for CO2, demonstrate crassulacean acid metabolism (CAM), to be
able to adapt to periods of high radiation, high temperature, and low moisture. These CAM plants
close their stomata during the day and assimilate during the night, to accumulate organic acids which
can be used to supply CO2 for producing carbohydrates by photosynthesis during the day.
Mesophytes have intermediate water requirements and well-developed root systems, and grow
mostly in temperate climates with mature, well-developed, moist but aerated soils, and include most
of our food crops. They regulate their root-to-shoot ratio to balance water uptake to water loss, and
by regulating the opening of their stomata to limit transpiration during periods of water shortages.
(Ehlers and Goss 2003; Hillel 2004; Decoteau 2005). Plants that have evolved in semi-arid areas
developed drought resistance traits that can be used in plant breeding such as increased WUE,
increased chlorophyll levels, maintenance of turgor during stress, a higher rate of photosynthesis
during stress, constitutive traits (phenology, root traits, plant and organ size, leaf surface properties,
non-senescence, stem reserve) and stress adaptive traits (osmotic adjustment by solute
accumulation (e.g. proline), increase in antioxidant agents, sustained plant water status), all of which
support sustained function under drought (Bartholomew et al. 1973; Blum 1996; Condon et al. 2004;
Gaur et al. 2008; Medrano et al. 2009; Pooja et al. 2010).
2.4 Flavonoid biochemistry
2.4.1 Secondary metabolites
Plant secondary metabolism has been a focus of research in recent years due to its significant roles in
plant defence and in human medicine and nutrition. Although secondary metabolites are not
necessary for basic plant growth and development, they do play important roles in helping plants
survive, in providing protection from biotic stresses (herbivores, insects, diseases) and abiotic
stresses (cold, heat, salinity, drought, etc). Three main categories of secondary metabolites exist:
phenolics, alkaloids and terpenoids. Phenolics are derived from the amino acids phenylalanine and
35
tyrosine (in grasses). They are a group of many different ringed hydrocarbons that do not have
nitrogen in their structure. Phenolics mostly strengthen plants or protect them from various threats.
In some cases where this is beneficial, the plant produces large amounts of them. Phenolics make up
around 40% of the carbon circulating the biosphere, typically in cell walls and vacuoles of cells that
produce them. The most important types of phenolics are lignins, flavonoids and allelopathics
(Buchanan et al. 2000; Heldt and Heldt 2005; Raven et al. 2005).
Secondary metabolites perform important roles in the adaptation and acclimation of plants to their
environment. Phenolics represent a major group of these metabolites (Aerts et al. 1999). Most plant
phenolics are derived from the phenylpropanoid and phenylpropanoid-acetate and related pathways
such as those leading to tannins (Weisshaar and Jenkins 1998). Phenolics are generally characterised
as aromatic metabolites with one or more hydroxyl groups attached to the aromatic arene (phenyl)
ring.
The first step in the biosynthesis of flavonoids is the conversion of phenylalanine (PAL), one of the
three aromatic amino acids synthesised by the shikimic-chorismic acid pathway, the other two being
tryptophan and tyrosine (Winkel 2004). See also
(http://www.genome.jp/kegg/pathway/map/map00400.html). The enzymes that catalyse the first
committed step in this secondary metabolic pathway are phenylalanine ammonia lyase (PAL),
cinnamate 4-hydroxylase (C4H) and 4-coumarate: CoA ligase (4CL) (Koes et al. 2005). PAL converts
phenylalanine to cinnamic acid, which in turn is converted by C4H to p-coumaric acid, which is
followed by conversion into 4-coumaroyl-CoA by 4CL.
2.4.2 Flavonoids
In the phenylpropanoid pathway there is a branch point which is split into the pathways for
flavonoids (Figure 2.10), the isoflavonoids and the group of stilbenes, coumarines and lignins (Davies
and Schwinn 2006). 4-coumaroyl-CoA and malonyl-CoA, derived from citrate produced by the TCA
cycle, are converted by chalcone synthase (CHS) into (tetrahydroxy-) chalcone, which in turn is
converted by chalcone isomerase (CHI) into naringenin (a flavonone), which can be hydroxylated by
flavonoid-3-hydroxylase (F3H) into dihydroflavonols, of which dihydrokaempferol (DHK) is an
example. Further hydroxylation of DHK by flavonoid 3’-hydroxylase (F3’H) leads to the formation of
dihydroquercetin (DHQ) (Winkel-Shirley 2001a) (Appendix D). Flavonol synthase (FLS) converts DHK
and DHQ into the flavonols kaempferol (K) and quercetin (Q), respectively (Winkel 2006) (Appendix
E). Rutin is the name of a Q glucoside that is often associated with the defence system against stress
conditions (Suzuki et al. 2005), and is used as a standard in HPLC analysis.
36
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Pourcel et al. 2007.
Figure 2.10 Structure, biosynthesis and oxidation of flavonols (Pourcel et al. 2007).
Simplified schematic of the flavonoid pathway (a). The main classes of end products are presented,
and their molecular structure shown for one example in each class. The structure of the flavonol
quercetin (b) is given as an example of carbon numbering (Pourcel et al. 2007).
Flavonoids include thousands of water-soluble molecules and are usually found in fruits, vegetables,
autumn leaves, and flowers. Some dissuade herbivores and prevent bacterial decay in the form of
the brown, bitter composites called tannins, which can be used to preserve leather. Many flavonoids,
such as lycopene in tomato and pro-cyanidins in apple, grapes and strawberries, are used in medicine
to help control and prevent cancer and cardiovascular diseases and as antiviral agents. Other
flavonoids can be found in flavouring substances and fragrances, such as inblack pepper, basil,
cloves, ginger, vanilla, cinnamon and maple syrup (Gang 2005). Flavonoids called anthocyanins cause
the red, blue and purple colours in flowers that attract pollinating insects (Bohm 1998; Winkel-Shirley
2001a).
Flavonoids are compounds with two benzene rings separated by a propane unit. Some flavonols have
a protective role as UV-B filters, and they could also work as copigments for anthocyanins in specific
tissues. To fulfil these functions, these flavonols are assumed to accumulate in the vacuole (Mol et al.
1998; Koes et al. 2005). The colourless to pale yellow flavonols kaempferol and quercitin, and some
of the pigments anthocyanins (Feild et al. 2001; Gould 2004) and proanthocyanidins (tannins)
presumably play important roles in the protection against UV radiation (Winkel-Shirley 1999;
37
Harborne and Williams 2000; Winkel-Shirley 2002b). See also
(http://www.genome.jp/kegg/pathway/map/map00400.html).
Synthesis of flavonoids takes place in the cytosol, the fluid part of the cytoplasm. As part of the first
committed step, the genes for the first three enzymes (PAL, C4H and 4CL) in the pathway for the
synthesis of flavonoids and lignins are turned on simultaneously. Presumably they have identical
promoters like intense light or stress and are activated by similar intracellular chemical messengers.
The mRNA synthesises the corresponding enzyme, which in turn catalyses the product, which
accumulates in vacuoles and epidermal tissue when the pathway is blocked, or acts as a substrate for
the next enzyme, which is produced at the same time (Ramsay and Glover 2005; Mano et al. 2007;
Park et al. 2007). Flavonols, flavonones, anthocyanins and proanthocyanidins accumulate in the
vacuoles. Vacuolar membrane bound permeases or transporters take up flavonoids, which are
synthesised in the cytosol, into the vacuole. One of the functions of vacuoles is to store various
secondary metabolites including flavonoids. The pH gradient between the cytosol and the acidic
vacuole provides the potential energy needed to take up substrates into the vacuole (Markham et al.
2000; Debeaujon et al. 2001; Winefield 2002; Kitamura 2006).
2.4.3 Flavonols
Kaempferol and Quercetin are two flavonols that have UV-B protective properties. UV light can
induce the preferential synthesis of those flavonols that have the higher levels of hydroxylation (e.g.
glycosides of quercetin instead of kaempferol), rather than a general increase in all flavonols. This is
likely to be related to the higher capacity of quercetin to scavenge UV-generated free radicals (Ryan
et al. 1998; Ryan et al. 2001; Ryan et al. 2002). Lewis et al. (2006) showed that altering the
expression of the flavonoid 3 '-hydroxylase gene by anti-sense technology modified flavonol ratios
and pollen germination in transgenic Mitchell petunia plants, leading to an increased production of
kaempferol and a decrease in the quercetin:kaempferol glycoside ratio (QKR) in flower limb tissue.
Quercetin as a biochemical response to UV-B stress
One of the biochemical plant responses to water deficit stress is increased production of specific
phenolic compounds called flavonols (Hernandez et al. 2004). Flavonols appear to be mainly involved
in the response mechanisms against abiotic and biotic stresses. In addition to variation in
morphological and physiological traits and interactions, abiotic stress in plants is partially mediated
by the production of phenolic compounds, and numerous studies suggest that the secondary
metabolite group of flavonoids have multiple functional roles in the responses of plants to adverse
environmental conditions (Winkel-Shirley 2002a; Treutter 2005; Roberts and Paul 2006; Kilian et al.
2007; Mellway et al. 2009).
38
Of note, flavonols have several photo-protection properties against excessive short wavelength solar
(UV-B) radiation in plants (Burchard et al. 2000). White clover cultivars selected for genetic diversity
in flavonoid expression exhibit variation for these metabolites (Carlsen et al. 2008), as do ecotypes
and new cultivars (Hofmann and Jahufer 2011). Hofmann et al. (2000) showed that variation in the
flavonol glycosides of quercetin (Q) and kaempferol (K) were implicated in the variable response of
multiple populations of white clover to enhanced UV-B radiation. They also demonstrated an inverse
relationship between plant dry matter (DM) production and Q glycoside accumulation among
populations (Hofmann and Jahufer 2011). Furthermore, higher Q glycoside accumulation under UV-B
was correlated with protection against UV-B-induced growth reduction (Hofmann and Jahufer 2011).
These studies suggest the metabolic cost of a plant strategy towards constitutive and abiotic stressinduced protection may be lower carbon allocation towards primary production, which is a known
general response (Grime 2001).
Flavonoids allegedly have signalling properties in protecting plant cells from oxidative stress (Taylor
and Grotewold 2005; Angeloni et al. 2007; Peer and Murphy 2007). Quercetin specifically regulates
auxin movement by acting as an endogenous negative regulator (inhibitor) of auxin transport (Jacobs
and Rubery 1988; Mathesius et al. 1998; Brown et al. 2001), and hence influences plant development
(Fiserova et al. 2006; Peer and Murphy 2007; Gao et al. 2008; Buer et al. 2010). Quercetin aglycone is
a putative inhibitor of auxin transport, and as auxin promotes growth in stems, while inhibiting
growth in roots in higher concentrations, plant development is reduced above ground and stimulated
below ground (Peer and Murphy 2007; Buer et al. 2010). Quercetin is also an antioxidant agent
against ROS, which helps reducing the damaging effects caused by abiotic stress in general (Agati et
al. 2009).
Quercetin and its association with yield
Treutter (2005) reviewed the roles of flavonoids in plant defence against pathogens, herbivores, and
environmental stress. From an evolutionary perspective, oxidative pressure from stress could have
had a key influence for the distribution and the abundance of flavonoids. Resistant plant genotypes
generally have high levels of flavone or flavonol synthesis which accumulate rapidly in epidermal
vacuoles and infrequently also in epicuticular waxes, and act as a “sunscreen” (Landry et al. 1995;
Strissel et al. 2005).
Photoprotection is suggested as a predominant role of flavonoids (Solovchenko and Schmitz-Eiberger
2003; Mahdavian et al. 2008). Q and K glycosides are two flavonol glycosides that were shown to
have UV-B protective properties, which is afforded by a high Q / K glycoside ratio in Petunia leaves
(Ryan et al. 2001; Ryan et al. 2002). UV radiation can induce the preferential synthesis of those
flavonols that have a higher level of hydroxylation (e.g. glycosides of Q instead of K), (Figure 2.10).
39
Hofmann et al. (2000) identified flavonol glycosides in the UV-B response of nine populations of
white clover. They found an inverse correlation between plant productivity and Q glycoside
accumulation. Furthermore, higher UV-B-induced Q glycoside accumulation related to tolerance of
UV-B-induced growth reduction. In another study (Hofmann et al. 2003a) where UV-B was combined
with drought, it was shown that higher levels of Q glycosides were correlated with lower plant
productivity and to higher UV-B tolerance under well-watered conditions. Exposure to higher levels
of UV-B or drought increased the synthesis of flavonones and flavonols. On the basis of these
ecotype and cultivar studies, high levels of Q glycosides appeared to be associated with high levels of
abiotic stress tolerance, and low levels of productivity.
2.4.4 Flavonoids in white clover ecotypes
Slow-growing and stress-resistant white clover ecotypes were characterised by UV-B tolerance under
well-watered conditions and under water limitation(Hofmann et al. 2000; Bouton et al. 2005). Water
deficit stress has been reported to increase flavonoid concentration in several plant families (Gray et
al. 2003; Hernandez et al. 2004; Tattini et al. 2004; Turtola et al. 2005; Scalabrelli et al. 2007). It is as
yet unknown how Q glycosides might mediate water deficit stress and what the relationship is
between Q glycosides and biomass production within a white clover population under induced water
deficit in pots, nor the value of abiotic stress-resistant white clover ecotypes in breeding programs
for sustainable and economically viable drought-stressed pasture mixtures. However, the
development and use of drought-resistant white clover cultivars would be clearly advantageous to
forage production in areas with marginal or erratic rainfall patterns.
2.4.5 Flavonoids and drought-induced stress
Regarding flavonoids, flavonols and Q, several explanations can be found why Q glycosides could be
associated with dehydration avoidance.. Flavonoids may behave as anti-oxidants by inhibiting the
production of UV-B-induced reactive oxygen species (ROS), although UV radiance is not a
prerequisite for the biosynthesis of flavonoids (Owens et al. 2008; Agati et al. 2009; Jenkins 2009).
Additionally, flavonoids may have signalling properties in protecting plant cells from oxidative stress
(Taylor and Grotewold 2005; Angeloni et al. 2007; Peer and Murphy 2007), and quercetin derivatives
specifically regulate auxin movement and hence influence plant development (Fiserova et al. 2006;
Gao et al. 2008).
Drought-induced stress has been reported to increase flavonoid concentration in several plant
families (Gray et al. 2003; Hernandez et al. 2004; Tattini et al. 2004; Turtola et al. 2005; Scalabrelli et
al. 2007; Huang et al. 2009). A recent finding by Gerhardt et al. (2008) suggested that the Q to
Kaempferol (K) glycoside ratio(QKR) increased fivefold under photosynthetically active radiation
40
(PAR) with added UV-B and far-red (FR) radiation, where non-acytelated Q was produced in
preference to acytelated K derivatives. Flavonols seem to be mainly involved in the response
mechanisms against abiotic and biotic stresses (Roberts and Paul 2006; Kilian et al. 2007; Mellway et
al. 2009; Agati and Tattini 2010). Recent findings of different MYB transcription factors explain how
MYB gene expression controls flavonol biosynthesis and thus contributes to abiotic stress resistance
(Mehrtens et al. 2005; Stracke et al. 2007; Czemmel et al. 2009; Yang et al. 2009; Stracke et al.
2010a). These findings indicate that Q glycosides can act in a similar way under drought stress as
under UV-B stress, as a moderator of abiotic stress sensitivity, as a putative regulator of auxin, which
influences plant root development, and as an anti-oxidant agent against ROS, expressed by the MYBfamily of genes. In addition, an increase of QKR is reported under UV stress (Gerhardt et al. 2008). It
appears that Q glycosides are the better protective compound compared to K glycosides in plants
stressed by UV-B, and drought as well. As there is always a UV-B stress component in plants growing
outdoors, the process of priming or hardening involves previous exposure to abiotic stress, which
would make a plant more tolerant againstforthcoming stress exposure. For example, one stress
component (e.g. UV-radiation) can trigger priming against another stress factor (e.g. drought)
(Conrath et al. 2006; Beckers and Conrath 2007; Bruce et al. 2007).
2.5 Plant breeding
Plant breeding is the development of new types of plants with improved characteristics. These
genetic changes made in plants are permanent and heritable, but their expression can be influenced
by the environment. Plant breeders manipulate plant attributes, structure and composition to
improve populations for certain human needs or to enhance existing qualities, and they are able to
create new variants that previously did not occur in natural populations (Acquaah 2007). The goals of
plant breeding are diverse, with breeding objectives formulated based on factors such as crop
producer needs, consumer preferences or needs, and ecological impact and efficiency. Resistance to
biotic and abiotic stresses or to lodging can be bred into plants, and higher yielding wheat cultivars or
a better tasting tomato can be created. Older breeding objectives like product appeal and adaptation
to production environment remain valid today, although some of the genetic alterations can now be
achieved with the availability of advanced breeding tools (Jauhar 2006; Acquaah 2007).
2.5.1 Traditional plant breeding
As part of what they call the “Evergreen Revolution”, Baenziger et al. (2006) describe the essential
framework of a plant improvement program consisting of three parts: defining breeding objectives,
creating genetic variability, and identifying superior new genotypes (Baenziger et al. 2006).
Moreover, conventional breeding programs for field crops consist of the following steps: evaluation,
selection and testing (Sleper and Poehlman 2006). Genetic variation is manipulated by crossing and
41
selection. What a plant breeder sees or measures is the phenotype of a plant. The relationship
between the genotype, the environment, and of their interaction is expressed as: phenotype =
genotype + environment + interaction (P = G + E + GE) or in variance component terms:
2
σ P2 = σ G2 + σ E2 + σ GE
Responses of genotypes and performance of varieties are different in each
.
environment. Genotype-by-environment interactions are often observed for most economically
importance quantitative traits. Thus, new breeding germplasm must be evaluated for several years
and in multiple locations in multi-environment experiments (Basford and Cooper 1998). Interactions
may be caused by heterogeneity of genotypic variance across environments or imperfect correlations
of genotypic performance across environments. Interactions can be non-crossover (rank changes do
not occur) or cross-over (rank changes do occur) i.e. rank changes in the performance of varieties in
different environments. For plant breeders, GEI (Genotype-by-Environment Interaction) may only be
problematic when they are crossover interactions(Baker 1988).
Variation in a population of phenotypes of plants may be normally distributed. The desirable
variation of genotypes is hidden in the variation of phenotypes: Variation of P = variation of G +
variation of E. This is the expression of difference in traits among individuals in a population. The part
of genetic variation called heritability = genetic variation / phenotypic variation, can range from 0 (no
genetic variation) to 1 (variation caused only by genetics). The plant breeder needs to select plants
that have desired characteristics, which can be a balancing act between acceptable costs and
selection progress depending on trait heritability (Chrispeels et al. 2003; Acquaah 2007).
The process of combining different varieties of organisms is an important aspect of plant breeding,
and is used to create genetic variation by sexual reproduction. Meiosis, the movement of
chromosomes during the production of natural gametes, is the primary driver of genetic variability in
conventional breeding programmes, although mutation can play a role. Selected parent plants are
cross-pollinated (even if they are naturally self-pollinating) to produce progeny with a range of
genetic differences. This is called crossing (Chrispeels et al. 2003). Plant breeding methods for crop
improvement depends on the natural pollination method of the plant:
1. Choose parents with individual traits of interest
2. Hybridise these parents
3. Breeding scheme: selecting among the progeny of those parents to recover the favourable
characteristics from both parents in one or more progeny plants
4. Release the best progeny as a variety (pureline, hybrid, clonal or population), or continue with
another cycle of crossing and selection.
The appropriate breeding scheme (3) and selection of the variety (4) depends on the method of
pollination and reproduction specific to the crop, either self-pollinated (homozygous) or cross42
pollinated (heterozygous). Cross-pollinated plants are sensitive to the increased homozygosis linked
with inbreeding and show inbreeding depression (yield capacity decrease in advanced generations of
hybrids in a narrow genetic base) (Fehr et al. 1987; Chrispeels et al. 2003).
F1 hybrid varieties often exhibit higher yield potential than either parent strain, and they are
produced by both cross- or self-pollinated plants. First inbred or pure lines are produced by selfpollination. Then two selected homozygous lines are crossed to produce heterozygous offspring. This
homogenous, heterozygous outcross or F1 offspring often shows hybrid vigour, also called
transgressive segregation (Rieseberg et al. 1999) or heterosis, resulting in the phenotypic superiority
of a hybrid’s traits over its parents’ traits (Lippman and Zamir 2007).
Backcrossing adds one or more desirable characteristic to an elite variety. When a plant breeder
wants to add just one characteristic to a plant, such as resistance to a virus disease, a single desirable
characteristic from a donor parent can be added to the genetic structure of a superior recurrent
parent. Although this breeding method comes close to manipulating single genes through sexual
reproduction, in fact many genes, often in large chromosome parts are being manipulated due to
linkage drag. Several backcrosses have to be made to reduce the genes (except those expressing the
desirable trait) contributed by the donor parent, although this can be accelerated by the use of
markers to increase the rate at which the recurrent parent genome is recovered. Backcrossing is not
useful for quantitative traits that are controlled by multiple genes or chromosome segments,
because during meiosis these larger chromosomal areas will be split up and recombined differently in
the offspring and the favourable quantitative trait will not be transferred as such (Gepts 2002;
Chrispeels et al. 2003; Acquaah 2007).
2.5.2 Quantitative genetics
Quantitative inheritance involves inheritance of multigenic (quantitative) traits, such as yield or
biomass production (Lynch and Walsh 1998), in contrast to inheritance of qualitative traits such as
disease resistance, which is typically confined to a small genomic region or is monogenic. Qualitative
resistance is often unstable when compared to quantitative resistance (St Clair 2010).
Heritability
Heritability of a quantitative trait is the concept of the reliability of the phenotypic value of a plant
population as a guide to the breeding value. It is the proportion of the observed variation in a
progeny that is inherited, and is expressed as the ratio of genetic variation to total variation, which
includes both environmental and genetic variation. (Kearsey and Farquhar 1998). If the genetic
variation in a progeny is large in relation to the environmental variation, heritability will be high.
Unfortunately, traits that are greatly influenced by the environment, such as yield or biomass
43
production, will often have low heritability (Sleper and Poehlman 2006), because the environmental
component is large, compared to the genetic component. With multiple-gene inheritance,
transgressive segregates may occur, which have trait values outside the range of the parents,
especially if the genetic factors conferring desirable traits are at different loci in the parents
(Rieseberg et al. 1999). This occurrence is useful for plant breeders to obtain superior cultivars
(Sleper and Poehlman 2006).
Crossing and Mendelian genetics
Plant breeding methods for crop improvement depends on the natural pollination method of the
plant; in the case of white clover this is cross-pollination. Parent plants are cross-pollinated to
produce variable offspring (Chrispeels et al. 2003). Progeny or offspring is a set of individuals arising
from a parental cross; a hybrid is any heterozygous offspring resulting from the mating of two
distinctly homozygous individuals (where identical alleles of the gene in question are present on both
homologous chromosomes); a genotype or individual is a genetically unique individual plant; a clone
or clonal copy is a vegetative (genetically identical) copy of a genotype; a mapping population is a sub
set of progeny that is sampled in the experiments. During meiosis, a form of nuclear division
associated with sexual reproduction in higher plants, two successive divisions take place: the first
reductional and the second equational (Majumdar et al. 2004). The parent cells of amphidiploid
white clover are allotetraploid (2n = 32), and at meiosis bivalents (n=16) (a pair of associated
homologous chromosomes) are formed, i.e. white clover is an allotetraploid that acts like a diploid
(Majumdar et al. 2004). The movement of chromosomes during the production of these natural
gametes and the successive recombination of genes leads to genetic variability (Sleper and Poehlman
2006).
The mechanism of Mendelian heredity is based on gene recombination following crossing, and
follows two laws discovered by Mendel for diploids: The law of segregation: paired factors (genes)
segregate during the formation of gametes in a random manner such that each gamete receives one
form or the other (allele); and the law of independent assortment: when two or more pairs of traits
are considered simultaneously, the factors (genes) for each pair of traits assort independently to the
gametes (Jain and Kharkwal 2004). In a random mating population, qualitative or simple traits, such
as morphological characteristics, are phenotypically expressed as dominant, recessive, or a defined
combination thereof. In contrast, phenotypic values of quantitative or complex traits, such as yield or
stress resistance, exhibit continuous variation which can be measured and statistically analysed
(Falconer and Mackay 1996; Sleper and Poehlman 2006; Acquaah 2007). See also the previous
paragraph.
44
2.5.3 Breeding for abiotic stress resistance
Stress resistance is an integral part of cultivar development programs (Pareek 2010). Before final
selection of a new cultivar, prospective genotypes are evaluated at different sites and over several
years to assess environmental adaptation (Abberton and Marshall 2005). Higher plants are not
mobile like animals and must adapt or acclimate to the environment when changes occur. They do
this by in a series of responses to cope with stressful situations. The underlying physiological basis of
these responses is a network of signalling pathways, from the perception of environmental signals,
via the generation of messengers and signal transduction to the final stress response (Seki et al.
2007; Wu et al. 2007).
Under conditions of severe stress, high-yielding genotypes often show severe yield depression (Blum
1988). Conversely, cultivars with higher stress tolerance often show very low yield potential (Grime
2001). It is desirable to combine stress tolerance traits with high yield potential (Hofmann et al.
2003a; Hofmann et al. 2003b; Sanderson et al. 2003).
Plant breeders commonly use two approaches in breeding for abiotic stress resistance: indirect
breeding and direct breeding (Abberton and Marshall 2005; Jenks and Hasegawa 2005; Acquaah
2007). Through indirect breeding, breeders expose plant genotypes to environmental stress under
atypical conditions using indirect selection pressure, e.g. by choosing thicker stolons in white clover
as an indirect selection criterion for increased root development, and consequently they investigate
the relationship between root development and drought tolerance (Annicchiarico and Piano 2004).
Direct breeding is carried out under uniform and predictable stress conditions (Herrmann et al.
2008). Selection methods used for direct breeding are field selection, selection under managed stress
environments, selection based on yield, selection based on developmental traits or selection based
on assessment of plant water status and plant function (Chaves and Oliveira 2004). Breeders may
select for early maturity (stress avoidance), or genotypes with proven stress resistance, and may seek
to transfer the resistance genes into superior cultivars (Levitt 1980; Price et al. 2002; Ehlers and Goss
2003; Vinocur and Altman 2005).
Breeding for drought resistance is closely linked to the environments in which the crop is growing,
not only in regard to its physical and biochemical qualities, but also needs related to cultural
behaviour (Blum 2001; Abbo et al. 2010; Messina et al. 2011). The development and use of droughtresistant cultivars is advantageous to crop production in areas with marginal or erratic rainfall
patterns. Abiotic water-related stresses, such as drought, salinity and waterlogging, are estimated to
reduce crop yield worldwide by up to 70% compared to optimal conditions (Bray 1997; Acquaah
2007), and breeding for stress resistance is becoming more important (Witcombe et al. 2008).
45
Transgenic approaches offer a realistic option to enhance drought tolerance in white clover (Wang et
al. 2009; Jiang et al. 2010b). Drought causes major yield losses and conventional breeding for
drought resistance has its limitations (Trethowan et al. 2001; Ashraf and Harris 2005). With
transgenic technology abiotic stress tolerance can be engineered into crop plants. In the meanwhile,
marker-assisted breeding and selection will enable ‘genomics-assisted breeding’ for crop
improvement (Varshney et al. 2005b). The combination of traditional marker-assisted breeding with
genetic engineering can accelerate development of plant types with abiotic stress tolerance (Vinocur
and Altman 2005; Miklas et al. 2006).
In dryland farming systems, drought resistance is the major breeding objective (Jahufer et al. 1999;
Lane et al. 2000a; Humphreys 2005; Williams et al. 2007), whereas in more advanced agricultural
production systems, the objective is to obtain economic plant production under drought conditions
(Jahufer et al. 2002; Passioura 2007). Previous breeding objectives for drought resistance were aimed
at developing a cultivar that had water-saving capacity (Condon et al. 2004). However, the relation
between crop yield and water use shows that the focus for drought resistance should be on efficient
water use (= high water productivity) (Passioura 2006; Blum 2009) when water is limited by drought,
rather than on water-saving capacity (Bacon 2004; Farooq et al. 2009a).
Formulating an ideotype with detailed morphological, physiological and developmental
characteristics for a drought target environment is a challenging task for plant breeders.
Complementing crop modelling with marker-aided breeding into an efficient breeding strategy to
efficiently define and develop crop matching ideotypes for different environments is a major
challenge for the future (Yin et al. 2003).
In drought-prone locations, usually a combination of stress factors takes place at the same time, such
as drought with high temperature (Mittler 2006), or drought and salinity stress under irrigation, and
may be unpredictable in occurrence, intensity and duration. As stated before, drought resistance is
location-specific, and new drought-resistant genotypes need to be evaluated under specific
environmental conditions. Genotype x location interaction is region-specific and may be wide enough
to warrant breeding for specific locational adaptation (Annicchiarico et al. 2010). As the nature of
drought changes with location, it is crucial that a plant breeder can determine which type of drought
is occurring and hence define a screening strategy for that location. In G×E analysis in plant breeding
there is a concept of “mega-environments”. These are groupings of environments in which responses
of genotypes are more or less similar (Gauch and Zobel 1997). Thus a plant breeder would say that
drought resistance is mega-environment specific. An example is CIMMYT's approach to breeding for
wide adaptation (Braun et al. 1996).
46
Several stress response genes have been discovered by researchers in other crops, e.g. barley and
tobacco (Senthil-Kumar et al. 2010; Morran et al. 2011), although these are not necessarily stress
adaptive in terms of drought resistance but rather constitutive (Blum 1996). Plant traits affecting
drought response include phenology (timing of vegetative and reproductive activities), plant
development and size, roots, plant surface, non-senescence mechanisms such as the “stay-green”
gene (Harris et al. 2007), stem reserve utilisation, photosynthetic systems and water use efficiency
(Chaves et al. 2003).
2.5.4 Molecular plant breeding
Molecular plant breeding is based on quantitative genetics, merged with molecular- and population
genetics. Quantitative genetics is concerned with the inheritance of those differences between
individuals that are of degree rather than of kind (Falconer and Mackay 1996). Molecular genetics is
focussed on genetics at the molecular level. Population genetics studies the genetic constitution of a
population, the frequencies of genes and genotypes and natural occurring variation (Dudley 2004).
Together, molecular and population genetics give rise to systems of marker-aided breeding.
The accuracy of molecular and phenotypic plant selection and the capacity to determine the
genotype by environmental interaction will decide the success of breeding for complex traits
(Hammer et al. 2005; Araus et al. 2008). Using the backcross approach with molecular markers for
QTLs and subsequent marker-assisted selection for improvement of polygenic traits such as drought
adaptation in maize is described by Ribaut and Ragot (2007). With the advent of newly developed
genomics resources, stable abiotic stress-resistant lines could be developed taking advantage of gene
pools using functional genomics or marker-assisted programs. The technique of QTL cloning in
marker-assisted breeding programs for drought resistance and application of marker-assisted
selection entails integrating QTL alleles for drought tolerance from one species into the gene pool of
another as a result of hybridisation (Salvi and Tuberosa 2005; Tuberosa and Salvi 2006).
Several stress-inducible genes introduced by gene-transfer have enhanced abiotic stress tolerance in
transgenic plants, and will make it possible to engineer drought-tolerant transgenic plants (Umezawa
et al. 2006; Valliyodan and Nguyen 2006). Functional genomic approaches can help reveal abiotic
stress resistance mechanisms, e.g. transcriptome analyses based on micro-arrays have led to the
discovery of stress-responsive genes in Arabidopsis and many crop plants and tree species (Shinozaki
and Yamaguchi-Shinozaki 2007).
Molecular markers
Molecular or genetic markers are stable differences in short DNA sequences that occur at specific
locations on chromosomes. Various types of molecular markers have been developed since the
47
1980s (Mohan et al. 1997). Because molecular markers follow the same rules of inheritance as genes,
they can be put on genetic maps, which are very useful to plant breeders (Jones et al. 1997; Jones et
al. 2009). Genetic or genome maps are graphic representations of the order and relative distances
among genetic locations on a chromosome. Molecular markers occur on many locations along the
chromosome, and genetic maps can be produced with markers covering all the chromosomes, close
together. Genetic linkage is very useful in marker-assisted breeding (Semagn et al. 2006a). For
example, a tightly linked marker and gene means that the plant breeder can look at the marker in the
analysis of transmission of the interesting gene, instead of at the trait expression itself which may be
expensive or difficult to accurately assess through phenotype alone. Another advantage of molecular
markers is the way they can be used in determining if two alleles influencing a single characteristic
are from the same or from different loci. If alleles are mapped at different locations using molecular
markers, they must vary at different loci. This makes it efficient to combine two differently mapped
genes from two parent plants with the same phenotype into one offspring with an improved
phenotype. The heritability of quantitative traits are analysed by combining phenotype testing and
molecular marker mapping. The main goal is breaking down the genetic control of a quantitative trait
into its component QTLs and combining favourable QTL alleles into future generations (Andersen and
Lubberstedt 2003; Chrispeels et al. 2003; Collard et al. 2005; Jones et al. 2009).
Several methods have been developed to identify molecular markers, e.g. by separating DNA
fragments to size by gel electrophoresis (Sato et al. 2005). When individuals can be distinguished
from each other because they have different sequences in the DNA at a particular locus, the
molecular marker locus is said to be polymorphic between those individuals (Dolanska and Curn
2004). Molecular markers follow the same rules of inheritance as genes (Chrispeels et al. 2003;
Collard et al. 2005). Microsatellites, also called short tandem repeats (STR) or simple sequence
repeats (SSR), are commonly used genetic markers in plants and are one to six nucleotides long for
each repeat unit (Kolliker et al. 2001). Polymorphism of SSR markers is generated by loss or gain of
repeat units. SSR marker assays use polymerase chain reaction (PCR) and are preferred for genetic
analysis because they are stable, polymorphic, easily analysed and occur regularly throughout the
genome (Hajeer et al. 2000). SSR markers can be used to define regions of the white clover genome
regulating the complex traits Q glycosides and biomass production (Forster et al. 2001).
Quantitative Trait Loci
Quantitative (continuously variable) traits are characteristics of progeny populations that are
controlled by multiple genes (Tanksley 1993). Quantitative Trait Loci (QTL) are statistically estimated
locations on chromosomes of one or more genes controlling a quantitative trait (such as yield,
resistance to stress), and are much more challenging to clarify than qualitative traits (Doerge 2002).
Because of the importance of quantitative traits, manipulating them is central to plant breeding. The
48
challenge of plant breeding for quantitative traits is to get most or all the best alleles controlling the
characteristic into a single plant variety. Because the desirable alleles are so difficult to locate, in a
conventional breeding program plant breeders have to make hundreds of crosses, from which
millions of F1 offspring have to be planted for the evaluation, selection and testing experiments,
which can take many years to complete. Furthermore, the process of selecting the best performing
phenotypes and crossing them has to be repeated several times because plant breeders do not know
which favourable alleles of what genes are present in the offspring, and because trait expression is
influenced by environment (Chrispeels et al. 2003; Collard et al. 2005; Baenziger et al. 2006).
An important trait in many plant breeding programmes is abiotic stress resistance, which is generally
polygenic, i.e. it is a trait that is expressed by several genes located at different locations on the
genome, or on QTL (Barrett et al. 2001). Transgenic plants could be of use, because they can over- or
under-express one or more stress-tolerance genes and can be selected for the selected trait in a
population (Jiang et al. 2010a). QTLs are the locations and characterisation of the genes causing
quantitative variation for these traits (Bernardo 2010). To be able to select for this trait in a breeding
program, genes that encode for the desired trait in different strains of white clover and their close
relatives have to be revealed (Barrett et al. 2005). Genetic analysis can be used to identify factors
that play important roles in the detection of this trait, and genes that are responsible for the
expression of the polygenic trait (Humphreys 2005). One of the important traits in abiotic stress
resistance is the production of dry matter (DM, biomass or herbage yield). An indicator of abiotic
stress resistance in white clover could be quercetin glycoside levels (Hofmann et al. 2000; Hofmann
et al. 2003a). However, Q related QTL have not been identified in white clover yet.
Genetic mapping
Genome maps allow plant breeders to partially break down the genetic control of complex traits into
discreet factors with Mendelian inheritance (Lander and Botstein 1989), each of which is a QTL
(Barrett et al. 2004a; Barrett et al. 2005; Zhang et al. 2007a). The use of gene mapping, MAS, and
map-based cloning, can theoretically increase breeding efficiency and the array of traits suited for
selection (Lebowitz et al. 1987; Barrett et al. 2001; Collard et al. 2005). An exampleof QTLs for carbon
isotope discrimination that are repeatable across environments and across wheat mapping
populations is given by Rebetzke et al. (2008a), and an example of QTLs in drought-stressed maize
regulating morpho-physiological traits and yield is stated by Tuberosa et al. (2002). However, QTL
analysis is still time-consuming and resource-demanding, which prevents the use of gene mapping
and marker-assisted selection techniques more widely in plant breeding (Tuberosa et al. 2002).
A logarithm of the odds ratio (LOD score) can be used for a test of significances. At a LOD of 2, it is
100x more likely that a QTL exists in the interval, and this is 1000x more likely at a LOD of 3 (Doerge
49
et al. 1997); Molecular Breeding
(http://www.knowledgebank.irri.org/ricebreedingcourse/QTL_mapping.htm). LOD-scores are also
used elsewhere in biostatistics, for instance to group and order markers in linkage groups of a
genetic linkage map.
Genome, linkage or genetic maps are pictographic representations of the order of genetic locations
on a chromosome (Barrett et al. 2007). Using linkage analysis, molecular markers are placed on these
maps and act as “flags” for the presence of an interesting gene, because they are often located
(linked) near such a gene (Jones et al. 2003). This is particularly useful for genes that are difficult or
expensive to reliably detect phenotypically (Henry 2001). If the gene and the marker are closely
linked to each other, the marker will generally be inherited along with the allele of interest (Barrett
et al. 2005). Precision can be increased by identifying markers flanking the gene or region of interest.
Molecular markers can be used to map alleles to determine if two alleles influencing the same trait
are mapped to different locations or not (Collard et al. 2005). Genetic linkage maps of white clover
have been constructed using SSR markers discovered in sequences from several species in the
Trifolieae tribe, including white clover, red clover (T. pratense L.), Medicago truncatula (Gaertn.) and
soybean (Glycine max L.) (Barrett et al. 2004a; Zhang et al. 2007a).
Experimental designs for estimating effects and map positions of QTLs are based on linkage
disequilibrium between alleles at a marker locus and gene loci at the linked QTL (Tanksley 1993;
Morgante and Salamini 2003). In linkage disequilibrium the haplotype frequencies in a population
differ from the values they would have if the alleles at each locus were combined at random. When
the linkage disequilibrium is zero, then the population is in linkage equilibrium (Gupta et al. 2005).
Marker-assisted breeding
Marker-assisted breeding is suited to help identify and manipulate QTLs and major genes to develop
improved cultivars (Gepts 2002; Chrispeels et al. 2003; Acquaah 2007). While some animal and plant
species now benefit from the use, or are rapidly developing genomic selection strategies, minor
species including white clover remain reliant on QTL approaches in the absence of concerted efforts
to develop and implement genomic selection by whole-genome sequencing and association scan
mapping (Rafalski 2010).
Unlike traits controlled by single genes, complex traits governed by quantitative trait loci (QTLs) and
their interactions are more difficult and expensive to manipulate. Backcrossing is not useful for
quantitative traits controlled by multiple genes or chromosome segments (such as abiotic stress
resistance), because just identifying the desirable alleles in phenotypes is a challenging task, although
progress has been made in marker-assisted backcrossing (Semagn et al. 2006b).
50
Molecular markers can be used in breeding programs aimed at introgressing resistance genes into an
improved cultivar with the desired traits. This process is called marker-assisted selection (MAS),
marker-assisted breeding or molecular plant breeding (Barrett et al. 2004b; Smith et al. 2007).
Because of the complexity of manipulating quantitative traits, integrated approaches are required to
increase the usefulness of marker-assisted selection (MAS) for QTLs (Collard and Mackill 2008).. MAS
allows for an acceleration of the breeding process. Finding qualitative traits controlled by single or
few genes could routinely be assisted by MAS (Francia et al. 2005).
However, for more complex traits like yield and abiotic stress tolerance, Francia et al (2005) pointed
out a number of limitations on efficiently using QTL mapping information in plant breeding by making
use of MAS in major crops. For less intensely studied crops such as white clover these limitations are
even greater, especially as QTL mapping is demanding on time and resources and thus high-cost.
More reliable and complete data about quantitative traits by using correctly sized mapping
populations are needed (Young 1999; Francia et al. 2005), together with a combination of crop
modelling and QTL mapping (Figure 2.11; (Yin et al. 2003)), to select a plant ideotype for a given
environment, set of environmental conditions or managed stress environments (MSE) (Morgante and
Salamini 2003; Campos et al. 2004; Francia et al. 2005; Lafitte et al. 2007).
A combination of phenotype screening (“phenotyping”) and molecular marker mapping is used by
plant breeders to analyse the inheritance of quantitative traits. Statistical analysis of the data is used
to detect marker alleles associated with phenotypic differences in these traits (Herrmann et al.
2008), also called marker-trait associations. After splitting up the genetic control of a quantitative
trait into its component QTLs, it will be theoretically possible to transfer those favourable alleles into
a superior progeny (Barrett et al. 2007), provided certain assumptions are satisfied e.g. the QTL has
positive genotypic effect in all genetic backgrounds. Requirements for mapping QTLs are the
availability of a linkage map of polymorphic marker loci that adequately covers the whole genome,
and variation for the quantitative trait within or between populations or strains (Paterson 1998;
Phillips and Vasil 2001).
With the advent of newly developed genomics resources, stable abiotic stress-resistant lines can
theoretically be developed taking advantage of gene pools using functional genomics or markerassisted programs (Richards et al. 2010; van Eeuwijk et al. 2010). Salekdeh et al. (2009) presented a
useful conceptual framework making use of molecular breeding for drought tolerance. This
framework settles on yield as the product of water use (WU), water use efficiency (WUE) and harvest
index (HI) (Passioura 2006; Salekdeh et al. 2009). Wide-ranging phenotyping protocols are needed to
make efficient use of this framework (Salekdeh et al. 2009). Figure 2.11 shows a conceptual overview
of marker development by Collard and Mackill (2008).
51
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Collard and Mackill 2008.
Figure 2.11 Marker development ‘pipeline’ (Collard and Mackill 2008).
52
53
Chapter 3
Pilot experiment: Phenotypic trait variation in novel white clover F1
progeny
Layout of the pilot experiment in the LU nursery enclosure.
54
3.1 Introduction
Biological productivity and resilience of pastoral systems is supportive of the primary industry in New
Zealand. Positive acclimation of plants to abiotic stress induced in both normative and harsh climatic
conditions is a key component of the consistent performance required from grazed forage as the
primary energy source for profitable farming. Recent white clover research (Hofmann et al. 2000)
demonstrated a relationship between levels of glycosides of the secondary metabolite Quercetin (Q
glycosides) and firstly, plant adaptive response to abiotic stress, secondly plant biomass production
under normative conditions. They found that high levels of Q glycosides were associated with high
levels of abiotic stress tolerance, and low levels of biomass production. It is unknown if this observed
relationship is causal or associative, an unintended and detrimental side-effect of natural and
artificial selection processes. If it is associative then plant breeders may be able to reverse these
relationships and develop forages with high levels of abiotic stress tolerance and high plant
production.
The pilot experiment described in this chapter has the purpose to establish the parameters needed
to decide on a go-ahead for a large-scale phenotyping experiment (Chapter 4): key trait
measurements, and the degree and range of clonal variation for these traits. Objectives are to
identify causal versus associative relationships of Q glycosides with plant dry matter (DM) production
and adaptive stress response, to identify levels of phenotypic variation for Q glycosides and DM
production, with high levels preferred, and find evidence (or lack thereof) for breakage of the
negative correlation between Q glycosides and productivity, with breakage being preferred. The
experiment will determine the effect of normative NZ East coast summer conditions (UV levels,
drought and heat) on a population of white clover F1 progeny genotypes in pots, and signify key
attributes of the progeny population: morphology, physiology and biochemistry relevant for the
large-scale phenotyping experiment, as well as establishing a range of clonal trait variation in an F1
subset for Q glycosides and DM production.
3.2 Materials and methods
3.2.1 Plant material
A New White Clover Hybrid
Using these two populations, a new pair cross between two individual genotypes was created at the
Grasslands Research Centre, AgResearch, Palmerston North, NZ. One parent genotype was taken
from the Kopu II synthetic cultivar, selected for morphology and vigour representative of the cultivar.
The second parent genotype was identified on the basis of high flavonoid accumulation from the
Tienshan ecotype. The F1 progeny that resulted from this pair-cross between Tienshan and Kopu II
exhibited variation for medium sized leaves, high stolon density and vigorous growth (Figure 3.1,
55
Figure 3.2). This F1 progeny will be used in this study to elucidate genetic control and interrelationships among plant characteristics, including biomass productivity and Quercetin (Q) levels. A
comparison of root and shoot characteristics of parents and progeny is shown in Figure 3.3.
Kopu II cultivar
X
Tienshan ecotype
KxT & TxK
Figure 3.1
Examples of phenotypes of Kopu II, Tienshan and their pair-crossed F1 progeny.
Figure 3.2
Two phenotypes of two TxK F1 progeny showing large variation for vigour.
56
Kopu II elite
cultivar
Figure 3.3
KxT F1 hybrid
Tienshan
ecotype
The distinctive phenotypic differences in roots and shoots between F1 progeny and
parents. (The white label measures 128 mm in length).
A population of 190 full-sibling F1 plants was created by a hand pollinated pair cross between two
phenotypically divergent, highly heterozygous white clover (Trifolium repens) genotypes (Barrett et
al. 2004a): “14-22” grown from the population C20133 of “Grasslands Kopu II”, a synthetic New
Zealand cultivar; and “28-6”, grown from the population C8749 of a Chinese high altitude ecotype
originating in the Tian Shan Mountain range, consequently named “Tienshan” (Hofmann et al. 2000).
Kopu II 14-22 exhibits large leaves, leaf fleck, leaf mark, many long thick stolons , and open growth;
whereas Tienshan 28-6 exhibits small yellow-green leaves, no leaf fleck, no leaf mark, many thin wiry
stolons, and compact slow growth. Accession numbers were from AgResearch’s Margot Forde Forage
Germplasm Centre in Palmerston North, New Zealand. The whole population of 190 F1 progeny
genotypes plus two parental genotypes were cloned by stolon cuttings and maintained in 7.5L pots in
the Nursery at Lincoln University. From these stock plants further stolon cuttings were made to
produce plant material for the experiments.
White clover plant material used was a random selection of 10 F1 genotypes from the 190 full-sibling
F1 population. The parental genotypes Kopu II and Tienshan were also included. Sixty stolon cuttings
were planted in pots in a glasshouse on 7 December 2007. A week later on 14 December 2007, the
plants in pots were placed outdoors in the layout described in paragraph 3.2.2 Experimental design.
Measurements were carried out on 19 December 2007, 2 and 17 January 2008. Harvest of leaves for
HPLC analysis was done on 18 January and the plants were destructively harvested for dry matter on
21 January 2008.
57
3.2.2 Experimental design
The experiment design for the pilot experiment was a 3 x 4 rectangular lattice design, with ten
randomly sampled F1 progeny genotypes and two parents, Tienshan & Kopu II, as internal checks, in
five replications (blocks), resulting in a total of G12 x R5 = 60 experimental units (Appendix F.1). The
plants were grown in large, wide 7.5 L pots using the soil mixture identified in the soil mix
experiment and the pots were randomised according to the design. The treatment structure was
single factor (genotype), and genotypes were grown for six weeks under Canterbury outdoor
summer conditions, without induced stress.
3.2.3 Soil mix test under drought
To determine the best available multi-purpose soil-mix for white clover testing in pots outside that
has the following qualities: reduced shrinking or clotting of the local silt loam, a fine, crumby
structure that washes well from the roots and allowing the establishment of roots throughout the
soil-mix. Ideally, white clover experiments in pots outdoors should reflect the situation in the field as
closely as possible. The soil in the local paddocks in Canterbury consists predominantly of a silt loam
(Francis and Kemp 1990).
Soils considered for the mix included mortar sand, standard potting mix, Wakanui sandy / silt loam,
Lake Lyndon brown allophanic silt loam, vermiculite, pumice, perlite and crusher dust in different
mixture ratios. Horticultural gypsum (Winstone Wallboards), wetting agent (Hydraflo, Scotts
Australia Pty Ltd) and fertiliser (Osmocote Exact) were added to the mix to improve soil structure,
water holding and rewetting capacities and nutrient content. White clover F1 progeny were grown in
large 7.5 litre pots in a glasshouse. A small-scale drought test was performed on some of the potted
plants and after growing them for four weeks in different soil-mixes, rooting tests were performed
and root-washing was tested at the Lincoln University Field Service Centre (FSC), Lincoln, New
Zealand.
3.2.4 Measurements
Every two weeks non-destructive morphological trait measurements were carried out including
stolon numbers per pot (St); primary stolon (the first or oldest stolon emerging from the original
clonal cutting) thickness measured by diameter at the centre of the internode behind the first fully
expanded (FFE) leaf in mm (StDiam), length of primary stolon from node of FFE leaf day 0 to node of
FFE leaf harvest date in mm (PrimStLength), where the FFE leaf on day 0 is marked with strung paper
tags, first measurement (day 0) is from the root/shoot boundary to the node of the FFE leaf; leaf
mark (LM) yes or no, leaflet width (LfltWidth) of the middle leaflet at its widest point of the first fully
expanded (FFE) leaf on the primary stolon in mm; number of inflorescences per primary stolon (InSt),
58
and per pot (In); number of growing points per primary stolon (GrP-St), and per pot (GrP-Pot); petiole
length (PetLength) from node to pulvinus of the FFE leaf on the primary stolon in mm. These
measurements were performed fortnightly. Physiological assessment was performed by examining
chlorophyll concentration in leaves (SPAD value, the optical density difference at two wavelengths:
650 nm and 940 nm) with a SPAD-502 leaf chlorophyll meter (Konica Minolta Sensing Inc., Osaka,
Japan).
Leaf area of laminae samples was measured with a portable leaf area meter (AM300, ADC
Bioscientific Ltd, Herts, England) to obtain Area (mm2), Width (mm) and Length (mm) measurements.
Dry matter (DM, g) was destructively measured for above ground parts or shoot DM (SDM) and
below ground parts or root DM (RDM) separately. The above ground parts (shoots) washed out, cut
off and dried, and the roots washed out of the silt loam and dried separately from the shoots. Rootwashing was greatly facilitated by the improved soil structure. The DM for above ground parts were
separated into leaf laminae (LamDM) including DM of the 10 laminae used for HPLC analysis, petioles
(PetDM), stolons (StDM), and inflorescences including peduncles (InDM), and were separately dried
for 48 hours at 70º C and weighed. Leaf DM (LeafDM) was calculated by adding up LamDM + PetDM
+ StDM. Specific leaf mass (SLM, g mm-2) was calculated by dividing leaf dry matter by the leaf area of
four laminae.
3.2.5 HPLC Analysis
Sample preparation and HPLC analysis follows established methods (Hofmann et al. 2000; Ryan et al.
2002; Dong 2006). White clover laminae (ten clean first fully expanded leaves) were harvested at the
nursery and put in vials containing liquid nitrogen. Immediately after collection, samples were frozen
in liquid nitrogen and stored at -32 ºC. The frozen samples were ground to a fine powder in a mortar
with pestle under liquid nitrogen, after which the ground samples were freeze-dried in a W.G.G.
Cuddon Ltd freeze-dryer at H218, Lincoln University. Flavonoids were extracted from freeze-dried 50
mg samples in MeOH-H2O-HOAc (79:20:1) for 12 hours in darkness. The centrifuged supernatant was
syringe-filtered into amber 1 ml HPLC vials, which were placed in the carousel of a Millipore Waters
717 plus auto-sampler for HPLC analysis, together with a series of standards with Rutin C27H30O16.
Solvents for analysis A (1.5% H3PO4) and B (HOAc-CH3CN-H3PO4-H2O, 20:24:1.5: 54.5)(Hofmann and
Jahufer 2011), and cleaning solvents C (10% CH3CN) and D (90% CH3CN), were prepared and filtered
(Hofmann et al. 2000). A Millipore Waters 600-MS system controller was used to distribute and mix
the solvents. A reverse phase separation column (Merck Supersphere Lichrocart 125-4 RP-18
endcapped column, 4 μm, 4 mm x 119 mm) suited for flavonoids was connected between the autosampler and the Millipore Waters 996 photodiode-array detector. A specific gradient for solvents A
59
and B was used to clearly separate the peaks for Q glycosides and K glycosides in the chromatogram,
starting with 80% A and 20% B, decreasing to 33% A at 30 min, 10% A at 33 min and 0% A at 39.3 min
(Hofmann et al. 2000; Hofmann et al. 2003a). Millennium HPLC analysis software was used to
produce a sample information report. This report, together with spectrum analysing software, was
used to analyse peak wavelengths for Q glycosides (257 & 357 nm) and K glycosides (266 & 347 nm).
The integrated areas of all flavonol peaks (measured at 352 nm) were added, and the result
compared to an external standard curve prepared using 0, 10, 25, 50 and 100 ppm rutin (quercetin 3rutinoside). Thus the flavonol glycoside levels are expressed here as rutin equivalents (Ryan et al.
2002; Olsen et al. 2010).
The author centrifuging samples for the extraction of flavonoids.
3.2.6 Statistical Analysis
The genotypes used in this experiment were a random sample representing the mapping population.
Therefore, analysing the genotypes as random effects gave the opportunity of estimating the
genotypic variation within the mapping population. These variance components were used in the
estimation of broad sense heritability. The BLUP values were adjusted means corrected for spatial
variation (row-column effects within replicates and across replicates), which were certainly more
accurate. The genotypes as random effects enabled a) estimation of genotypic variances and b) the
60
BLUP values were adjusted means corrected for spatial variation. For data analysis and tests of
significance for main and interaction effects, the restricted maximum likelihood / best linear unbiased
prediction estimates of the random effects (REML/BLUP) in a linear mixed model, and analysis of
variance (ANOVA) procedures in the GenStat statistical software package were used, while correlative
studies were conducted with the GenStat Regression Analysis and Correlation procedures.
Homogeneity of variances was tested by examining the residual plots of the ANOVA and REML
analyses (Smith et al. 2005; Payne et al. 2009).
3.3 Results
The new soil matrix (Appendix C.1) was used satisfactorily in the growth - and root-washing phases of
the pilot experiment. The pilot study demonstrated evidence of significant variation for the
biochemical traits. The results between the three fortnightly morphology measurements were either
having too many missing values or were not significantly different between genotypes. In the third
and last measurement series, genotypic variation among the F1 genotypes for the traits Area, GrPPot, GrP-St, InDM, LamDM, LeafDM, Length, LfltWidth, PetDM, RDM, SDM, SLM, St, SPAD, StDM,
StDiam, TDM and Width (Table 3.1) were all non-significant. There were not enough measurements
for the traits In and InSt to analyse. All of the plants had leaf mark (LM) except Tienshan. Genotypic
variation of the traits PetLength, PrimStLength, K, Q, QKR and RSR (Table 3.1) among the F1
genotypes were significant (P < 0.05). Genotypic variation within the whole population of the F1
progeny including the parental genotypes increased the significant differences of the traits (Table
3.1), columns “Progeny + Parents” and “P Prog + Par”). There was no significant correlation between
Q glycosides and SDM in the pilot experiment.
61
Table 3.1
BLUP estimates for the trait means in the pilot experiment.
Stolon numbers per pot (St); primary stolon thickness (StDiam, mm); length of primary stolon
(PrimStLength, mm); leaflet width (LfltWidth, mm); number of growing points per primary stolon
(GrP-St), and per pot (GrP-Pot); petiole length (PetLength, mm); chlorophyll concentration in leaves
(SPAD-value); flavonol glycosides quercetin (Q, mg g-1 dry matter) and kaempferol (K, mg g-1 DM);
quercetin:kaempferol glycoside ratio(QKR); leaf area of laminae, (Area, mm2), leaf width (Width,
mm), leaf length (Length, mm); shoot dry matter (SDM, g), root dry matter (RDM, g), root-to-shoot
ratio (RSR); leaf laminae dry matter (LamDM, g), petioles dry matter (PetDM, g), stolons dry matter
(StDM, g), inflorescences dry matter including peduncles (InDM, g); Leaf DM (LeafDM, g) was
calculated by adding up LamDM + PetDM + StDM; Specific leaf mass (SLM, g mm-2); P, probability; ns,
non-significant.
Trait
Progeny
Tienshan
Kopu II
P Progeny
Area
GrP-Pot
GrP-St
InDM
K
LamDM
LeafDM
Length
Width
PetDM
PetLength
PrimStLength
Q
QKR
RDM
RSR
SDM
SLM
SPAD
St
StDM
StDiam
TDM
Width
1438
26.68
13.92
0.008
0.511
1.6
2.207
46.439
11.693
0.607
32.685
104
1.919
4.647
1.209
0.339
3.6
1.469
58.028
5.508
2.002
1.394
4.81
29.626
1618
4.2
3.4
0.085
0.624
0.202
0.234
52.1
10.83
0.032
18.68
35.4
2.225
3.541
0.106
0.275
0.368
2.76E-05
51.64
1.731
0.134
1.382
0.474
30.48
1544
12
9.8
0
0.63
0.674
0.896
48.64
10.82
0.222
34.86
39.2
1.606
2.557
0.428
0.329
1.316
3.85E-05
60.79
2.186
0.42
2.27
1.744
29.04
ns
ns
ns
ns
< 0.001
ns
ns
ns
ns
ns
0.024
0.003
< 0.001
< 0.001
ns
0.01
ns
ns
ns
ns
ns
ns
ns
ns
62
Progeny +
Parents
1461.83
23.58
12.7
0.014
0.53
1.406
1.933
47.094
11.548
0.527
31.699
92.883
1.918
4.381
1.052
0.333
3.141
1.224
57.726
4.917
1.973
1.208
4.193
29.648
P Prog + Par
Ns
< 0.001
< 0.001
0.019
< 0.001
< 0.001
< 0.001
Ns
0.029
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.003
< 0.001
< 0.001
0.007
< 0.001
< 0.001
< 0.001
< 0.001
Ns
5
4.5
4
Q (mg g-1 DM)
SDM (g)
3.5
3
2.5
2
1.5
1
0.5
0
Figure 3.4
Comparison of phenotypic variation of quercetin glycosides (Q) and shoot dry matter
(SDM) between parental genotypes Tienshan and Kopu II, and their F1 progeny
genotypes KxT. Error bars are SEM, n=5.
A comparison between the phenotypic variation for the traits Q glycosides and SDM (Figure 3.4)
showed that the difference between parents and progeny was significant for Q glycosides and SDM,
but difference among the progeny was only significant for Q, not SDM, and there was no significant
correlation among the progeny for Q glycosides and SDM either (data not shown). However, RSR was
significantly different (Table 3.1) for both between parents and progeny and among progeny (Figure
3.5). Although SDM, RDM and TDM were not significantly different among progeny (Table 3.1), they
were between parents and progeny (Figure 3.6). Phenotypic variation for Q, K and QKR were
significantly different among progeny, as well as between parents and progeny (Table 3.1, Figure
3.7). The same could be said for PrimStLength and PetLength (Table 3.1, Figure 3.8).
63
0.45
0.4
LSD 0.071
(0.05)
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Figure 3.5
64
Phenotypic variation in root:shoot ratio (RSR).
6
SDM (g)
5
RDM (g)
4
3
TDM LSD
1.433
(0.05)
2
1
0
Figure 3.6
Phenotypic variation for root- (RDM) and shoot dry matter (SDM) together making up
total DM (TDM).
65
12
K (mg g-1 DM)
10
8
Q (mg g-1 DM)
QKR
6
4
2
0
Figure 3.7
66
Phenotypic variation for the flavonol glycosides quercetin (Q), kaempferol (K) and the
Q:K ratio. Error bars are SEM, n=5.
160
140
PetLength (mm)
PrimStLength (mm)
120
100
80
60
40
20
0
Figure 3.8
Phenotypic variation for petiole length and primary stolon length. Error bars are SEM,
n=5.
The PCA biplot (Figure 3.9) that correlated all 9 significant traits with each other showed that Q
glycosides is positioned opposite the DM traits and thus was negatively associated with them. K and
PetLength (PL) were closely linked with each other, while they were positioned opposite and
negatively associated with QKR and PrimStLength (PSL), which were closely linked. Remarkably, QKR
and K were not linked to Q, as the angle of their vectors was close to 90 degrees. PL and PSL had a
negative correlation as they were positioned opposite of each other. Genotypes that were close to,
or in the area a trait vector points towards, had higher expression for this trait than the others. If a
genotype was within the area two or more trait vectors pointed towards, they showed equal
expression for these traits (Figure 3.9).
67
Figure 3.9
PCA biplot summarising correlations between nine traits for the 10 F1 progeny
genotypes.
Q, quercetin; K, kaempferol; QKR, quercetin:kaempferol glycoside ratio; PSL, primary stolon length;
PL, petiole length; SDM, shoot dry matter; RDM, root DM, TDM, total DM; RSR, root:shoot DM ratio.
3.4 Discussion
The pilot experiment had as objective to investigate phenotypic variation for a range of physiological,
morphological and biochemical traits, to determine if the results would validate up-scaling the
number of individual plants (genotypes) in a larger experiment. Sanderson et al. (2003) noted that
biotic and abiotic stresses reduce white clover production under grazing by breaking up plants into
smaller individuals that are less able to compete with grasses (Sanderson et al. 2003). In view of that,
all of the measured traits were based on independent clonal plants. The white clover plant in the
field normally transforms into clonal plants rooted from stolon fragments when the taproot dies off
(Caradus and Woodfield 1998). Thus, the plants used in this experiment were one to two years in
advance of experiments normally using seedlings with tap-roots. Instead, the shorter secondary root
systems on the stolon fragments had taken over (Brock and Tilbrook 2000). Because the secondary
roots of most white clover cultivars are not as deep as the original taproot, plus the fact that the
roots were enclosed in a limited amount of soil in pots, the white clover plants were additionally
strained compared to field conditions (Baker and Williams 1987). In the field, white clover shows
68
large phenotypic plasticity due to the ability to adapt to different environmental conditions (Collins
et al. 2002). Most morphological traits, such as stolon density, branching and thickness, plant spread
and height as well as leaf length often express genotypic correlation with herbage yield in different
environments (Jahufer et al. 1994; Lane et al. 2000b).
However, most of the trait means were not significantly different from each other among the F1
progeny genotypes, most likely due to the relatively small sample size of 10 F1 plants, even though
they were replicated (cloned) five times (Snedecor and Cochran 1989). Of the traits that were
significantly different, RSR is of note as the composite traits SDM and RDM were not significantly
different from each other (Table 3.1). The trait primary stolon length (PrimStLength) is of note, as the
phenotypic differences between the F1 progeny were substantial (Figure 3.8). As a genotype grows a
longer primary stolon, the shorter is the petiole length of the FFE leaf and vice versa. This can be an
important trait of value as stolon density ensures the ongoing persistence of white clover, where
selection for reduced internode length rather than increased proportion of nodes branching will
result in increased stolon density, especially in large-leaved types (Caradus and Mackay 1989). The
biochemical traits Q, K and predominantly QKR were all significantly different among the progeny
(Table 3.1), which points to large variability for these traits, and this is desirable for selection in plant
breeding for abiotic stress tolerance (Carlsen et al. 2008). As QKR was not correlated with Q
glycosides in the biplot (Figure 3.9), this trait can be investigated separately from Q glycosides in
plant selection experiments. The negative correlation of QKR with K showed that there is a
preference for Q- instead of K-production in this pot experiment: dehydration stress induced the
accumulation of dihydroxylated flavonols rather than of their mono-hydroxylated equivalents. This is
similar to other studies where the ratio of quercetin to kaempferol increased in response to stress
(Ryan et al. 2002).
Although the F1 progeny genotypes showed apparent heterosis for DM when compared to their
parents, in particular from the parental genotype Tienshan (Figure 3.6), the DM traits did not differ
significantly among the F1 genotypes (Table 3.1). The large differences in DM of F1 genotypes
compared to the parental genotypes (Figure 3.6) are in all probability not due to transgressive
segregation (Rieseberg et al. 1999) or heterosis (hybrid vigour) (MichaelsonYeates et al. 1997). The
reason behind this phenomenon is most likely to be found in the differences in the age of the plant
material: the parents were cloned a year before their progeny and hence were a year older and most
likely also suffered from more virus-related pressure. To make a direct comparison, the clonal DM of
the F1 progeny would have to be compared to the parental clonal DM one year after the latter was
measured. The loss of heterosis and resulting reduced individual fitness in inbred white clover is
likely to limit the suitability of F2 or backcross mapping strategies for this species (Jones et al. 2003).
69
However, the effect in this population is significantly large to merit further investigation (Woodfield
and Brummer 2001).
The ecotype of white clover (Tienshan) exhibited greater stolon branching than the cultivar Kopu II,
and the stolon nodes were both branched and rooted, which is an important attribute in white clover
ecotypes (Brink et al. 1999; Collins et al. 2002). In effect, the incorporation of novel germplasm from
the ecotype white clover Tienshan into the synthetic cultivar Kopu II could have caused a beneficial
accumulation of recessive alleles associated with herbage yield (Abberton et al. 1998). The nonsignificant correlation between Q glycosides and SDM (data not shown) in this pilot experiment is
also desirable, but due to the DM traits among the F1 genotypes not having enough variation, this
needs further investigation. As the sample size of only 10 F1 genotypes in this experiment was on the
low side to produce variation, an experiment with a sufficiently large size is needed to validate any
findings in this pilot experiment.
70
71
Chapter 4
Phenotyping experiment: Phenotypic trait variation in novel white
clover F1 progeny
Start of the phenotyping experiment with white clover clones planted in pots.
72
4.1 Introduction
White clover (Trifolium repens L.) is a winter-hardy, perennial legume that can potentially add to high
livestock production. White clover cultivars are often classified by their leaf size, where large-leaved
varieties are characterised by their upright growth habit, lower stolon density (stolons per square
meter) and are more suitable for lax grazing systems. Small-leaved varieties often show higher stolon
density, short growing shapes and are better suited for continuous, intense grazing. White clover is
also a good break crop for grasses in rotational arable cropping systems with the added benefit of
adding reasonable amounts of nitrogen to following crops. New Zealand has traditionally been a
dominant producer and global marketer of high quality white clover seeds (Thomas et al. 2009).
Ideally, the amount of white clover in grazed mixed swards combined with grass species should
remain at around one third (Elgersma and Li 1997). However, white clover is not reliably present in
the sward over dry summer periods, which is evident in Australia (Lane et al. 2000a) and New
Zealand (Knowles et al. 2003).
Experiments conducted in New Zealand by Woodward and Caradus (2000) showed that for profitable
dairy farming, breeding for persistence and drought tolerance should be an important part of white
clover cultivar improvement. Novel strategies to exploit potential new cultivars in breeding
programmes to improve the persistence and drought tolerance of white clover will have major
implications for forage production (Marshall et al. 2004). Because white clover is a self-incompatible
cross-breeding species, a wide cross between two phenotypically divergent, highly heterozygous
genotypes as described in paragraph 3.2.1 was used to produce offspring with high genetic variability
for this experiment.
The objectives of the phenotyping experiment were to use information collected from the pilot study
in Chapter 3, to expand the number of plant individuals and expose this population to non-stress
conditions in pots over a Canterbury summer period, to (i) test phenotypic variation in the important
morphological, physical and biochemical traits, and to (ii) select genotypes with a number of around
10 % in each category of highest and lowest attribute levels for DNA bulk samples in selective
genotyping (Chapter 7).
This study intended to test the following hypotheses:
•
Phenotypic variation in a replicated random sample of 130 F1 genotypes for quercetin (Q)
glycosides, dry matter (DM) and other traits is significant.
•
The correlation between Q glycosides × DM is not significant.
73
•
The attribute responses to environmental exposure are at variance among the F1 progeny
individuals and between the parents and the F1 progeny, depending on the levels of Q glycosides
found.
4.2 Materials and methods
4.2.1 Plant material
The plant material used was a random selection of 130 F1 genotypes from the 190 full-sibling F1
population as described in paragraph 3.2.1. The parental genotypes Kopu II and Tienshan were also
included.
The 396 stolon cuttings were planted in pots outside in February 2008. The plants in pots were
placed outdoors in the layout described in paragraph 4.2.2, and the phenotyping experiment
commenced on 29 February 2008. Plants were maintained by watering and weeding. Harvest of
leaves for HPLC analysis was done on 14 April 2008 and the plants were destructively harvested for
dry matter from 15 April 2008.
4.2.2 Experimental design
The experiment design for the phenotyping experiment was a randomised complete block design
(RCBD), with 130 randomly sampled F1 genotypes (65 KxT and 65 TxK) and the two parents Tienshan
& Kopu II as checks, grown in three replications (blocks) (Appendix F.2). In total there were 396
experimental units. The plants were grown in 7.5 L pots, using the same soil mixture as in the pilot
experiment, and the pots were completely randomised between and within blocks. The treatment
structure was single factor, and genotypes were grown for nine weeks under Canterbury outdoor
conditions, without induced stress.
4.2.3 Measurements
Measurements were above ground parts or shoot dry matter (SDM), below ground parts or root DM
(RDM) (g). Shoots were cut off at the nursery, put in bags and oven-dried. The pots containing roots
were transported to the FSC for root washing and stored in a cooling room at 4 °C. After root washing
the roots were oven-dried for 48 hours at 70º C. Roots and shoots were weighed separately for total
DM (TDM) and root:shoot ratio (RSR) determination. The flavonol glycosides (mg g-1 DM) of quercetin
(Q) and kaempferol (K) were measured with HPLC analysis as described below. Quercetin:kaempferol
glycoside ratio was subsequently determined by Q/K.
74
4.2.4 Quantitative flavonoid analysis with HPLC
Sample preparation and HPLC analysis follows established methods (Hofmann et al. 2000; Ryan et al.
2002; Dong 2006). White clover laminae (ten clean first fully expanded leaves) were harvested at the
nursery and put in vials in liquid nitrogen. Immediately after collection, samples were frozen in liquid
nitrogen and stored at -32 ºC. The frozen samples were ground to a fine powder in a mortar with
pestle under liquid nitrogen, after which the ground samples were freeze-dried in a W.G.G. Cuddon
Ltd freeze-dryer at H218, Lincoln University. Flavonoids were extracted from freeze-dried 50 mg
samples in MeOH-H2O-HOAc (79:20:1) for 12 hours in darkness. The centrifuged supernatant was
syringe-filtered into amber 1 ml HPLC vials, which were placed in the carousel of a Millipore Waters
717 plus auto-sampler for HPLC analysis, together with a series of standards with Rutin C27H30O16.
Solvents for analysis A (1.5% H3PO4) and B (HOAc-CH3CN-H3PO4-H2O, 20:24:1.5: 54.5), and cleaning
solvents C (10% CH3CN) and D (90% CH3CN), were prepared and filtered (Hofmann et al. 2000). A
Millipore Waters 600-MS system controller was used to distribute and mix the solvents. A reverse
phase separation column (Merck Supersphere Lichrocart 125-4 RP-18 endcapped column, 4 μm, 4
mm x 119 mm) suited for flavonoids was connected between the auto-sampler and the Millipore
Waters 996 photodiode-array detector. A specific gradient for solvents A and B was used to clearly
separate the peaks for Q glycosides and K glycosides in the chromatogram, starting with 80% A and
20% B, decreasing to 33% A at 30 min, 10% A at 33 min and 0% A at 39.3 min (Hofmann et al. 2000;
Hofmann et al. 2003a). Millennium HPLC analysis software was used to produce a sample
information report. This report, together with spectrum analysing software, was used to analyse
peak wavelengths for Q glycosides (257 & 357 nm) and K glycosides (266 & 347 nm). The integrated
areas of all flavonol peaks (measured at 352 nm) were added, and the result compared to an external
standard curve prepared using 0, 10, 25, 50 and 100 ppm rutin (quercetin 3-rutinoside). Thus the
flavonol glycoside levels are expressed here as rutin equivalents (Ryan et al. 2002; Olsen et al. 2010).
4.2.5 Statistical analyses
For data analysis and tests of significance for main and interaction effects, the restricted maximum
likelihood / best linear unbiased prediction (REML/BLUP) and analysis of variance (ANOVA)
procedures in the GenStat statistical software package were used, while correlative studies were
conducted with the GenStat Regression Analysis and Correlation procedures. Homogeneity of
variances was tested by examining the residual plots of the ANOVA and REML analyses (Payne et al.
2009).
75
4.3 Results
The phenotyping experiment showed repeated highly significant variation for the traits Q, K, QKR,
SDM, RDM, TDM and RSR (Figure 4.1) between parents (P < 0.001), between parents and F1 progeny
(P < 0.001) and among the progeny (P < 0.001) (Table 4.1). Quercetin glycosides (Q) and SDM were
only weakly (albeit significantly P < 0.001) correlated under low stress conditions, with levels of one
explaining less than 10% of the variation in levels of the other (Figure 4.2).
Table 4.1
BLUP means for the traits in the phenotyping experiment.
Flavonol glycosides quercetin (Q, mg g-1 DM) and kaempferol (K, mg g-1 DM); quercetin:kaempferol
glycoside ratio (QKR); shoot dry matter (SDM, g), root dry matter (RDM, g); total dry matter, (TDM =
SDM + RDM, g); root:shoot DM ratio (RSR).
Trait
K
Q
QKR
RDM
RSR
SDM
TDM
Mean with
Parents
Mean
Progeny
Mean Parents
Mean
Tienshan
Mean Kopu II
P
0.82
2.37
3.65
1.26
0.39
3.54
4.80
0.81
2.37
3.67
1.27
0.39
3.58
4.84
1.03
2.60
2.15
0.42
0.50
1.19
1.61
1.36
3.62
2.67
0.22
0.63
0.38
0.60
0.69
1.59
1.64
0.62
0.37
2.00
2.62
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
6
Mean Tienshan
5
Mean F1 Progeny
Mean Kopu II
4
3
2
1
0
K
Figure 4.1
76
Q
QKR
RDM
Traits for the F1 progeny and parents.
RSR
SDM
TDM
Flavonol glycosides quercetin (Q) and kaempferol (K) (mg g-1 DM); Q:K glycoside ratio (QKR); dry
matter (DM) (g), shoot DM (SDM), root DM (RDM); total DM, (TDM); root:shoot ratio (RSR). Error
bars are SEM, n=3 for both Tienshan and Kopu II; n=390 for F1 progeny.
The levels of flavonol glycosides Q were on average higher than those for K in all three populations of
Tienshan, Kopu II and F1 progeny (Figure 4.1), although due to the small sample size of the parents
(three individuals), the standard error of the means was quite large.
SDM vs Q
8
Shoot Dry Matter (g)
7
y = -0.6301x + 5.0365
R² = 0.0903
P < 0.001
6
5
4
3
2
1
0
0
1
2
3
Quercetin glycoside (mg
Figure 4.2
4
g-1
5
6
DM)
Correlation between the traits quercetin glycosides (Q) and shoot dry matter (SDM)
for the F1 progeny and parent (not indicated) genotypes.
The biplot from principal component analysis (Figure 4.3) for all the traits showed their mutual
association based on the angle of the trait vectors. All DM traits cohered strongly together and hence
were positively correlated, while the RSR vector was positioned opposite the other DM traits and was
thus negatively correlated with those. K glycosides and QKR were also negatively correlated, but
from a large angle with the vectors of RSR and DM traits, and were therefore not correlated with
either of them. The smaller angles between the traits Q glycosides and K glycosides and RSR meant
they had a weak positive correlation with each other, but RSR and K glycosides had lesser association
as they were further apart.
77
Figure 4.3
Biplot from PCA of seven traits for the F1 progeny including parental (not indicated)
genotypes.
Flavonol glycosides quercetin (Q) and kaempferol (K) (mg g-1 DM); Q:K glycoside ratio (QKR); dry
matter (DM) (g), shoot DM (SDM), root DM (RDM); total DM, (TDM); root:shoot ratio (RSR).
4.4 Discussion
Phenotypic assessment among 130 F1 progeny in pots showed highly significant differences, both in
the genetic variation for quercetin (Q) glycosides and for biomass production (DM). In addition, Q
glycosides and biomass only showed a weak negative correlation, with levels of one explaining less
than 10% of the variation in levels of the other, indicating that there was no link between Q
glycosides and biomass under these experimental conditions. This finding is of profound
consequence, providing the first evidence that forage products, both high yielding and with high Q
glycosides, are possible. Prior to this finding, it was generally accepted that a trade-off between
biomass production and flavonoid accumulation was necessary (Hofmann et al. 2000; Hofmann and
Jahufer 2011).
In contrast to the trait means in the pilot experiment (Chapter 3), where most of the trait means
were not significantly different, in this larger-scale experiment the phenotypic variation for all of the
traits was significantly different (Table 4.1), most likely due to a larger sample size of 130 F1
genotypes (Snedecor and Cochran 1989). As a consequence, the traits means were still highly
78
significantly different when the two parental genotype results were included (Table 4.1). Remarkably,
the QKR in the progeny was higher than those of both parents, and similar findings were reported
elsewhere (Gerhardt et al. 2008). The differences between the populations for the other trait means
were significant except RSR, and of note is that the means of the progeny traits SDM, RDM and TDM
were significantly higher than those of either parent (Figure 4.1). This is consistent with the finding in
the pilot experiment, and is likely due to the difference in age between parents and progeny, as older
plants in general show a higher presence of mosaic virus. Substantial gains can be expected from
cultivars resistant to this pathogen (Dudas et al. 1998).
The association between traits shown in the biplot from principal component analysis (Figure 4.3)
were consistent with those in the pilot experiment (Chapter 3), except that the trait RSR was not
positioned opposite the DM traits in the pilot biplot. This consistency is worth mentioning, as it
means that there is constant genotypic variation for these traits over different experiments
(Hofmann et al. 2000; Carlsen et al. 2008). Root and shoot dry matter, and root-to-shoot ratio
relating to biomass are important parameters to get an understanding of partitioning of assimilates
and yield levels (Hay and Porter 2006), water uptake and water use efficiency (WUE) (Lucero et al.
2000; Siddique et al. 2001). White clover individuals that demonstrate an advantageous root-toshoot ratio could play an important role in breeding programs by improving yield stability in less
favourable environments. The genotypic variation estimated for herbage production and plant traits
can be used as a source of variation for white clover genetic improvement programmes for adverse
environments (Jahufer et al. 1994; Jahufer et al. 1997). Furthermore, white clover ecotypes such as
Tienshan represent valuable germplasm for breeding programs and the development of more
persistent cultivars (Brink et al. 1999; Frankow-Lindberg 1999).
The main question arises if the weak correlation between the progeny traits Q glycosides and DM
(Figure 4.2) holds up under applied water deficit stress conditions. Can we breed for high yield
combined with high quercetin successfully? Next steps needed to answer these questions are a
physiological drought study on F1 progeny and a field experiment under adverse conditions.
79
Chapter 5
Journal article:
“Multivariate associations of flavonoid and biomass accumulation
in white clover (Trifolium repens) under drought”
Ballizany, W. L., Hofmann, R. W., Jahufer, M. Z. Z., & Barrett, B. A. (2012). Multivariate associations of
flavonoid and biomass accumulation in white clover (Trifolium repens) under drought Functional
Plant Biology, 39(2), 167–177. doi:http://dx.doi.org/10.1071/FP11193
The drought experiment in February 2009.
80
5.1 Abstract
White clover (Trifolium repens L.) is an important pasture legume in temperate regions, but growth is
often strongly reduced under summer drought. Cloned individuals from a full-sibling progeny of a
pair cross between two phenotypically distinct white clover populations were exposed to water
deficit in pots under outdoor conditions for nine weeks, while control pots were maintained at field
capacity. Water deficit decreased leaf water potential by more than 50% overall, but increased the
levels of flavonol glycosides quercetin (Q) and the ratio of quercetin and kaempferol glycosides (QKR)
by 111% and by 90%, respectively. Water deficit reduced dry matter (DM) by 21%, with the most
productive genotypes in the controls showing the greatest proportional reduction. The full-sibling
progeny displayed a significant increase in the root:shoot ratio by 53% under water deficit. Droughtinduced changes in plant morphology were associated to changes in Q, but not kaempferol (K)
glycosides. Genotypes with high QKR levels reduced their DM production least under water deficit,
and in turn increased their Q glycosides and QKR most. These data show at the individual genotype
level that increased Q glycoside accumulation in response to water deficit stress can be positively
related to retaining higher levels of DM production.
5.2 Additional keywords
Biomass, abiotic stress, secondary metabolism, flavonoids
5.3 Introduction
White clover (Trifolium repens L. 2n = 4x = 32) is an economically significant forage source in
temperate grasslands. The perennial species grows and persists via stoloniferous growth on fertile,
moist soil, but is severely constrained under abiotic stress (Sanderson et al. 2003; Hofmann et al.
2007). While some white clover germplasm selections can adapt to dryland summer rainfall
environments (Jahufer et al. 1995), critical levels of summer moisture stress can reduce the presence
of white clover in pastures (Knowles et al. 2003). Consequently, users of white clover will benefit
from new cultivars with the genetic capacity for adaptation to higher levels of abiotic stress to persist
and produce vegetatively under increasingly variable and challenging conditions (Humphreys et al.
2006).
Drought is by far the most significant environmental cause of plant stress in worldwide agriculture
(Bartels and Sunkar 2005). Osmotic stress caused by drought (water deficit) makes up the most
serious limitation to plant growth, production and distribution, and causes serious physiological
damage or even total crop loss. Plants develop water deficit when the demand for water exceeds
supply. Water deficit-induced changes in plants include physiological responses such as reductions in
81
leaf water potential, stomatal conductance, internal CO2 concentration, net photosynthesis, growth
rates and leaf area, and increased root:shoot ratio. (Bray 1997; Reddy et al. 2004).
One of the biochemical plant responses to water deficit stress is increased production of specific
phenolic compounds called flavonols (Hernandez et al. 2004). Flavonols appear to be mainly involved
in the response mechanisms against abiotic stresses (Agati et al. 2011), and numerous studies
suggest that flavonoids have multiple functional roles in the responses of plants to adverse
environmental conditions (Winkel-Shirley 2002a; Treutter 2005). For example, flavonoids have
signalling properties in protecting plant cells from oxidative stress (Taylor and Grotewold 2005; Buer
et al. 2010), in addition to absorbing the most energetic solar wavelengths (Rozema et al. 1997).
Quercetin glycosides (Q) specifically regulate auxin movement by acting as an endogenous inhibitor
of auxin transport (Mathesius et al. 1998; Brown et al. 2001), and hence influence plant development
(Gao et al. 2008). Q glycosides are also antioxidant agents against reactive oxygen species (ROS), and
help reduce the damaging effects caused by abiotic stress in general (Agati et al. 2009). Water deficit
stress has been reported to increase flavonoid concentration in several plant species (Turtola et al.
2005).
Hofmann et al. (2000) showed that variations in flavonol glycosides quercetin (Q) and kaempferol (K)
were implicated in the responses of white clover populations to enhanced UV-B radiation. They also
demonstrated an inverse relationship between plant dry matter (DM) production and Q glycoside
accumulation among multiple populations (Hofmann and Jahufer 2011). Furthermore, higher Q
glycoside accumulation under UV-B and lower plant production correlated with protection against
UV-B-induced growth reduction (Hofmann et al. 2000; Hofmann and Jahufer 2011). These studies
suggested that the metabolic cost of a plant strategy towards constitutive and abiotic stress-induced
protection may be lower carbon allocation towards primary production, which is in line with
ecological plant strategy theory (Grime 2001).
However, in contrast to the existing knowledge on the association of flavonoids such as Q glycosides
with UV-B sensitivity, little is known about the role of these compounds in drought adaptation. We
thus hypothesised that their accumulation would be associated with biomass accumulation and
drought-induced DM decreases in the novel white clover F1 progeny investigated here. The
objectives of this study were; a) to quantify the relationship between DM production and flavonoid
accumulation in white clover under water deficit and well-watered conditions, b) to examine the
potential role of constitutive and induced flavonol glycosides in drought adaptation of plants and
their association with biomass production.
82
5.4 Materials and methods
5.4.1 Plant material, experimental design and treatment conditions
The plant material used in this study was a random selection of 32 F1 genotypes from the 190 fullsibling F1 population as described in paragraph 3.2.1. The parental genotypes Kopu II and Tienshan
were also included. The plants were grown from stolon cuttings which produced nodal root systems.
Progeny and parent genotypes were used in this experiment. A pair cross between two
morphologically contrasting, highly heterozygous white clover parent plants was used to generate a
segregating population of full-sibling F1 progeny.
A random sample of 32 F1 progeny seed was germinated and propagated for clonal replication in the
experiment. Propagated clonal copies of both parents were used to investigate the parent-toprogeny broad sense heritability of traits and to use clonal copies in both water treatments. A pot
experiment started on 16 December 2008 with a length of 17 weeks from planting to harvest with
two treatments; 1) a well-watered control with ample soil water to keep the pot soil near field
capacity (FC), and 2) induced water deficit in half of the plants during the last nine weeks of the
experiment, with limited soil water availability just above wilting point (WP).
Each of the 34 genotypes (32 F1's + 2 parents) was cloned three times per treatment. For each clonal
copy of each genotype, one 10 cm long stolon cutting was transplanted into a 7.5 L pot filled with a
soil mix of 75% Wakanui silt loam (Francis and Kemp 1990) and 25% mortar sand (0 - 3 mm), and
included 2 g L-1 Osmocote Exact Standard slow release fertiliser.
The potted plants were arranged in a randomised complete block design with three replications
(Appendix F.2). The plants were defoliated three times after 9, 13 and 17 weeks to a height of 1 cm
from the soil surface using hand-clippers. This was done to mimic grazing and to prevent the shoots
outgrowing the pot size.
In the control treatment, pots were maintained at a soil moisture level of just below FC (38%
volumetric soil moisture content) with an automated irrigation system (Figure 5.1). In the water
deficit treatment, pots were maintained at a soil moisture level just above WP (10% volumetric soil
moisture content), using the automated irrigation system, together with a portable translucent roof
that was placed above the water deficit plots during precipitation events (Figure 5.2). Both
treatments were compensated for daily potential evapotranspiration (Penman PE), and the control
treatment for rainfall, which was measured with rain gauges attached to a control system (CR1000,
Campbell Scientific Inc., Logan, UT, USA). The experiment was conducted in an open air enclosure at
the Lincoln University Nursery, Canterbury, New Zealand (43° 38.7’ S, 172° 27.7’ E, elevation 12 m).
83
Site meteorological data were recorded at a meteorological station approximately 3 km from the
experimental site.
Figure 5.1
Close-up of a well-watered sub-plot in the drought experiment.
The irrigation system can be seen as black pipes with smaller tubes leading to emitters in the pots.
The grey object is one of two rain gauges for the two treatments, connected to a data logger. In the
background, custom-built rain shelters for the water-limited sub-plots can be observed.
84
Figure 5.2
A water-limited sub-plot in the drought experiment.
5.4.2 Measurements
Soil moisture content in one randomly chosen pot per replication per treatment was measured every
week using Time Domain Reflectometry (TDR, % volumetric water content; HydroSense, Campbell
Scientific Inc., Logan, UT, USA). To obtain a measure of plant water deficit stress, several
physiological measurements were carried out in the parental genotypes Tienshan and Kopu II, and in
one randomly chosen F1 genotype from each replication. A Li-Cor 6400 portable photosynthesis
system (LI-COR Biosciences Inc., Lincoln, NE, USA) was used to measure Pn, photosynthesis (μmol CO2
m-2 sec-1); g, stomatal conductance (mol H2O m-2 sec-1); and E, transpiration (mmol H2O m-2 sec-1).
Physiological water use efficiency (WUE) was calculated from Pn E-1. A plant water status console
(3000 Series, Soil moisture Equipment Corp., Santa Barbara, CA, USA) was used to measure water
potential Ψ (MPa) (Turner 1990).
At 17 weeks, plants were destructively harvested and dry matter (DM) was measured for above- and
below ground biomass. Shoots were oven-dried for 48 hours at 70 °C. Roots were washed free of soil
and oven-dried for 48 hours at 70 °C. Roots and shoots were weighed separately for root DM (RDM,
g), shoot DM (SDM, g), and the data were used to calculate root:shoot ratio (RSR) and total DM
(TDM, g). The index of relative chlorophyll content (SPAD value, the optical density difference at two
85
wavelengths: 650 nm and 940 nm) was measured with a SPAD-502 leaf chlorophyll meter (Konica
Minolta Sensing Inc., Osaka, Japan).
5.4.3 Analysis of flavonoids by high performance liquid chromatography (HPLC)
Sample preparation and HPLC analysis followed established methods (Hofmann et al. 2000; Ryan et
al. 2002; Dong 2006). Plant material was sampled for flavonoid leaf analysis immediately prior to the
final harvest. Ten first fully expanded (FFE) trifoliate laminae from each individual were collected.
Immediately after collection, samples were flash-frozen in liquid nitrogen and stored at -34 °C. The
frozen samples were ground to a fine leaf powder with a mortar and pestle in liquid nitrogen, after
which the ground samples were freeze-dried for 48 hours in a freeze-dryer (W.G.G. Cuddon Ltd.,
Blenheim, New Zealand) and stored at -34 °C until analysis. The freeze-dried, ground plant material
(50 ± 2 mg) was extracted in darkness with occasional vortex shaking for 12 hours in 3 ml MeOH-H2OHOAc (79:20:1). Following centrifugation, 2 ml of the supernatant was syringe-filtered into amber 2
ml HPLC vials with caps and used for HPLC analysis. Analytical HPLC was performed on an integrated
HPLC machine (Agilent 1100 series, Agilent Technologies, Waldbronn, Germany). Chromatography
was carried out with an injection volume of 10 µl and a flow rate of 0.8 mL min-1, on a Merck
LiChroCART 125-4 RP-18 end-capped column (4 µm, 4 mm x 119 mm), filled with Superspher 100
silica gel fitted with a LiChrospher 100 RP-18e 5 µm guard column. The gradient solvent system
comprised filtered solvent A (1.5% H3PO4) and filtered solvent B (HOAc-CH3CN-H3PO4-H2O
(20:24:1.5:54.5)), mixed using a linear gradient starting with 80% A and 20% B, increasing to 67% B at
30 min, 90% B at 33 min, 100% B at 39.3 min, and back to 20% B at 41 min to 47 min total. In order to
maintain the integrity of naturally-occurring flavonoid compounds in the samples, the leaf extracts
were not hydrolysed. The resulting chromatograms thus contained numerous flavonoid peaks. These
were examined individually for their online spectra, which clearly identified them as derivatives of
the flavonols quercetin and kaempferol (Markham 1982). The integrated areas of all flavonol peaks
(measured at 352 nm) were added, and the result compared to an external standard curve prepared
using 0, 10, 25, 50 and 100 ppm rutin (quercetin 3-rutinoside). Thus the flavonol glycoside levels are
expressed here as rutin equivalents (Ryan et al. 2002; Olsen et al. 2010).
5.4.4 Statistical analyses
The statistical analysis included (i) estimation of phenotypic correlation coefficients, (ii) analysis of
variance, (iii) estimation of broad sense heritability based on plant means within and across
treatments, and (iv) multivariate analysis including principal component analysis and cluster analysis.
86
Phenotypic correlation coefficients between the traits Q glycosides and TDM, and between principal
components 1 and 2 from the principal component analysis and the traits Δ% Q, Δ% K, Δ% QKR, Δ%
RDM, Δ% SDM, Δ% TDM, Δ% SPAD were estimated according to Falconer and Mackay (1996).
The analysis of variance for the traits Q, K, QKR, RDM, SDM, TDM, SPAD was carried out using the
Residual Maximum Likelihood (REML) (Virk et al. 2009) option in GenStat 12 (VSN International Ltd.,
Hemel Hempstead, United Kingdom). The Best Linear Unbiased Predictor (BLUP) values (Piepho et al.
2008) from the REML analysis were used in the generation of adjusted means. The linear models
used in the variance component analysis were completely random. The linear models used in the
variance component analysis within individual treatments (environments) and across treatments
were completely random.
Equation 5.1
The linear model used in the analysis across environments.
Yijk = M + gi + ej + bjk + (ge)ij + (gb)ijk + εijk,
Where:
Yijk
is the value of an attribute measured from plant i in replicate k in environment (treatment) j,
and i=1,...,ng, j=1,...,ne, and k=1,...,nb, where g, e, and b are plants, environments and replicates,
respectively,
M
is the overall mean,
gi
is the effect of plant i, N(0,σ2g),
ej
is the effect of environment j, N(0,σ2e),
bjk
is the effect of replicate k within environment j, N(0,σ2b),
(ge)ij
is the effect of the interaction between plant i and environment j, N(0,σ2ge),
(gb)ijk
is the effect of the interaction between plant i and replicate k in environment j, N(0,σ2gb),
εijk
is the residual effect associated with plant i in replicate k in environment j, N(0,σ2ε).
Including the treatments (environments) as random effects enabled estimation of the type and
magnitude of genotype-by-treatment interaction.
The plant mean across-replicates broad sense heritability (Hb) within individual treatments for the
traits Q, K, QKR, RDM, SDM, TDM, SPAD was estimated (Falconer and Mackay 1996) using:
87
Equation 5.2
𝐻𝑏 =
𝜎𝑔2
Broad-sense heritability within treatments.
2
𝜎
𝜎𝑔2 + ɛ
𝑛𝑟
where 𝜎𝑔² is the within treatment genotypic variance component, 𝜎𝜀² is the error variance component,
and nr is the number of replicates.
The plant mean across-replicates and across-treatments broad sense heritability (Hb) for the traits Q,
K, QKR, RDM, SDM, TDM, SPAD was estimated (Falconer and Mackay 1996) using:
Equation 5.3
𝐻𝑏 =
𝜎𝑔2 +
𝜎𝑔2
𝜎2
𝑔.𝑡
𝑛𝑡
+
Broad-sense heritability across treatments.
𝜎2
ɛ
𝑛𝑡 .𝑛𝑟
²
is the genotype-by-treatment
where 𝜎𝑔² is the across treatments genotypic variance component, 𝜎𝑔.𝑡
interaction variance component, 𝜎𝜀² is the error variance component, nt is the number of treatments,
and nr is the number of replicates. Variance components were considered significant if they were ≥ 2
× standard error of difference (SE), as an estimated least significant difference (LSD) at the 5% level
(Jahufer et al. 1997).
Principal component analysis (PCA) was performed using the BLUP values obtained from the REML
analysis for all the traits. The BLUP values were standardised to a mean of zero and a variance of 1, to
reduce possible correlations between the traits and convert the traits into new, combined variables.
The biplots generated with the software packages GenStat and SigmaPlot enabled assessment of the
genotypic variation on a multivariate scale, and significance testing of effects in traits. The trait
means of each treatment, well-watered and water deficit, were correlated in an across-treatments
PCA, as was the ratio for each trait between water deficit and well-watered treatments, expressed as
percent change or delta percent (Δ%), calculated by ((water deficit - well watered)/|(well watered)|)
* 100 (Hewett and Moeschberger 1985). Clustering of the genotypes was performed using an
agglomerative hierarchical clustering procedure with squared Euclidean distance as a measure of
dissimilarity and incremental sums of squares as a grouping strategy. The GEBEI package was used to
perform the clustering (Watson et al. 1995).
88
5.5 Results
5.5.1 Results soil dehydration test
The soil test results were consistent with those found in literature for sandy silt loam (Francis and
Kemp 1990; McLaren and Cameron 1990; Baumhardt et al. 1992) as seen in the drying curve (Figure
5.3).
Soil Moisture Content (Average of 10 pots)
Soil Moisture Content (g)
2500
2000
1500
y = 25.413x2 - 2E+06x + 4E+10
2
R = 0.9982
1000
500
09
20
/0
1
/2
0
/0
1
19
/2
0
09
09
/0
1
18
/0
1
17
/2
0
09
/2
0
09
16
/0
1
/2
0
09
15
/0
1
/2
0
/0
1
14
/2
0
09
09
/2
0
/0
1
13
12
/0
1
/2
0
09
0
Date
Figure 5.3
Soil moisture content per pot (averaged over 10 pots) over nine days of pot-soil
drying.
Plants started wilting on 16/01/2009.
5.5.2 White clover population germplasm source effects
Measured across white clover plants, most traits were affected by the water deficit (Table 5.1). The
flavonoids identified by HPLC analysis were glycosides of the flavonols quercetin (Q) and kaempferol
(K). The largest water deficit-induced increase of more than 100% was observed for Q glycoside
accumulation. This was in contrast to the relatively small water deficit-generated increase for K, thus
increasing QKR (Table 5.1). TDM as a summation of SDM and RDM was reduced by water deficit, due
to significantly decreased SDM production by 21%. The trait RDM was not significantly affected by
the water deficit treatment across all three white clover populations. The significant decrease in SDM
caused a significant increase by 50% in RSR across populations (Table 5.1).
89
Table 5.1
Trait means across 32 white clover full-sibling F1 progeny (Pr) and the means of their
parents Kopu II (K) and Tienshan (T).
Traits: Q, quercetin glycosides (mg g-1 DM); K, kaempferol glycosides (mg g-1 DM); QKR,
quercetin:kaempferol glycoside ratio; SDM, shoot dry matter (g); RDM, root dry matter (g); RSR,
root:shoot ratio; TDM, total dry matter (g) = SDM + RDM; SPAD, chlorophyll index; P, probability.
Traits are ranked by the degree of change. Δ%, water deficit-induced percent changes in eight traits.
In the column “Δ populations”, water deficit-induced changes of the trait means among the separate
populations T, K and Pr are compared.
Trait
description
Well
watered
Water
deficit
Δ%
P
Δ populations 1
Q
2.17
4.57
110.81
<0.001
K↑, T↑, Pr ↑
QKR
1.07
2.05
90.49
<0.001
K↑, T↑, Pr ↑
RSR
0.28
0.42
50.08
0.004
K ns, T ns, Pr ↑
K
RDM
SPAD
TDM
2.14
13.93
47.20
64.27
2.49
14.98
49.94
50.54
16.16
7.52
5.79
-21.38
0.024
0.476
0.010
<0.001
K↑, T↑, Pr ↑
K ns, T ↓ , Pr ↑
K↑, T ns, Pr ↑
K↓, T↓, Pr ↓
SDM
50.34
35.55
-29.37
<0.001
K↓, T↓, Pr ↓
1
Based on the LSD treatment × population interaction (P < 0.05); ↑ significant increase; ↓ significant reduction; ns, non-significant
Examination of the water × population interaction showed that this effect was due to a significant
increase in RSR by 53% under water deficit in the F1 progeny (Figure 5.4). The correlation between
the final harvest SDM and the total accumulated SDM over three cuttings was highly significant
(coefficient of determination r2 = 0.91, P < 0.001). There was no phenotypic correlation between Q
glycosides and TDM in the well-watered or water deficit treatments (data not shown).
90
Figure 5.4
Root:shoot ratio (RSR) means of the white clover parents Kopu II and Tienshan, and
their full sib F1 progeny.
Error bars are ± SE.
5.5.3 Physiological effects of drought
Physiological measurements indicated significant differences across white clover populations for the
traits WUE, Pn, E, g, and Ψ (Table 5.2), where WUE was increased by water deficit and the other traits
all decreased, with a reduction in Ψ by more than 50% the strongest response. There was no
significant difference for any of the traits WUE, Pn, E, g, and Ψ among the parents and progeny
populations individually and also no significant difference in the drought-induced DM decrease
among the three populations for these traits (non-significant water deficit × population term).
91
Table 5.2
Trait means and water deficit-induced percent change.
Five physiological traits in clonal copies of white clover plants potted in soil under both well watered
and water deficit treatments. Data were averaged across 3 white clover genotypes (three full-sibling
F1 progeny, the parent Kopu II, and the parent Tienshan), and ranked by the degree of change. Traits:
Ψ, water potential (MPa); E, transpiration (mmol H2O m-2 sec-1); Pn, photosynthesis (μmol CO2 m-2 sec1
); g, stomatal conductance (mol H2O m-2 sec-1); WUE, physiological water use efficiency, (Pn E-1). Δ%,
percent change; P, probability.
Trait
Well watered
Water deficit
Δ%
P
WUE
4.86
5.57
14.56
< 0.05
Pn
22.08
15.32
-30.6
< 0.05
E
4.79
2.87
-39.99
< 0.05
g
0.61
0.30
-50.71
< 0.05
Ψ
-1.28
-1.96
-53.04
< 0.01
5.5.4 Variance and heritability
There was significant (P < 0.05) genotypic variance (𝜎𝑔² ) among the white clover genotypes for the
physiological traits RDM, SDM, TDM, RSR, SPAD and the biochemical traits Q and K glycosides and
QKR within individual treatments (Table 5.3).
92
Table 5.3
Within-treatments genotypic (𝝈²𝒈 ), and experimental error (𝝈²𝜺 ) variance components,
standard errors (±SE) and broad sense heritability (Hb).
Eight physiological traits measured across 2 parents and 32 white clover progeny cloned and tested
under water deficit and well watered treatments. Traits: Q, quercetin glycosides; K, kaempferol
glycosides; QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry matter; RDM, root dry matter;
RSR, root:shoot ratio; TDM, total dry matter = SDM + RDM; SPAD, chlorophyll index.
Well watered
Traits
Q
(𝜎𝑔² ±SE)
Water deficit
(𝜎𝜀² ±SE)
Hb
(𝜎𝑔² ±SE)
(𝜎𝜀² ±SE)
Hb
0.25 ± 0.11
0.56 ± 0.10
0.57
2.29 ± 0.67
1.27 ± 0.22
0.84
K
0.54 ± 0.15
0.24 ± 0.04
0.87
0.78 ± 0.22
0.31 ± 0.05
0.88
QKR
0.09 ± 0.02
0.04 ± 0.01
0.85
0.61 ± 0.16
0.16 ± 0.03
0.92
RDM
23.01 ± 8.04
27.10 ± 4.72
0.72
13.15 ± 3.85
7.25 ± 1.26
0.84
SDM
81.59 ± 25.06
57.85 ± 10.07
0.81
23.20 ± 6.72
11.91 ± 2.07
0.85
RSR
0.005 ± 0.002
0.012 ± 0.002
0.54
0.006 ± 0.002
0.004 ± 0.001
0.82
TDM
166.08 ± 47.95
83.05 ± 14.46
0.86
59.04 ± 16.98
28.75 ± 5.01
0.86
SPAD
6.43 ± 2.42
9.47 ± 1.65
0.67
11.07 ± 4.40
18.75 ± 3.26
0.64
Across treatments, all traits except SPAD had significant (P < 0.05) genotypic variance (Table 5.4). The
genotypic variance for the percent change (Δ%), was only significant (P < 0.05) for the traits QKR,
²
RDM and SPAD (Table 5.4). There was significant (P < 0.05) genotype-by-treatment (𝜎𝑔.𝑡
) interaction
for the traits Q, QKR, TDM and SPAD (Table 5.4).
The broad-sense heritability Hb for most of the traits measured from the plants within the water
deficit treatment, with the exception of SPAD, were higher than 80% (Table 5.3). Under the wellwatered treatment, Hb for the traits K, QKR, SDM and TDM were at a similar level. Broad-sense
heritability for Q glycosides in the well-watered treatment was 57%, and 84% under water deficit.
The Hb for the traits across treatments was significantly lower than under both individual treatments,
K glycosides being the only one exceeding 50% (Table 5.4). The Hb for the Δ% traits QKR, RDM and
SPAD all exceeded 50%.
93
Table 5.4
Across-treatments genotypic (𝝈²𝒈 ), genotype-by-treatment interaction (𝝈²𝒈.𝒕 ), and
experimental error (𝝈²𝜺 ) components of variance.
Standard errors (±SE), as well as broad sense heritability (Hb) for eight physiological traits measured
across 2 parents and 32 white clover full sibs cloned and tested under water deficit and well watered
treatments. Traits: Q, quercetin glycosides; K, kaempferol glycosides; QKR, quercetin:kaempferol
glycoside ratio; SDM, shoot dry matter; RDM, root dry matter; RSR, root:shoot ratio; TDM, total dry
matter = SDM + RDM; SPAD, chlorophyll index. Δ%, percent change of each of the eight traits across
treatments. Only traits with significant (LSD P<0.05) broad sense heritability detected for 𝜎𝑔² ,
genotypic variation are presented.
Traits
Q
𝜎𝑔² ±SE
²
𝜎𝑔.𝑡
±SE
𝜎𝜀² ±SE
Hb
Δ% (𝜎𝑔² ±SE)
Δ% (𝜎𝜀² ±SE)
Δ% Hb
0.67 ± 0.30
0.59 ± 0.22
0.91 ± 0.11
0.60
2293 ± 1886
13892 ± 2418
-
K
0.61 ± 0.17
0.04 ± 0.04
0.28 ± 0.03
0.90
634 ± 381
2416 ± 421
-
QKR
0.20 ± 0.07
0.15 ± 0.05
0.10 ± 0.01
0.68
1604 ± 683
3196 ± 556
0.60
0.75
682 ± 315
1622 ± 282
0.56
0.79
1 ± 21
207 ± 36
-
0.71
561 ± 416
2943 ± 512
-
0.79
58 ± 40
270 ± 47
-
-
52 ± 26
143 ± 25
0.52
RDM
SDM
RSR
TDM
SPAD
14.32 ±
4.84
41.89 ±
13.32
0.005 ±
0.002
85.18 ±
27.23
3.68 ± 2.43
3.76 ± 2.44
10.50 ± 5.63
0.0009 ±
0.0010
27.38 ± 11.56
5.08 ± 2.48
17.17 ±
2.11
34.88 ±
4.29
0.008 ±
0.001
55.90 ±
6.88
14.11 ±
1.74
5.5.5 Percent change trait correlations
There was a significant (R² = 0.236, P = 0.003) positive correlation between the TDM drought
response (percent TDM reduction from the well-watered control, or Δ% TDM) and the Q glycosides
drought response (percent Q increase from the well-watered control, or Δ% Q) (Figure 5.5). Thus,
genotypes that increased their Q glycoside levels more were able to maintain more of their TDM
under water deficit.
94
Figure 5.5
White clover dry matter response under water deficit.
Dry matter response expressed as a percent of total dry matter (TDM) production of the well
watered control (Δ% TDM), versus quercetin glycoside (Q) response (Δ% Q, Q glycoside accumulation
under water deficit expressed as a percent of Q glycosides of the well watered control). The
phenotypic correlation was significant (R2 = 0.236, P = 0.003).
5.5.6 Multivariate trait responses to drought across treatments
The across-treatments biplot (Figure 5.6) is based on traits for which there was significant (P < 0.05)
genotypic variation among the 32 full-sibling progeny and the two parents across both the water
deficit and well-watered treatments. Principal component 1 explained 39% of the total trait variation,
and the second principal component explained 23% of the variation. Based on the angles between
the directional vectors in the PCA, trait Q glycosides had a negative association with the traits related
to dry matter; RSR, RDM, and TDM. K glycosides was negatively associated with QKR and SPAD
(Figure 5.6).
There were three genotype groups generated from cluster analysis (Figure 5.6). A number of
individuals in group 2 had above average expression for the traits RSR, RDM, TDM, SDM, SPAD and
QKR. Most members in genotype group 3 and some members in group 1 had above average Q
95
glycosides expression. Parental genotype Kopu II was a member of group 1 and the parental
genotype Tienshan stood separately from all other groups.
Figure 5.6
Biplot representing 34 white clover genotypes evaluated across the well watered and
water deficit treatments.
Chart data were generated from a principal component analysis of adjusted means (BLUPs from
REML analysis). Each directional vector represents a trait: Q, quercetin glycosides; K, kaempferol
glycosides; QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry matter; RDM, root dry matter;
RSR, root:shoot ratio; TDM, total dry matter = SDM + RDM; SPAD, chlorophyll index. The symbols
indicate three genotype groups detected using cluster analysis.
5.5.7 Multivariate trait responses to drought percent changes between treatments
The overall plant responses to water deficit are shown in the delta % PCA biplot (Figure 5.7). Here,
the principal components were calculated from the percent changes (Δ% between the two
treatments for each genotype and trait. The first principal component accounted for 54% of the
variation, and 21% was explained by the second principal component. The delta biplot reveals close
proximity of changes in morphological traits with changes in Q glycosides and QKR (Figure 5.7). For
example, the higher the increase in Q glycosides production (Δ% Q), the higher was maintenance of
96
TDM production (i.e. less pronounced decrease in Δ% TDM), thus reflecting the finding from Figure
5.5.
Five genotype groups were generated from cluster analysis (Figure 5.7). A number of genotypes in
group 4 had above-average expression for the traits Δ% RSR, Δ% QKR, Δ% RDM, Δ% SPAD, Δ% TDM
and Δ% SDM. A number of members in genotype group 3 had above average Δ% Q glycosides and Δ%
K glycosides expression; and all group 5 genotypes showed high Δ% Q glycosides expression. Both
parental genotypes Tienshan and Kopu II were included in group 2.
Figure 5.7
Delta biplot representing 34 white clover genotypes, evaluated for the percent change
(Δ%) between the well watered and water deficit treatments.
The directional vectors represent Δ% for each trait: Q, quercetin glycosides; K, kaempferol glycosides;
QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry matter; RDM, root dry matter; RSR,
root:shoot ratio; TDM, total dry matter = SDM + RDM; SPAD, chlorophyll index. The symbols indicate
five genotype groups detected using cluster analysis.
5.5.8 Plant traits linked to the principal water deficit measure Δ% PC1
Plant trait responses in correlation with Δ% PC1 explained the majority of the variance in Δ% TDM,
Δ% RDM, Δ% RSR, Δ% SDM, Δ% QKR, and Δ% SPAD (Table 5.5), while correlation with Δ% PC2
explained the majority of variance in Δ% K. PC1 and PC2 both explained the majority of variance in
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Δ% Q. Δ% K glycosides was not linked to any of the other traits, and was not related to PC1 (Table
5.5).
Table 5.5
Correlation coefficients (r) and probabilities P for the drought-induced percent change
(Δ%)
Eight plant traits linked to principal components PC1 and PC2 of the delta PCA biplot. The Δ% means
were regressed against PC1 and PC2 scores. Traits are ranked by their Pearson correlation
coefficients r with PC1.
PC1
PC2
Trait
r
P
R
P
Δ% TDM
0.941
< 0.001
0.083
0.642
Δ% RDM
0.919
< 0.001
-0.212
0.230
Δ% RSR
0.797
< 0.001
-0.368
0.032
Δ% SDM
0.792
< 0.001
0.322
0.063
Δ% QKR
0.759
< 0.001
-0.248
0.157
Δ% SPAD
0.647
< 0.001
0.039
0.827
Δ% Q
0.571
< 0.001
0.673
< 0.001
Δ% K
-0.070
0.696
0.952
< 0.001
The first principal component (PC1) from the delta % PCA (Figure 5.7) represented the overall
principal water deficit measure for all eight traits. Genotypes located towards the negative end of
PC1 showed, for example, less drought-induced reduction in TDM, together with more pronounced
increases in Q glycosides and QKR, whereas the opposite held true for the positive end of PC1. In
Figure 5.8, Figure 5.9 and Figure 5.10, this PC1 was correlated against well-watered trait data that
showed significant variation of means. Under the well-watered treatment, TDM had a significant
negative correlation (P = 0.001) with the principal water deficit measure PC1 (Figure 5.7), which
indicated that plants with higher DM production under the well-watered treatment had more
positive PC1 scores (e.g. by decreasing DM) under water deficit. The ratio between Q glycosides and
K glycosides showed inverse relationships with PC1 (Figures Figure 5.9, Figure 5.10). Thus genotypes
that had high QKR levels under well-watered (Figure 5.9) and water deficit treatments (Figure 5.10)
reduced their DM production least under water deficit, and in turn increased their Q glycosides and
QKR most. Compared to the principal water deficit measure PC1, QKR under water deficit (P < 0.001)
increased from well-watered (P < 0.05), showing up to a two-fold shift from K glycosides towards Q
glycosides (Figures 5.9, 5.10).
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Figure 5.8
Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and total dry matter (TDM) production.
Well watered treatment of 32 F1 progeny and two parent white clover genotypes. Drought-induced
DM decrease as estimated on the PC1 axis altered from less change to more change as dry matter
production increased (R2 = 0.29, P = 0.001).
99
Figure 5.9
Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and QKR in the well watered treatment.
Correlation for 32 F1 progeny and their two parent white clover genotypes, as observed in the well
watered treatment (a) (R2 = 0.16, P < 0.05)
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Figure 5.10 Relationship between overall drought-induced DM decrease delta PC1 biplot scores
and QKR in the water deficit treatment.
Correlation for 32 F1 progeny and their two parent white clover genotypes, as observed in the water
deficit treatment (b) (R2 = 0.62, P < 0.001).
5.6 Discussion
5.6.1 Responses to drought
Water deficit resulted in a number of significant morphological and physiological changes in the
white clover populations (Tables Table 5.1, Table 5.2). The strong decrease in water potential (Table
5.2) is in line with other studies illustrating a high degree of water stress in white clover (Turner
1991; Hofmann et al. 2003a). As expected, stomatal opening was reduced (Table 5.2), resulting in
decreased transpiration and limited photosynthesis, similar to other findings in white clover (Grieu et
al. 1995). Because of a larger water deficit effect on transpiration compared to photosynthesis, WUE
increased (Table 5.2), reflecting other observations in water deficit-stressed white clover (Barbour et
al. 1996). It was of further interest to note population-specific water deficit responses in the form of
increased RSR in the F1 progeny (Figure 5.4). Other studies have also indicated enhanced RSR under
water deficit in white clover (Caradus and Woodfield 1998) , and the findings for the F1 progeny can
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be utilised in plant breeding selection processes, as higher RSR often implies improved moisture
balance under water deficit (Chaves et al. 2002; Blum 2005).
5.6.2 Heritability of traits
The high broad-sense heritability (Hb) for most traits under the water deficit treatment (Table 5.3)
and across treatments (Table 5.4) indicates significant genotypic variation and genotype-bytreatment interaction, which is useful for white clover breeding programs focussed on abiotic stress
resistance, as it allows for a broad range of genotype selection. However, high G×E variance reduces
overall heritability, as it is in the denominator of Equation 5.3. From the percent change (Δ%) traits
with significant genotypic variation (Table 5.4), Δ% QKR showed the highest Hb which is important as
QKR increased significantly under water deficit and this was related to maintenance of growth under
drought (Figures Figure 5.7, Figure 5.9, Figure 5.10). The estimated significant genotype-bytreatment interaction for the traits Q, QKR, TDM and SPAD (Table 5.4), indicate a relative change in
ranking among the F1 progeny for the expression of these traits across the water deficit and wellwatered treatments. This also indicates the sensitivity of these traits to the two treatments applied.
Furthermore, the genetic factor G, and therefore heritability, will most likely have been increased by
the choice of two extreme parents (Kopu II and Tienshan) for the wide cross, compared to when two
similar parents would have been used (Abberton 2007).
5.6.3 Trait correlations and plant breeding perspective
Because no significant direct association between the traits Q glycosides and DM was discovered in
this F1 population within either treatment, plant breeders can select white clover genotypes showing
high Q glycoside levels separately from the trait DM. Furthermore, water deficit significantly
increased Q glycoside accumulation and the significant positive correlation between the droughtinduced DM decrease and the drought-induced Q glycosides increase (Figure 5.5) indicates that plant
breeders should select for the difference in Q glycosides between treatments (Δ% Q), not just for the
highest Q glycoside levels absolute. This can have profound implications for breeding programs with
the opportunity of selecting genotypes that show high Δ% Q, together with combined sustainable
herbage yield and maintenance of DM under drought.
5.6.4 Multivariate analyses
In the across-treatments biplot (Figure 5.6), the significant genotype-by-treatment interaction
indicates a change in relative ranking of genotypes across the two treatments. For example, the
genotype performance for DM and Q glycosides expression changed across the two treatments.
However, the across-treatments analysis has provided an estimate of average performance of each
genotype and thus reduced individual treatment effects, which may have also resulted in the change
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in type and magnitude of association among traits such as Q glycosides and DM as observed in the
delta % biplot (Figure 5.7).
5.6.5 Cluster analyses
From the five genotype groups generated from cluster analysis in the delta % biplot (Figure 5.7), a
number of genotypes in group 4 with above average expression for the traits Δ% RSR, Δ% QKR, Δ%
RDM, Δ% SPAD, Δ% TDM and Δ% SDM, and all group 5 genotypes which showed high Δ% Q
glycosides expression, could be used as potential breeding material.
5.6.6 Plant traits linked to the principal water deficit measure Δ% PC1
The principal water deficit measure (Δ% PC1) (Figure 5.7, Table 5.5) involved most of the important
agronomic traits, including Δ% DM production. This is similar to observations of responses in UV-Btreated white clover populations (Hofmann et al. 2001). Yield potential (YP) is defined as the
maximum DM yield realised under non-stress (well-watered) conditions, and reduced biomass or YP
under drought represents the integrated response to water deficit stress of all biochemical,
physiological and growth processes (Blum 2005). It was of particular interest to note that
maintenance of dry matter production under water deficit was related to increases of Q glycosides
and QKR (Figure 5.7). The differential response within the F1 progeny to water deficit (Figure 5.7)
further indicates underlying potential genetic variation that would enhance plant selection focused
on improving white clover performance under moisture stress. The correlation of Δ% PC1 with wellwatered TDM data (Figure 5.8), is reflective of suggestions that rapid growth rate renders plants
more sensitive to abiotic stress (Smith et al. 2000).
5.6.7 Metabolic implications of flavonol activity
Gene expression of the key enzymes flavonoid 3’-hydroxylase (F3’H) and flavonol synthase (FLS)
(Winkel 2006) in the quercetin biosynthetic pathway is up-regulated by the effects of drought stress
(Fan et al. 2008), and was demonstrated by anti-sense technology (Lewis et al. 2006). Recent findings
on different MYB transcription factors explain how MYB gene expression controls flavonol
biosynthesis and thus contributes to abiotic stress resistance (Mehrtens et al. 2005; Stracke et al.
2007; Czemmel et al. 2009; Yang et al. 2009; Stracke et al. 2010a). The ratio between Q glycosides
and K glycosides showed negative association with Δ% PC1 demonstrating that genotypes with high
QKR reduced their DM production least under water deficit, and in turn increased their Q glycosides
and QKR most (Figure 5.7 and Figure 5.10a, b). The ratio between Q glycosides and K glycosides
under the water deficit treatment showed a marked shift from K glycosides towards Q glycosides
(Figures Figure 5.9, Figure 5.10), similar to increases of QKR under UV radiation in white clover and
other species (Hofmann et al. 2000; Scalabrelli et al. 2007; Gerhardt et al. 2008). The antioxidant and
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radical scavenging activities of quercetin-3-O-glucosides are seen to provide superior protection
against abiotic stress compared to kaempferol-3-O-glucosides (Yamasaki et al. 1997; Fiorentino et al.
2007). The positive association between higher maintenance of DM and Q glycoside accumulation
under drought (Figure 5.5, Figure 5.7) could suggest a particularly efficient mediator role for Q
glycosides in stress protection, considering its generally low levels in herbaceous plants. For example,
Q glycosides specifically regulate auxin movement by acting as an endogenous inhibitor of auxin
transport (Agati and Tattini 2010).
5.6.8 Conclusions
The results of this experiment show evidence of white clover plants within an F1 population that are
stress-resistant and highly productive simultaneously. Our findings considered together with
previous work (Hofmann et al. 2000), indicate that Q glycosides act in a similar way under water
deficit stress as under UV-B radiation stress, correlated with stress sensitivity. The genotypic
variation detected here also signifies the potential use of new germplasm in selecting for increased Q
glycoside accumulation as well as herbage production in future breeding programs. As Δ% Q
glycosides and DM under the well-watered treatment do not seem significantly correlated, plants
with high Δ% Q glycosides could be selected first, followed by further selection for high herbage
yield. The levels of Q glycosides in white clover and many other herbaceous plants are too low to
suggest a direct trade-off in carbon flow between the accumulation of DM and Q. The functions of
flavonoids as developmental regulators are very complex (Kuhn et al. 2011; Lewis et al. 2011) and
need to be investigated further. The different environmental responses of the individual genotypes
and their trait-grouping in the biplots have not been identified due to limited resources for this
experiment. A follow-up with larger-scale field experiments in two contrasting environments will
shed more light on genotype-by-environment interaction effects. Genetic analysis may well elucidate
the role and regulation of several transcription factors involved in abiotic stress response pathways
(Ashraf 2010).
104
105
Chapter 6
Journal article:
“Genotype × Environment analysis of flavonoid accumulation and
morphology in white clover under contrasting field conditions”
Ballizany, W. L., Hofmann, R. W., Jahufer, M. Z. Z., & Barrett, B. A. (2012). Genotype × environment
analysis of flavonoid accumulation and morphology in white clover under contrasting field
conditions. Field Crops Research, 128, 156-166. doi:http://dx.doi.org/10.1016/j.fcr.2011.12.006
The author taking measurements in the field at Ashley Dene dryland farm.
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6.1 Abstract
White clover (Trifolium repens) is a beneficial legume in temperate pastures as a provider of high
quality feed to stock and nitrogen to grassland. However, its growth is often strongly impaired by
summer drought. The aim of this study was to investigate the association of flavonoids and biomass
production in environments contrasting in moisture availability, and to identify trait ideotypes to be
used in breeding plants for stress environments. In two separate field experiments, cloned
individuals from a full sib progeny generated by a cross between two phenotypically distinct and
highly heterozygous white clover genotypes, were exposed to two contrasting field environments:
water-limited (WL) and water-sufficient (WS). Most measured morphological and physiological traits
and flavonol glycoside accumulation were affected by the contrasting WL and WS environments. WL
decreased carbon isotope discrimination by 5.5 %. Under WL the levels of quercetin glycoside (Q)
and of the quercetin:kaempferol glycoside ratio (QKR) increased by 62 % and by 36 %, respectively,
relative to the WS environment. The WL environment reduced shoot dry matter (SDM) by 68 %, with
the most productive genotypes showing the greatest proportional reduction. Stolon numbers per
plant decreased by 48 % under WL, but stolon density (stolon number m-2 canopy area) increased by
48 %. In principal component analysis, drought-induced changes in plant morphology were closely
aligned to changes in Q, but not kaempferol glycosides. The increased induction of Q glycoside
accumulation in response to water deficit stress was related to retaining higher levels of SDM
production. This study also enabled the identification of individual plant trait combinations useful for
white clover breeding programmes focussed on developing improved cultivars for dryland
environments.
6.1.1 Keywords
Quercetin glycoside, drought, yield, Trifolium repens, biomass, abiotic stress, water deficit, genetic
variation, secondary metabolism, oxidative stress, flavonoids
6.2 Introduction
The perennial forage legume white clover (Trifolium repens L.) is an important component in many
pasture environments as a supplier of high quality feed to livestock and nitrogen to plants. White
clover is extensively used in temperate latitudes due to its wide range of climatic adaptation and
ability to form permanent mixed swards with a range of perennial grasses (Frankow-Lindberg 2001).
However, it is less tolerant of drought compared with other perennial temperate forage legumes
such as lucerne (Medicago sativa L.), because of its shallow root system and inability to effectively
control transpiration (Burch and Johns 1978; Baker and Williams 1987; Annicchiarico and Piano
2004). Consequently, new white clover cultivars with improved tolerance of drought-induced stress
are needed (Humphreys et al. 2006).
107
There is a general trade-off between the genetic potential for high plant herbage yield and its
adaptation to drought (Grime 1989). Leaf size is a major determining factor in white clover
productivity, which is greater with larger leaf size, but leaf size has a negative correlation with
persistence (Annicchiarico 1993). The expansion of a strong grid of stolons is essential for persistence
and stolon traits have long been a focus in breeding programs (Caradus and Chapman 1996; Collins et
al. 1997). For white clover stolons to be able to survive over a dry summer period, a combination of
yield and vegetative persistence under moisture stress conditions is needed. Thus plant breeders
select for increased herbage yield under summer moisture stress conditions (Chapman and Williams
1990). Plants with adaptation to moisture stress may be those individuals that avoid stress by
switching from an active vegetative mode to a dormant mode. The efficiency of selection programs
that are yield-based can be improved by observing plants in stress environments, and the use of
physiological selection criteria for stress tolerance (Blum 2011a).
Plant morphology has a major impact on water-use and dry matter yield in clover. Small plant size
and reduced leaf area limit water use and these are representative of low productivity, and typical of
ecotypes from adverse environments (Caradus and Mackay 1989). An example is the Chinese
highland T. repens ecotype ‘Tienshan’ (Hofmann et al. 2000). While such plants can withstand
drought very well, their biomass production is relatively low (Lane et al. 2000b). Cultivars developed
for dryland conditions often display moderate yield and increased water use efficiency. For example,
this can be seen in temperate dryland legumes, where small plants and small leaf area are critical for
improved stress adaptation, but exhibit lower potential yield (Thomas 2003; Jahufer et al. 2009;
Hofmann and Jahufer 2011). Stolon branching and stolon numbers in white clover are significantly
reduced under water deficit stress (Widdup and Turner 1990). However, stolon numbers (Belaygue et
al. 1996) and stolon density (Sanderson et al. 2003) can recover after rain. Alternatively, sexual
reproductive development of white clover was not impaired by a moderate water deficit that
reduced vegetative growth (Bissuel-Belaygue et al. 2002).
The functions of flavonoids have been widely reviewed with regard to their protective roles against
pathogens, herbivores, and environmental stress (Dixon et al. 2002; Carlsen and Fomsgaard 2008;
Agati et al. 2011). The accumulation of defence-related flavonoids is influenced by complex
interactions, and competition between primary and secondary metabolism may exist (Treutter 2005).
Flavonols are catalysed in the flavonoid biosynthetic pathway from naringenin by the enzyme
flavanone 3-hydroxylase (F3H) synthesising dihydrokaempferol, from which flavonoid 3’-hydroxylase
(F3’H) catalyses dihydroquercetin, and flavonol synthase (FLS) synthesises respectively the flavonols
kaempferol and quercetin (Winkel 2006). These are in turn converted to their respective glycosides
by UDP dependent glycosyltransferase (UGT) in Arabidopsis thaliana (Stracke et al. 2007). The R2R3-
108
MYB transcription factors referred to as PFG1–3 for ‘PRODUCTION OF FLAVONOL GLYCOSIDES’
(Mehrtens et al. 2005; Stracke et al. 2010b) express for accumulation of distinct flavonol glycosides.
Flavonols and their glycosides appear to be mainly involved in the response mechanisms against
abiotic stresses (Agati et al. 2011), and numerous studies suggest that flavonoids have multiple
functional roles in the responses of plants to adverse environmental conditions (Winkel-Shirley
2002a; Treutter 2005; Martens et al. 2010). Although it has been reported that antioxidant flavonoid
biosynthesis are mostly enhanced in stress-sensitive and not in stress-tolerant species, (e.g. solar
irradiation-stressed Mediterranean shrubs (Tattini et al. 2005) and salinity-stressed rice (Walia et al.
2005)), soybean cultivars (Glycine max L.) with enhanced tolerance to UV–B irradiation accumulated
significantly more flavonols than sensitive cultivars, and the expression of flavonol biosynthesis was
induced upon UV treatment (Reed et al. 1992; Kim et al. 2008). The flavonol glycosides of quercetin
(Q) and kaempferol (K) have UV-B protective properties (Ryan et al. 1998), a trait also conferred by a
high Q:K glycoside ratio(QKR) in Petunia (Lewis et al. 2006). UV radiation can induce the preferential
synthesis of flavonols that have a higher level of hydroxylation, i.e. quercetin-3-O-glucoside instead
of kaempferol-3-O-glucoside (Olsson et al. 1998; Fiorentino et al. 2007). This physiological adaptation
is likely to be related to the higher capacity of Q glycosides to scavenge stress-generated free radicals
(Scalabrelli et al. 2007).
Hofmann et al. (2000; Hofmann and Jahufer 2011) identified flavonols involved in the response of a
number of white clover populations to UV-B, and reported a trade-off between plant dry matter
(DM) production and induced Q glycoside accumulation. Furthermore, higher Q glycoside
accumulation under UV-B was linked to tolerance against UV-B-induced growth reduction (Hofmann
et al. 2003a), although the UV-B sensitivity depended on water availability, plant productivity and
duration of stress (Hofmann et al. 2003b). On the basis of these ecotype and cultivar studies, high
levels of Q glycosides appeared to be associated with high levels of abiotic stress tolerance, and
lower levels of productivity.
We hypothesise that flavonoid accumulation is associated with differential biomass production of
white clover plants growing in contrasting soil moisture environments under field conditions. The
objective of this research was to assess the plant response and genotype by environment (G x E)
interaction in water-sufficient (WS) and water-limited (WL) environments in terms of flavonoid
accumulation, its association with DM yield and stress acclimatisation, the inheritance of flavonoid and
DM production, and the magnitude and type of G×E for a range of other morphological, physiological
and biochemical traits.
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6.3 Materials & methods
Field experiments on two contrasting experimental sites were established in mid-October 2009 with
a duration of 27 weeks from planting to harvest. One site was a summer water-sufficient (WS) site
(Figure 6.1) established at AgResearch Farm in Canterbury, New Zealand (43° 37’ S, 172° 28’ E,
elevation 14 m), the other a summer water-limited (WL) site (Figure 6.2) at the Lincoln University
Dryland Research Farm at Ashley Dene in Canterbury, New Zealand (43° 38’ S, 172° 21’ E, elevation
34 m). The sites are 9.64 km apart and meteorological data were recorded at a weather station
between the two experimental sites.
Figure 6.1
The experimental site at the AgResearch farm in January 2010.
6.3.1 Field preparation
Both experimental sites were ploughed and cultivated to establish optimum seed bed conditions for
plant establishment. At both the WL and WS sites, soil samples were taken to determine soil
moisture, fertility and pH. The soil type at the WS site was a deep Templeton silt loam (Francis and
Kemp 1990) with a pH of 5.4, an average volumetric soil moisture of 30 % from October to January,
medium to low nitrogen and high potassium and medium Olsen phosphorus (29 mg L-1) levels
(Analysis Report Lab Number 729584.3, Hill Laboratories Ltd., Hamilton, New Zealand, Appendix G).
At the WL site the soil was a Lismore stony silt loam (Hewitt 2010) with a pH of 5.5, an average
110
volumetric soil moisture of 15 % from October to January, medium to low nitrogen, high potassium
levels and high Olsen phosphorous (54 mg L-1) levels (Analysis Report Lab Number 729584.1, Hill
Laboratories Ltd., Hamilton, New Zealand, Appendix G). The Olsen phosphorus content would not
limit white clover growth in the drying soils present in the experiments (Singh and Sale 1998).
Figure 6.2
The experimental site at Ashley Dene dryland farm in January 2010.
111
Figure 6.3
Selecting cloned white clover genotypes for planting in the field.
After transplanting (Figure 6.3), sites were irrigated in the first week of December 2009 with 30 mm
of water, after which there was sufficient rain for plant establishment of the experiments at both
sites. A wire mesh electric fence was placed around each site to exclude grazing animals. Broadleaf
weed seedlings were controlled with the pre-emergence herbicide Treflan 480 (Dow AgroSciences)
before experiment establishment. After transplanting, small dicotyledonous weed seedlings were
controlled by the clover compatible post-emergent broadleaf herbicide Preside® (Dow AgroSciences).
During the experiments, the non-selective contact herbicide Buster® (Bayer CropScience) was
sprayed monthly between the experimental plants to eradicate white clover volunteer seedlings and
other weeds. All herbicides were sprayed from a 15 L knapsack sprayer with recommended rates.
Further hand-weeding was used within the perimeter of the growing plants every two to three
weeks.
6.3.2 Plant Material
The plant material used in this study was the entire mapping population of 190 F1 genotypes from
the full-sibling F1 population as described in paragraph 3.2.1, plus the parents Kopu II and Tienshan.
The F1 progeny plants were vegetatively propagated to create clonal replication for the field
experiments. Each of the 190 F1 genotypes and the two parent individuals were cloned three times
112
per site and grown in trays. For each clonal copy of each genotype, one 10 cm-long stolon cutting,
measured from the apical end, was transplanted into root trainers in July 2009 to establish roots and
facilitate subsequent transplanting and establishment in the field. The plants in root trainers were
randomised in trays according to a row-column experimental design, described below. Plant plugs
were transplanted into the WL site on 13 October 2009 and into the WS site on 15 October 2009. The
plants were allowed to grow until the end of February, when they were grazed once. Regrowth of the
stubble occurred until end of April 2010. The final harvests were 21 and 28 April 2010 at the WL and
WS sites, respectively.
6.3.3 Experimental design
The plots at the two evaluation sites consisted of spaced plants. The experimental layout at both
sites was a row-column spatial design (Gleeson 1997) consisting of three replicates with repeated
clonal checks (Appendix F.4). The plots were on 60 × 60 cm centres at the dry and stony WL site, and
on 75 × 75 cm centres at the WS site, to ensure the plants were kept separate whilst growing.
Twenty-seven clonally propagated repeated check plantlets from each of the two parents were used.
In addition, clones produced from a single genotype of each of two elite white clover cultivars,
Crusader C20132 (Woodfield et al. 2001) and Tahora II which contains small leaved selections
(Caradus and Woodfield 1997) were included as repeated checks (27 and 21 clones per genotype per
site, respectively). The 102 check plots in total per site were systematically distributed among the
570 (190 per replicate) progeny plots. The entire experiment was surrounded by a row of border
plots to reduce edge effects in the experimental material (Cullis et al. 2006). The experiment was
carried out under ambient conditions (e.g. standard UV-B, rainfall and biotic stress pressure in a New
Zealand South Island East coast summer) without further imposed stress factors. The experiment was
grazed once at the end of February 2010, when a flock of 12 sheep was introduced for 24 hours to
simulate a more realistic pasture environment, to stimulate stolon growth, and to limit the lateral
expansion of the plants. After grazing the residual shoots were observed to be short and even.
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Figure 6.4
Weeding was an important part of experiment maintenance.
6.3.4 Measurements
Soil moisture content
Volumetric soil moisture content was estimated every two weeks at both sites using Time Domain
Reflectometry (TDR HydroSense, Campbell Scientific Inc., USA), by taking 20 measurements
diagonally across each site with a probe rod length of 12 cm, similar to the depth of roots observed in
white clover grown in the field (Baker and Williams 1987).
Carbon isotope discrimination
Changes in the 13C/12C ratio (δ13C) in plants relative to the ambient CO2 are correlated with long term
plant water use efficiency (WUE; net carbon fixation per unit water transpired) (Hall et al. 1994). To
obtain a measure of plant water deficit stress by distinguishing stomatal conditions or plant water
status for the plants grown at the different sites, carbon isotope discrimination measurements
(Farquhar et al. 1989; Ehleringer 1991) were carried out in the parental genotypes Tienshan and
Kopu II, and in five randomly sampled F1 genotypes from each replication, and each site, with a total
of 30 genotypes per site. From each plant, 2 mg ground, freeze-dried leaf powder was used for
elemental analyser isotope ratio mass spectrometry (EA-IRMS) (Halket and Zaikin 2006). The ground
and dried plant samples were weighed into tin capsules, sealed and loaded into the auto-sampler of
114
the PDZ Europa (Crewe, UK) GSL elemental analyser located in Lincoln University, Lincoln, New
Zealand. A reference material was also weighed out in the same manner to match the composition
and abundance of the samples. The samples were combusted in the presence of oxygen to convert
the carbon in the material to CO2 gas. The resultant CO2 gas was resolved on a gas chromatograph
packed column and passed into a PDZ Europa (Crewe, UK) 20-20 IRMS, where masses 44, 45 and 46
were determined. Both references and samples were analysed in this manner in a batch process,
whereby a number of samples was bracketed between references. The samples were measured with
a duplication rate of 1 in 8. The reference material used during analysis of all samples was EMWHEAT (δ 13CV-PDB = -25.12 ‰). EM-WHEAT has been normalised to the international reference
material IAEA-CH-6 (Sucrose, δ 13CV-PDB = -10.43 ‰). EM-WHEAT was also run as dummy samples to
check precision and accuracy. Repeated measurements of EM-WHEAT yielded a precision of ± 0.1 ‰.
The 13C/12C isotope composition was measured by use of a mass spectrometer, which yielded the
following ratio:
Equation 6.1
𝑅=
Molar ratio 13C/12C in CO2.
13
𝐶𝑂2
12𝐶𝑂
2
The carbon isotope ratio of the plants, delta 13C (δ13C), was quantified on a parts per thousand (‰)
basis:
Equation 6.2
Carbon isotope ratio δ13C deviation from a standard.
𝑅𝑠𝑎𝑚𝑝𝑙𝑒
δ 13𝐶 (‰) = �𝑅
𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑
− 1� × 1000
where the standard represents the carbon isotopes contained in the reference material EM-WHEAT
(δ 13CV-PDB = -25.12‰).
Equation 6.3
𝛼 =
Isotope effect, also called fractionation factor.
𝑅𝑎
𝑅𝑝
Where Ra is the isotopic abundance ratio 13C/12C in the air CO2 and Rp is the isotopic abundance ratio
13
C/12C in the plant CO2. Instead of using the isotope effect (α = Ra/Rp), Farquhar & Richards (1984)
proposed the use of ∆, the deviation of α from unity, as:
Equation 6.4
∆= 𝛼– 1 =
The measure of the carbon isotope discrimination by the plant.
𝑅𝑎
−1
𝑅𝑝
115
Typically for C3 plants ∆ = 20‰ (20 × 10-3). As the absolute isotopic sample composition is difficult to
measure, the mass spectrometer measures the deviation (δ) of the isotopic composition of the
material from a standard:
Equation 6.5
𝛿𝑎 =
𝑅𝑎
−1
𝑅𝑠
Equation 6.6
𝛿𝑝 =
Deviation of the isotopic composition of the air from a standard.
Deviation of the isotopic composition of the plant from a standard.
𝑅𝑝
−1
𝑅𝑠
Where Rs is the isotopic abundance ratio 13C/12C in the standard. Factors of 1000 in other equations
involving the δ notation should be deleted when comparing to the equations here. In contrast to δ,
the discrimination (∆) is independent of the isotopic composition of the standard used for the
measurement of Rp and Ra, and is also is independent of Ra. Plants show positive discrimination (∆)
against 13C. All compositions with δ are compared to the PDB standard, a fossil belemnite from the
Pee Dee Formation for which R = 0.01124. The value of ∆ is obtained using Equation 6.4, Equation 6.5
and Equation 6.6.
Equation 6.7
∆ (‰) =
Carbon isotope discrimination in the plant sample.
𝛿𝑎 − 𝛿𝑝
1 + 𝛿𝑝
On the PDB scale, free atmospheric CO2 (Ra ≈ 0. 01115 in 1988) currently has a deviation, 𝛿𝑎 of
approximately -8‰, and typical C3 material (Rp ≈ 0. 01093) a deviation, 𝛿𝑝 , of -27.6%, which yields ∆
= (-0.008 + 0.0276) / (l - 0.0276) = 20.1‰.
Water use efficiency (WUE), expressed as mmol C mol-1 H2O, is calculated as (Farquhar and Richards
1984):
Equation 6.8
WUE =
Water use efficiency as calculated from ∆.
𝑐𝑎 × (27−∆)
1.6 × 𝑣 × 22.6
where ca is the ambient CO2 concentration, v is the gradient in H2O-vapour between the leaf and the
atmosphere, and 1.6 is the ratio of gaseous diffusivities of CO2 and H2O-vapour in air. As it is assumed
that ca and v are constants (Farquhar and Richards 1984; Farquhar et al. 1989; White et al. 1995),
WUE is negatively correlated with ∆.
116
Morphology
Visual scoring of morphological traits was carried out every four weeks. Additionally, selected plants
were harvested destructively for DM in February and March 2010 to empirically measure and
calibrate visually-scored traits. Visual scoring for all traits was done on a scale of 1 to 5 (1 = smallest;
5 = largest) and calibrated using three or more samples per scale unit, resulting in a minimum of 15
samples. The traits of the rest of the individuals were determined by correlating the curve-fitted
regression equations of actual means to scores. For DM measurements, the herbage was cut off at 2
cm height using electric clippers, put in paper bags and dried in an oven for 48 hours at 70 °C.
Scoring, counting and measuring at both sites was carried out for the traits shoot dry matter (SDM,
g); leaf size averaged over the length of three middle leaflets per plant (LS, mm); number of
inflorescences (IN); canopy data were collected by scoring the openness of the leaf canopy and
measuring leaf-canopy area, (CA, m2); leaf-canopy volume (CV, m3); number of stolons (ST); stolon
density (SD) as number of stolons per square meter leaf-canopy area.
6.3.5 Analysis of flavonols by high performance liquid chromatography (HPLC)
Sample preparation and HPLC analysis followed established methods (Hofmann et al. 2000; Ryan et
al. 2002; Dong 2006). Plant material was sampled for flavonoid leaf analysis immediately prior to the
final harvest. Ten first fully expanded (FFE) trifoliate laminae from each individual were collected.
Immediately after collection, samples were flash-frozen in liquid nitrogen and stored at -34 °C. The
frozen samples were ground to a fine leaf powder with a mortar and pestle in liquid nitrogen, after
which the ground samples were freeze-dried for 48 hours in a freeze-dryer (W.G.G. Cuddon Ltd.,
Blenheim, New Zealand) and stored at -34 °C until analysis. The freeze-dried, ground plant material
(50 ± 2 mg) was extracted in darkness with occasional vortex shaking for 12 hours in 3 ml MeOH-H2OHOAc (79:20:1). Following centrifugation, 2 ml of the supernatant was syringe-filtered into amber 2
ml HPLC vials with caps and used for HPLC analysis. Analytical HPLC was performed on an integrated
HPLC machine (Agilent 1100 series, Agilent Technologies, Waldbronn, Germany). Chromatography
was carried out with an injection volume of 10 µl and a flow rate of 0.8 mL min-1, on a Merck
LiChroCART 125-4 RP-18 end-capped column (4 µm, 4 mm x 119 mm), filled with Superspher 100
silica gel fitted with a LiChrospher 100 RP-18e 5 µm guard column. The gradient solvent system
comprised filtered solvent A (1.5% H3PO4) and filtered solvent B (HOAc-CH3CN-H3PO4-H2O
(20:24:1.5:54.5)), mixed using a linear gradient starting with 80% A and 20% B, increasing to 67% B at
30 min, 90% B at 33 min, 100% B at 39.3 min, and back to 20% B at 41 min to 47 min total. In order to
maintain the integrity of naturally-occurring flavonoid compounds in the samples, the leaf extracts
were not hydrolysed. The resulting chromatograms thus contained numerous flavonoid peaks. These
were examined individually for their online spectra, which clearly identified them as derivatives of
the flavonols quercetin and kaempferol (Markham 1982). The integrated areas of all flavonol peaks
117
(measured at 352 nm) were added, and the result compared to an external standard curve prepared
using 0, 10, 25, 50 and 100 ppm rutin (quercetin 3-rutinoside). Thus the flavonol glycoside levels are
expressed here as rutin equivalents (Ryan et al. 2002; Olsen et al. 2010).
6.3.6 Statistical analysis
Traits were compared per genotype between sites. Genotypic effect, genotype × environment
interaction, and broad-sense heritability were determined. The data for the traits Q, K, QKR, SDM, LS,
IN, CA, CV, ST, SD were examined using the variance component analysis procedure, REML option, in
GenStat 12 (VSN International Ltd.). A completely random linear model was used in the analysis of all
traits within and across the two sites, using the REML algorithm (Virk et al. 2009). The REML analysis
enabled the generation of adjusted means (Best Linear Unbiased Predictors or BLUPs) for the range
of traits examined and pattern analysis thereof (Watson et al. 1995; Jahufer et al. 2006). The
statistical analysis of the phenotypic traits included (i) estimation of phenotypic correlation
coefficients, (ii) analysis of variance, (iii) estimation of broad-sense heritability based on plant mean
within and across-environment, and (iv) multivariate analysis including principal component analysis
and cluster analysis. Slope analysis (Kempthorne and Folks 1971) between the two linear regression
lines showed a significant difference (P < 0.05) between environments. While the genetic variance of
the Kopu II parental genotype was close to the mean of the population and is not shown separately
in the figures, the Tienshan parental genotype differed significantly (P < 0.05) (Kempthorne 1957).
Phenotypic correlation coefficients between the percentage change (delta percent) traits Δ% Q
glycosides and Δ% SDM, were calculated by ((WL - WS)/|(WS)|) * 100 and expressed as a percentage.
Correlations between principal components 1 and 2 from the principal component analysis and the
traits Q, K, QKR, SDM, LS, CA, CV, ST, SD were estimated according to Falconer and Mackay (1996).
The plant mean across replicate broad-sense heritability (Hb) within individual environments for the
traits Q, K, QKR, SDM, LS, IN, CA, CV, ST, SD was estimated (Falconer and Mackay 1996) using:
Equation 6.9
𝐻𝑏 =
Broad-sense heritability within environments
𝜎𝑔2
𝜎2
𝜎𝑔2 + 𝑛ɛ
𝑟
Where 𝜎𝑔² is the within environment genotypic variance component, 𝜎𝜀² is the pooled error variance
component, and nr is the number of replicates.
The plant mean across replicates and across-environment broad-sense heritability (Hb) for the traits
Q, K, QKR, SDM, LS, CA, CV, ST, SD was estimated (Falconer and Mackay 1996) using:
118
Equation 6.10 Broad-sense heritability across environments
𝐻𝑏 =
𝜎𝑔2 +
𝜎𝑔2
2
𝜎𝑔.𝑒
𝜎2
+ ɛ
𝑛𝑡
𝑛𝑡 . 𝑛𝑟
²
is the genotype ×
Where 𝜎𝑔² is the across-environment genotypic variance component, 𝜎𝑔.𝑒
environment (G×E) interaction variance component, 𝜎𝜀² is the error variance component, nt is the
number of environments (sites) and nr is the number of replicates.
Principal component analysis (PCA) was conducted using BLUP values from REML analysis for each
trait. The biplots enabled assessment of the genotypic variation on a multivariate scale, and the
association among traits. The trait means from WS and WL were correlated in an across-environment
PCA. Clustering of genotypes was performed using an agglomerative hierarchical clustering
procedure, using squared Euclidean distance as a measure of dissimilarity, and incremental sums of
squares as a grouping strategy. The GEBEI package (Watson et al. 1995) was used to perform the
clustering.
6.4 Results
The difference in volumetric soil moisture between environments was significant (P< 0.001) at all
times. Volumetric soil moisture was relatively stable between October 2009 and January 2010, ≈15%
and ≈32% in the WL and WS environments, respectively (Figure 6.5). From February to April 2010 soil
moisture decreased to 10 % in the WL environment and to 19 % in the WS environment (Figure 6.5)
and in April 2010 the difference between the environments turned out to be 44% (Table 6.1).
119
35
TDRWL
TDR Volumetric Soil Moisture (%)
30
TDRWS
25
20
15
10
5
0
Oct-09
Figure 6.5
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
Volumetric soil moisture (%) measured by TDR in the water-limited (TDRWL) and
water-sufficient (TDRWS) environments.
6.4.1 Physiological traits
The carbon isotope discrimination against 13C (∆) values for WL (18.20 ‰) and WS (19.27 ‰) in
plant samples indicated that mean plant water status between environments differed significantly (P
< 0.001) (Table 6.1). The ∆ was 1.07 parts per thousand lower in plants in the WL environment than
in plants in the WS environment. This reduction in ∆ corresponded to a 13.8% higher water use
efficiency (WUE) (Equation 6.8) in samples from plants growing in the WL environment (HoghJensen
and Schjoerring 1997).
6.4.2 Morphological and biochemical traits
With the exception of Inflorescences (In), all traits differed significantly (P<0.05) between the WL and
WS environments (Table 6.1). The large relative increase in Q glycosides (62%) from the WS to the
WL environment contrasted with the relatively small increase of K glycosides by 21 %, thus increasing
QKR by 36 % (Table 6.1). SDM production was highly significantly reduced by 61 % in the WL
environment at the final harvest (Table 6.1). The correlation between the final harvest SDM and the
accumulated total dry matter (TDM) over two harvests in both the WL (R2 = 0.26) and the WS (R2 =
0.36) environments was highly significant (P<0.001). The number of stolons per plant (ST) was
reduced by more than 48 % in the WL environment (P < 0.001), whereas stolon density (SD, stolons
per m2 leaf canopy area) increased by 48 % (P < 0.001) (Table 6.1), due to a stronger reduced leaf
canopy area (CA) by 65%.
120
Table 6.1
Trait means of 190 white clover full-siblings and two parent genotypes across two
environments, and environment-induced percent changes (Δ%) in 10 traits.
Traits: Q, quercetin glycosides (mg g-1 DM); K, kaempferol glycosides (mg g-1 DM); QKR,
quercetin:kaempferol glycoside ratio; SDM, shoot dry matter (g); TDM, total dry matter SDM1 +
SDM2 (g); St, number of stolons; SD, stolon density, number of stolons per square meter leaf-canopy
area; LS, leaf size averaged over 3 middle leaflets (mm); In, number of inflorescences, CA, leaf-canopy
area (m2); CV, leaf-canopy volume (m3); TDR, time domain reflectrometry (% volumetric soil
moisture); ∆, carbon isotope discrimination (‰); WUE, water use efficiency (mmol C mol-1 H2O). WL,
water-limited environment; WS, water-sufficient environment; H1, first harvest; H2, second harvest;
ns, non-significant.
Trait
WS
H1
WL
H2
H1
Δ%
H2
H1
P
H2
H1
H2
∆
-
19.27
-
18.20
-
-5.54
-
<0.001
CA
0.41
0.42
0.13
0.15
-67.75
-65.2
<0.001
<0.001
CV
0.05
0.05
0.01
0.01
-77.25
-75.14
<0.001
<0.001
In
31.93
-
22.31
-
-30.13
-
ns
-
K
-
2.19
-
2.65
-
20.90
-
0.008
LS
15.39
14.21
11.36
11.85
-26.18
-16.64
<0.001
<0.001
Q
-
2.96
-
4.78
-
61.61
-
0.007
QKR
-
1.48
-
2.02
-
36.18
-
0.034
SD
172.71
173.95
220.51
257.34
27.68
47.94
0.009
<0.001
SDM
29.93
73.78
9.49
28.83
-68.29
-60.92
<0.001
<0.001
St
71.74
74.67
30.30
38.49
-57.77
-48.46
<0.001
<0.001
TDM
-
103.71
-
38.32
-
-63.05
-
<0.001
TDR
31.85
19.25
15.24
9.64
-53.12
-44.44
<0.001
<0.001
WUE
-
0.21
-
0.24
-
13.80
-
<0.001
6.4.3 Heritability
There was significant (P < 0.05) genotypic variance (𝜎𝑔² ) among the full sibs for all the traits within the
individual environments, with the exception of SD2 and CV2 in the WS environment (Table 6.2).
Across environments, all traits except SD, ST, CA and CV had significant (P < 0.05) genotypic variance
²
(Table 6.3). There was significant (P < 0.05) G×E (𝜎𝑔.𝑒
) interaction for the traits SD, LS, IN, CA, and
SDM (Table 6.3).
121
Table 6.2
Within individual environment genotypic (𝝈²𝒈 ), and experimental error (𝝈²𝜺 )
components of variance.
Associated standard errors (±SE) and broad sense heritability (Hb) (based on plant mean across
replicates) for 10 traits among 190 white clover full sibs and two parents in two contrasting
environments. Traits (second harvest only): Q, quercetin glycosides (mg g-1 DM); K, kaempferol
glycosides (mg g-1 DM); QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry matter (g); St,
number of stolons; SD, stolon density, number of stolons per square meter leaf-canopy area; LS, leaf
size over the middle leaflet (mm); CA, leaf-canopy area (m2); CV, leaf-canopy volume (m3); WL, waterlimited environment; WS, water-sufficient environment. Only traits with significant (P < 0.05)
heritability for genotypic variation are presented.
WS
Traits (𝜎𝑔² ±SE)
(𝜎𝜀² ±SE)
WL
Hb
CA
0.03 (×10-2) ±
0.01(×10-2)
0.28 (×10-2) ±
0.02 (×10-2)
0.27
CV
6.73 ± 4.57
106.57 ± 7.18
-
K
0.31 ± 0.05
LS
(𝜎𝑔² ±SE)
(𝜎𝜀² ±SE)
Hb
0.04(×10-3) ±
0.01(×10-3)
0.25 (×10-3) ± 0.02
(×10-3)
0.34
0.11 ± 0.04
0.69 ± 0.05
0.31
0.52 ± 0.04
0.64 0.51 ± 0.07
0.54 ± 0.04
0.74
1.07 ± 0.23
3.29 ± 0.24
0.49 1.02 ± 0.20
2.43 ± 0.18
0.56
Q
0.71 ± 0.14
1.62 ± 0.12
0.57 0.95 ± 0.17
1.78 ± 0.13
0.62
QKR
0.24 ± 0.04
0.37 ± 0.03
0.67 0.45 ± 0.06
0.41 ± 0.03
0.77
SD
37.50 ± 23.30
530.20 ± 35.70
-
2105.00 ± 150.00
0.39
SDM
54.80 ± 10.80
137.90 ± 10.20
0.54 5.50 ± 1.03
11.98 ± 0.86
0.58
St
28.30 ± 13.70
290.90 ± 19.70
0.23 15.62 ± 4.17
67.68 ± 4.82
0.41
447.00 ± 125.00
The broad-sense heritability Hb for most of the traits measured from the plants within the WL
environment, with the exception of SD, ST, CA and CV, were higher than 0.55 (Table 6.2), with Q
glycosides 0.62 and QKR 0.77. In the WS environment, Hb estimates were lower than in the WL
environment for all traits measured (Table 6.2) except In, which was non-significant. The Hb
estimated across-environment was comparable to the WL environment, with Q K glycosides and QKR
respectively 0.68, 0.76 and 0.80, while and LS and SDM were lower. CA, CV, SD and St were nonsignificant (Table 6.3).
122
Across environment genotypic (𝝈²𝒈 ), genotype × environment (G×E) interaction (𝝈²𝒈.𝒆 ),
and experimental error (𝝈²𝜺 ) components of variance.
Table 6.3
Associated standard errors (±SE) and broad sense heritability (Hb) (based on plant mean across
replicates and treatments) for 10 traits among 190 white clover full sibs and two parents evaluated in
a water-limited and a water-sufficient environment. Traits (second harvest only): Q, quercetin
glycosides (mg g-1 DM); K, kaempferol glycosides (mg g-1 DM); QKR, quercetin:kaempferol glycoside
ratio; SDM, shoot dry matter (g); St, number of stolons; SD, stolon density, number of stolons per
square meter leaf-canopy area; LS, leaf size over the middle leaflet (mm); CA, leaf-canopy area (m2);
CV, leaf-canopy volume (m3). Only traits with significant (P<0.05) heritability for genotypic variation
are presented.
𝜎𝑔² ±SE
²
𝜎𝑔.𝑒
±SE
Hb
0.04(×10-3) ± 0.05 (×10-3) 0.14 (×10-3) ± 0.07 (×10-3)
𝜎𝜀² ±SE
0.15(×10-2) ± 0.01 (×10-2) -
CV
0
3.63 ± 2.22
53.77 ± 2.57
-
K
0.36 ± 0.05
0.05 ± 0.02
0.53 ± 0.03
0.76
LS
0.66 ± 0.15
0.33 ± 0.14
2.88 ± 0.15
0.51
Q
0.70 ± 0.11
0.08 ± 0.07
1.72 ± 0.09
0.68
QKR
0.33 ± 0.04
0.03 ± 0.02
0.38 ± 0.02
0.80
SD
34.00 ± 49.00
210.00 ± 68.00
1315.00 ± 66.00
-
SDM
9.04 ± 4.07
21.07 ± 4.90
75.05 ± 3.95
0.28
St
7.80 ± 5.70
13.60 ± 7.60
180.30 ± 9.00
-
Traits
CA
6.4.4 Univariate analyses and correlations
There was a weak negative, but significant (P < 0.001) phenotypic correlation between Q glycosides
and SDM in the WS (Figure 6.6) and the WL (Figure 6.7) environments. Tienshan was included in the
regressions, but its presence did not skew the analyses (data not presented).
123
7
Tienshan
6
WS Q (mg g-1 DM)
5
4
3
2
1
0
0
5
10
15
20
25
30
35
40
45
50
WS SDM H2 (g)
Figure 6.6
Phenotypic correlation between quercetin glycosides (Q) accumulation and shoot dry
matter (SDM).
White clover grown in a field experiment in a water-sufficient environment (WS). H2, second harvest.
124
9
Tienshan
8
WL Q (mg g-1 DM)
7
6
5
4
3
2
1
0
0
2
4
6
8
10
12
14
16
18
WL SDM H2 (g)
Figure 6.7
Phenotypic correlation between quercetin glycosides (Q) accumulation and shoot dry
matter (SDM).
White clover grown in a field experiment in a water-limited (WL) environment. H2, second harvest.
SDM accumulation in the WS environment and the water deficit induced growth response (SDM
production in the WL environment as a proportion of SDM in the WS environment, or Δ% SDM)
showed a moderate negative phenotypic correlation (Figure 6.8). There was a weak positive
phenotypic correlation between the drought-induced reduction of Δ% SDM and Q glycoside
accumulation in the WS environment (Figure 6.9). As SDM at harvest 1 (H1) and harvest 2 (H2) were
highly correlated with total dry matter (TDM), the sum of both SDM harvests (Table 6.1), SDM at H2
was used for comparison with flavonoids, as the leaves for this trait were harvested in the same
period.
125
-30
0
5
10
15
20
25
30
35
40
45
50
-40
Δ% SDM (%)
-50
-60
-70
Tienshan
-80
-90
Figure 6.8
WS SDM H2 (g)
Phenotypic correlation between water deficit growth response (shoot dry matter
(SDM) production in the water-limited (WL) environment.
This correlation is expressed as a percentage of SDM in the water-sufficient (WS) environment, or Δ%
SDM) versus SDM production in the WS environment. H2, second harvest. Δ% SDM is between
second harvests.
An important trait in physiological plant drought response is the difference in e.g. yield compared
between the water-limited environment and the control environment, which is water-sufficient
(Dudley 1996a, 1996b). This trait can be expressed as a percentage of the control, also called percent
change or delta percent. Obviously a positive (or at least less negative) percentage change in a
variable, e.g. yield under drought, is the more important for plant physiology and –breeding.
126
-30
0
1
2
3
4
5
6
7
-40
Δ% SDM (%)
-50
-60
-70
Tienshan
-80
-90
Figure 6.9
WS Q (mg g-1 DM)
Phenotypic correlation between water deficit growth response (shoot dry matter
(SDM) production in the water-limited (WL) environment.
This correlation is expressed as a percentage of SDM in the water-sufficient (WS) environment, or Δ%
SDM) versus quercetin glycosides (Q) accumulation in the WS environment. Δ% SDM is between
second harvests.
6.4.5 Multivariate analyses
The plant trait responses to the WS environment are shown in the PCA biplot (Figure 6.10). The first
principal component accounted for 48 % of the variation, and 19 % was explained by the second
principal component. The plant trait responses to the WL environment are shown in the PCA biplot
(Figure 6.11). The first principal component accounted for 46 % of the variation, and 19 % was
explained by the second principal component.
There were differences in trait association across the two environments. The stolon and canopy
related traits associated together in the WS environment, while Q and K glycosides and QKR correlate
together opposite from SDM and LS (Figure 6.10). In the WL environment (Figure 6.11), all the dry
matter and morphological traits correlated tightly together and were positioned opposite Q, showing
a negative correlation. K glycosides and QKR were negatively correlated with each other, and both
showed no correlation with the rest of the traits (Figure 6.11). QKR altered from a negative
association with SDM in the WS environment (Figure 6.10), to no association at all with SDM in the
WL environment (Figure 6.11).
127
The across-environment biplot (Figure 6.12) contains traits with significant (P < 0.05) genotypic
variation among the full-sibling progeny and the two parents across environments. Principal
component 1 explained 43 % of the total trait variation, and the second principal component
explained 19 %. Based on the angles between the directional vectors in the PCA, Q glycosides had a
negative association with traits related to dry matter and morphology, namely LS, SDM, SD, ST, CV,
and CA. K glycosides was negatively associated with QKR. However, QKR was not correlated with
SDM (Figure 6.12).
0.4
LS2
Component II (19%)
0.3
0.2
Group 1
SDM2
Group 4
0.1
0.0
-0.1
-0.2
CA2
St2
SD2
CV2
K
Group 3
Group 5
-0.3
K
Group 2
QKR
Tienshan
Q
-0.4
-0.5
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
Component I (48%)
Figure 6.10 Water-sufficient environment biplot generated from principal component analysis.
Biplot based on adjusted means using BLUP values with variance component analysis of trait data.
The 190 white clover full sibs and two parent genotypes were evaluated in a field experiment with
sufficient moisture to prevent physiological stress. The directional vectors represent the traits
(second harvest only): Q, quercetin glycosides; K, kaempferol glycosides; QKR, quercetin:kaempferol
glycoside ratio; SDM, shoot dry matter; ST, number of stolons; SD, stolon density, number of stolons
per square meter leaf-canopy area; LS, leaf size over the middle leaflet; CA, leaf-canopy area; CV,
leaf-canopy volume. 1, first harvest; 2, second harvest.
128
0.5
Component II (19%)
0.4
QKR
0.3
0.2
0.1
Group 4
Group 2
Q
Tienshan
CV2
St2
CA2
SD2SD2
LS2
0.0
-0.1
-0.2
Group 1
Group 3
-0.3
K
Directional vectors for traits: SDM2,
CA2, CV2, St2 & SD2
-0.4
-0.5
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Component I (46%)
Figure 6.11 Water-limited environment biplot generated from principal component analysis.
Biplot based on adjusted means using BLUP values with variance component analysis of trait data.
The 190 white clover full sibs and two parent genotypes were grown in a field experiment with
sufficient moisture limitations to induce a physiological response. The directional vectors represent
the traits (second harvest only): Q, quercetin glycosides ; K, kaempferol glycosides; QKR,
quercetin:kaempferol glycoside ratio; SDM, shoot dry matter; ST, number of stolons; SD, stolon
density, number of stolons per square meter leaf-canopy area; LS,leaf size over the middle leaflet;
CA, leaf-canopy area; CV, leaf-canopy volume. 1, first harvest; 2, second harvest.
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0.5
Component II (19%)
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.5
-0.5
Group 3
LS2
SDM2
SD2
CA2
St2
CV2
Tienshan
Q
Group 4
Group 2
-0.3
-0.4
K
Group 1
Directional vectors for traits: St2,
CA2 & SD2
-0.4
-0.3
-0.2
-0.1
QKR
0.0
0.1
0.2
0.3
0.4
0.5
Component I (43%)
Figure 6.12 Across-environment biplot generated from principal component analysis.
Biplot based on adjusted means using BLUP values with variance component analysis of trait data.
190 white clover full sibs and two parent genotypes were evaluated in a water-limited and a watersufficient environment. Directional vectors represent the traits (second harvest only): Q, quercetin
glycosides; K, kaempferol glycosides; QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry
matter; ST, number of stolons; SD, stolon density, number of stolons per square meter leaf-canopy
area; LS, leaf size over the middle leaflet; CA, leaf-canopy area; CV, leaf-canopy volume. 1, first
harvest; 2, second harvest.
6.4.6 Cluster analyses
The parental genotype Kopu II in all the biplots was positioned close to the mean of the population in
all PCAs, in contrast to the parental genotype Tienshan, which was consistently distinct from the
main cluster (Figure 6.10, Figure 6.11, Figure 6.12).
In the WS biplot, five groups were detected using cluster analysis (Figure 6.10). Most of the
genotypes in group 3 had above-average expression for the traits CA2, ST2, SD2, CV2 and SDM2. A
number of members in genotype group 1 had above average expression for LS. A number of
members in group 4 and group 5 had above average Q and K glycosides and QKR expression. All
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group 2 genotypes showed high Q glycosides expression. The parental genotype Tienshan was
included in group 4 here.
In the WL biplot, four groups were identified (Figure 6.11). Some of the genotypes in groups 3 and 4
had above average expression for the traits SDM2, CA2, CV2, ST2, SD2 and LS2. Groups 2 and 4 had
above average expression for QKR. Most of group 2 had above average Q glycosides expression; and
all group 1 genotypes showed high Q glycosides expression. The parental genotype Tienshan was in
group 1 in the WL biplot.
In the across-environment biplot, four genotype groups were detected using cluster analysis (Figure
6.12). A number of individuals in group 4 had above average expression for the morphological traits
ST2, CA2, CV2, SD2, SDM2 and LS2. Most members in genotype group 3, and some in group 2, had
above average Q glycosides expression. Parental genotype Tienshan was in group 3.
6.4.7 Trait correlations with across-environment PC1
Plant trait responses in correlation with the across-environment PC1 (Figure 6.12) explained the
majority of the variance in ST, CA, SD, CV, SDM and Q glycosides (Table 6.4), where the correlation of
Q glycosides with PC1 was negative and with PC2 positive. Correlation with PC2 explained the
majority of variance in K glycosides and QKR. PC1 and PC2 both explained the majority of variance in
LS. K glycosides was not correlated to any of the traits (Figure 6.12), and was not related to PC1
(Table 6.4).
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Table 6.4
Correlation coefficients (r) and probabilities (P) for nine plant traits linked to the first
two principal components (PC1, PC2) of the across-environment (G×E) PCA.
Differences between means were regressed against the scores of PC1 and PC2. Traits are ranked by
their PC1 Pearson correlation coefficients (r). Traits (second harvest only): Q, quercetin glycosides
(mg g-1 DM); K, kaempferol glycosides (mg g-1 DM); QKR, quercetin:kaempferol glycoside ratio; SDM,
shoot dry matter (g); St, number of stolons; SD, stolon density, number of stolons per square meter
leaf-canopy area; LS, leaf size over the middle leaflet (mm); CA, leaf-canopy area (m2); CV, leafcanopy volume (cm3).
Correlation with PC1
Correlation with PC2
G×E Traits
r
P
R
P
St
0.946
< 0.001
0.142
0.049
CA
0.904
< 0.001
0.104
0.152
SD
0.794
< 0.001
0.069
0.342
CV
0.714
< 0.001
0.269
< 0.001
SDM
0.633
< 0.001
-0.133
0.067
LS
0.418
< 0.001
-0.337
< 0.001
K
-0.200
0.006
-0.739
< 0.001
QKR
-0.236
0.001
0.916
< 0.001
Q
-0.566
< 0.001
0.322
< 0.001
6.5 Discussion
6.5.1 Physiology
The WL environment resulted in a number of significant physiological changes in the white clover
plants (Table 6.1). The difference in volumetric soil moisture between the two environments was
significant, with a strong decrease of volumetric soil moisture percentage in the WL environment
(Figure 6.5, Table 6.1). Changes in the 13C discrimination (∆, ‰) in plants relative to the ambient CO2
are thought to be negatively correlated with long term plant water use efficiency (WUE; net carbon
fixation per unit water transpired) (Farquhar and Richards 1984; Hall et al. 1994). The lower carbon
isotope discrimination (∆) in white clover in plants in the WL environment and higher WUE
(HoghJensen and Schjoerring 1997) (Table 6.1) is in line with other studies illustrating a relatively low
tolerance of water stress in white clover (Barbour et al. 1996; HoghJensen and Schjoerring 1997).
Lucero et al. (2000) and Inostroza and Acuna (2010) found that a high WUE was associated with
white clover populations that had a low leaf area DM investment and high stolon DM investment
(Inostroza and Acuna 2010). This is similar to the observations in our experiment, as is apparent in
the decrease in SDM and the increase in SD in the WL compared to the WS environment (Table 6.1).
6.5.2 White clover morphology
Reduced stolon numbers (ST) and therefore reduced stolon branching was evident in the WL
environment (Table 6.1), as reported elsewhere (Belaygue et al. 1996). The significant increase of
stolon density (SD) ensures the on-going persistence of white clover under water deficit stress
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(Caradus and Mackay 1989). The occurrence of reduced stolon numbers and increased stolon density
at the same time can be explained by the decrease of the canopy area. As the number of stolons is
reduced by drought (H1 -58%; H2 -49%), the canopy area reduced more (H1 -68%; H2 -65%), so the
stolon density (number of stolons per m2 canopy area) increased (H1 +28%; H2 +48%). As expected,
the morphological traits LS, IN, CA, SDM and CV were significantly reduced by water limitation,
suggesting similar effects of water-stress on the vegetative growth of white clover as were observed
by Turner (1991).
6.5.3 Biochemistry
Quercetin acts as a modulator and endogenous inhibitor of auxin transport (Jacobs and Rubery,
1988; Mathesius et al., 1998; Brown et al., 2001), and hence influences plant development. These
findings, together with the significant increases in Q glycosides consistent in the results reported in
this work (Table 6.1), suggest that Q glycosides can be a modulator of plant developmental processes
under water deficit stress.
The ratio between Q glycosides and K glycosides in the WL environment showed a marked shift from
K glycosides towards Q glycosides (Table 6.1). As kaempferol 3-O-glycosides have silenced the OHgroup in the 3’-position (i.e. the 3-position in the B-ring of the flavonoid skeleton), Q glycosides
provides better protection against abiotic stress, due to a higher anti-oxidant capacity conferred by
the extra hydroxyl group compared to K glycosides (Ryan et al. 1998; Scalabrelli et al. 2007; Agati et
al. 2009). Our results suggest that QKR as observed under limited soil moisture may potentially be a
useful breeding target for drought-tolerance, in addition to Q glycosides per se.
6.5.4 Heritability
The broad-sense heritability (Hb) for the traits Q, QKR, K, ST, CA and SDM in the WL environment,
which increased compared to those in the WS environment (Table 6.2), and the higher Hb for Q, QKR
and K glycosides across environments (Table 6.3) indicate the potential genetic variation for these
traits within the population. From the traits with significant genotypic variation (Tables 6.2, 6.3), Q
and K glycosides and QKR showed the highest Hb, which is important as Q and K glycosides and QKR
increased significantly in the WL environment as well (Table 6.1). In the WS environment, Hb
estimates were lower than in the WL environment for all traits measured (Table 6.2) except In, which
was non-significant. Effects of environmental stress on the expression of genetic variation for traits
likely to be under selection in natural populations causes field heritability to be higher for stress
response traits in contrast to morphological traits, which tend to show lower levels of heritable
variation in nature (Jenkins et al. 1997).The estimated significant genotype × environment interaction
for the traits SD, LS, CA and SDM (Table 6.3), indicated a relative change in ranking among genotypes
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for the expression of these traits across the WL and WS environments (Table 6.3), confirming the
importance of environment-specific adaptation to drought. Overall, the results indicate genetic
variation for key traits that could be used in improving white clover performance under moisture
stress by using genotypes selected for these improved traits.
6.5.5 Univariate analyses and correlations
The weak negative phenotypic correlation between Q glycosides and SDM in both the WS and the
WL environments (Figure 6.6, Figure 6.7) may indicate a trade-off between the investment in abiotic
stress protection (e.g. flavonoids) and morphological (e.g. biomass) drought adaption (Grime 1989).
The significantly different slopes between the two linear regression lines (Figure 6.6, Figure 6.7) of
the WL and WS environments indicated contrasting variability around that trade-off. For instance,
the phenotypic variation of the plant trait data was greater in the WL environment (Figure 6.7). In
fact, water-limitation increased variability of the traits Q glycosides and SDM. Consequently,
genotypes that have ideal qualities, with high Q glycosides and high SDM, can be selected under
more challenging environments (Kempthorne 1957).
The strong negative correlation with an R2 of 31% between SDM accumulation in the WS
environment and drought sensitivity (Δ% SDM) (Figure 6.8) shows a clear trade-off based on growth.
Genotypes showing higher Q glycoside levels in the WS environment were likely to maintain more of
their SDM in the WL environment (Figure 6.9). However, there was only a weak positive phenotypic
correlation between the drought-induced reduction of SDM versus Q glycoside accumulation in the
WS environment with an R2 of 7% (Figure 6.9). Since Q glycosides is a plant constitutive trait that is
stress-responsive, but is controlled by genes that do not require stress for their expression (Veitch
and Grayer 2008), improved adaptation may be achieved by selecting populations that express high
Q glycoside levels under water-sufficient conditions. These Q-selected phenotypes are more likely to
maintain SDM under water-limited conditions, and can subsequently be selected for herbage yield in
a drought cycle. However, this is only true if the absolute growth is also high (Turner 1991).
6.5.6 Multivariate analyses
QKR in the WL environment (Figure 6.11) was not correlated to SDM, thus in a water-limited
environment QKR can be selected for separately or simultaneously with SDM. In the acrossenvironments biplot (Figure 6.12), the significant G×E interaction indicates a change in relative
ranking of genotypes across the two environments, e.g. for SDM and Q glycosides expression.
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6.5.7 Cluster analyses
From the four genotype groups detected using cluster analysis in the WL biplot (Figure 6.11), all
group 1 and most of group 2 showed high expression for Q glycosides and QKR and a number of
genotypes in group 3 showed above average expression for ST2, SD2 and SDM2. A similar effect can
be observed in the across-environment biplot (Figure 6.12), a number of plants in group 4 showed
above average expression for the traits ST2, SD2 and SDM2, and individuals in groups 2 and 3 showed
high Q glycosides expression. This demonstrates the potential for this analytical approach to identify
material that can be used in breeding for increasing SD, Q glycosides and K glycosides expression, as
well as high QKR, as all these traits were related to persistence of white clover plants in WL
environments (Figure 6.12).
6.5.8 Plant traits associated with to the across-environment PC1
The principal water deficit measure PC1 from the across-environment biplot (Figure 6.12, Table 6.4)
involved most of the important agronomic traits, including biomass production. Yield potential (YP) is
the maximum DM yield realised in a water-sufficient environment (Figure 6.10), and reduced YP in a
water-limited environment (Figure 6.11) represents the integrated response to water deficit stress of
all biochemical, physiological and growth processes (Blum 2005). It was of particular interest to note
that maintenance of SDM production under water deficit was related to an increase of SD (Figure
6.11, Figure 6.12). The differential response within the F1 progeny to a WL environment (Figure 6.11)
further indicates underlying potential genetic variation that would enhance plant selection focused
on improving white clover performance under summer moisture stress.
6.5.9 Conclusions
These results indicate that increased induction of Q glycoside accumulation in response to water
deficit is positively related to retaining higher proportionate levels of SDM production in white
clover. In addition, genotypes with high QKR levels decreased their SDM production least in a waterlimited environment, and increased their Q glycoside levels and stolon density most, relative to a
water-sufficient environment.
The significant G×E interaction estimated for the traits SD, LS, CA, and SDM across the WL and WS
environments indicates the plant physiological responses of different genotypes to abiotic stress.
The increased heritability for traits other than yield in the WL environment indicated the potential
genetic variation which could be used in white clover breeding programmes focussed on abiotic
stress adaptation. The significant genotypic diversity and significant broad-sense heritability
estimated for stolon density in the WL environment also suggests the potential use of this white
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clover ecotype and elite germplasm as a source of variation for the genetic improvement of
productivity and persistence of white clover for water-limited environments. However, it should be
noted that the full sib population used in this study should only be used as a source of variation and
not a breeding population due to the possibility of inbreeding depression.
Combined selection for increased flavonol and stolon density expression may result in the
development of new white clover cultivars with increased potential for herbage production and
persistence in water-limited environments. The functions of flavonoids as developmental regulators
are very complex (Kuhn et al. 2011; Lewis et al. 2011) and need to be investigated further. Genetic
analysis may well elucidate the role and regulation of several transcription factors involved in abiotic
stress response pathways. The results from this field experiment in a full sibling population provide
the groundwork for further research to identify quantitative trait loci (QTL) for key traits.
The individual performances, groupings and G×E effects of the 190 genotypes were not analysed due
to time constraints and could be presented in a follow-up manuscript.
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137
Chapter 7
Genetic mapping of flavonoids, biomass production and
morphological attributes under water deficit in white clover
(Trifolium repens L.)
Parts of this chapter will be submitted to the Journal of Experimental Botany in 2012.
The author diluting DNA to a common solution examined with a NanoDrop spectrometer.
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7.1 Abstract
Improving genetic value of forages will warrant grasslands production and sustainability. Lifting the
rate of genetic gain for production and sustainability related traits by integration of marker aided
breeding approaches, innovative field evaluation strategies, and introgression of genetic resources is
the major objective of the white clover breeding program at AgResearch Grasslands, New Zealand.
Molecular approaches in white clover breeding research have delivered parallel improvements in
herbage yield and persistence, improvement of forage quality, utilisation of molecular markers in
complex populations to implement selection indices, and abiotic stress tolerance toward utilisation in
improved grasslands. Field and nursery-based studies have linked flavonoid metabolites to drought
tolerance.
Five main results were obtained from this study: (1) Only 27 of 237 marker-trait associations (11%)
were common to both water-limited and water-sufficient environments, confirming the existence of
stress specific genotypic effects; (2) the marker-trait associations tended to form clusters of genomic
locations, frequently consisting of marker-trait associations from different trait classes (biochemistry,
growth, morphology); (3) biochemical marker-trait associations (specifically for kaempferol
glycosides) were more frequently co-located with growth marker-trait associations than
morphological related ones, especially in the water-limited environment; (4) one co-location was
observed between quercetin glycosides and leaf size quantitative trait loci (QTL), whereas a number
of biomass and flavonoid specific QTL were discovered, which can be considered as candidate
genomic locations for explaining part of the variability of persistence related to leaf size and biomass;
(5) Several promising genetic regions were discovered in the parental ecotype “Tienshan”
homoeologues groups with positive genotypic effects for important traits such as quercetin
glycosides, biomass production, stolon density and leaf size, which is remarkable for such a small
white clover ecotype.
These results support the hypothesis that flavonoid metabolism provides valuable adaptive capacity
for improving white clover responses to water deficit induced stress, and is largely independent of
genetic factors controlling yield per se. Molecular markers associated with genome regions with
effects of up to 110% for biochemical traits have been identified in the complex white clover
populations used in this study and these can be integrated in breeding programs.
7.2 Introduction
Plant breeding has the potential to be revolutionised by the push of non-GM genetic technologies
such as DNA markers (Jones et al. 1997) in marker-aided breeding to accelerate selection of superior
forages for commercialisation (Barrett et al. 2009), and by the pull of market demand to develop
139
plants resilient to increased challenges from intensive management, extension onto marginal lands,
and increasing climate instability (Humphreys et al. 2006). These forces focus product development
targeting forages that exhibit adaptive responses to production environment stresses such as heat,
cold, ultraviolet radiation, and drought (Williams et al. 2007) in addition to other characteristics
determining economic value including yield and quality.
Genomics-based approaches can help identify and exploit agronomically desirable alleles at
quantitative trait loci (QTLs) of relevance to drought resistance and yield. A multidisciplinary
approach is needed that combines knowledge of the molecular and physiological processes in order
to utilise the full potential of genomics-assisted breeding (Tuberosa and Salvi 2006).
However, there still is a gap between plant breeding and genomics methods in many species
(Sreenivasulu et al. 2007). The method clearly works for qualitative traits such as disease resistance
(Miedaner and Korzun 2012), and although there are many challenges to overcome for quantitative
traits such as yield under drought (Messmer and Stamp 2010), there are opportunities as well
(Holland 2004; Moose and Mumm 2008). For example, an integrated approach towards breeding and
genomics could be implemented (Collard and Mackill 2008) to improve drought tolerance in crop
plants (Cattivelli et al. 2008).
The allotetraploid genome of white clover with two homoeologous sets of chromosomes derived
from two different ancestral species (Williams 1987a, 1987b), four haploid sets accompanying an
allogamous (cross-fertilisation) disomic breeding system and a general intolerance of inbreeding
(Williams 1987a) defines the parameters of genetic linkage analyses and marker aided breeding.
Barrett et al. (2004a) developed a linkage map based on simple sequence repeat (SSR) genetic
markers of white clover and putative macro-colinearity was found in a comparison of marker map
locations from white clover, red clover, and alfalfa (Medicago sativa) (Zhang et al. 2007a). These and
other white clover maps have been used to link quantitative trait loci with genetic markers (Forster
et al. 2001; Cogan et al. 2006; Abberton et al. 2009; Tashiro et al. 2010).
Marker-based mapping experiments with outcrossing plant species such as white clover have in
principle an identical set-up: parents are chosen that are different for a quantitative or qualitative
trait, e.g. Q glycosides; the two parents are screened for polymorphic marker loci M and m linked to
Q glycosides: MQ and mq; F1 progeny is generated with MM and mm recombination; phenotypes are
screened for Q glycosides in the field; the mean of the MM and mm individuals are contrasted at
every marker locus; and QTL are declared where the difference between MM and mm is greatest, as
explained in detail by Bernardo (2010).
140
In addition to inherent problems of only sampling the alleles present in two parents out of a large
and diverse population, single-marker analysis is limited due to recombination between marker allele
and QTL, resulting in inaccurate assigning of QTL positions. A common problem with QTLs is that
many false positives are found after carrying out a statistical test at every marker without adjustment
of critical probability thresholds for declaration of significance (Xu 2010). Interval mapping improves
resolution by integrating among markers, but interval mapping has problems as well: it cannot
resolve two QTLs in a marker interval; all methods declare too many QTLs although the LOD
thresholds seem very high; it does not consider epistasis; no methods are accurate for QTL with small
effects ; and it requires complete genome coverage with no gaps over 10 cM in the region of interest
(Kearsey and Pooni 1998). A further challenge of linkage mapping is that double crossover products
can look like parental types, leading to underestimates of the map distance. However, recombination
frequencies can be corrected by the Haldane and Kosambi mapping functions (Collard et al. 2005).
The important QTL allele must be tightly linked to a marker to be benificial in breeding applications.It
is even better when the allele is bracketed by marker alleles in coupling phase on both sides. Ideally,
the favourable gene allele itself is used as a marker, which can be facilitated by comparative mapping
and candidate gene analysis. QTL mapping is inexact for identifying, localising, and approximating the
extent of genotypic effects of genes with a small effect. Rebetzke et al. (2007) noted that if the
repeatability (heritability) of QTL in a phenotyping experiment is low, the QTL map may be unreliable
and unsuitable for use in plant breeding. QTL mapping is suitable for identifying genes with relatively
large effects (Bernier et al. 2009), and requires a phenotypic screening system with high heritability
(Rebetzke et al. 2008b). To summarise, QTL mapping is most successful when focussing qualitative
traits in lines that are easily detectable in a good screen, when crosses between highly divergent
plants are used, when highly reliable screening methods are used that differentiate the trait, and
when QTL analysis is based on the means of repeated tests rather than single experiments to ensure
that heritability is high (0.7 or more) (Jones et al. 1997; Jones et al. 2009).
Where QTL are used in breeding, Semagn et al. (2006b) explained that QTLs with small effects are
difficult to map accurately. QTLs can only be successfully used in marker-aided backcrossing when
they are localised to very small chromosome segments. Fine-mapped QTLs are most useful when
they represent large effects in multiple genetic backgrounds and across environments, e.g. disease
resistance factors (Torres et al. 2006), but they are often not necessary because the resistances are
selected phenotypically.
Complex or quantitative plant traits such as yield components that display significant genetic
variation, i.e. phenotypic traits that are controlled by polygenes, several alleles or quantitative trait
loci (QTL), are suited for molecular breeding techniques using markers and mapping (Prioul et al.
141
1997; Mackay 2001). Individual genes contributing to complex traits can be discovered through their
association with genetic markers, and can theoretically be resolved into simple Mendelian genetics
(Lander and Botstein 1989).
While individual marker:trait associations in the absence of genetic map locations are possible for
discovery of QTL effects, genetic linkage maps greatly enhance the power and value of QTL analysis.
Tanksley (1993) referred to the determination of the relative position and distances between
markersas genetic linkage mapping (meiotic mapping). . The mean number of recombination events
per meiosis were defined by Collard et al. (2005) as genetic map distances between two markers and
are expressed in the unit centiMorgan (cM). Genetic map construction necessitates the development
of an appropriate mapping population, an appropriate sample size and decision on the type of
molecular markers used for genotyping, then genotyping the mapping population with a sufficient
quantity of markers, and identifying statistical linkage between markers and QTL using
computationally intensive algorithms (Doerge 2002; Semagn et al. 2006a).
The first step for marker-assisted selection (MAS) and to identify genes or QTLs linked to
economically important traits is the development of comprehensive genetic maps (Collard and
Mackill 2008). Genetic maps require molecular markers that are highly reproducible, high
throughput, co-dominant, transferable, and especially developed from expressed regions of a
genome (Varshney et al. 2005a).
Polymerase chain reaction (PCR)-based single sequence repeat (SSR) markers, also called
microsatellites, are commonly used in genetic mapping projects because they are abundant, can
detect polymorphism between closely related individuals, are co-dominant, highly reproducible and
can distinguish multiple alleles in plant species due to variation in the number of repeat units, which
are made up of 1 – 6 base pairs (bp) short DNA sequences, and disperse mainly in the regions
between genes and un-coding regions throughout genomes (Li et al. 2002; Ellis and Burke 2007),
although they are well represented in genic sequence as well (Barrett et al. 2004a). SSR markers are
valuable tools for genetic dissection of agronomic traits and analysis of genetic diversity in clovers
(Sato et al. 2005; Kölliker et al. 2010). One of the drawbacks of SSR markers is their laborious
development (Barrett et al. 2004a; Dracatos et al. 2006). The sequential nature of these gel-based
marker systems brings about reduced throughput and relatively high costs per assay, although that
can be addressed in part by capillary electrophoresis systems.
Starting from serial systems based on the separation of DNA fragments such as SSR, more recent
molecular marker technology has moved towards high throughput, parallel, hybridisation and
sequence based systems that can examine up to tens of thousands of markers simultaneously (Wenzl
et al. 2006). Diversity arrays technology (DArT) is a hybridisation-based technology that utilises the
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parallel capability of the microarray platform (Jaccoud et al. 2001). Up to several thousands of loci
are typed by a single DArT assay simultaneously, and this technology can generate whole-genome
fingerprints by scoring samples of genomic DNA for presence versus absence of DNA fragments
(Akbari et al. 2006). DArT markers have recently been used in genetic mapping and fingerprinting
studies in various crops (Mace et al. 2008; Kopecky et al. 2009; Whittock et al. 2009; Truong et al.
2010; Badea et al. 2011; Supriya et al. 2011), often combined with SSR-markers (Semagn et al. 2006c;
Wenzl et al. 2006; Hearnden et al. 2007; Mantovani et al. 2008; Peleg et al. 2008; Jing et al. 2009;
Hippolyte et al. 2010). DArT technology largely overcomes the challenges intrinsic with the complex
genome of white clover, which has rendered the development of large scale single nucleotide
polymorphism (SNP) marker resources cost prohibitive in the absence of a full genome sequence
(Cogan et al. 2007; Lawless et al. 2009). In species such as white clover for which there is limited
genomic sequence information available, anonymous markers such as DArT are more cost-effective
compared to microsatellites in terms of machine time, reagents, and staff input (Gupta et al. 2008;
Appleby et al. 2009), although they are dominant instead of co-dominant markers. While
microsatellite markers retain their role as a marker system with utility in specific applications, DArT
potentially offers a ready source of inexpensive and reliable marker data, with the convenience and
ease of outsourcing (www.diversityarrays.com).
By solely genotyping individuals with extreme, and hence most informative, quantitative trait values,
Darvasi and Soller (1994) described selective DNA pooling and -genotyping as a method to reduce
costs in marker- QTL linkage determination . The linkage test is based on estimates of the frequencies
of marker alleles from the pooled samples only. DNA is pooled from the selected individuals at each
of the two phenotypic extremes, and this can reduce genotyping costs of marker-QTL linkage
determination considerably while maintaining high statistical power. Dekkers et al. (2000) showed
that for two flanking markers between individuals with high and low phenotypes, differences in allele
frequencies can be used to estimate the position of a QTL and that this is independent from the
phenotypic means of the selected clusters. Wang et al. (2007) described a method for interval
mapping of QTL with selective DNA pooling data suitable for large families. The number of required
genotypings can thus be greatly reduced by selective DNA pooling.
Phenotypic assessment among 130 F1 progeny in pots (Chapter 4) showed highly significant effects in
the genetic variation for quercetin (Q) glycosides and for biomass production (herbage yield). In
addition, Q glycosides and biomass only showed a weak negative correlation, indicating that the
association between Q glycosides and biomass had been broken under these experimental
conditions. These findings were confirmed in a phenotypic assessment among 190 F1 progeny under
contrasting field conditions (Chapter 6), suggesting the opportunity for co-selection of types with
high levels of Q and biomass production potential.
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Although genetic mapping of biomass and persistence has been explored in a Trifolieae relative of
clover, Medicago sativa L. (Robins et al. 2007b; Robins et al. 2008) and red clover (Herrmann et al.
2007; Riday 2011), marker-trait associations and QTLs for the biochemical traits Q and K glycosides in
combination with biomass production and other morphological traits have not been investigated in
white clover. The aim of this study was to investigate the suitability of Q glycosides, known stress
protection compounds naturally found at varying levels in plants (Veitch and Grayer 2008), for use in
marker-aided breeding of high performance forage products. Several objectives were formulated: (i)
to initiate development of a DNA marker testing capability to cost-effectively and rapidly select for
levels of Q glycosides in white clover product development processes, and if successful, (ii) to expand
the marker analysis to quantify effects of putatively linked markers to all the individuals in the
mapping population; and (iii) for linkage analysis: to complete a genome-scan using DNA markers of
DNA bulks to identify those putatively linked with Q glycosides and herbage yield and other traits of
interest; (iv) to identify significant marker-trait associations for the traits of interest; (v) for QTLanalysis: to detect and assign robust QTLs to linkage groups, to be used in marker-assisted forage
breeding programs.
7.3 Materials and methods
7.3.1 Plant material and DNA extraction
White clover is a self-incompatible, out-crossing species. To get as much variability as possible for the
important traits, two highly heterozygous parental plants were crossed to derive a mapping
population consisting of full-siblingling F1 genotypes. The plant material was a random selection of
130 F1 genotypes from the 190 full-sibling F1 population as described in paragraph 3.2.1, which was
used in the phenotyping experiment in pots (Chapter 4). The full mapping population of 190 F1
genotypes was used in the field experiment (Chapter 6). The parental genotypes Kopu II and
Tienshan were also included in all phenotypic and marker assessments. Genomic DNA was extracted
and purified from young leaves with a FastPrep 24 Instrument (MP Biomedicals) homogeniser and a
Plant DNAzol kit (Invitrogen New Zealand Limited, Auckland, NZ) (Chomczynski et al. 1997; Wilfinger et
al. 1997) at AgResearch Grasslands.
7.3.2 Genotyping and genetic linkage mapping
Methodology followed established protocols (Faville et al. 2003; Barrett et al. 2004a). A
representative set of 96 SSR markers from a genetic linkage map constructed with SSR markers
derived from expressed sequence tags (Barrett et al. 2004a) were selected to scan the genome at
intervals across all 16 linkage groups, making use of automated DNA marker systems for maximum
efficiency. Criteria used for selection of the SSR markers were that they were informative in both
homeologues of the Pastoral Genomics Consortium population ‘MP1’ (about 70 markers) or the
144
AgResearch BB linkage maps (Barrett et al. 2004a) (35-40 markers); spaced evenly along the
chromosome, with preferably less than 10 cM spacing (where the MP1-BB converged map was used
for spacing information); polymerase chain reaction (PCR) products to produce clean, easy to score
electropherograms; clean peaks (preferably no stutter and no peaks 1bp apart); if possible mapped in
Trifolium occidentale as well (40 markers).
SSR forward primers were modified to incorporate a 6-carboxy-fluorescein (FAM ) M13(-21)
fluorescent dye-label at the 5’ end (Schuelke 2000). The reverse primers of the SSR markers were
modified as described in Brownstein et al. (1996).The Taq DNA polymerase (Invitrogen) PCR
amplifications of SSR markers were based on the method of Schuelke (2000), modified with a 30 min
final extension at 72°C to promote non-templated adenylation of the amplicons (Brownstein et al.
1996), using a 2720 thermal cycler (Applied Biosystems) or icycler (Biorad). After PCR, DNA samples
were denatured with HiDi Formamide (Apllied Biosystems), an injection solvent; and GeneScan 500LIZ (Applied Biosystems), a size standard used as an internal reference to size and correct for mobility
differences between capillaries, was added in separate plates prior to capillary electrophoresis
(Barrett et al. 2004a). PCR products were size resolved and detected in an ABI 3100 Genetic Analyzer
(Applied Biosystems). DNA genotyping results were scored from electropherograms using ABI
GeneScan/Genotyper software version 3.7 (Applied Biosystems) or GeneMapper (Applied
Biosystems) for data collection. GeneMarker software version 1.75 (Softgenetics) was used for
fragment analysis and quality screening, after which the data was inspected to pick out which PCR
products were segregating (i.e. were informative alleles in this population) and which were allelic
within a marker. The computer program JoinMap 3.0 (www.kyazma.nl) (Van Ooijen and Voorrips
2001) was used for linkage analysis. JoinMap 3.0 was used to arrange the marker data within linkage
groups, using LOD values of 3.0 and higher, and recombination frequencies of 0.40 and lower (Van
Ooijen and Voorrips 2001; Robins et al. 2008). Linkage group numbering corresponded to that of
Trifolium repens (Barrett et al. 2004a).
To initially save cost and rapidly discover relevant polymorphism in markers, a selective DNA pooling
(Ronin et al. 1998; Dekkers 2000) approach was taken to identify markers putatively associated with
genome regions influencing the traits of interest. Selective genotype analysis based on Michelmore et
al. (1991), as adapted by Darvasi et al. (1994), DNA pooling, PCR and genotyping was used to test SSR
markers from a genetic linkage map constructed with SSR markers derived from expressed sequence
tags (Barrett et al. 2004a) across the white clover genome against those DNA pools using ~10 % high
and low tail ends of the population for each of the seven quantitative traits measured (Q and K
glycosides, QKR, SDM, RDM, RS and TDM), resulting in 14 pools. There were 14 individuals in each
pool and the pools were balanced in the number of individuals. DNA concentrations extracted from
each genotype were balanced to a common dilution making use of a NanoDrop (Thermo Scientific)
145
micro-volume spectrophotometer, and DNA from 14 genotypes was mixed in each separate bulk
containing high or low end genotype mixes. Each bulk concentration and separate samples of DNA
from the two parents was diluted to PCR concentration prior to marker analsyis.
JoinMap 3.0 (www.kyazma.nl) (Van Ooijen and Voorrips 2001) was used for linkage analysis as
described above. Linkage group identification corresponded to that of Trifolium repens as utilised in
Barrett et al. (2004a) and modified by George et al. (2008).
Each allele was then scored for its presence or absence in each progeny and parental genotype, and
the phenotypic data were classified into a 1 and a 0 treatment and a test of significance was done to
see if there were treatment mean differences. The computer program MapQTL 4.0 (www.kyazma.nl)
(Van Ooijen et al. 2002) was used to identify QTLs for the traits Q, quercetin glycosides (mg g-1 DM);
K, kaempferol glycosides (mg g-1 DM); QKR, quercetin:kaempferol glycoside ratio; SDM, shoot dry
matter (g); RDM, root dry matter (g); RSR, root:shoot ratio and TDM, total dry matter (RDM + SDM
(g)) from the phenotyping pot experiment (Chapter 4), and for the traits Q glycosides, K glycosides,
QKR, SDM, TDM, total dry matter (SDM harvest 1 + SDM harvest 2 (g)); St, number of stolons; SD,
stolon density, number of stolons per square meter leaf-canopy area; LS, leaf size averaged over
three middle leaflets (mm); IN, number of inflorescences, CA, leaf-canopy area (m2); CV, leaf-canopy
volume (m3) from the field experiment (Chapter 6), for WL, water-limited environment; WS, watersufficient environment; across environments and percentage change between environments.
Suitable markers were used to build a medium resolution linkage map to be able to more accurately
estimate genome locations containing QTLs with the traits of interest. Linkage maps were rendered
visually using MapChart software for the graphical presentation of linkage maps and QTLs (Voorrips
2002).
Overview of DArT technology
Diversity Arrays Technlogy Pty Ltd (http://www.diversityarrays.com/molecularprincip.html) was used
for the analysis of white clover genotypic DNA by making use of DArT technology consisting of a
number of steps: ”complexity reduction of the DNA of interest; library creation; microarraying
libraries onto glass slides; hybridisation of fluoro-labelled DNA onto slides; scanning of slides for
hybridisation signal, and data extraction and analysis”;(Jaccoud et al. 2001; Wenzl et al. 2004).
There were three major issues with the DArT markers: firstly there was a large amount of
redundancy removed (markers mapping on the same position in a linkage map), secondly, most of
the DArT markers had not yet been mapped on the reference map to assign linkage group or map
order information, and thirdly, due to phenotyping data available from 131 progeny individuals for
the SSR marker analysis and only 92 for the DArT analysis, there was a high proportion of missing
values in the DArT analysis. This largely was resolved by combining the marker data sets to generate
146
two SSR-DArT linkage maps, one for each of the two parents. No distinction was made between the
two homoeologues for each linkage group due to limited SSR marker datasets, and the DArT markers
not being mapped completely. Spurious associations were corrected for by setting the threshold
value for significance to a probability level of 0.005. DArT markers that showed 100% similarity with
another DArT locus were excluded, and all of the SSR markers were left in. The effects reported are
the percentage differences between segregating progeny individuals having a marker allele, and
those without that particular allele. Presence of the marker increases (positive effect) or reduces
(negative effect) the trait value compared to the means of the individuals with the percentage
indicated.
7.3.3 Statistical analyses
Marker-trait associations using 131 random individual plants typed with 96 SSR markers, and a
subset of 92 random individual plants typed with a DArT micro-array with 800+ markers (including
some redundant makers), were identified using Kruskal-Wallis single-factor analysis of variance in
MapQTL 4.0 (Van Ooijen et al. 2002). The mean value of genotypes containing the allele was
contrasted with the mean value of individuals without the allele for each marker. The analysis was
carried out for each environment and each harvest in the field experiment, and for the phenotyping
experiment in pots. Alleles were declared to be significantly linked to a trait at P ≤ 0.005. Results for
quantitative traits were analysed with the chi-square test for heterogeneity and with the KruskalWallis one-way analysis of variance by ranks for single markers. This is a non-parametric (or
“distribution-free”) method to test whether samples derive from the same (non-normal) distribution.
This test is used when comparing more than two independent samples. The parametric (assuming a
normal distribution or “normality assumption”) equivalent of the Kruskal-Wallis test is the one-way
analysis of variance (ANOVA) (Van Ooijen et al. 2002; Kang 2003; Meksem and Kahl 2005; Brummer
and Casler 2009; Klimenko et al. 2010).
7.4 Results
The results described in this section represent analyses by selective genotyping of bulks a limited
number of individuals; then a comprehensive linkage analysis with 131 individuals combined with 96
SSR markers, and the subset 92 individuals combined with a DArT marker array.
7.4.1 Selective DNA pooling
The means for each of the seven traits showed differences of up to 90% between the high and low
DNA pools (Table 7.1). Specifically K (90%), QKR (89%), SDM (72%) and TDM (70%) are noteworthy.
DNA analysis using selective DNA pooling and subsequent genotyping produced five putative
marker:trait associations for TDM production and 11 for Q glycosides in DNA bulks, predictive of
147
levels of Q glycosides and TDM in this population. The DNA markers identified appeared to be useful,
but are only putative identification of regions in the genome influencing Q glycosides and TDM in this
pair cross. The data collected was nearly complete, with less than 1% missing data from the plant
experiments, and less than 5% missing data from the DNA marker datasets. Markers that showed
polymorphism in both parents and in progeny bulks for TDM and Q glycosides were detected (Table
7.2). Many markers appeared in both homoeologues of white clover, however only one locus for
each marker is entered as it is not possible to distinguish which homoeologue the allele resides on
without a comprehensive mapping exercise. Only one marker (gtrs967) showed polymorphism for
both TDM and Q (Table 7.2) in the bulks.
Table 7.1
Trait means in the high and low bulk DNA pools used in selective DNA pooling.
Q
K
QKR
RDM
SDM
RSR
TDM
High
3.61
1.83
7.81
2.10
6.08
0.52
8.08
Low
1.55
0.19
1.55
0.63
1.71
0.27
2.43
∆%
-57
-90
-80
-70
-72
-48
-70
Table 7.2
Markers that showed polymorphism (allelic variation) in both parents and in progeny
bulks.
Dry matter (DM) and quercetin glycosides (Q) from the phenotyping experiment (Chapter 4).
Marker
gtrs757
gtrs887
prs484
gtrs524
prs786
gtrs168
ats003
gtrs967
gtrs776
gtrs167
prs465
gtrs355
prs639
gtrs395
gtrs780
148
Polymorphism for trait
Linkage group
Position (cM)
Q
1
18.7
Q
1
31.5
Q
1
67.4
Q
1
79.3
TDM
3
72.6
TDM
4
32.9
TDM
4
65.7
Q, TDM
4
48.0
TDM
4
76.5
Q
5
8.0
Q
6
0.8
Q
6
5.7
Q
7
52.2
Q
7
26.6
Q
7
69.9
7.4.2 Genetic linkage mapping
Linkage mapping was successful in identifying a number of linkage groups. Out of 237 markers
tested, 151 (64%) were polymorphic in parent Kopu II, and 86 (36%) were polymorphic in parent
Tienshan. The maternal parent Kopu II (K) map (Figure 7.1) had four consensus homoeologous
groups, and the paternal parent Tienshan (T) map (Figure 7.2) had six linkage groups of the 16 paired
homoeologues, representing the eight progenitor chromosomes of the white clover genome.
From a total of 280 maternal (Kopu II) markers, 58 (21%) and from a total of 192 paternal (Tienshan)
markers, 42 (22%) had high segregation distortion (probably because of the wide difference between
parents), but the markers were left in the mapping process as the position and linkage phase of most
of the DArT markers were unknown at that stage, and we wanted to identify putative genomic
regions associated with the traits of interest rather than focus on the genetic resolution of new
marker locations.
The difference in the number of segregating marker alleles between the two parents, 59% for Kopu II
and 41% for Tienshan from a total number of 472, explained the difference in the number of nonredundant markers mapped in the linkage groups: Eighty-one markers in the Kopu II linkage maps
(Figure 7.1) and 63 markers in the Tienshan linkage maps (Figure 7.2).
Recombination frequency was estimated with LOD scores between 4 and 8 in Kopu II and with a LOD
scores between 3 and 10 in Tienshan. At lower LOD scores the putative groups showed insufficient
linkage and some of these groups had to be split up in smaller groups with higher LOD scores to be
able to map them. Markers were mainly clustered around homoeologous Groups 4 and 7 in Kopu II
(Figure 7.1) and around Groups 2 and 7 in Tienshan (Figure 7.2), while the rest of the markers were
more distributed on the rest of the groups. Tienshan displayed six groups (Figure 7.2) compared to
four groups in Kopu II (Figure 7.1) of the possible 16 groups (including two homoeologues), with a
shorter length than expected.
149
K-LG1
0.2
0.5
1.6
K-LG4
clPt-845684
clPt-845174
clPt-820474
34.4
clPt-821855
50.1
52.5
clPt-843729
clPt-845949
66.9
69.5
72.6
clPt-821306
clPt-873193
clPt-821025
81.2
85.1
87.4
clPt-843887
clPt-846087
clPt-845059
Figure 7.1
150
0.0
0.2
0.7
2.8
4.6
8.8
9.8
10.7
12.2
13.5
23.0
27.3
30.5
35.8
37.1
39.1
44.1
45.1
47.1
49.7
50.5
55.0
56.0
56.8
59.4
62.3
70.3
gtrs721_303
clPt-843182
clPt-873140
clPt-845024
clPt-872171
clPt-871925
clPt-843703
clPt-820623
gtrs721_304
gtrs721_295
clPt-845169
clPt-844129
gtrs168_148
gtrs168_156
gtrs168_140
clPt-822077
clPt-821930
gtrs967_349 gtrs967_346
gtrs967_341
gtrs140_137
gtrs140_133
clPt-820542
clPt-872299
clPt-820761
clPt-872519
uga003_188
clPt-842908
79.3
gtrs140_135
86.3
uga003_124
K-LG6
0.0
0.8
3.6
3.9
prs465_404 prs465_368
prs465_363
prs465_407
gtrs355_310
10.3
gtrs355_228
23.2
clPt-843861
33.4
clPt-843781
48.3
51.9
54.2
clPt-845458
clPt-821875
clPt-821805
K-LG7
0.0
2.3
6.5
9.3
12.6
15.0
18.1
18.2
21.7
22.3
22.6
clPt-820568 prs575_268
clPt-822196
prs575_278
clPt-872581
gtrs524_303
gtrs395_401
gtrs395_316
gtrs395_303
gtrs524_316
clPt-843754
gtrs524_401
30.7
clPt-844166
36.5
44.4
44.7
49.1
50.9
52.1
57.2
58.3
59.1
65.0
66.6
67.1
69.9
71.0
prs639_225
prs639_205
prs639_218
clPt-872195
clPt-820768
clPt-844750
clPt-844675
clPt-845409
clPt-844324
clPt-872572
gtrs780_297
gtrs780_288
clPt-842411
clPt-820601
82.3
82.6
clPt-844322
clPt-845721
Kopu II (KII) maternal partial linkage map with four linkage groups.
T-LG1
0.0
1.0
3.2
T-LG2
prs484_269 clPt-873334
clPt-843570
clPt-821744
23.0
24.1
clPt-873107
clPt-820694
30.6
clPt-821580
36.8
gtrs887_348
43.4
clPt-872700
70.1
clPt-843682
0.0
1.2
3.3
4.7
6.2
13.3
17.0
19.4
21.0
gtrs374_299 clPt-872454
clPt-845384
clPt-844746
clPt-845384
clPt-872454
clPt-844746
clPt-873384
clPt-821820
clPt-871902
clPt-871886
38.7
40.1
44.5
46.5
clPt-873384
clPt-843028
clPt-873181
clPt-844125
85.5
prs329_202
T-LG3
0.0
clPt-842469
T-LG4
0.0
0.8
gtrs168_135
gtrs168_144
16.4
18.8
20.2
clPt-844307
clPt-842837
gtrs967_328
26.9
27.8
clPt-842539
clPt-872737
T-LG6
0.0
clPt-820947
11.4
clPt-845385
31.7
clPt-872332
T-LG7
0.8
5.4
8.2
9.7
10.2
16.9
17.0
18.0
19.3
26.0
26.8
28.9
32.7
34.6
35.2
39.5
41.7
42.1
46.6
48.1
gtrs395_313 gtrs241_203
prs575_274
gtrs241_215
gtrs395_389
gtrs524_389
clPt-845995
gtrs524_312
clPt-873162
gtrs395_323
clPt-820057
gtrs524_323
prs639_197
clPt-873207
clPt-820296
clPt-843765
prs639_203
prs639_207
prs639_209
prs639_184
clPt-845636
clPt-843932
clPt-843905
59.1
59.9
gtrs780_300
gtrs780_294
67.8
clPt-845476
0.0
Figure 7.2
Tienshan (T) paternal partial linkage map with six linkage groups.
151
7.4.3 Marker-trait associations
Linkage mapping in JoinMap was used to identify genome locations of previously unmapped
markers. Subsequent analysis in MapQTL, identified 237 marker-trait associations using the
microsatellite (SSR) and DArT markers for single-locus analysis relating to yield, drought response,
and flavonoids. Only associations from the Kruskal-Wallis analysis at P < 0.005 were declared
significant. These 237 significant associations were based on data derived from the 96 SSR markers
typed in 131 of the 190 F1 genotypes tested in the field, combined with 139 DArT markers and 92 F1
progeny and the two parental genotypes.
Both parents (Kopu II and Tienshan) contributed alleles for each trait with positive and negative
phenotypic effects (Robins et al. 2007a). The values for the biochemical traits Q and K glycosides
(Table 7.3) were higher than the biomass traits (Table 7.4).
For most traits, at least one QTL was identified on each of the substantive homoeologous groups
identified in this experiment, and there were QTL in the same genome locations identified
independently in the field traits and pot experiment. In at least one of the two environments and
during at least one of the two harvests and in pots, a number of marker-alleles were associated with
the following traits:
11 marker-alleles associated with quercetin glycosides (Q), 28 with kaempferol glycosides (K), and 21
with quercetin:kaempferol glycoside ratio(QKR) (Table 7.3); 29 alleles associated with shoot dry
matter (SDM) and 17 alleles with total dry matter (TDM) (Table 7.4); 42 alleles associated with stolon
density (SD) and 28 with stolon numbers (ST) (Table 7.5, Table 7.6); 44 alleles associated with leaf
size (LS) (Table 7.7); 19 alleles associated with canopy area (CA) and 14 with canopy volume (CV)
(Table 7.8).
Qu and Hancock (2001) explained that linkage phase is the way alleles were joined in the parental
generation. The gene pairs are in coupling phase when AB and ab are joined in the parental gametes.
Otherwise, the gene pairs are in repulsion phase, as in the cross AAbb x aaBB (Qu and Hancock
2001). Linkage phase codes were determined by the software Joinmap 4.0 (Van Ooijen and Voorrips
2001). Q glycosides associated with marker alleles showed predominantly repulsion phase with
decreased trait value in both field environments ((Table 7.3). A major finding was that of a number of
DNA markers predictive of levels of Q glycosides in this population, which were consistent over field
environments (Table 7.3), the pot experiment (Table 7.9) and the bulk genotyping results (Table 7.2).
Specifically SSR marker gtrs355 was associated with Q glycosides in all experiments, although with a
negative effect for the allele detected in this population. These markers were identified in more than
one year and environment. Together with the fact that the marker alleles associating with Q
152
glycosides were distributed over the Tienshan linkage groups 4 and 7, and Kopu II linkage groups 1
and 6.
Some DArT marker alleles that associated with Q glycosides in the pot experiment, associated with K
glycosides or QKR in the field experiment, or did not reach the significance threshold. The SSR marker
gtrs967 that showed polymorphism for the trait Q glycosides as well as DM in the progeny bulks
(Table 7.2) had an allele that associated negatively with K glycosides in the pot experiment (Table
7.9) and did not appear at all in the field results (Table 7.3). Of note was the SSR marker gtrs168 in
LG4 in both the KII (Figure 7.1) and T (Figure 7.2) linkage maps with two different alleles of which one
associated positively, the other negatively with the trait RSR in the phenotyping pot experiment
(Table 7.9), and associated with DM production in the bulk genotyping results (Table 7.2). As RDM
was not measured in the field experiment, field RSR was not calculated. Another interesting markerallele was gtrs168_156 associating negatively in the WLE with SD (Table 7.5) and also negatively with
SDΔ% (Table 7.6).
The T-derived marker allele clPt-843765 in LG7 (Figure 7.2) in the water-sufficient environment was
the only marker allele with a positive genotypic effect for Q glycosides of 14% (Table 7.3). In contrast,
all of the marker alleles associated to K glycosides showed increased trait values for both parents
(Table 7.3). However, the T derived marker allele gtrs241_203 showed an increased trait value for
QKR-WLE, a decreased trait value for K-∆% and a positive trait value for QKR-AE, while the T derived
marker with opposite phase gtrs241_215 showed a negative trait value for QKR-WLE and a positive
trait value for K-∆% (Table 7.3).
Both parents contributed alleles with positive and negative effects for the DM traits, often on the
same LG (Table 7.4), but mostly in Harvest 1. The SSR T marker gtrs241with two different alleles (203,
215) showed both negative and positive associations with DM (Table 7.4). The magnitude of most
genotypic effects for DM traits (Table 7.4) had lower values than for the biochemical traits (Table
7.3). In particular, the regions on Groups 1 and 7 (Table 7.4) in which numerous markers identified
QTL for the DM traits within harvests and environments, appeared to be important regions
harbouring loci controlling biomass production. The genotypic effect in both parents for TDM across
environments had negative associations with only two marker alleles (Table 7.4).
KII marker alleles associated with stolon density (SD) and stolon numbers (ST) (Table 7.5) showed a
number of highly significantly positive effects of over ±8% in Group 1, while T markers had both
positive and negative effects of over ±8% in Groups 3 and 6. For the associations with stolon
percentage change (∆%St) and across environment (AE) traits (Table 7.6) the situation was different:
most of the phenotypic effects were over ±5%, mainly distributed over the linkage groups, except
some of the highest KII values in Group 1 for Harvest 1 (Table 7.6). The associations for both SD and
153
ST showed positive harvest × environment interaction for Harvest 1. A notable marker was the
Tienshan DArT marker clPt-872332 (Table 7.10), which had a positive genotypic effect on both SD
and LS.
Alleles associated with leaf size (LS) (Table 7.7) were distributed over the linkage groups, but mostly
in Harvest 1 in the water-sufficient environment. Across environments, positive associations were
detected in Linkage groups 1 and 6, both T, and both for Harvest 2. Plant spread (canopy area and
volume) was severely diminished by drought by up to 75% (Table 6.1, Chapter 6). Marker alleles
associated with canopy traits (Table 7.8) were predominantly in coupling phase with positive effects
on trait value. Of note were the regions on linkage groups 1 (K) and 6 (T), in which numerous alleles
for canopy area (CA) and canopy volume (CV) were identified in Harvest 1 across environments.
A further 27 marker-trait associations were detected in data from the phenotyping pot experiments
(Table 7.9), of which four SSR markers were also found in the field experiment data. Nine alleles were
associated with K, five with Q, seven with QKR, two with root:shoot ratio (RSR), two with SDM and
two with TDM (Table 7.9).
154
Table 7.3
Marker alleles associated with the field experiment biochemical traits.
Means of quercetin glycosides (Q), kaempferol glycosides (K) and quercetin:kaempferol ratio (QKR), in the watersufficient environment (WSE) and water-limited environment (WLE) with the allele effects (%) based on singlemarker Kruskal-Wallis analysis. ∆%, percentage change between environments; AE, across environments. Traits
missing because of too low significance: K-WSE, Q-∆%, QKR-∆%. No distinction was made between the two sets of
homoeologous chromosomes.
Linkage Posn K
Q
Q
QKR
QKR
K
K
Q
QKR
Marker allele
Group
(cM) WLE
WSE WLE WSE
WLE
∆%
AE
AE
AE
clPt-820694-p
1
24
59*
gtrs887_343-m
clPt-845949-m
clPt-821025-m
clPt-843887-m
clPt-820266-m
clPt-844948-m
clPt-845059-m
uga066_235-p
clPt-842469-p
gtrs203_166-m
gtrs203_169-p
gtrs250_242-p
gtrs355_228-m
gtrs355_294-m
clPt-843765-p
prs639_218-m
gtrs241_203-p
gtrs241_215-p
1
32
12*
1
53
14*
1
73
24****
-23***
-33****
51**
23****
-35****
1
81
25****
-26****
-35****
43*
26****
-39****
1
89
27****
-26****
1
89
26****
1
89
27****
2
44
3
0
3
14
3
14
6
9
6
9
-10*
6
9
-12*
7
33
14*
7
36
7
0
12*
-167**
7
1
-13*
62**
15*
54*
-38****
-41****
25****
-110*
-15*
-13*
-8*
14*
-12*
11*
12*
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM)
155
156
Table 7.4
Marker alleles associated with field experiment dry matter traits.
Means of shoot dry matter (SDM), total dry matter (TDM, Harvest 1 + Harvest 2) with the allele effects (%) based on single-marker Kruskal-Wallis analysis. Traits missing
because of too low significance: SDM WLE Harvest 2. ∆%, percentage change between environments; AE, across environments. No distinction was made between the two
sets of homoeologous chromosomes.
Posn SDM WSE
TDM WSE
SDM WLE
TDM WLE
SDM ∆%
TDM ∆%
SDM AE
TDM AE
Marker allele
LG (cM) Harvest 1 Harvest 2
Harvest 1
Harvest 1 Harvest 2
Harvest 1 Harvest 2
gtrs524_316-m 1
76
8*
8*
gtrs524_389-p 1
76
-10*
gtrs524_401-m 1
76
-9*
-8*
clPt-871902-p
2
19
-12*
-12*
-12*
prs329_202-p
2
68
7*
gtrs203_175-p 3
14
-9*
-9*
clPt-822077-m 4
39
-7*
clPt-820542-m 4
55
11*
-7*
clPt-820761-m 4
57
-12*
-13*
gtrs395_316-m 7
23
8*
8*
gtrs395_401-m 7
23
-9*
-8*
clPt-843765-p
7
33
-15*
-14*
-8*
prs639_205-m 7
36
9**
7*
6*
prs639_225-m 7
36
-9*
clPt-845636-p
7
42
9**
clPt-843905-p
7
48
-8**
clPt-842411-m 7
70
-16*
-13*
-16****
-13*
-10**
clPt-820601-m 7
71
-12****
gtrs241_203-p 7
0
-11*
-11*
8***
8****
gtrs241_215-p 7
1
9*
-7*
-7***
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
Table 7.5
Marker alleles associated with the field experiment stolon traits (1/2).
Means of stolon density (SD) and number of stolons per plant (ST), in the water-sufficient
environment (WSE) and water-limited environment (WLE) with the allele effects (%) based on singlemarker Kruskal-Wallis analysis. No distinction was made between the two sets of homoeologous
chromosomes.
Posn
SD WSE
Marker allele
LG
(cM)
Harvest 1
Harvest 2
gtrs757_185-m
1
19
clPt-873193-m
1
70
clPt-821025-m
1
73
clPt-821104-m
1
76
clPt-843887-m
1
81
4*
clPt-844948-m
1
89
4*
clPt-845059-m
1
89
gtrs374_296-m
2
13
-1*
gtrs374_303-p
2
13
-1*
SD WLE
Harvest 1
Harvest 2
ST WSE
Harvest 1
Harvest 2
ST WLE
Harvest 1
Harvest 2
4*
10*
8*
14**
11***
9***
16****
8**
17****
12****
-3*
3*
uga066_235-p
2
44
gtrs203_181-p
3
14
uga032_177-m
3
24
clPt-844307-p
4
16
clPt-842837-p
4
19
gtrs168_140-m
4
33
4*
gtrs168_156-m
4
33
-4*
clPt-872519-m
4
47
gtrs967_341-m
4
50
-11*
-4*
uga003_188-m
4
66
4*
clPt-842908-m
4
70
3*
clPt-872830-m
4
71
3*
clPt-820947-p
6
0
clPt-845385-p
6
11
clPt-872332-p
6
32
clPt-872581-m
7
9
clPt-843478-p
7
13
clPt-820296-p
7
29
clPt-844166-m
7
31
clPt-843932-p
7
47
5*
4*
-11*
4*
-16*
8*
8*
4*
3*
4*
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
157
Table 7.6
Marker alleles associated with the field experiment stolon traits (2/2).
Means of percentage change (∆%) and cross environments (AE) of stolon density (SD) and number of
stolons per plant (ST), in the water-sufficient environment (WSE) and water-limited environment
(WLE) with the allele effects (%) based on single-marker Kruskal-Wallis analysis. ∆%, percentage
change between environments; AE, across environments. No distinction was made between the two
sets of homoeologous chromosomes.
Posn
SD ∆%
Marker allele
LG
(cM)
Harvest 1
gtrs757_185-m
1
19
-52**
clPt-873193-m
1
70
clPt-821025-m
1
73
clPt-821104-m
1
76
clPt-843887-m
1
81
clPt-844948-m
1
89
Harvest 2
ST ∆%
Harvest 1
Harvest 2
SD AE
Harvest 1
Harvest 2
Harvest 1
Harvest 2
-8***
12***
3*
4*
36*
13****
4*
clPt-845059-m
1
89
gtrs374_296-m
2
13
gtrs374_303-p
2
13
10***
6***
uga066_235-p
2
44
10*
5*
gtrs203_181-p
3
14
uga032_177-m
3
24
clPt-844307-p
4
16
clPt-842837-p
4
19
-12*
gtrs168_140-m
4
33
12***
6**
gtrs168_156-m
4
33
-13**
-5*
12****
-7*
clPt-872519-m
4
47
-12*
gtrs967_341-m
4
50
9*
uga003_188-m
4
66
11*
clPt-842908-m
4
70
9**
clPt-872830-m
4
71
9**
clPt-820947-p
6
0
10*
clPt-845385-p
6
11
clPt-872332-p
6
32
2*
5*
-12*
4*
clPt-872581-m
7
9
12**
clPt-843478-p
7
13
11**
clPt-820296-p
7
29
10*
clPt-844166-m
7
31
12*
clPt-843932-p
7
47
11*
6*
6*
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
158
ST AE
11**
Table 7.7
Marker alleles associated with the field experiment leaf size traits.
Means of leaf size (LS), in the water-sufficient environment (WSE) and water-limited environment
(WLE) with the allele effects (%) based on single-marker Kruskal-Wallis analysis. ∆%, percentage
change between environments; AE, across environments. No distinction was made between the two
sets of homoeologous chromosomes.
Posn
LS WSE
Harvest 1
Harvest 2
LS WLE
Harvest 1
Marker allele
LG
(cM)
clPt-821580-p
1
31
gtrs524_303-m
1
76
clPt-844746-p
2
4
20*
clPt-844514-p
3
1
-18*
prs575_278-m
5
66
prs465_404-m
6
0
prs465_407-m
6
0
gtrs355_228-m
6
9
5****
gtrs355_279-m
6
9
-4*
gtrs355_294-m
6
9
4**
clPt-872332-p
6
32
clPt-843222-m
6
33
clPt-843781-m
6
33
clPt-872997-m
6
33
clPt-820917-p
7
0
clPt-842994-p
7
2
-5**
clPt-872581-m
7
9
-4*
prs705_223-m
7
17
-3*
clPt-843754-m
7
22
-4*
clPt-872732-p
7
22
4*
clPt-842411-m
7
70
gtrs241_203-p
7
0
-4*
-3***
14***
gtrs241_215-p
7
1
4*
3**
-12***
3*
Harvest 2
LS ∆%
Harvest 1
Harvest 2
LS AE
Harvest 1
3*
Harvest 2
4***
3*
3*
-3*
-3*
-3*
4*
3*
-9*
4***
-4*
-8*
4*
4*
4*
3*
3*
-3*
-5*
12*
-4*
12*
-4**
4**
-4*
-2*
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
159
160
Table 7.8
Marker alleles associated with the field experiment canopy traits.
Canopy area (CA) and canopy volume (CV), in the water-sufficient environment (WSE) and water-limited environment (WLE) with the allele effects (%) based on
single-marker Kruskal-Wallis analysis. ∆%, percentage change between environments; AE, across environments. Traits missing because of too low significance: CAWSE-Harvest 2, CA-WLE-Harvest 2, CV-WSE-Harvest 1, CV-WLE-Harvest 2, CV-∆%-Harvest 1, CA-AE-Harvest 2. No distinction was made between the two sets of
homoeologous chromosomes.
Posn
CA WSE
CA WLE
CV WSE
CV WLE
CA ∆%
CA ∆%
CV ∆%
CA AE
CV AE
CV AE
Marker allele
LG
(cM)
Harvest 1 Harvest 1 Harvest 2 Harvest 1 Harvest 1
Harvest 2 Harvest 2 Harvest 1 Harvest 1 Harvest 2
clPt-872700-p
1
43
-1*
-1*
clPt-873193-m 1
70
2*
4*
clPt-821025-m 1
73
5**
5**
6****
clPt-843887-m 1
81
5***
5****
2**
6****
5*
clPt-820266-m 1
89
5**
2***
clPt-820266-m 1
89
clPt-845059-m 1
89
5****
6****
gtrs374_296-m 2
13
-2**
2*
1*
-2*
gtrs374_303-p 2
13
1*
1**
gtrs203_181-p 3
14
-4*
-1*
prs575_274-p
5
66
-3*
clPt-820957-m 6
0
3**
clPt-845385-p
6
11
-6*
clPt-872332-p
6
32
5**
5**
5*
clPt-821875-m 6
52
2*
-1*
clPt-845721-m 7
83
3***
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
Table 7.9
Marker alleles associated with the phenotyping pot experiment traits.
Quercetin glycosides (Q), kaempferol glycosides (K), quercetin:kaempferol glycoside ratio (QKR),
root:shoot ratio (RSR), shoot dry matter (SDM) and total dry matter (TDM, root + shoot dry matter)
with the allele effects (%) based on single-marker Kruskal-Wallis analysis. Traits missing because of
too low significance: root dry matter (RDM). No distinction was made between the two sets of
homoeologous chromosomes.
Linkage
Posn K
Q
QKR
RSR
SDM
TDM
Marker allele
Group
(cM)
Pot
Pot
Pot
Pot
Pot
Pot
clPt-845949-m
1
53
47**** -57****
clPt-821306-m
1
67
51****
-88****
prs484_264-m
1
67
-33*
clPt-873193-m
1
70
51****
-99****
18*
16*
clPt-821025-m
1
73
55****
-106****
clPt-843887-m
1
81
64****
-120****
clPt-846087-m
1
85
60**** 18**
-87****
clPt-820266-m
1
89
61****
-100****
clPt-844948-m
1
89
15*
gtrs203_175-p
3
14
-20*
-19*
gtrs168_135-p
4
33
-13*
gtrs168_144-p
4
35
11*
gtrs967_351-p
4
50
-35*
gtrs250_242-p
6
9
26*
gtrs355_228-m
6
9
-16*
clPt-845458-m
6
48
18***
* Marker-trait association is significant at the 0.005 probability level.
** Marker-trait association is significant at the 0.001 probability level.
*** Marker-trait association is significant at the 0.0005 probability level.
**** Marker-trait association is significant at the 0.0001 probability level.
Allele designations following the locus name are as follows: m, maternal, Kopu II; p, paternal,
Tienshan.
Positions are indicated in centiMorgan (cM).
7.4.4 Common markers for multiple traits and parents
A number of marker loci in homoeologous Linkage groups 1, 6, 4 and 7 were associated with QTLs for
more than one of the traits analysed in this study (Table 7.10). Only one marker in Group 6 - gtrs355 showed associations with Q glycosides and another trait - leaf size - with both positive and negative
effects for the latter due to contrasting allele phases. Another marker in Group 7 - prs639 - showed
associations with both K glycosides and shoot dry matter production. Several alleles in LG1 showed
associations with K glycosides and multiple other traits with both positive and negative phenotypic
effects. The marker prs639 showed two different linkage phases (1- and 0-), while marker gtrs241
showed linkage in both LG4 and LG7.
161
Table 7.10 Alleles associated with more than one trait across two harvests, averaged across two
environments in the field experiment.
Quercetin glycosides (Q), kaempferol glycosides (K), quercetin:kaempferol ratio (QKR), shoot dry
matter (SDM), stolon density (SD), number of stolons per plant (ST), leaf size (LS), canopy volume
(CV) and canopy area (CA), with the phenotypic effects positive (+) or negative (-) based on singlemarker Kruskal-Wallis analysis. No distinction was made between the two sets of homoeologous
chromosomes. LG, linkage group; LPh, linkage phase, determined by JoinMap 3.0.
Marker allele
clPt-821025
clPt-843887
clPt-844948
clPt-845059
gtrs355
clPt-845385
clPt-872332
prs639
gtrs241
LPh
Class
LG
Pos.
0-
m
1
0-
m
0-
Q
K
QKR
73
+
-
1
81
+
-
m
1
89
+
0-
m
1
89
0-
m
6
9
-1
p
6
11
-0
p
6
32
1-/0-
m
7
36
-0
p
7
1
SDM
SD
ST
LS
CV
+
+
CA
+
+
+
+
+
+
-
+
+/-
+
+
+
+
+
+
+
+
-
Allele class designations are as follows: m, maternal, Kopu II; p, paternal, Tienshan.
Positions are indicated in centiMorgan (cM).
7.5 Discussion
This is the first study examining molecular markers associated with levels of Q and K glycosides in
pasture plants. As explained in the introduction (paragraph 7.1), QTL detection in mapping families
derived from single pair crosses, in particular highly heterozygous out-breeders such as white clover,
provides unique challenges for developing molecular tools applied in plant breeding programmes
(Abberton et al. 2009). The principle of linkage mapping is identifying simply inherited markers in
close proximity to genetic factors within QTL via processes that identify a statistical association
between marker and QTL alleles, selectively reducing the intensity of that association as a function of
the distance between the marker and the QTL (Chapman et al. 2007; Xu 2010). Although genetic
mapping of complex traits is possible in allotetraploid populations such as white clover, the failure to
find more than one QTL in Tienshan with positive genotypic effect for the trait Q glycosides is of
interest, as were the alleles with positive effect for Q in Kopu II in the pot experiment. This might be
a consequence of using an F1 (high Q × low Q) family. If a QTL locus has two strong alleles with
positive genotypic effect in Tienshan but two contrastingly strong alleles with negative genotypic
effect in Kopu II, they would tend to cancel each other out in the F1, and give intermediate
phenotypes (i.e. the extreme phenotypes disappear and so does the locus). Failing to detect the
162
strongest QTL alleles could be one of the main likely disadvantages of using an F1 family. This, along
with resource constraints, is why we chose single-marker analysis, although precise localisation or
quantification of QTLs in a specific region is not possible in this system due to confounding of
genotypic value and frequency of recombination (Bernardo 2010). However, initial QTL detection and
identification of associations between the markers and phenotypic data can be achieved by singlemarker analysis (Lilley et al. 1996; Cogan et al. 2005).
In this experiment, 237 marker-trait associations have been defined, which is important for making
further steps in QTL localisation for marker-assisted selection and back-crossing programmes in
white clover (Semagn et al. 2006b; Collard and Mackill 2008). However, a number of these were
time-point or location specific, or among correlated characters, resulting in a substantially lower
number of genome regions strongly implicated in economically significant traits.
7.5.1 Quantitative genetic analysis
Heritability of desirable traits is useful for the determination of genetic gain. The estimates for
heritability are broad-sense estimates, due to the fact that the phenotypic data were derived from
clonal copies of a full-sibling population. Whilst narrow-sense heritability would be more useful for
breeding purposes, the narrow-sense heritability estimates would be smaller than the broad-sense
estimates. Broad-sense heritabilities (Hb) based on plant means within field environments (Table 6.2,
Chapter 6) for most of the traits with sufficient water was moderate to high, whereas under water
deficit higher Hb was revealed for most traits. The Hb based on plant mean across field environments
(Table 6.3) showed intermediate data between those in the separate environments. The estimated
significant genotype × environment interaction for the traits (Table 6.3) indicates a relative change in
ranking among the F1 progeny for the expression of these traits across the field environments (Kumar
et al. 2008). This also indicates the sensitivity of these traits, and the genetic factors conditioning the
traits, to the contrasting environments. Partitioning the confounding environment and genotype ×
environment interaction effects from the genotypic effects, in the variance component analysis
across the water deficit and water-sufficient environments, would have contributed to more precise
Hb estimates (Caradus and Chapman 1996). The high Hb estimates emphasise the significance of
genes versus the environment on the relevant traits, although these are broad-sense heritabilities.
However, precisely estimating gain from selection for these traits would be problematic, as the
influence of dominant gene action and gene interaction (epistasis) on heritability could not be
determined (Robins et al. 2007a). The moderate to high heritability found in the field screening
means that in this mapping population all these traits are under strong genetic control, which is
beneficial for the reliability of the linkage maps (Rebetzke et al. 2008b). Although there is significant
163
G × E interaction, the data does not give the genetic correlation attributable to change in ranking.
The effects may be magnitude change-related.
7.5.2 QTL effects and interactions
This experiment identified QTL effects for ten traits including quercetin glycosides and shoot dry
matter within two environments and two harvests. Analysis identified regions on five KII and six T
white clover linkage groups that were associated with at least one of the traits in one harvest and
one environment. The finding that markers were identified in more than one year and environment
(Tables 7.2, 7.3, 7.9) provides support that they are linked to QTL and that genetic control of Q
glycosides can be independent from biomass production, as they are associated on different regions
of the genome. This forms a basis for enhanced plant breeding systems for parallel improvement of
drought tolerance and yield.
Several marker alleles were negatively associated with key traits such as Q glycosides and biomass.
As an increased trait value is desirable, for breeding purposes the use of reverse selection for alleles
with a negative genotypic effectgenotypic effect on Q glycosides will select individuals without the
marker allele for reduced Q glycosides. Importantly, the T-derived marker allele clPt-843765 in Group
7 (Figure 7.2) was the only marker allele with a positive genotypic effect for Q glycosides (Table 7.3).
However, this same marker had a negative genotypic effect for SDM and TDM (Table 7.4), which
reduces its suitability for plant improvement towards enhanced plant persistence. A selection on
gtrs241 would be desirable for an increased QKR across environments (Table 7.3), as this can result in
individuals with higher Q glycoside content, most likely afforded by the large negative genotypic
effect on KΔ% by the same marker (Table 7.3).
It is specifically interesting to note that the low-yielding Tienshan ecotype contained eight alleles
conferring an increase in DM traits (Table 7.4), as it is a plant with small leaves and stolons, and with
a low biomass production and slow growth. The allelic diversity occurring in the progeny is related to
the recombination of heterozygous alleles originating in the contrasting ecological and morphological
backgrounds of the two parents. It is possible that genetic material from isolated regions such as the
Tienshan region of China harbour less allelic diversity than do elite breeding populations such as
Kopu II. It may well be possible to dissect the physiological and biochemical basis for traits like yield
(Robins et al. 2007a; Robins et al. 2007b) or persistence (Robins et al. 2008) by assessing their
various components in succession. Genome locations with QTL for multiple component traits (e.g.
leaf size, stolon density or Q glycosides levels) that are part of the complex trait persistence under
drought can then be combined to investigate and influence the trait in question.
164
Stolon density is related to persistence of T. repens, but is associated with small leaf size which can
lead to a low harvest index (Caradus and Mackay 1989). In this study we have identified genome
regions that influence one trait (SD, Tables 7.5, 7.6) without influencing the other (LS, Table 7.7),
again offering the potential to use QTL for precision selection to simultaneously select to improve
traits that have a negative correlation.
Taken together with the finding of the positive genotypic effect on both SD and LS (Table 7.10) by the
T-derived DArT marker clPt-872332, these results make it possible to use a trait-combo to select for
reduced internode length combined with increased leaf size to increase stolon density for
persistence and higher herbage yield. Although stolon-based traits like density and numbers per
individual might be interesting for selection purposes, heritability for these traits across
environments was too low to be significant (Table 6.3, Chapter 6), which would impact the efficiency
of marker-aided selection (Chapman et al. 2007). Further research should focus on improvement of
(narrow sense) heritability for SD (Caradus and Chapman 1996).
The positive associations of several alleles with leaf size (LS) indicate an important selection criterion
for LS in Tienshan, as the association was stable across environments and heritability was moderately
high at 0.51 (Table 6.3, Chapter 6). Increasing leaf size correlated to persistence offers an opportunity
in the development of new white clover cultivars (Gustine et al. 2002; Ayres et al. 2007), and these
trait-specific genome regions and associated markers may enable more efficient co-selection of these
traits. This discovery of genetic factors in Tienshan that can increase leaf size in a cross to elite
material, is another example of the value of genetic resources with small phenotypes for yield
harbouring genetic factors that can enhance yield (Xiao et al. 1996; Bouton et al. 2005; Helgadottir et
al. 2008).
In full-sibling families derived from cross-pollinated (out-breeding) species such as white clover,
markers may vary in the number of alleles that segregate (up to four are possible), by heterozygosity
of one or both parents , markers being dominant or co-dominant, and unkown linkage phases of
marker pairs. Different types of categories and crossings may occur in the general case of multi-allelic
systems with four or more alleles (Haseman and Elston 1972). When considering an A locus with i, j, k
and l alleles, there are seven possible types of crosses (Bhering et al. 2008), but only four are
considered to be informative, since they segregate for at least one parent.
The marker alleles associated with canopy traits (Table 7.8) that were in coupling phase with positive
effects on trait value are important candidates for loci controlling these traits. However, as broadsense heritability was too low to be significant across environments (Table 6.3), canopy traits may
not be well suited to marker-aided selection or breeding programs (Chapman et al. 2007).
165
The genotypic effects of marker alleles associated with multiple traits (Table 7.10) tended towards a
comparable direction, with positive or negative effects in all traits, which can be utilised for selection
purposes. One exception was the derived trait QKR, where the associated markers showed a
negative effect, while for other traits (K, SD, ST, CV and CA) a positive effect was found (Table 7.10).
Such improvement of any of these other traits could potentially lead to a reduction in QKR (Collard
and Mackill 2008). Reverse selection for this QKR allele selects individuals without the marker for
reduced QKR, if that is the breeding focus. A logical physiological connection is that when K
glycosides increase, QKR is reduced. The association with SD, ST, CV and CA could be a genetic factor
implicated in environment interaction. The genotypic effects of genes associated with several KII
DArT markers in Group 1 (Table 7.10) are in the same phase (0-), which indicates that all marker
alleles associated with a positive effect on a trait (e.g. K) in the same parental group with similar
significance could in fact be a cluster of markers effected by linkage phase, and not with separate
effects. The Kopu II marker in Group 6, gtrs355 (Table 7.10), showed associations with Q glycosides
and leaf size, with both positive and negative effects for the latter due to contrasting allele phases.
This allele could be investigated further, as the association between Q glycosides and leaf size could
be important to improve persistence under drought, and may be a factor in the negative correlation
observed between Q glycosides and DM in white clover populations (Hofmann et al. 2000). The
Tienshan marker in Group 4/7, gtrs241 (Table 7.10), showed associations with QKR and leaf size, with
a positive effect for QKR and a negative effect for LS, which would be a normal biological process, as
the higher the level of Q glycosides compared to K glycosides, the more the plants appear to be
stressed and as a consequence reduce their LS. One marker allele in Group 4/7, gtrs241, showed
both a positive effect towards QKR, and a negative effect towards leaf size (Table 7.10).
Biological processes can be examined using the linkage groups in the marker-trait association
matrices e.g. lesser variation / significance for certain traits in the water-limited environment than in
the water-sufficient environment, which is in concordance with the phenotyping results in this field
study (Chapter 6). Markers associated to just one harvest for one trait and not linked to other
markers also displaying associations with that trait, may well be false positives (Tuberosa et al. 2002)
or indicate transient genotypic effects related to season, growth phase, or other environmental cues.
However, the identification of identical markers for the traits at multiple harvests, and in multiple
environments in the field experiment and the phenotyping pot experiment support the argument
that markers are in fact linked to QTLs that are stable across environments, and are less likely to be
false associations (Podlich et al. 2004). The genetic control of biomass production would be
influenced by different environments. Because different associations are detected at different
harvests, it is not yet clear which would be the best markers to use as candidates in MAS schemes
(Lukens and Doebley 1999; Robins et al. 2007a).
166
White clover often has two different alleles of the same locus, and orthologs between homoeologues
that may contain QTL alleles with contrasting expression or effects. As the homoeologues could not
be separated in this analysis, further investigation is needed to examine whether alleles associated
with positive trait effects are on separate homoeologues from alleles associated with negative trait
effects. In either one of the parents, the marker alleles with both negative and positive genotypic
trait-effects could be confined to separate homoeologous groups (Humphreys et al. 2005). However,
this might be a non-issue, as inheritance in white clover is disomic, i.e. during meiosis pairing
behaviour resembles that of non-homologous pairs of chromosomes in diploids (Voisey et al.
1994).This study is the first of its kind and is therefore inescapably empirical, which is reflected in the
relative scarcity of literature on this subject. Nevertheless, the enhancement of the value of the DNA
marker resource by the addition of information about markers linked to desirable traits has
considerable value for development of systems for more rapidly achieving genetic gain and cultivar
development goals in white clover breeding programmes. The present partial map, based on a cross
between the commercial white clover cultivar Kopu II and the wild ecotype Tienshan remains
preliminary for QTL detection, as it covers only part of the tetraploid white clover genome. This map
needs a larger number of markers to be mapped, and in some cases QTL detection would appear to
benefit from additional progeny being genotyped and included in the analysis. This is the first genetic
map for white clover that combines SSR and DART markers which can turn into a valuable resource
for further white clover genetic research, and can be used for additional QTL detection (e.g. other
drought response traits such as specific leaf mass, stomatal conductance, proline, glutathione)(Peleg
et al. 2008).
7.5.3 Breeding perspectives
Although the genotypic effects in the alleles associated with the biochemical traits were moderate to
large in magnitude, be it in coupling or repulsion phase, the magnitude of effects in the other traits
were predominantly smaller. QTL mapping is less accurate for genes with small effects (Kearsey and
Pooni 1998; Lynch and Walsh 1998), and may require larger population sizes to accurately assess the
magnitude and location of QTL effects. Heritability across environments in the field phenotyping
experiment was large for most traits, which would make the QTL map more reliable (Rebetzke et al.
2008b; Bernier et al. 2009). To be successful in plant breeding programs, only QTLs that are restricted
to very small chromosome sections should be used in marker-aided backcrossing (Semagn et al.
2006b) and marker-aided selection for drought-related traits (Sinclair 2011). Hence, the so-called
“hot-spots” for alleles associating with Q glycosides, K glycosides and QKR in Linkage groups 1, 3, 6
and 7 (Table 7.3) would be starting areas to identify candidate genes for those traits (Kane and
Rieseberg 2007), or to source markers to explore in complex populations. For most of the other
167
traits, the largest genotypic effects are the best starting points for further investigation, such as
those for biomass production in Linkage groups 1, 4 and 7 (Table 7.4).
While this work shows evidence that marker-trait associations related to flavonoids and
morphological traits can be identified, the experiment was limited as we used clonally propagated
spaced plants, differing from a commercial sward where a mix of clover and grasses is used. Further
research is needed with progeny families grown in grazed, mixed swards. Another limitation in this
study was the evaluation of only two parental genotypes which restricts the allelic diversity for QTL
detection and for any subsequent white clover breeding. Association mapping and genomic selection
using complex populations (Smith et al. 2009), preferably combined with recurrent selection in this
outcrossing population (Brummer and Casler 2009), would alleviate this bottleneck, though it would
complicate marker analysis. The biparental approach was probably the most sensible for the
biochemical traits, at least for a start.
Future work is also needed to independently confirm and accurately assign QTLs to the genomic
regions associated with markers for important traits; to further elucidate the genetic regulation of
levels of Q glycosides; determine additive genotypic effects to be able to calculate narrow-sense
heritability; examine the inheritance of Q glycosides and dry matter production in other genetic
backgrounds, and investigate improved systems to develop forages with high levels of Q-mediated
abiotic stress resistance and high plant productivity for pastoral systems. These findings may have
profound implications for drought tolerance breeding in other economically important plant species.
7.5.4 Conclusion
Five main results were obtained:
(1) Only 27 of 237 marker-trait associations (11%) were common to both water-limited and watersufficient environments, confirming the existence of stress specific genotypic effects;
(2) The marker-trait associations tended to form clusters of genomic locations, frequently consisting
of marker-trait associations from different trait classes (biochemistry, growth, morphology);
(3) Biochemical marker-trait associations (specifically for K glycosides) were more frequently colocated with growth marker-trait associations than morphological related ones, especially in the
water-limited environment;
(4) One co-location was observed between Q glycosides and leaf size QTL, whereas a number of
biomass and flavonoid specific QTL were discovered, which can be considered as candidate genomic
168
locations for explaining part of the variability of persistence related to leaf size and biomass
(Tuberosa and Salvi 2007);
(5) Several promising genetic regions were discovered in the Tienshan homoeologous groups with
positive genotypic effects for important traits such as quercetin glycosides, biomass production,
stolon density and leaf size, which shows a disconnect between phenotype and genotype in this
small white clover ecotype.
These results support the hypothesis that flavonoid metabolism provides valuable adaptive capacity
for improving white clover responses to water deficit induced stress, and is largely independent of
genetic factors controlling yield per se. However, the biochemical QTLs Q-and K-glycosides and their
ratio QKR have not yet been clearly associated with drought stress resistance.
Furtnermore, the QTLs identified need to be validated in a follow-up experiment. The fact that QTLs
are independent does not dismiss that there are regions not identified that show strong genetic
correlation for the two traits quercetin glycosides and biomass production (i.e. part genetic control).
169
Chapter 8
Overall Discussion
Tools for DNA marker work.
170
8.1 Summary of findings relative to research objectives
The results indicated that, for the family studied, there were adequate amounts of genetic variation
for morphological, physiological and metabolic traits, and that the apparent association between low
yield and high flavonoid concentrations could be broken. Although limited in its value by the choice
for analysis of a single bi-parental F1 family in a species known for its heterogeneity and
heterozygosity, the research carried out in the course of this Ph.D. study provided two major
findings. First was the discovery of a number of DNA markers predictive of levels of Q glycosides,
biomass and other important traits in this F1 population. The second major finding was that there
was only a weak correlation between dry matter (DM) yield and quercetin (Q), glycosides with levels
of one explaining less than 10% of the variation in levels of the other. This finding provides the first
evidence that forage products are possible that are both high yielding and high in Q glycosides. Prior
to this finding, it was generally accepted that a trade-off was necessary (Hofmann et al. 2000). The
reasons why the association between Q glycosides and DM was in such contrast with previous
reports (Hofmann et al. 2000) were most likely due to the choice of using an artificial cross between
two parents that were showing widely contrasting trait data for Q and DM (wide cross), the choice of
investigating individuals within an F1 full-sib progeny population in this project versus contrasting
several different white clover populations, the sampling of the population (and alleles), and the
choice of environments (applied drought in this project versus normative conditions). However, the
results of using a single bi-parental F1 family need to be interpreted very conservatively, and cannot
give conclusions of a profound general applicability. It represents a first step toward clarifying our
understanding of important polygenic traits in a complex amphidiploid hybrid species. Nevertheless,
some very useful indications have been achieved.
All required technical achievements in this project were met in the context of an adaptive plan. After
the pilot experiment it became clear that the possibility of having broken the negative correlation
between Q glycosides and DM production had presented itself, and the programme was adapted to
pursue larger numbers of plants from within the same F1 family in a single experiment, solidifying
that finding. As the focus developed on this finding, the area of work in trait measurement was
expanded to include related traits such as kaempferol glycosides (K), quercetin:kaempferol glycoside
ratio (QKR) and other morphological traits such as root DM, shoot DM and root to shoot ratio. A new
growth matrix mixture that was developed during the latter experiments has proven to be valuable
for the testing of containerised plants, which will increase the efficiency and reliability of future
development work in this area.
Subsequently, water deficit was applied in a separate drought experiment in pots and physiological
measurements were carried out to determine how the important traits performed under these
171
conditions. Crosses were made with the best performing genotypes out of this experiment and a
selection of elite white clover cultivars for later use. Finally, a field experiment at two sites, one with
limited soil moisture, was conducted to study trait behaviour under field conditions over summer.
The most important finding with this mapping population was that the weak correlation between Q
glycosides and DM held up consistently over all the experiments, and hence results should be
repeatable in future breeding experiments. Marker:trait associations were identified with traits from
the population used in the field experiment and compared with the population used in the
phenotyping pot experiment.
This project provided several commercial development opportunities: a new breeding target leading
to a forage product combining high yield and high Q glycosides, the enhancement of the value of the
DNA marker resource by the addition of information about markers linked to these traits in one F1
family, and development of systems for more rapidly achieving product development goals. The
completion of the DNA test capability will have two outcomes for systems enhancement. It will
theoretically increase speed to market, and it will theoretically increase the incremental gain from
selection during that time by increasing the precision with which superior plants and populations of
plants can be identified.
A conceptual model linking all traits and results in the experiments mentioned in this thesis is shown
in Figure 8.1. Water deficit increased the biochemical traits (Q and K glycosides, QKR) plus the
morphological traits stolon density (SD) and root:shoot ratio (RSR), and the physiological trait water
use efficiency (WUE). Correlations between the traits SD and WUE indicated that they were
associated with persistence of white clover under these experimental dryland conditions, as was
found in other studies (Jahufer et al. 1999; Ayres et al. 2007). Potential QTLs linked to WUE have
been found in white clover by Abberton et al. (2009). Nodal root growth in that study decreased less
under drought than above ground parts, increasing RSR. This is an important trait for white clover as
the taproot dies off after 12-18 months, and can be used to increase persistence and drought
tolerance (Thomas 2003). Most marker alleles showed phenotypic associations to the traits with both
negative and positive phenotypic trait data. Even if a phenotypic trait such as biomass would
decrease, there would be alleles having a positive genotypic effect (Figure 8.1). In contrast, for the
traits that increased under water deficit, there were also alleles associating negatively with those
traits. Marker alleles with positive genotypic effects on decreasing traits and alleles with negative
effects on decreasing traits should be reverse selected, i.e. plant breeders could select genotypes not
showing these alleles for the desired genotypic effect (be it positive or negative) on a specific trait
(Agbicodo et al. 2009). Understanding the genetics of drought resistance and identification of DNA
markers linked to QTLs and localising chromosomal regions or candidate genes involved in drought
172
resistance will help white clover breeders to develop improved cultivars that combine drought
resistance with other desired traits using marker assisted selection.
Environmental stress
Drought / Water deficit
Decreases
• Aboveground plant
Increases
• Quercetin, kaempferol
growth
glycosides
•
Root growth
•
Carbon isotope
•
•
Quercetin:kaempferol
glycoside ratio
discrimination
•
Stolon density
Leaf size
•
Root:shoot ratio
l
b
ff
Novel marker-trait
associations
• Positive
genotypic effect
•
Negative
genotypic effect
Figure 8.1
Conceptual model linking morphological, physiological, biochemical and genetical
responses in white clover to water deficit stress.
8.2 Similarities and contrasts between experiments
Some of the important results collected in the field experiment were similar to the results from the
phenotyping experiment in pots. Specifically, the weak correlation between biochemical traits and
biomass production in pots held up in the field. Biochemical traits increased and most morphological
traits, including herbage yield, decreased in both experiments. Identical genetic markers in similar
genomic regions were associated to the same traits in both experiments. There were also profound
differences: the most important one being environmental; the plants in the pot phenotyping
experiment were watered regularly, while in the field, the plants were only irrigated twice after
173
planting to stimulate establishment of the plant plugs. After that, the plants were rain-fed, and only
the differences in soil structure and soil moisture (as both sites were similar in soil fertility) instigated
the differences in trait results. There were significant differences in the genotype rankings for all of
the trait means (Appendix H).
8.3 Context and limitations of the findings
The experimental conditions for the duration of the three main experiments are likely to have
affected outcomes, as some of the cloned plant material was genotypically identical between
experiments. The progeny population in the drought experiment in pots was 32, compared to 130 in
the phenotyping experiment and 190 the field experiment. A further difference was the strong water
deficit treatment in the drought pot experiment compared to the phenotyping experiment in pots
and the field experiment, where in both cases there was no specific water deficit treatment.
However, the Ashley Dene Farm site did represent a water-limited environment, compared to the
water-sufficient AgResearch Farm site (Chapter 6). The reason behind the low number of individual
progeny genotypes in the pot experiments was to allow a focus on additional physiological
measurements related to water deficit-induced stress.
Pot studies and field studies with the same plant genotypes or even species can yield dissimilar
results when artificial drought in pots is applied over a short period of time, in contrast to drought in
the field, which builds up more gradually. The genotype trait rankings showed similarities but also
difference between the experiments (Appendix H).Tissue desiccation under natural conditions is slow
(Barker et al. 2005). The evaluation of plant response to drought stress and plant adaptation requires
sufficient duration of stress (Basnayake et al. 1996). However, some of the same markers had
associations with identical traits, such as Q glycosides, K and SDM, in the phenotyping pot
experiment as in the field experiment, so it could well be possible that key conditions were similar.
The relationship between DM and constitutive levels of Q glycoside accumulation does not appear to
be causal. In fact, the results of this white clover drought experiment show clearly for the first time,
evidence of plants within an F1 population which are stress-resistant and productive simultaneously.
Our findings considered together with previous work (Hofmann et al. 2000), indicate that in white
clover and other species, Q glycosides act in a similar way under water deficit stress as under UV-B
stress as a predictor of abiotic stress sensitivity. Q glycosides could have a role as a general abiotic
stress moderator in white clover, and this may be of use in other crop species as well. The genotypic
variation also signifies the potential use of the Tienshan × Kopu II F1 population in selecting for high Q
glycoside accumulation as well as herbage production in a breeding program with future crosses with
elite white clover cultivars. Plants with high TDM in a water-sufficient environment are frequently
strongly affected under water deficit. As Δ Q glycosides and TDM under water-sufficient conditions
174
seem not to be significantly correlated, plants with high Δ Q glycosides could be selected first,
followed by further selection for high herbage yield. Together with revealing a high degree of genetic
variation for most of the measured traits, these findings suggest Tienshan as a useful source of new
genetics into white clover, and Q glycosides as a potential selection criterion in drought-resistance
plant experiments.
8.4 Recommendations for further investigation
Most of the results in these experiments were focussed on the DM yield concept where single plant
yield is not necessarily related to annual DM yields in swards under grazing (Williams et al. 2000). The
small leaf types can yield well under grazing, provided they are grazed more often, e.g. a large leaftype may get grazed six times a year while a small leaf-type 12 times with half the yield each time
(Woodfield et al. 2001). When allowed to grow as single plants, the large leaf-type will often be more
successful, although the yield of large-leaf plants is reduced relatively more under drought. It is
essential to differentiate the single-plant yield from complex sward/grazing yields, and this needs to
be investigated. Further suggested areas of research could be:
•
to validate the DNA markers found and analyse QTL – marker relationships,
•
to test back-crosses of the hybrids used here under duress in the field,
•
investigating predicted responses to selection (narrow sense heritability of desirable traits from
parents to offspring) and calculation of expected genetic gain in a new population,
•
index selection (combining several weighted traits simultaneously),
•
how to use molecular markers and QTLs found in selection programs (MAS),
•
to elucidate the genetic regulation of levels of Q by the MYB complex of genes by means of
candidate gene technology,
•
to combine HPLC profiling techniques with transcriptome data, quantitative genetics, and QTL
analysis to thoroughly investigate the intricacies of the flavonoid biosynthetic pathway in white
clover
•
to examine the inheritance of Q and biomass productivity from a parental to a progeny
generation by means of quantitative molecular genetics, and
•
to investigate the likelihood of developing forages with high levels of Q-mediated abiotic stress
resistance and high plant productivity by using a suitable new population derived from the best
genotypes (ideotypes) crossed with elite cultivars.
Outcomes of these areas of research can strongly improve targets and precision in the cultivar
selection process. Combined selection for increased flavonol and stolon density expression may
result in the development of new white clover cultivars with increased potential for herbage
production and persistence in water-limited environments. The functions of flavonoids as
175
developmental regulators are very complex (Kuhn et al. 2011; Lewis et al. 2011) and need to be
investigated further. To ensure the wide applicability of these results they would have to be
replicated with other species as well, such as luzerne (Medicago truncatula) as legume model plant
or other related legumes in the Trifoliae tribe, and polyploid forage grasses such as allohexaploid tall
fescue (Lolium arundinaceum Schreb.).
The focus on QTL analysis for the two combined traits Q glycosides and DM production at more
different experimental sites based on the data collected from the F1 mapping population could
theoretically further elucidate the role and regulation of several transcription factors involved in
abiotic stress response pathways with emphasis on expression of MYB genes (Appendix D) related to
both DM and Q glycosides production (Ashraf 2010). It would be desirable to detect QTLs showing
large effects on DM production, overlapping with those for persistence relating to Q glycosides, and
explaining most of the total phenotypic variance for DM and Q glycosides. Implications of what this
would mean for selection methods in plant breeding would need to be investigated in markerassisted selection (MAS) experiments at a later stage. The results from the experiments in a full
sibling population described in this thesis provide the groundwork for further research to identify
quantitative trait loci (QTL) for key traits in other plant populations (Brummer and Casler 2009).
As there is always a UV-B component in plants growing outdoors, the process of priming or
hardening needs to be considered. Thus, prior exposure to an abiotic stress factor can render a plant
more resistant to future stress exposure (Bruce et al. 2007). Interactions between responses to
different stress types can often be synergistic; they boost each other to strengthen the plant by
priming the defence mechanisms (Stratmann 2003; Roberts and Paul 2006). The question arises if in
these experiments, one stress component (UV-radiation) triggered priming against another stress
factor (water deficit) (Conrath et al. 2006; Beckers and Conrath 2007; Bruce et al. 2007). The priming
of induced plant defence responses or stress anticipation has not been investigated in this
experiment, and could be integrated in future research. This could be combined with experiments to
investigate other abiotic stress-induced compounds to further elucidate the physiological and
biochemical processes behind stress resistance related to the compound Q glycosides. Genetically
primed plants could be useful for breeder and farmers, as priming can also increase the resistance of
field-grown crops to biotic and abiotic stress factors (Beckers and Conrath 2007).
Concluding from recent literature, the main goal for the future of drought-resistance breeding would
be enhancing yield stability of crops under drought stress in field conditions. As drought stress
seldom occurs alone, combinations of occurrence of abiotic stresses and plant responses to these
conditions could focus of future at the development of novel crops with enhanced resistance against
176
environmental stress factors in the field. Future challenges need to address adjustment and
coordination of these multiple protective mechanisms.
177
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Appendix A
Terms of reference
A.1
Stress tolerance in forage legumes - white clover quercetin genetics
investigation (Hofmann & Barrett)
Biological productivity and resilience of pastoral systems underpins primary industry in New Zealand.
Positive adaptive plant response to stress, including abiotic stress induced in both normative and
harsh climatic conditions, is a key component of the consistent performance required from grazed
forage as the primary energy source for profitable farming.
Recent white clover research demonstrates a relationship between levels of the secondary
metabolite Quercetin (Q) and 1/ plant adaptive response to abiotic stress and 2/ plant productivity
under normative conditions. High levels of Q are associated with high levels of abiotic stress
tolerance, and low levels of productivity. It is important to realise that at this point, it is unknown if
this observed relationship is causal or associative, an unintended and deleterious by-product of
natural and artificial selection processes. If it is associative then plant breeders may be able to
reverse these relationships and develop forages with high levels of abiotic stress tolerance and high
plant productivity.
We propose work to elucidate the genetic regulation of levels of Q, and tests to identify causal versus
associative relationships of Q with plant productivity and adaptive stress response. This work will use
plant families with known structure to observe the inheritance of Q and productivity from a parental
to a progeny generation, and DNA markers to define regions of the white clover genome regulating Q
and productivity.
Multiple crosses among possible parents are available for consideration, and we propose a pilot
experiment using two criteria to identify a full-sibling family that is most suited. The first criterion is
levels of genetic variation for Q and productivity, with high levels preferred. The second criterion will
be evidence (or lack thereof) for breakage of the negative correlation between Q and productivity,
with breakage being preferred.
The selected family will be grown in replicated experiments, preferably under standard and inductive
conditions, where plant performance and Q levels will be monitored. Concurrently, a medium
resolution genetic map of the population will be built, such that variation among the progeny
performance and Q levels data may be used in tandem with the genetic map information to identify
202
regions of the genome (called quantitative trait loci - QTLs) which exert genetic influence on trait
performance related to productivity and Q.
203
Appendix B
Evapotranspiration
Estimates of the rate and amount of ET are used for irrigation projects, managing water quality, in
litigation and in negotiations of contracts and treaties involving water, to predict melt water yields
from mountain watersheds or to plan for forest fire prevention (Ward and Trimble 2004).
There are several methods which can be used to estimate ET: the energy balance or radiation
method, the mass transfer method, and the soil water balance method. In the past, calculation of ET
with the soil water balance method in the field was often quite unreliable because seepage, surface
runoff and other field variables could not be measured properly. Separation of transpiration (T) and
soil evaporation (E) was difficult (Briggs and Shanz 1913a, 1913b, 1914; de Wit 1958). Evaporation
needed to be estimated or determined separately, so that it could be subtracted from total water
use (ET) (Hanks et al. 1967). Lysimeters, big containers which were put level with the soil surface and
from which the drainage water could be captured and even could be weighted (Roth et al. 1994),
were used to solve the water balance equation by separating the seepage (or drainage or deep
percolation) from ET. Surface runoff and lateral flow do not occur due to the restricted space inside
lysimeters. This means that if all fluxes other than ET are known, ET can be deduced from the change
in soil water storage (∆s) over the time period.
Water balance equation
P = E + I + T + D + R + ∆s (mm)
P: precipitation (rain, snow, condensation, irrigation)
E: evaporation (loss of water from the soil surface to the atmosphere)
I: interception (evaporation of precipitation on leaves)
T: transpiration (loss of water by the plants to the atmosphere via stomata)
D: subsoil drainage
R: runoff
∆s: change in soil water storage
Relatively large amounts of energy are needed to evaporate water. Therefore, ET is limited by the
amount of energy available, and can be estimated by applying the energy balance principle. The
204
terms in the energy balance equation can either be positive or negative. Advection, the transport of
energy horizontally, is ignored. If all other components are known, latent heat flux representing the
evapotranspiration fraction can be calculated.
Energy balance equation
Rn – G – λET – H = 0
Rn: net radiation
H: sensible heat
G: soil heat flux
λET: latent heat flux
B.1
Potential versus reference evapotranspiration
Penman (1948) explained the potential evapotranspiration rate (PET) as the evaporation from a
green plant stand, well supplied with water and cut short, similar to a trimmed lawn. Actual
transpiration is determined by PET and soil water content. Leaves are the main plant parts that cause
water loss. The actual transpiration rate per soil surface unit will increase with the leaf area per unit
soil area, the leaf area index (LAI). Water loss also depends on the amount of received radiation.
When the soil is completely covered by the leaf canopy, and the LAI and the radiation inception are
maximal, the transpiration rate per unit soil surface cannot be significantly increased. PET is a
combination of potential evaporation from the soil and transpiration from the crop’s green leaves at
a maximal rate, dependent on meteorological circumstances like initial soil moist content, radiation,
temperature and wind speed (Ehlers and Goss 2003; Kirkham 2005).
The Penman equation for daily evaporation from a free water surface is given by:
LE p =
(Δ/γ )(R Ν − G ) + LEa
Δ/γ + 1
Penman evaporation equation
L
latent heat of vaporization (J g-1)
Ep
potential evaporation rate (g cm-2 day-1)
∆
slope of the function of saturated vapour pressure versus temperature (mbar ºC-1)
205
γ
psychrometric constant (mbar ºC-1)
RΝ
energy flux of net radiation (J cm-2 day-1)
G
energy flux of heating the soil (J cm-2 day-1)
Ea
ventilation-humidity term = 0.263(0.5 + 0.00622 U)(es -e) (mm day-1)
U is the daily wind speed; es –e is the saturation deficit of the air during the day, where es is the
saturated vapour pressure of the air, and e the actual vapour pressure (mbar).
To calculate potential evaporation with the Penman equation, the following parameters need to be
measured in the field: net radiation, air temperature, wind run (daily wind speed) and relative
humidity, while normally soil heating (G) is ignored. For the calculation of PET, the ventilationhumidity term Ea is changed into 0.263(1 + 0.00622 U)(es -e).
Although Penman combined both aspects of evaporation, the required energy source and a
mechanism to remove vapour away from the evaporating surface, the equation was empirical for
wind to account for removal of the water vapour in order to allow ET to continue. Penman did not
have much theoretical basis for this presupposition. The equation did not include an aerodynamic
resistance function to quantify boundary layer resistance, and it did not have surface resistance to
vapour transfer to account for stomatal resistance. Several researchers have proposed equations to
remedy these omissions. The most often cited equation is that of Monteith (Jensen et al. 1990;
Ventura et al. 1999). Monteith (1981) introduced an equation for evapotranspiration, taking into
account the mechanism of stomatal control of water loss integrated into a canopy resistance. This
was a modification of the Penman equation, and is called the general Penman-Monteith equation
(Hillel 1998; Gieske 2003).
LE =
(es − ea )
ra
 r 
Δ + γ1 + s 
 ra 
∆ ( Rn − G ) + ρ a c p
Penman-Monteith equation
LE
latent heat required for evapotranspiration
Rn
net radiation
206
G
soil heat flux
(es − ea ) vapour pressure deficit of the air = saturation deficit of water vapour in the air
ρa
mean air density at constant pressure
cp
specific heat of the air
∆
represents the slope of the saturation vapour pressure temperature relationship
γ
psychrometric constant
rs
(bulk) surface resistance
ra
aerodynamic resistance
Over time, a large number of reference evapotranspiration (ET0) estimation methods were developed
and enhanced. These methods often had locally adapted or modified parameters, and therefore
were not suitable for global utilisation. In an extensive study undertaken with backing by the
Committee on Irrigation Water Requirements of the American Society of Civil Engineers (ASCE),
Jensen (1990) reviewed 20 different methods to estimate ET0, using very detailed procedures. A
parallel evaluation of ET0 methods by a consortium of European research institutes, using data from
different lysimeter studies, was carried out under commission of the European Community (Choisnel
et al. 1992). Overestimation of the Modified Penman method was confirmed by both studies, as well
as significant variability in the methods depending on climatic conditions (Smith et al. 1996; Allen et
al. 1998). The performance of various ET0 methods indicates the excellent performance of the
Penman-Monteith method in both arid and humid climates, which was credibly shown in both the
ASCE and the European study.
The adapted Penman-Monteith equation was accepted by the Food and Agricultural Organization of
the United Nations (FAO) as the preferred method to calculate water requirements for irrigated
crops (Smith et al. 1996). The term reference crop evapotranspiration (ET0) represents the
evaporative power of the atmosphere under standardised conditions. This method is now widely
accepted and the use of the earlier terms potential ET and actual ET is strongly discouraged because
of ambiguities in the definitions (Allen et al. 1998).
207
Performance of various ET0 methods (after Jensen(1990)) (Smith et al. 1996)
Table removed because of copy-right issues.
The original image/graph/figure can be found in: Smith et al. 1996.
ET0 =
900
u2 (es − ea )
T + 273
Δ + γ(1 + 0.34u2 )
0.408∆( Rn − G ) + γ
FAO-56 Penman-Monteith equation
ET0
reference evapotranspiration (mm d-1)
Rn
net radiation at the crop surface (MJ m-2 d-1)
G
soil heat flux density
T
air temperature at 2 m height (°C)
u2
wind speed at 2 m height (m s-1)
es
saturation vapour pressure (kPa)
ea
actual vapour pressure (kPa)
(es-ea) saturation vapour pressure deficit (kPa)
Δ
208
slope vapour pressure curve (kPa °C-1)
γ
psychrometric constant (kPa °C-1)
900
conversion factor
Several variations of the FAO-56 Penman-Monteith equation exist depending on the type of
reference crop considered (grass or alfalfa) (Allen et al. 2000; Walter et al. 2000; Allen et al. 2005).
B.2
FAO crop evapotranspiration concepts
Table removed because of copy-right issues.
The original image/graph/figure can be found in: Gieske 2003.
Parameters needed for calculation of ET0 (Gieske 2003).
Reference ET0, crop evapotranspiration under standard (ETc) and non-standard conditions (ETc,adj) can
be calculated in three steps (after Allen (1998)):
1. Reference crop evapotranspiration (ET0) is calculated based on climate data plus a well-watered
grass reference crop, with the FAO-56 Penman-Monteith method. Monthly and ten-daily input data
are included for maximum and minimum temperature, humidity, radiation, and wind-speed.
2. Crop water requirements (ETc) for a specific crop under standard conditions are determined from
ET0 and estimates of crop evaporation rates, expressed as crop coefficients (Kc), based on a wellwatered crop and optimal agronomic conditions: disease-free, well-fertilised, grown in large fields.
209
Crop evapotranspiration
ETc = ET0 x Kc (mm d-1)
Kc : crop coefficient (dimensionless)
The crop coefficient varies during the growing period. Factors determining Kc are shown:
•
crop type: annuals or perennials (rangeland, deciduous trees and shrubs, evergreens)
•
climate, irrigation
•
soil evaporation
•
crop growth stages (with main factors affecting Kc)
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Allen et al. 1998.
Typical ranges expected in Kc for the four growth stages (Allen et al. 1998).
Single crop coefficients for the initial (Kc ini), mid-season (Kc mid), and end of late season (Kc end) growth
stages are typically selected from crop tables, and adjusted to reflect wetting frequency and local
climatic conditions, and for dual crop coefficients, surface evaporation (Allen et al. 1998; Allen et al.
2005).
3. Crop evapotranspiration under non-standard conditions leads to an ETc adjusted for water stress,
environmental and management conditions (ETc,adj) with coefficients Kc,adj and Ks .
210
Adjusted crop evapotranspiration
ETc,adj = ET0 x Kc,adj x Ks (mm d-1)
Ks: water stress factor, describes the effect of water stress on crop transpiration
For soil water limiting conditions, Ks < 1; where there is no soil water stress, Ks = 1.
211
Appendix C
Silt loam with gypsum and wetting agent
C.1
Potting mix
1) 75 % Wakanui silt loam obtained locally from a paddock at the Horticultural Research site
2) 25 % mortar sand 0-3 mm
3) 2 g / l Osmocote Exact Standard 3-4 months., N-P-K 16-5.0-9.2 + 1.8 mg trace elements +
1.8% Magnesium:
16%
Nitrogen Total (N)
7.1% Nitrate nitrogen (NO3-N)
8.9% Ammoniacal nitrogen (NH4-N)
5%
Phosphorus Pentoxide (P2O5)
5% Water soluble
9.2% Potassium oxide (K2O)
9.2% Water soluble
1.8% Magnesium oxide (MgO)
1.3% Water soluble
0.40% Iron (Fe)
0.20% Chelated by EDTA
0.06% Manganese (Mn)
0.02% Boron (B)
0.02% Water soluble
0.047% Copper (Cu)
0.03% Water soluble
0.02% Molybdenum (Mo)
0.014% Water soluble
0.015% Zinc (Zn)
4) 1 g / l Hydraflo wetting agent
5) 30 g / l gypsum (calcium sulphate, CaSO4)
212
C.2
Soil texture triangle
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Regents of the University of Minnesota
http://www.swac.umn.edu/classes/soil2125/img/txttrgl.jpg.
USDA Soil Texture Triangle. Particle diameters: clay: <0.002mm, silt: 0.002mm to .05mm, sand:
0.05mm to 2mm
C.3
Gypsum addition to silt loam
Gypsum (calcium sulphate di-hydrate, CaSO4 - 2H2O, pH neutral) can reduce surface sealing, and
improves water infiltration and reduces erosion in the field. In lab studies, gypsum released
electrolytes that keep clay particles clumped together, which reduced crusting (Hank Becker
"Stopping erosion with gypsum and PAM - polyacrylamide", Agricultural Research, Sept 1997).
Application of powdered gypsum on a Hoban silt loam (fine-silty, mixed, thermic Ustollic Calciorthid)
at Pecos, TX, increased infiltration amount by an average of 38% in tilled or compacted soil, where
the surfaces had been disturbed (Baumhardt RL, Wendt C, and Moore J, Infiltration in response to
water quality, tillage, and gypsum, Soil Science Society of America Journal 0361-5995 vol: 56 (1) 1992
p:261 -266).
213
C.4
Hydraflo addition to the soil mix
C.4.1 (Scotts Australia Pty Ltd © 2003)Wetting agent benefits
•
Improves water use and fertiliser efficiency
•
Provides an even wetting front in the soil profile regardless of irrigation frequency and times
•
Amends water repellent particles in the root zone and thatch
•
Improved performance of fertilisers and pesticides
•
Aids drainage and prevents water logging
•
Improves crop uniformity, no dry spots
•
Long lasting - 8 months+ per application
•
Safe on plants
•
Increases plant uniformity
•
Improved plant growth & performance
•
Reduces hand watering requirements on hot spots
214
Appendix D
The flavonoid biochemical pathway
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Winkel-Shirley 2001b.
Schematic of the major branch pathways of flavonoid biosynthesis (Winkel-Shirley 2001b).
215
216
Appendix E
Metabolon formation
Figure removed because of copy-right issues.
The original image/graph/figure can be found in: Jorgensen et al. 2005.
Organization of the branched pathways of phenylpropanoid metabolism within separate individual
metabolons. Based on the model of Winkel, 2004 (Jorgensen et al. 2005).
217
Appendix F
Experimental designs
F.1
Design pilot experiment
Treatment structure and randomised layout of the genotypes in a rectangular lattice design. R(1, 2, 3, 4, 5):
replications.
Genotype
R1
R2
1
Tienshan 28-6
1
2
3
4
7
10
2
Kopu II 14-22
4
5
6
1
8
11
3
KxT F1 # 3
7
8
9
2
5
12
4
KxT F1 # 5
10
11
12
3
6
9
5
KxT F1 # 18
6
KxT F1 # 27
R3
R4
R5
7
KxT F1 # 30
6
8
12
1
4
9
1
6
10
8
KxT F1 # 42
2
9
10
3
7
12
2
7
11
9
KxT F1 # 50
3
4
11
5
8
10
3
5
8
10
KxT F1 # 52
1
5
7
2
6
11
4
9
12
11
KxT F1 # 60
12
KxT F1 # 62
218
F.2
Design phenotyping experiment
REP2
REP1
TxK81
KxT92
KxT10
TxK75
KxT21
TxK10
TxK18
TxK43
KxT3
TxK92
TxK82
KxT49
KxT53
TxK14
KxT10
TxK16
KxT18
KxT14
KxT93
TxK12
TxK56
KxT13
KxT3
TxK5
KxT85
KxT54
Tienshan
TxK91
TxK16
TxK80
KxT60
TxK14
KxT76
TxK58
TxK48
KxT69
KxT55
KxT70
TxK63
KxT34
TxK41
KxT16
TxK2
TxK3
KxT36
KxT23
Kopu II
TxK61
TxK59
TxK53
KxT66
KxT19
KxT73
TxK73
TxK6
TxK20
TxK12
TxK39
TxK93
KxT20
KxT66
TxK45
TxK26
KxT91
TxK73
KxT9
TxK66
TxK6
KxT85
KxT37
KxT68
KxT20
KxT37
KxT39
TxK70
TxK26
TxK23
TxK76
KxT80
TxK96
KxT82
KxT26
KxT14
TxK34
KxT49
KxT72
TxK7
TxK20
TxK57
TxK9
KxT84
TxK15
TxK84
KxT4
KxT48
TxK39
KxT13
KxT91
TxK19
TxK42
TxK57
KxT96
KxT84
TxK85
TxK90
KxT43
TxK50
KxT64
KxT2
KxT39
KxT42
TxK60
TxK19
KxT82
TxK54
KxT92
TxK55
TxK76
TxK81
TxK42
KxT53
TxK61
TxK54
Kopu II
TxK55
KxT57
KxT34
KxT56
KxT90
KxT81
KxT45
KxT23
TxK34
KxT81
KxT41
KxT12
TxK24
KxT78
KxT24
KxT5
KxT75
TxK13
TxK69
TxK40
KxT6
KxT46
KxT75
TxK88
TxK56
KxT78
KxT50
KxT63
TxK66
TxK46
TxK7
KxT72
TxK46
TxK64
TxK21
TxK43
KxT50
KxT7
KxT88
TxK23
KxT6
KxT43
TxK48
TxK92
TxK13
KxT24
KxT7
TxK84
TxK24
KxT15
KxT68
TxK45
KxT2
TxK60
KxT5
KxT40
KxT19
TxK50
TxK10
KxT73
TxK68
TxK4
TxK62
KxT69
KxT21
KxT46
TxK90
KxT76
TxK40
TxK3
KxT16
KxT88
KxT58
KxT55
TxK36
TxK21
TxK63
KxT12
TxK2
TxK62
TxK82
TxK96
TxK70
KxT26
TxK58
KxT54
KxT40
TxK85
TxK78
TxK91
KxT57
TxK80
TxK68
TxK69
KxT62
TxK78
TxK64
KxT36
TxK4
KxT93
KxT41
TxK41
TxK5
KxT4
TxK93
KxT56
TxK18
KxT45
KxT96
TxK37
KxT64
KxT15
Tienshan
TxK53
KxT90
KxT59
TxK72
KxT70
KxT18
TxK9
TxK37
TxK49
TxK15
KxT42
KxT61
KxT48
KxT59
KxT9
KxT58
KxT62
TxK36
TxK59
KxT61
TxK88
TxK75
TxK49
KxT60
KxT80
TxK72
KxT63
REP3
KxT61
KxT70
TxK24
KxT20
TxK39
TxK21
TxK15
TxK48
TxK90
TxK76
TxK85
TxK34
TxK37
TxK23
TxK63
KxT96
TxK4
KxT6
TxK2
KxT80
KxT18
TxK45
TxK60
TxK96
TxK50
KxT2
TxK54
KxT42
KxT63
KxT76
KxT39
TxK59
KxT84
TxK93
TxK41
TxK3
KxT40
TxK19
TxK6
Kopu II
KxT43
KxT66
KxT37
KxT54
TxK78
KxT23
KxT5
KxT3
TxK26
KxT21
TxK92
TxK5
KxT50
KxT48
KxT36
KxT53
KxT91
TxK58
KxT7
TxK43
KxT57
TxK84
TxK69
KxT69
TxK20
KxT14
KxT13
TxK68
TxK61
KxT15
KxT82
KxT73
KxT58
TxK42
KxT78
TxK64
Tienshan
KxT34
TxK73
KxT16
TxK75
TxK72
KxT46
KxT9
KxT24
KxT4
KxT90
TxK46
KxT26
KxT60
KxT55
KxT49
TxK14
KxT92
KxT85
KxT68
TxK57
TxK80
TxK66
KxT75
KxT59
TxK53
TxK81
KxT81
KxT19
TxK56
TxK70
TxK49
KxT72
KxT45
KxT62
TxK13
KxT93
KxT12
KxT41
TxK55
TxK9
TxK40
TxK12
KxT10
TxK62
KxT88
TxK91
KxT64
TxK16
TxK36
KxT56
TxK88
TxK7
TxK82
TxK18
TxK10
219
F.3
Design drought experiment
F.3.1 Plant material
The plant material for the drought experiment consisted of 32 F1 genotypes eight each from all four quadrants Q/DW
of the correlation matrix (Figure 8), outside a smaller circle. One KopuII, one Tienshan, and two lines of Zulfi Jahufer's
Australian clovers: C23810 and ATRC807 (these two had the highest quercetin levels) were added as checks.
Selection of F1 genotypes
Q/DM
1
2
3
4
5
6
7
8
H/H
TxK88
TxK54
TxK92
KxT14
KxT58
KxT46
TxK26
KxT42
L/L
TxK16
TxK40
TxK04
TxK43
TxK07
TxK81
KxT78
KxT36
L/H
TxK96
KxT26
TxK85
KxT85
KxT24
TxK90
KxT96
TxK55
H/L
TxK42
KxT07
KxT03
TxK14
KxT05
KxT73
TxK20
KxT72
220
Genotypes in the drought experiment in a spatial RCBD.
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
G18
G19
G20
G21
G22
G23
G24
G25
G26
G27
G28
G29
G30
G31
G32
G33
G34
G35
TxK88
TxK54
TxK92
KxT14
KxT58
KxT46
TxK26
KxT42
TxK16
TxK40
TxK04
TxK43
TxK07
TxK81
KxT78
KxT36
TxK96
KxT26
TxK85
KxT85
KxT24
TxK90
KxT96
TxK55
TxK42
KxT07
KxT03
TxK14
KxT05
KxT73
TxK20
KxT72
Kopu_II
Tienshan
Zulfi_A
G36
Zulfi_B
Rep_1_D
Dry
Rep_2_D
Dry
Rep_3_M
Moist
G12
G32
G22
G29
G17
G1
G9
G11
G21
G10
G7
G31
G30
G2
G26
G36
G34
G5
G18
G27
G16
G13
G35
G28
G25
G4
G15
G6
G8
G33
G24
G14
G23
G3
G20
G19
G14
G27
G11
G25
G12
G32
G13
G20
G30
G29
G19
G2
G16
G1
G26
G21
G8
G5
G33
G28
G23
G36
G6
G3
G24
G4
G35
G18
G17
G9
G31
G10
G34
G22
G15
G7
G18
G35
G9
G24
G10
G23
G12
G15
G8
G28
G33
G5
G14
G16
G30
G2
G19
G29
G20
G1
G36
G11
G32
G25
G21
G34
G17
G22
G7
G4
G6
G26
G31
G3
G13
G27
Rep_1_M
Moist
Rep_2_M
Moist
Rep_3_D
Dry
G29
G24
G31
G26
G17
G1
G3
G16
G19
G14
G21
G13
G9
G15
G32
G25
G10
G5
G8
G20
G22
G33
G30
G34
G23
G11
G2
G28
G6
G12
G35
G27
G36
G4
G18
G7
G34
G15
G7
G22
G31
G8
G2
G25
G10
G12
G19
G36
G28
G14
G23
G33
G20
G26
G6
G11
G27
G3
G24
G32
G13
G4
G21
G35
G30
G29
G1
G18
G5
G17
G9
G16
G32
G3
G1
G21
G30
G12
G34
G13
G19
G26
G28
G4
G7
G9
G35
G23
G33
G20
G29
G2
G17
G11
G8
G5
G10
G6
G31
G27
G14
G25
G22
G16
G24
G15
G18
G36
221
F.4
Design field experiment
Row - column experimental design with repeated checks
(TRIAL SITE 1) Ashley Dene farm
Number of mapping population genotypes = 190
Number of replicates = 3 Number of Rows per Replicate = 14
Number of columns = 16
X : Buffer row of plants
Repeated clonal checks
191 : 9 clones per replicate
192 : 9 clones per replicate
193 : 9 clones per replicate
194 : 7 clones per replicate
Col
Row
Replicate 1
Replicate 2
Replicate 3
222
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
X
20
64
45
192
119
74
164
193
174
36
188
194
154
14
192
88
167
70
2
76
29
18
191
153
106
142
130
193
113
192
163
1
58
190
178
191
54
150
27
149
182
193
X
X
191
19
56
139
11
108
186
23
172
189
24
15
169
2
54
44
183
145
192
52
124
122
126
43
55
19
5
47
68
74
91
70
194
84
176
41
86
115
192
93
152
132
X
X
115
42
193
159
124
191
18
75
163
62
127
177
191
12
14
194
137
102
105
155
30
96
113
123
129
194
109
132
193
168
37
81
51
77
88
126
194
110
104
108
3
63
X
X
110
121
130
155
69
99
111
25
55
191
68
32
178
179
170
39
59
162
27
159
193
84
169
36
98
103
185
67
17
96
85
192
20
125
102
120
128
153
134
33
191
180
X
X
173
30
61
193
82
65
193
170
71
118
27
161
166
194
168
28
163
191
34
99
42
64
191
184
186
165
86
192
92
4
139
43
46
12
194
119
175
39
25
167
95
31
X
X
194
80
190
136
152
181
3
171
193
88
125
192
52
146
193
33
61
117
151
194
74
92
172
63
38
191
160
21
191
56
36
155
183
127
71
50
48
191
78
9
112
193
X
X
66
78
49
72
98
87
160
120
132
150
44
138
168
5
90
23
180
13
174
107
9
110
60
193
58
17
136
6
10
135
32
16
193
164
34
80
76
38
114
170
62
145
X
X
117
100
105
194
26
116
192
106
33
1
191
123
4
86
24
91
193
77
138
65
20
194
182
49
12
114
192
156
121
89
192
174
169
148
160
101
193
165
8
193
94
75
X
X
142
37
58
7
34
90
16
176
157
47
31
182
137
193
131
26
150
40
192
94
81
166
41
144
191
139
154
32
147
22
133
173
186
191
107
162
72
156
14
122
29
192
X
X
192
91
95
148
193
57
67
191
76
22
63
192
134
131
191
178
48
45
46
100
62
120
173
152
8
108
157
191
194
7
129
179
117
144
79
23
98
52
193
177
151
65
X
X
39
93
194
17
53
29
149
162
143
129
89
48
114
112
125
164
35
112
128
193
4
79
161
57
193
121
22
187
159
21
66
192
109
83
15
191
140
131
18
171
6
45
X
X
101
184
141
84
113
107
193
35
46
191
81
145
6
126
111
192
181
89
78
85
73
191
175
176
3
177
83
190
188
185
97
49
146
192
19
142
187
30
26
99
189
194
X
X
128
59
8
54
73
50
97
156
144
135
109
92
70
191
93
127
104
193
95
189
16
179
171
147
69
192
101
116
192
166
191
28
47
82
60
181
194
11
87
193
123
130
X
X
193
60
194
104
183
43
192
40
140
165
38
185
85
180
75
10
68
11
51
192
66
146
7
149
134
141
71
194
118
73
35
103
191
138
116
55
24
157
13
105
64
158
X
X
151
167
102
9
192
83
41
28
187
13
192
51
147
103
158
53
192
118
135
72
25
194
133
15
119
50
37
115
2
5
61
161
42
100
143
137
40
192
136
44
69
67
X
X
133
21
192
153
122
10
194
96
77
175
79
94
158
191
194
140
148
80
193
188
56
143
97
31
191
82
1
87
124
194
90
53
106
193
59
57
141
111
172
154
184
191
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Row - column experimental design with repeated checks
(TRIAL SITE 2) AgResearch Lincoln Farm
Number of mapping population genotypes = 190
Number of replicates = 3 Number of Rows per Replicate = 14
Number of columns = 16
X : Buffer row of plants
Repeated clonal checks
191 : 9 clones per replicate
192 : 9 clones per replicate
193 : 9 clones per replicate
194 : 7 clones per replicate
Col
Row
Replicate 1
Replicate 2
Replicate 3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
X
39
59
170
194
159
162
185
193
67
84
169
98
103
191
41
190
58
54
150
27
113
192
163
1
178
67
149
193
117
100
194
105
26
116
192
106
33
1
191
123
4
86
X
X
10
68
75
51
70
11
71
7
66
146
192
134
141
149
192
84
115
86
194
119
68
74
91
70
191
182
93
152
142
37
58
7
34
90
16
176
157
47
31
182
137
193
X
X
88
192
173
2
76
192
130
26
29
18
142
106
193
153
126
77
51
176
110
104
193
168
37
81
88
128
108
3
192
167
102
9
192
83
41
28
192
187
13
51
147
103
X
X
167
181
111
78
85
89
83
190
73
191
175
3
177
176
120
125
20
192
153
134
17
96
85
194
102
132
33
95
110
121
130
155
69
99
111
25
55
141
68
191
178
179
X
X
193
163
168
194
99
36
86
192
42
64
164
186
165
192
194
12
46
175
39
25
92
4
139
43
56
63
167
191
173
30
193
61
82
65
193
170
71
193
27
161
166
194
X
X
33
61
28
151
34
117
160
21
74
92
172
38
184
63
50
127
183
48
191
78
71
191
36
155
192
180
9
112
66
80
190
136
152
181
3
171
98
118
125
138
52
146
X
X
178
48
191
46
100
45
157
191
62
120
8
192
108
152
80
193
164
76
38
114
10
135
32
16
34
31
170
62
194
78
49
72
191
87
160
120
132
150
44
192
168
5
X
X
193
35
125
128
193
112
22
187
4
79
161
27
121
57
101
148
169
193
165
8
121
89
194
174
160
193
179
94
101
184
88
84
113
107
193
35
46
191
81
145
6
126
X
X
44
183
54
93
52
145
5
47
193
122
126
55
19
194
162
186
116
72
156
14
147
22
133
173
107
145
122
192
128
59
192
54
73
50
97
156
144
135
109
92
70
191
X
X
127
104
192
95
189
194
101
116
16
179
69
191
171
147
23
144
192
98
52
193
29
7
129
193
79
75
177
151
20
64
45
8
191
74
164
174
193
36
188
194
154
14
X
X
53
137
14
105
155
102
109
191
30
96
113
129
43
123
191
83
109
140
131
18
159
191
66
21
15
192
171
6
191
19
56
139
11
108
186
23
172
189
24
15
169
2
X
X
194
132
158
135
72
118
37
115
25
193
133
119
191
15
142
117
146
187
194
26
188
185
97
49
19
65
99
189
133
21
192
153
122
10
194
96
77
175
79
94
158
192
X
X
140
148
1
193
188
80
194
87
56
143
97
50
82
31
181
82
47
30
11
87
118
166
28
194
60
45
123
193
151
91
95
148
193
57
67
32
76
192
63
22
134
131
X
X
23
180
90
174
107
13
136
6
9
110
60
58
17
193
55
138
191
24
157
13
192
73
35
103
184
191
105
64
115
60
149
104
183
43
191
40
140
165
38
185
85
180
X
X
91
191
24
138
65
77
192
156
20
194
182
12
114
49
137
100
42
40
111
136
2
5
61
161
143
130
44
69
193
42
119
159
124
89
18
75
163
62
127
177
194
12
X
X
124
150
131
40
192
94
154
32
81
166
41
191
139
144
193
57
106
141
192
172
124
90
191
53
59
158
154
194
39
93
194
17
53
29
193
162
143
129
191
48
114
112
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
223
Appendix G
Soil fertility analysis reports
225
Appendix H
Crossing genotypes
The author assessing white clover plants suitable for crossing in a bee-free tunnel house.
226
H.1
Introduction
Crosses of F1 ideotypes (ideal genotypes) for traits selected from the drought experiment (Chapter 5) to
suitable elite parents were created in the 2010/2011 summer growing season, to produce a stock of seed
for later research.
H.2
Materials & methods
H.2.1 Plant material
The plant material used was a selection suitable ideotypes from the 190 full-sibling F1 population as
described in paragraph 3.2.1, Chapter Methodology. A selection was made from the genotypes in the
drought experiment (Chapter 5) showing high expression for the percent change traits ∆%Q glycosides
(Figure 5.6, Group 5) and ∆%DM (Figure 5.6, Group 4). The selected individuals in group 5 were TxK88,
KxT73 and TxK20. The selected individuals in group 4 were KxT96, TxK90 and TxK92. Stolon cuttings were
planted in 5L pots and maintained for a month from 3 December 2010. Elite white clover cultivars were
chosen on the basis of availability at the time of flowering, and included the cross Kopu II × Barblanca,
Bounty, Tribute, Trophy and Crusader (Table 1). The plants were grown from seeds in August 2010 in roottrainers.
Table 1.
White clover genotypes for crossing.
Name
Kopu II × Tienshan # 73, 96
Tienshan × Kopu II # 20, 88, 90, 92
Type
F1 hybrids, clones
F1 hybrids, clones
Kopu II × Barblanca
Crusader
Tribute
Trophy
Bounty
Tienshan
Breeding line, genotype
Elite cultivar, genotype
Elite cultivar, genotype
Elite cultivar, genotype
Elite cultivar, genotype
Ecotype, clone
Accession number
C20133 × C8749
C8749 × C20133
(Maternal × Paternal)
C25638
C20132
C21905
C23513
C19737
C8749
Accession numbers were from AgResearch’s Margot Forde Forage Germplasm Centre in Palmerston North,
New Zealand.
The plants were transported to the bee-free tunnel house at AgResearch farm, Lincoln, NZ to start
flowering. After two weeks enough flowers appeared to start crossing.
H.2.2 Crossing method
Half-sibs were produced by using one or two F1 clones (depending on the number available) and two elite
cultivars in one cage (Figure 1). This is in contrast to pair-crossed full-siblings, which is suitable to produce a
mapping population, but not to study the segregation of traits or genes to the next generation. The ecotype
parent Tienshan was also crossed with Crusader and Kopu II × Barblanca. Bumble bees were caught on
lavender plants away from white clover volunteers every week and put into the cages (Figure 3). The
227
crossing started on 23 December 2010 and every week the F1 plants were replaced with flowering ones
from the bee-free tunnel house (Figure 2) until 17 January 2011. The pollinated plants were placed in the
same tunnel to let the seeds ripen. Flowers that were not pollinated or that appeared after crossing were
removed to prevent accidental pollination.
228
Keith Widdup of AgResearch Lincoln opening a crossing cage to let bumble bees in.
229
White clover plants flowering in a bee-free tunnel house.
230
White clover plants in the crossing cage. In the background luzerne can be seen.
H.3
Results
After ripening for a month on the plants the seed-heads were harvested and dried for two weeks after
which the seeds were rubbed out, put in paper satchels and weighed. The results are presented in Table 2.
231
Table 2. Crosses made with days in cage and seed weights summed per group of plants.
Cross
Bounty
Crusader
Kopu II x Barblanca
Tribute
Trophy
Days in
Cage
24
Seed
weight (g)
1.12
Days in
Cage
16
Seed
weight (g)
1.26
Days in
Cage
18
Seed
weight (g)
1.1
Days in
Cage
16
Seed
weight (g)
0.77
Days in
Cage
24
Seed
weight (g)
0.71
KxT96
Tienshan
TxK20
16
1.4
16
0.64
26
0.702
18
0.51
24
1.35
-
-
16
0.04
16
0.3
-
-
-
-
16
0.5
9
0.25
27
0.219
18
0.62
9
0.29
TxK88
18
1.83
22
0.115
18
2.71
24
1.43
16
1.11
TxK90
-
-
28
2.87
26
2.42
-
-
-
-
TxK92
16
1.92
16
3.52
24
3.119
24
2.34
16
2.67
KxT73
See next table for all crossing results.
H.4
Discussion
The F1 plants from the drought experiment that were subjected to applied drought and performed well
were crossed with elite cultivars that performed well under drought stress (Woodfield et al. 2001; Ayres et
al. 2007; Jahufer et al. 2009). From the F1 hybrids, TxK92 produced by far the most seeds per day in the
cage (0.14 g d-1), followed by TxK90 (0.10 g d-1) and TxK88 (0.07 g d-1). The seeds were transported to the
Margot Forde Germplasm Centre, AgResearch, Palmerston North, NZ, where they will be kept under
controlled conditions for later use by forage scientists.
232
All crossing results.
KxT73
Crossing
Cultivar
Genotype
Bounty
KxT73
Bounty
3
10/12/2010
9/01/2011
17/01/2011
8
0.18
KxT73
Bounty
3
2/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.42
KxT73
Crusader
5
3/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.63
KxT73
5
11/12/2010
23/12/2010
2/01/2011
9
0.63
31/12/2010
1
12/12/2010
31/12/2010
9/01/2011
9
0.51
2/01/2011
1
6/12/2010
23/12/2010
2/01/2011
17/01/2011
9
0.59
KxT73
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Tribute
4
9/12/2010
23/12/2010
2/01/2011
2/01/2011
9
0.34
KxT73
Tribute
4
4/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.43
KxT73
Trophy
2
5/12/2010
23/12/2010
2/01/2011
17/01/2011
9
0.26
KxT73
Trophy
2
1/12/2010
9/01/2011
17/01/2011
8
0.14
KxT73
Trophy
2
8/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.31
KxT96
Bounty
8
11/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.48
KxT96
Bounty
8
4/12/2010
23/12/2010
2/01/2011
9
0.92
KxT96
Crusader
10
1/12/2010
23/12/2010
2/01/2011
9
0.51
KxT96
10
2/12/2010
2/01/2011
9/01/2011
7
0.13
6
8/12/2010
7/01/2011
17/01/2011
10
0.072
6
7/12/2010
23/12/2010
2/01/2011
9
0.41
6
6/12/2010
2/01/2011
9/01/2011
7
0.22
Kxt96
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Kopu II x
Barblanca
Tribute
9
5/12/2010
31/12/2010
9/01/2011
9
0.29
KxT96
Tribute
9
9/12/2010
23/12/2010
KxT96
Trophy
7
12/12/2010
9/01/2011
F1
KxT73
KxT73
KxT96
KxT96
KxT96
Change Cultivar
Genotype
2/01/2011
Cage
Nr.
Plant potted
Start Date
3
7/12/2010
23/12/2010
First Refresh F1
Second
Refresh F1
Third Refresh
F1
2/01/2011
9/01/2011
31/12/2010
9/01/2011
9/01/2011
2/01/2011
2/01/2011
31/12/2010
2/01/2011
9/01/2011
2/01/2011
Days
in
Cage
9
Seed
weight
(g)
0.52
End Date
2/01/2011
9
0.22
17/01/2011
8
0.03
233
234
KxT96
Crossing
Cultivar
Genotype
Trophy
7
3/12/2010
23/12/2010
KxT96
Trophy
7
10/12/2010
2/01/2011
Tienshan
Crusader
11
8/12/2010
9/01/2011
17/01/2011
8
0.02
Tienshan
11
5/12/2010
9/01/2011
17/01/2011
8
0.02
14
10/12/2010
9/01/2011
17/01/2011
8
0.18
14
6/12/2010
9/01/2011
17/01/2011
8
0.12
TxK20
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Bounty
13
4/12/2010
23/12/2010
2/01/2011
9
0.4
TxK20
Bounty
13
6/12/2010
2/01/2011
9/01/2011
7
0.1
TxK20
15
5/12/2010
23/12/2010
2/01/2011
9
0.25
11
12/12/2010
31/12/2010
31/12/2010
9/01/2011
9
0.02
11
12/12/2010
31/12/2010
31/12/2010
9/01/2011
9
0.03
11
10/12/2010
23/12/2010
2/01/2011
9
0.169
TxK20
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Kopu II x
Barblanca
Tribute
14
1/12/2010
23/12/2010
2/01/2011
9
0.61
TxK20
Tribute
14
9/12/2010
31/12/2010
9/01/2011
9
0.01
TxK20
Trophy
12
3/12/2010
23/12/2010
2/01/2011
9
0.29
TxK88
Bounty
23
1/12/2010
31/12/2010
9/01/2011
9
0.67
TxK88
Bounty
23
12/12/2010
23/12/2010
2/01/2011
9
1.16
TxK88
Crusader
25
3/12/2010
9/01/2011
17/01/2011
8
0.04
TxK88
Crusader
25
6/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.07
TxK88
25
2/12/2010
2/01/2011
2/01/2011
9/01/2011
7
0.005
21
6/12/2010
31/12/2010
9/01/2011
9
1.38
21
7/12/2010
23/12/2010
2/01/2011
9
1.33
TxK88
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Tribute
24
4/12/2010
9/01/2011
17/01/2011
8
0.06
TxK88
Tribute
24
11/12/2010
2/01/2011
9/01/2011
7
0.51
TxK88
Tribute
24
9/12/2010
23/12/2010
2/01/2011
9
0.86
F1
Tienshan
Tienshan
TxK20
TxK20
TxK20
TxK88
TxK88
Change Cultivar
Genotype
7/01/2011
2/01/2011
Cage
Nr.
Plant potted
Start Date
First Refresh F1
Second
Refresh F1
Third Refresh
F1
2/01/2011
2/01/2011
2/01/2011
31/12/2010
31/12/2010
31/12/2010
9/01/2011
2/01/2011
2/01/2011
Days
in
Cage
9
Seed
weight
(g)
0.9
9/01/2011
7
0.42
End Date
TxK88
Crossing
Cultivar
Genotype
Trophy
TxK88
Trophy
22
5/12/2010
23/12/2010
TxK90
Crusader
27
11/12/2010
9/01/2011
TxK90
Crusader
TxK90
Crusader
TxK90
F1
Change Cultivar
Genotype
Cage
Nr.
Plant potted
Start Date
22
10/12/2010
2/01/2011
First Refresh F1
Second
Refresh F1
Third Refresh
F1
2/01/2011
9/01/2011
Seed
weight
(g)
0.2
2/01/2011
9
0.91
17/01/2011
8
0.36
27
1/12/2010
7/01/2011
9/01/2011
2
0.75
27
8/12/2010
31/12/2010
31/12/2010
9/01/2011
9
0.74
27
12/12/2010
31/12/2010
31/12/2010
9/01/2011
9
1.02
26
2/12/2010
31/12/2010
31/12/2010
9/01/2011
9
0.85
26
4/12/2010
23/12/2010
2/01/2011
9
1.34
26
9/12/2010
9/01/2011
17/01/2011
8
0.23
TxK92
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Kopu II x
Barblanca
Bounty
18
11/12/2010
2/01/2011
9/01/2011
7
0.73
TxK92
Bounty
18
2/12/2010
23/12/2010
2/01/2011
9
1.19
TxK92
Crusader
20
9/12/2010
2/01/2011
9/01/2011
7
1.94
TxK92
20
7/12/2010
23/12/2010
2/01/2011
9
1.58
16
12/12/2010
23/12/2010
2/01/2011
9
1.369
16
4/12/2010
9/01/2011
17/01/2011
8
0.94
16
3/12/2010
2/01/2011
9/01/2011
7
0.81
TxK92
Crusader
Kopu II x
Barblanca
Kopu II x
Barblanca
Kopu II x
Barblanca
Tribute
19
8/12/2010
9/01/2011
17/01/2011
8
0.44
TxK92
Tribute
19
5/12/2010
2/01/2011
9/01/2011
7
0.72
TxK92
Tribute
19
1/12/2010
23/12/2010
2/01/2011
9
1.18
Txk92
Trophy
2/01/2011
17
10/12/2010
2/01/2011
9/01/2011
7
1.62
TxK92
Trophy
31/12/2010
17
6/12/2010
23/12/2010
2/01/2011
9
1.05
TxK90
TxK90
TxK90
TxK92
TxK92
TxK92
2/01/2011
2/01/2011
9/01/2011
Days
in
Cage
7
End Date
9/01/2011
2/01/2011
2/01/2011
2/01/2011
9/01/2011
2/01/2011
2/01/2011
235
236
Appendix I
Genetic analysis
I.1
Selective DNA pooling, PCR and genotyping protocols
I.1.1 DNAzol Clover DNA extraction
Extract DNA from 132 white clover genotypes, including 2 parents (already done by technician)
I.1.2 Selective DNA pooling
SDP saves time and money by pooling DNA in bulks
132 white clover genotypes x 96 markers x 7 traits = 88704 tests!
SDP only looks at the 10% tail ends of the traits in question = 20% of the population
Genotypes in bulks of interest can be investigated separately
Determine 10 % high and low tail ends of the population for each trait:
Dry Weight Roots
DW Shoots
Total DW
R / S Ratio
Q
K
Q / K Ratio
Measure DNA concentration of each genotype
Balance DNA concentrations to a common dilution
Pool 14 genotype DNA in each bulk
Dilute bulk to PCR concentration
237
Dilute parent DNA to identical PCR concentration
Test microsatellite DNA markers from across the white clover genome against DNA pools or bulks
White Clover PCR recipe per rxn.
SSR Primer Cocktail Recipe
Reagent and Stock [ ] 1X
Water
3.89
Buffer (10X)
1
dNTPs (5 mM each)
0.4
Mg (50 mM)
0.5
Primer Cocktail
2
Colour (V) (10 uM)
0.15
Platinum Taq (5U/ul)
0.06
DNA (2 ng/ul)
2
I.1.3 Polymerase Chain Reaction (PCR) in thermal cycler
The PCR method used here is the one-tube, single-reaction nested PCR method.
Use 96 well PCR plate
7 bulks high / low = 14 DNA bulks
2 parent DNA
96 markers
Total: (14 + 2) * 96 = 1536 tests
1536 / 96 wells = 16 PCR plates
Add water, PCR buffer, dNTPs, Mg, colour, Platinum Taq
Add primer cocktail (reverse / forward)
Add bulk DNA
Run appropriate thermal cycler program
Denature with HiDi and LIZ in separate plate
238
I.1.4 Genetic analyser or genotyper
Generate genotyper 96-well-plate record
Run appropriate genotyper program using capillary electrophoresis
Check for possible PCR errors
Analyse genotyper results from electropherograms
Determine genotypes with high Q and high DW
Identify linkage between molecular markers and QTLs for the 7 traits mentioned earlier
Identify marker differences between parents
Identify marker differences among bulks
Look for markers that are polymorphic for traits in parents as well as bulks
Take most promising markers and test the individuals within bulks.
Identify potential DNA marker tags for Q and DM
HiDi Formamide is used to denature DNA samples prior to capillary electrophoresis and serves as an
injection solvent.
GeneScan 500-LIZ is a size standard used as an internal reference to size and correct for mobility
differences between capillaries. A size standard of 100-500 base-pairs is recommended for
microsatellite analyses.
The genotyper uses a linear flowable liquid POP-7 polymer as sieving matrix.
Sample loading is performed by electrokinetic injection.
GeneScan is used for fragment analysis.
Results will be analysed with Kruskal Wallis single marker tests.
I.1.5 Markers and primers
Criteria to choose these markers:
Informative in both sub-genomes of MP1 or BB
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Spaced evenly along chromosome - prefer less than 10 centiMorgan (cM) spacing
PCR products produce clean, easy to score electropherogram, clean peaks (prefer no stutter or peaks
1 basepair apart)
Mapped in Trifolium occidentale or MP2 or BB as well
The colour used is a 6-carboxy-fluorrescine (FAM) fluorescent dye label, which shows up on the
genotyper laser detection system as blue.
The primers are oligonucleotide primers, selected for even distribution along the white clover
genome.
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Appendix J
Published papers
“Multivariate associations of flavonoid and biomass accumulation in white clover (Trifolium
repens) under drought”
Ballizany, W. L., Hofmann, R. W., Jahufer, M. Z. Z., & Barrett, B. A. (2012).
Functional Plant Biology, 39(2), 167–177. doi:http://dx.doi.org/10.1071/FP11193
“Genotype × Environment analysis of flavonoid accumulation and morphology in white clover
under contrasting field conditions”
Ballizany, W. L., Hofmann, R. W., Jahufer, M. Z. Z., & Barrett, B. A. (2012).
Field Crops Research, 128, 156-166. doi:http://dx.doi.org/10.1016/j.fcr.2011.12.006
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