AN ABSTRACT OF THE THESIS OF
Chatchawan Jantasuriyarat for the degree of Master of Science in Crop Science
presented on September 5, 2001.
Title: Quantitative Trait Loci Tnfluencing Free-Threshing Habit and Spike
Morphology in Wheat (Triticum aestivum L.).
Abstract approved:
Redacted for Privacy
Oscar Riera-Lizarazu
Spike morphology characteristics and the free-threshing habit of wheat have
been extensively investigated because of their evolutionary significance and practical
importance. Several genetic systems that govern these traits have been reported. Some
studies suggest polygenic inheritance while others have identified major genes. This
study was conducted to identify and locate quantitative trait loci (QTL) affecting the
free-threshing habit and spike morphology characteristics in the International Triticeae
Mapping Initiative (ITtvll) recombinant inbred line (RIL) mapping population. The
ITMI population was planted in three environments in 1999 and 2000. The ITMT RILs
were evaluated for threshability and spike morphology characters. QTL analyses were
performed using simple and composite interval mapping procedures. Two QTLs, one
on chromosome lB and one on 4A, affecting spike length were identified. The QTL
on chromosome I B has not been described previously. One QTL controlling spikelet
number was also detected on chromosome 4A. This QTL coincided in location with
the QTL on chromosome 4A that affected spike length. One QTL controlling rachis
internode length, a measure of spike compactness, was detected on chromosome 6A.
The location of QTLs that affected spike length, spiketet number, and spike
compactness did not coincide with the location of major genes (Q,
C, Si, PpdI, and
Ppd2) known to affect these traits. Two QTLs, one on chromosome 2D and one on
4D, affecting threshability were identified. The QTL on chromosome 4D has not been
described previously. A QTL that affected glume tenacity was also detected on
chromosome 2D. Coincident QTLs on chromosome 2D that affected both threshability
and glume tenacity are believed to correspond to Tg, a gene for tenacious glumes. In
addition, an amplified fragment length polymorphism (AFLP) marker
(XorstP3 74 7207) that was putatively associated with Tg was identified using bulked
segregant analysis. A QTL on chromosome 5A affecting glume tenacity was also
identified. The QTL on chromosome 5A is believed to represent Q, a gene known to
affect rachis fragility and glume tenacity. Information on the number, position, and
effect of QTLs determining these traits and their associated molecular markers may
facilitate their manipulation for wheat improvement purposes.
Quantitative Trait Loci Influencing Free-Threshing Habit and Spike Morphology
in Wheat (Triticum aestivum L.)
by
Chatchawan Jantasuriyarat
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Presented September 5, 2001
Commencement June 2002
Master of Science thesis of Chatchawan Jantasuriyarat presented on September 5,
2001
Redacted for Privacy
Major Profesor, representing Crop Science
Redacted for Privacy
Head of Department of Crop and Soil Science
Redacted for Privacy
Dean of GradâteSchoo1
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Redacted for Privacy
Chatchawan Jantasuriyarat, Author
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation and gratitude to my major
advisor, Dr. Oscar Riera-Lizarazu, for his advice, encouragement and support
throughout my graduate studies at Oregon State University.
I would like to express my sincere appreciation to Dr. Christopher Mundt, Dr.
M. Isabel Vales, Dr. Patrick M. Hayes, and Dr. Gary P. Klinkhammer for serving on
my graduate committee. Thanks are also extended to Elizabeth Yoon, Christy J.
Watson, and Dan Coyle for their time, their advice and their assistance.
I would like to thank the Development and Promotion of Science and
Technology Program (DPST), the Royal Thai Government and Kasetsart University
for their financial support for the first two and one-half years of my graduate studies,
and thanks again to my major advisor, Dr. Oscar Riera-Lizarazu for the supplemented
financial support during my second year and the financial support during my last six
months of my graduate studies.
Special thanks are extended to my fellow graduate students for their friendship
and help, and to my sister, Dr. Sureeporn Katengam, for her advice and support.
Very special thanks go to my mother and my grandmother for their love,
understanding and unconditional support for all my life.
CONTRIBUTION OF AUTHORS
Dr. Oscar Riera-Lizarazu and Dr. M. Isabel Vales proposed the subject of this
research and were involved in all genetic, statistical analysis, and writing of each
manuscript. Elizabeth Yoon was involved in phenotypic data gathering and provided
lab support and useful discussion. Christy J. W. Watson was involved in development
of the AFLP procedures used in this study and also provided useful discussion.
TABLE OF CONTENTS
Page
Chapter 1
Introduction.....................................................................................................................
1
GENETIC MARKERS AND GENETIC LINKAGE
MAPCONSTRUCTION ............................................................................... 2
QUANTITATIVE TRAIT LOCUS ANALYSIS .......................................... 6
QTL STUDIES USING THE ITMT WHEAT
MAPPING POPULATION ............................................................................ 8
OBJECTIVES OF THIS STUDY .................................................................. 9
Chapter 2
Quantitative Trait Loci Influencing Spike Morphology
in Wheat (Triticum aestivum L.) ................................................................................... 11
ABSTRACT ................................................................................................. 12
INTRODUCTION ........................................................................................ 13
MATERIALS AND METHODS ................................................................. 15
Mappingpopulation..................................................................... 15
Experimental design and phenotypic assessment ........................ 16
Statistical analysis ....................................................................... 17
QTLanalysis ............................................................................... 17
DNA extraction and microsatellite genotyping ........................... 19
RESULTS ..................................................................................................... 22
Analysis of phenotypic data ........................................................ 22
Trait correlations ......................................................................... 24
QTLdetection .............................................................................. 30
DISCUSSION.............................................................................................. 36
ACKNOWLEDGEMENTS ......................................................................... 38
REFERENCES ............................................................................................. 39
TABLE OF CONTENTS (continued)
Page
Chapter 3
Quantitative Trait Loci Influencing Free-Threshing Habit
in Wheat (Triticum aestivum L.) ...................................................................................42
ABSTRACT ................................................................................................. 43
INTRODUCTION ........................................................................................44
MATERIALS AND METHODS ................................................................. 47
Mappingpopulation ..................................................................... 47
Experimental design and phenotypic assessment........................ 48
Statistical analysis ....................................................................... 49
QTLanalysis ............................................................................... 50
DNA extraction and microsatellite genotyping ........................... 51
Bulked segregant analysis ........................................................... 53
AFLPanalysis ............................................................................. 54
RESULTS ..................................................................................................... 58
Analysis of phenotypic data ........................................................ 58
QTLdetection .............................................................................. 59
Bulked segregant analysis ........................................................... 67
AFLPanalysis ............................................................................. 67
DISCUSSION.............................................................................................. 68
ACKNOWLEDGEMENTS......................................................................... 73
REFERENCES ............................................................................................. 73
Chapter 4
Conclusion..................................................................................................................... 77
Bibliography .................................................................................................................. 80
LIST OF FIGURES
Figure
Page
2.1
Phenotypic frequency distributions for three spike
morphology traits for Hyslop farm, East farm, and greenhouse ...................... 25
2.2
Spikes of the parental lines W-7984, Opata 85, and Altar 84,
and some ITIvIT RILs showing differences for spike length ............................ 28
2.3
Spikes of the parental lines W-7984, Opata 85, and Altar 84,
and some ITMI RILs showing differences for spikelet number ...................... 28
2.4
Spikes of the parental lines W-7984, Opata 85, and Altar 84,
and some ITMI RILs showing differences for rachis internode length ............ 29
2.5
Scans of test statistics across environments for simple interval
mapping (SIM), simplified composite interval mapping (sCilvI)
and QTL x environment interaction for spike length of the
Opata 85 x W-7984 recombinant inbred lines (RILs) ....................................... 33
2.6
Scans of test statistics across environments for simple interval
mapping (SIM), simplified composite interval mapping (sCIM)
and QTL x environment interaction for number of spikelets per
spike of the Opata 85 x W-7984 recombinant inbred lines (RILs) ................... 34
2.7
Scans of test statistics across environments for simple interval
mapping (SIN'I), simplified composite interval mapping (5CIM)
and QTL x environment interaction for rachis internode length
of the Opata 85 x W-7984 recombinant inbred lines (RILs) ........................... 35
3.1
Phenotypic frequency distributions for threshability and
glume tenacity for 1-lyslop farm, East farm, and greenhouse............................ 62
3.2
Threshed spikes and their components [threshed seeds (Sd), chaff
(Ch), spikelets (Spl), spike fragments (Spk)] after threshing on a
petrol-powered Vogel plantlhead thresher ........................................................ 63
LIST OF FIGURES (continued)
Figure
Page
3.3
Scans of test statistics across environments for simple interval
mapping (SIM), simplified composite interval mapping (sCIM)
and QTL x environment interaction for glume tenacity of the
Opata 85 x W-7984 recombinant inbred lines (RILs) ....................................... 65
3.4
Scans of test statistics across environments for simple interval
mapping (SIM), simplified composite interval mapping (sCIM)
and QTL x environment interaction for percent threshability of
the Opata 85 x W-7984 recombinant inbred lines (RILs) ................................. 66
3.5
Autoradiograph of amplified fragment length polymorphisms
(AFLPs) associated with a threshability QTL on chromosome 2D ................. 69
3.4
Genetic map of the chromosome 2D showing location of AFLP
marker: XorstP374 7207................................................................................... 70
LIST OF TABLES
Table
Page
2.1
Primer sequences and annealing temperatures for SSR loci
in chromosome regions that were significantly associated
with spike morphology characteristics .............................................................. 20
2.2
Mean trait values of the parental lines W-7984, Opata 85, and
Altar 84, and 110 recombinant inbred lines (RILs) derived from
the cross between W-7984 and Opata 85 from three environments................. 27
2.3
Phenotypic correlation coefficients among spike length,
number of spikelets per spike, and rachis internode length
for 110 recombinant inbred lines (RILs) derived from the
cross between Opata 85 and W-7984 ................................................................ 29
2.4
Quantitative trait locus significance, effect, and percentage
of phenotypic variation accounted for for spike morphology
based on simple interval mapping analysis of 110 recombinant
inbredlines (RILs) ............................................................................................ 32
3.1
Wheat microsatellite loci, primer sequences, and annealing
temperatures ...................................................................................................... 52
3.2
Oligonucleotide sequences used for amplified fragment
length polymorphism (AFLP) analysis ............................................................. 57
3.3
Mean trait values of the parental lines W-7984, Opata 85, and
Altar 84, and I 10 recombinant inbred lines (RILs) derived from
the cross between W-7984 and Opata 85 .......................................................... 61
3.4
Quantitative trait locus significance, effect, and percentage
of phenotypic variation accounted for for free-threshing habit
based on simple interval mapping analysis of 110 recombinant
inbredlines (RIL5) ............................................................................................ 64
QUANTITATIVE TRAIT LOCI INFLUENCING FREE-THRESHING HABIT
AND SPIKE MORPHOLOGY IN WHEAT (Triticum aestivum L.)
Chapter 1
Introduction
Wheat (Triticum aestivum L.) is one of the most important food crops in the
world with an estimated world annual production of 550 million metric tons (Morris et
al. 1996). It is grown across a wide range of environments around the world and it is
the number one food grain consumed directly by humans. Wheat production leads all
crops, including rice, maize, and potatoes (Briggle et al. 1987). It is believed that
wheat was domesticated and used as a food crop approximately 9 to 10 thousand years
ago (Smith 1995). Wheat is an allohexaploid (2n=6x=42) with the three genomes A,
B, and D. Hexaploid wheat is believed to have arisen from two stepwise interspecific
hybridization events. First, a diploid wheat, Triticum urartu (2n=2x=14, AA),
hybridized with a second diploid species with the B genome to produce an
allotetraploid wheat (2n=4x=24, AABB). The source of the B genome remains
unknown but it is closely related to Aegilops speltoides (2n2x1 4, SS). Second, the
allotetraploid wheat hybridized with a third diploid species, Aegilops tauschii L.
(2n=2x=14, DD), to produce hexaploid wheat (2wr6x42, AABBDD).
Important agronomic and economic characters in wheat such as yield and yield
components, plant height, and days to flowering, are generally complex and controlled
by several genes. The nature of genes that affect complex traits is poorly understood.
2
However, tools such as DNA based markers, genetic linkage maps, and quantitative
trait locus analysis have helped advance our understanding with regard to the genetic
basis of some complex traits, such as grain yield and its components (Kato et al.
2000), bread-making quality (Perretant et al. 2000; Campbell et al. 2001), and disease
resistance (Singh et al. 2000; Huang et al. 2000).
GENETIC MARKERS AND GENETIC LINKAGE MAP CONSTRUCTION
There are two major classes of genetic markers used in the development of
genetic maps: morphological and molecular markers. A morphological marker is a
mutation of a particular gene, which impacts a discrete, easily identified phenotype to
an organism. The morphological marker is easy to use in the construction of genetic
linkage maps but they have several limitations. Morphological markers often have
deleterious effects on the study organism, there are low numbers of morphological
markers that are available for genetic map construction, and they can be influenced by
environmental and genetic factors. The discovery of DNA-based markers has
facilitated the construction of genetic linkage maps by overcoming some of the
limitations of morphological markers.
There are two types of molecular markers: biochemical and DNA-based
markers. Biochemical markers are represented by different allelic forms of enzymes,
proteins, lipids, sugars, and other biochemicals. DNA-based markers are based on
genetic variation at the DNA sequence level. The problem with the morphological
marker having a larger phenotypic effect than the locus of interest is largely overcome
3
by DNA-based markers. Molecular markers also have the advantage of being more
abundant than morphological markers.
Among the various DNA-based markers developed, restriction fragment length
polymorphism (RFLPs) were the first to be used in genetic linkage map construction
(Botstein et al. 1980). RFLP markers are very reliable because their detection depends
on variation in the DNA sequence recognition site by specific restriction
endonucleases and hybridization with a specific DNA or RNA probe. RFLPs are
codominant and locus specific. A disadvantage of RFLP analysis is that they are labor
intensive and time consuming. The newer approaches based on the polymerase chain
reaction (PCR) are relatively simpler. Random amplified polymorphic DNAs
(RAPDs), a PCR-based technique, involves the generation of random genomic DNA
segments with single primers (usually 10 nucleotides long) of arbitrary sequence
(Williams et al. 1990). RAPDs are in general dominant markers. Because of their
random amplification, RAPDs have disadvantages in poor reliability and
reproducibility, and they are sensitive to experimental conditions.
Another DNA-based marker system, amplified fragment length
polymorphisms (AFLP), is based on PCR amplification of restriction fragments
generated by specific restriction enzymes (Vos et al. 1995). AFLP markers have
recently become widely used for genetic mapping. Outstanding features of AFLPs are
that they are highly abundant, reproducible, they have a relatively high multiplex ratio,
the process requires small amounts of DNA, and the system offers extensive genome
coverage (Vos et al. 1995). However, the AFLP assay can be time consuming and they
are reported to cause map expansions (Becker et al. 1995).
4
Simple sequence repeats (SSR), or microsatellites, are composed of tandem
repeats of one to six nucleotides. These short tandem repeat elements have been found
to be both abundant and widely distributed throughout the genomes of many higher
plants (Wang et al. 1994). Nucleotide sequences flanking the repeat are used to design
primers to amplify the different number of repeat units. Microsatellites have the
advantage of being codominant. In addition, they are simple, PCR-based, extremely
polymorphic, and highly informative. Microsatellites are, however, expensive to
develop.
The development of a plethora of genetic marker systems and the possibility of
constructing saturated linkage maps has fostered increased activity and interest in
genetic mapping. Genetic linkage maps of about 66 economically important plant
species have been produced (see Riera-Lizarazu et al. 2001). Genetic mapping
requires the selection of an appropriate mapping population, calculations of
recombination, the establishment of linkage groups, calculations of map distances, and
the determination of genetic locus order. Selection of mapping population is important
to successful map construction. There are several types of mapping populations: F2,
recombinant inbred line (RIL), double haploids, and backcross populations. F2
populations give more genetic information on a per individual basis than RIL
populations, doubled haploid populations, and backcross populations. Codominant
marker systems provide more information than dominant markers. Maximum genetic
information is obtained from a completely classified F2 population using a
codominant marker system (Staub and Serquen 1996).
Calculation of recombinant fraction is based on crossover events. A maximum
of half of the meiotic products will be crossover types (recombinants) when chiasma
form between two loci. The frequency of crossovers or recombinant chromosomes
defines the map distance between two loci. In general, one map unit is a distance that
yields one percent recombinant chromosomes or gametes. The frequency of
recombination and map distance is not directly proportionate as distance becomes
larger, due to the occurrence of double crossovers. Recombination frequency is
equivalent to map distance when two loci are closely linked because a double
crossover occurs at significantly lower frequency. To overcome the nonlinear
relationship between distance and recombination frequencies, equations to calculate
map distances that are linear and additive are used (Haldane 1919; Kosambi 1944).
Estimation of linkage groups can be determined by mathematical tests. There
are several mathematical methods used for linkage estimation but maximum
likelihood (Mather 1938) is the most commonly used. The maximum likelihood
method is used for the estimation of the recombination frequency between two
particular loci. For each pair of loci, the log likelihood of recombination frequency is
calculated and then compared with the log likelihood of the two loci being unlinked.
The ratio between the log likelihood of recombination frequency and log likelihood of
the two loci being unlinked is called likelihood odds (LOD) scores. The combination
of recombination frequency and LOD scores are used to construct linkage groups.
The final step in the construction of a genetic linkage map is determining map
order of loci within a linkage group. In order to find the proper order of the loci in a
linkage group, the order of two loci is first determined based on a test statistic [i.e.
sum of the adjacent recombination frequencies (SAR) or sum of adjacent LOD scores
(SAL) (Edwards 1987)1. An additional locus is then added to the two-locus order and
tested for its most likely position. Additional loci are then added one by one and their
order is determined.
QUANTITATiVE TRAIT LOCUS ANALYSIS
A quantitative trait locus (QTL) is the location of a genetic factor that affects a
trait that is measured on a quantitative (linear) scale. These traits are typically affected
by more than one gene, and also by the environment. Thus, mapping QTL is not as
simple as mapping a single gene that affects a qualitative trait. Classical studies of
genetically complex traits were studied by statistical analysis of appropriate
experimental populations. Using controlled crosses between parents of dissimilar
traits, heritability, the number of genetic loci, the degree of dominance, additivity, and
heterosis, and the role of gene x gene and gene x environment interactions could be
estimated. These studies used statistical methods to describe the phenotypic
distribution of populations. This classical method did not allow the estimation of the
magnitude and effect, the inheritance, and the gene action of any specific locus that
was affecting a quantitative trait.
The theory of QTL mapping was first described in 1923 by Sax, who found the
association between seed-coat pigmentation (a simple, qualitative trait) and seed size
in beans (a complex trait). Thoday (1961) proposed the idea of using a single marker
to systematically characterize and map individual QTL controlling quantitative traits.
7
The advent of DNA-based markers and linkage maps has made it feasible to scan an
entire genome and thus, localize and characterize genetic loci that affected quantitative
traits.
The underlying assumption of using marker loci to detect QTL is that linkage
disequilibrium exists between alleles at the marker locus and alleles of the linked
QTL. Linkage disequilibrium can be defined as the nonrandom association of alleles at
different loci in a population and can be caused by a number of factors including
selection and genetic drift. There are several statistical procedures for determining the
linkage between QTL and a marker locus. In single marker analysis, individual DNA
marker loci are tested throughout the genome for the likelihood that they are
associated with a QTL. For each genetic marker, the individuals of a segregating
population are split into classes according to their marker genotype. Mean and
variance parameters are calculated and compared among the classes. A significant
difference between classes suggests that there is a relationship between the genetic
marker and the trait of interest, and that the genetic marker is probably linked to a
QTL. There are few disadvantages of single marker analysis. The further a QTL is
from the marker, the less likely it is to be detected and the magnitude of the effect of
any detected QTL will normally be underestimated.
Lander & Botstein (1989) proposed a method called interval analysis. Sets of
linked markers are analyzed simultaneously with regard to their effects on quantitative
traits. By using linked markers for analysis, it is possible to compensate for
recombination between the markers and the QTL. There are two main interval
analyses for QTL mapping. Simple interval mapping (SIM) is essentially the same as
ri]
single marker analysis except that phenotypes are regressed on the QTL genotypes.
Since the QTL genotypes are unknown, they are replaced by probabilities estimated
from the nearest flanking markers. Another method for interval analysis is composite
interval mapping (CIIM). One of the factors that weakens simple interval mapping is
that the procedure fits a model for QTL at only one location. There are two problems
with this: the effects of additional QTL will contribute to sampling variance and, if
two QTLs are linked, their combined effects will cause biased estimates. Jansen and
Stam (1994) proposed solution for this problem, where by simple interval mapping
(SilvI) is performed in the usual way, except that the variance from other QTLs are
accounted for by including partial regression coefficients from markers in other
regions of the genome.
QTL STUDIES USING THE ITMI WHEAT MAPPING POPULATION
There has been a flurry of linkage map construction in wheat (Gale et al. 1995;
Cadalen et al. 1997; VanDeynze et al. 1995; Nelson et al. 1995a, 1995b, 1995c,
Marino et al. 1996; Blanco et al. 1998; Dubcovsky et al. 1996; Gill et al. 1991;
Lagudah et al. 1991; Boyko et al. 1999) and QTL studies (Sourdille et al. 1996; Fans
et al. 1997; Martinant et al. 1998; Campbell et al. 2001) in wheat. A wheat population
that is particularly interesting is the International Triticeae Mapping Initiative (ITMI)
wheat mapping population that consists of recombinant inbred lines (RILs) developed
from a cross between the hard red spring wheat (Triticum aestivum L. 2n=6x=42,
AABBDD genomes) cultivar Opata 85 and a synthetic hexaploid wheat, W-7984
(Nelson et al. 1995a; Nelson et al. 1995b; Nelson et al; 1995c; Marino et al. 1996; Van
Deynze et al. 1995). The synthetic wheat parent of the mapping population was
produced by crossing a modern short-statured durum wheat ( Triticum turgidum L.;
2n=4x==24, AABB), Altar 84, with an accession of Aegilops tauschii (2n=2x=14, DD)
followed by chromosome doubling. In addition to the development of a
comprehensive map of the wheat genome, this population has been successfully used
for the detection of quantitative trait loci (QTLs) affecting kernel hardness (Sourdille
et al. 1996), chiorosis caused by Pyrenophora tritici-repentis (Fans et al. 1997),
Karnal bunt resistance (Nelson et al. 1998) leaf and stripe rust resistance (Nelson et al.
1997; Singh et al. 2000), and pentosan content (Martinant et al. 1998). This population
is highly variable for other numerous traits since genetic variation contributed by the
common wheat parent, Opata 85, and the parents of W-7984, Altar 84 and Ae.
tauschii, is present in this population.
OBJECTIVES OF THIS STUDY
Spike morphology and the free-threshing habit are important in wheat breeding
programs. Spike morphology including, spike length, spikelet number, and spike
compactness, may have direct or indirect effects on grain yield. Theoretically,
increasing spike length but keeping spike density constant might be a way to increase
grain yield if floret fertility is also maximized. Thus, knowing where genes that affect
these traits are located, and having markers to manipulate them, has practical
importance.
10
Interspecific hybridization may be used to widen the genetic base of wheat. For
example, synthetic wheat hexaploids (derived from crosses between tetraploid wheat
with Ae.
tauschii)
have been shown to have potentially useful genetic variation for
wheat improvement (del Blanco et al. 2001) but obstacles to their utilization in wheat
improvement programs also exist. These obstacles include the introduction of alleles
from the non-adapted tetraploid durum wheat parent (Cox 1998) and the low
proportion of threshable progeny from wheat x synthetic crosses (Villareal et al.
1996). Better understanding of the genetic basis of threshability in synthetic hexaploid
germplasm and the ability to use molecular markers to select for threshable progeny
from larger wheat x synthetic populations might alleviate these problems.
For these reasons, the International Triticeae Mapping Initiative (ITMII)
mapping population and QTL analysis were used to identify genes or genetic factors
affecting spike morphology and threshability.
The specific objectives of this study were to identify:
I. regions of the wheat genome associated with the spike compactness
components.
2.
regions of the wheat genome associated with the free-threshing habit.
3.
additional makers linked to a major QTL affecting threshability in wheat.
The first objective was achieved and is described in Chapter 2 of this thesis.
The second and the third objectives were achieved and are described in Chapter 3 of
this thesis.
11
Chapter 2
Quantitative Trait Loci Influencing Spike Morphology in Wheat
(Triticum aestivum L.)
C. Jantasuriyarat, M. I. Vales, E. Yoon, and 0. Riera-Lizarazu
12
ABSTRACT
Spike morphology has practical importance because it is associated with grain
yield. Quantitative trait loci (QTL) that affected spike morphology parameters, such as
spike length, spikelet number, and rachis internode length in wheat (Triticum aestivum
L.), were identified in a recombinant inbred line (RIL) mapping population developed
by the International Triticeae Mapping Initiative (ITMT). The ITMII population was
planted in three environments during 1999 and 2000. QTL analyses were performed
using simple interval mapping (SIM) and simplified composite interval mapping
(sCIM) procedures. Two QTLs, one on chromosome lB and one on 4A, affecting
spike length were identified. The proportion of the phenotypic variance explained by
each QTL ranged from 15 to 22%. Collectively, these QTLs explained up to 44% of
the total phenotypic variation. The QTL on chromosome lB has not been described
previously. One QTL controlling spikelet number was also detected on chromosome
4A. The proportion of the phenotypic variance explained by this QTL ranged from 17
to 24%. This QTL coincided in location with the QTL on chromosome 4A that
affected spike length. One QTL controlling rachis internode length, a measure of spike
compactness, was detected on chromosome 6A. This QTL accounted for 14 to 18% of
the total phenotypic variation. Information on the number, position, and effect of
QTLs determining spike morphology characteristics and their associated molecular
markers may facilitate their manipulation in wheat improvement programs.
13
INTRODUCTION
Spike morphology parameters are widely used criteria for species
differentiation and have been extensively investigated (Miller 1987). The wheat spike
has also practical importance because variation in its form has been associated with
grain yield. Kato et al. (2000), using single chromosome recombinant lines (SCRs),
found a positive correlation between grain yield and yield components such as plant
height, spike length, and spikelet number. Spike morphology may not only be
associated with the yield potential but may also be associated with disease resistance.
In barley, Zhu et al. (1999) found coincident QTLs for Fusarium head blight (FHB)
resistance and QTLs for plant height and inflorescence traits, including seeds per
inflorescence, inflorescence density, and lateral floret size. The various measures of
FHB resistance were associated with taller plants, more seeds per inflorescence, lowdensity inflorescences, and small lateral florets. Thus, understanding the genetic
control of spike morphological parameters including spike length, spikelet number,
and spike compactness may be of interest for their direct and indirect effects on the
yield potential andlor disease resistance.
Spike morphology in wheat is influenced by at least three major genes,
including Q for square-headed spikes, C for compactoid spikes, and si for rounded
glumes and spherical or shot-like grains (Worland et al. 1987; McIntosh et al. 1998).
Bread wheat of the Triticum aestivum group is square-headed and has the constitution:
QQ, cc, SJSJ. The dominant
Q allele, located on the long arm of chromosome 5A
(Sears 1947), confers a free-threshing grain and a tough rachis. Snape et al. (1985)
studied the pleiotropic effects of Q and found that Q reduced spike length, but also
14
induced changes of spike morphology that permitted the development of more
spikelets, thus increasing yield potential. The dominant C allele, located on
chromosome 2D (Ausemus et al. 1967), results in a free-threshing grain with short
compact spikes (club wheats of the Triticum compactum class with the constitution:
QQ, CC, SJSJ). The recessive si allele, located on the short arm of chromosome 3D
(Schmidt et al. 1963), produces spherical-grain with short dense awned spikes
(belonging to the Triticum sphaerococcum class with the constitution: QQ, CC, sisi).
Kuspira and Unrau (1957) showed that there were a number of other minor genes also
involved in the expression of spike compactness components. Sears (1947) found that
chromosome 2D and 2B, carrying genes for photoperiodic response, PpdI and Ppd2,
respectively, affected the development of the spikelets. Pugsley (1966) was the first
person to associate day-length sensitivity with the number of spikelets. The
photoperiodic response gene, Ppdl, reduces plant height and tillenng, together with
spikelet number by shortening the plant's life cycle (Worland 1996).
More recently, Sourdille et al. (2000) identified quantitative trait loci
controlling spike compactness using a wheat double haploid mapping population. Five
QTLs on chromosome 1A, 2B, 2D, 4A, and 5A were detected for spike length, three
QTLs on chromosome 2A, 2B, and 5A affected the number of spikelets per spike, and
six QTLs on chromosome 1A, 2A, 2B, 2D, 4A, and 5A affected the compactness of
the spike. One QTL on chromosome 2D was associated with a microsatellite locus
(Xgwm26l-2D), which was located next to Rht8 (Korzun et al. 1998), a gene that
confers reduced height in wheat and is linked to PpdI (Worland et al. 1998).
15
A molecular marker linkage map of wheat composed of restriction fragment
length polymorphism (RFLP) (Nelson et al. 1995a; Nelson et al. 1995b; Nelson et al;
1995c; Marino et al. 1996; Van Deyrze et al. 1995) and simple sequence repeat (SSR)
(ROder et al. 1998; Pestsova et al. 2000) markers was constructed using a recombinant
inbred line population derived from a cross between a CIMIIvIYT wheat cultivar, Opata
85, and a synthetic hexaploid wheat, W-7984, by a collaborative genome mapping
project of the International Triticeae Mapping Initiative (ITMII). The ITIvil mapping
population and the DNA-based marker information used in the construction of the
linkage map are publicly available (GrainGenes database-http://wheat.pw.usda. gov/).
This valuable reference mapping population has already been used to unveil the
genetic determinants of a number of traits, including kernel hardness (Sourdille et al.
1996), chlorosis resistance caused by Pyrenophora tritici-repentis (Fans et al. 1997),
and pentosan content (Martinant et al. 1998). In this study the ITMIT mapping
population was used to identify regions of the wheat genome associated with spike
compactness components.
MATERIALS AND METHODS
Mapping population
The ITMI mapping population consists of recombinant inbred lines (RIL5)
developed from a cross between the hard red spring wheat cultivar Opata 85 (Triticum
aestivum L. 2n=6x=42, AABBDD genomes) and a synthetic hexaploid wheat, W-7984
(Nelson et al. 1995a; Nelson et al. 1995b; Nelson et al; 1995c; Marino et al. 1996; Van
16
Deynze et al. 1995). For this experiment, seeds for the ITMI population, the synthetic
hexaploid (W-7984), and Opata 85 were provided by Dr. C. Qualset, University of
California, Davis. Seeds for the durum wheat cultivar Altar 84 (Triticum turgidum L.
2n=4x=28, AABB) were provided by Dr. C. J. Peterson, Oregon State University.
Experimental design and phenotypic assessment
In April 1999, one-hundred and ten recombinant inbred lines (RILs), their
parents (Opata 85 and W-7984), and the durum cultivar Altar 84 (a parent of W-7984)
were planted at Hyslop Farm Field Laboratory, Corvallis, Oregon (referred to as
Hyslop farm in the rest of this paper). Opata 85, W-7984, and Altar 84 were planted in
three replications. Due to the limited seed available in 1999, the RILs were planted in
unreplicated 15-foot (4.6 m) rows. In April 2000, the mapping population, their
parents (Opata 85 and W-7984), and the durum cultivar Altar 84 were planted at the
University East Farm, Corvallis, Oregon (referred to as East farm in the rest of this
paper). Each line was planted in four-row plots (4 m2) in a randomized complete block
design with two replications. The mapping population and parental lines were also
planted in the greenhouse at Oregon State University (referred to as greenhouse in the
rest of this paper) in February 2000. Each line was represented by one pot containing
four plants arranged in a randomized complete block design with two replications.
Spike length was evaluated by measuring the length of the spike (cm) from the
base of the rachis to the tip of the uppermost spikelet, without including the awns. The
number of spikelets was counted for each spike. The average rachis internode length
was calculated by dividing the length of the spike by the number of rachis intemodes.
17
In 1999, five randomly selected mature spikes from each line were evaluated. In 2000,
eight and four randomly selected mature spikes from each line were evaluated in
materials planted at the East farm and the greenhouse, respectively.
Statistical analysis
The normal distribution and homogeneity of variances were tested for the three
phenotypic data sets (environments). The test of normality was performed using
PROC UNTVARIATE in SAS Version 6.12 (Statistical Analysis System, SAS
Institute 1996) and the test of homogeneity of variances was performed using the
software program hov Version 1.0 (Lim and Loh 1996). The three phenotypic data
sets showed normal distributions but they were not homogeneous for their variances.
Accordingly, the analysis of variance was performed on each data set separately using
the General Linear Model (PROC GLM) procedure of SAS. Considering the effects of
the genotypes random, estimates of variance components were computed by equating
mean squares to their expectations. Estimates of heritability were calculated as:
O2g1 (a2g
+
x2er)
h2 =
where r is the variance among recombinant inbred lines, U2e is the
error variance among recombinant inbred lines, and r is the number of replications.
QTL analysis
To identif' QTL5 controlling spike length, spikelet number, and rachis
internode length, phenotypic evaluations were used in combination with public DNA
marker data for the ITMI population (GrainGenes database-
18
http://wheat.pw.usda.gov/). QTL analyses were performed using the Simple Interval
Mapping (SIM) and simplified Composite Interval Mapping (sCIM) procedures in the
software package MQTL (Tinker and Mather 1995) and in the software package QTL
Cartographer (Basten et al. 1999). Each linkage group was scanned at 5-cM intervals
with I ,000 permutations and a Type I error rate of 5%. A forward backward stepwise
regression procedure in the software package QTL Cartographer (Basten et al. 1999)
was used to identify co-factors for composite interval mapping. Based on this analysis,
a maximum of three background markers per linkage group were used as co-factors.
The significance of QTL regions is reported as a test statistics (Tinker and Mather
1995). A primary QTL was declared at position where both SIM and sCIM gave
evidence for the presence of a QTL. Individual and joint additive effects of QTLs were
used to estimate the proportion of phenotypic variation (R2) accounted for by
significant QTLs. Detected QTLs were paired and tested for epistatic interaction by
using analysis of variance to test for differences between means of each genotypic
combination using the PROC GLM procedure in SAS Version 6.12.
After QTL detection, the entire ITMII population was genotyped with simple
sequence repeat (SSR) markers (ROder et al. 1998; Pestsova et al. 2000), in regions
where QTLs had been detected. A total of 23 SSR markers (Table 2.1) were used to
genotype the entire ITh'll population and linkage analysis was performed using
GMendel 3.0 (Hollaway and Knapp 1994). Linkage groups were calculated using
LOD 4.0 and rmax 0.22. Marker order was checked by Monte Carlo Bootstrap
simulation, using annealing temperatures of 300 for the inner loop and 200 for the
outer loop. The augmented genetic linkage map was reanalyzed as described earlier.
19
DNA extraction and microsatellite genotyping
Genomic DNA was extracted from 5 g of young fresh leaf tissue that was
ground in liquid nitrogen. Powdered leaf tissue was transferred to a 35-ml centrifuge
tube on ice and mixed with 10 ml of lysis buffer [8 ml of extraction buffer (0.03 M
Tris HC1 pH 9.5, 0.03 M EDTA, 0.3 M KC1, 1.5 M Sucrose, 3 iM spermidine, and 12
tM spermine), 2 ml of 10% sarkosyl] plus I tl of 0.1% f3-mercaptoethanol. About 5
ml of 1:1 mixture of phenol and chloroform was added and mixed carefully and then
centrifuged at 10,lOOg for 10 mm in a J2-HC BEKMAN ultracentrifuge. The aqueous
layer was transferred to a clean centrifuge tube, and re-extracted with 1:1
phenol/chloroform as described above. The aqueous layer was transferred to a clean
centrifuge tube and 1 ml of 3 M sodium acetate, pH 5.0, and 25 ml of ice-cold
absolute ethanol were added and mixed well. Precipitated DNA was spooled out using
a hooked pasteur pipette. The isolated DNA was rinsed three times with 70% ice-cold
ethanol, and allowed to dry for 10 mm. Finally, the DNA was resuspended in 200 il of
TE pH 8.0 (10 mM Tris HC1 and II mM EDTA) containing 10 xg/m1 of RNase.
The polymerase chain reaction (PCR) amplification of SSRs were performed
in a MJ Research PTC- 100 (MJ Research, Inc., Watertown, MA) thermocycler using
condition described by ROder et al. (1998). Fluorescent detection of PCR amplification
products was achieved by using reverse primers labeled with either 5-carboxyfluorescein (5-FAM) or 4,7,2' ,4' ,5 ',7' -hexachloro-6-carboxyrhodamin (HEX). PCR
amplification products were prepared and sent to the Central Service Laboratory
(CSL) at the Center for Gene Research & Biotechnology (CGRB), Oregon State
University. At the CSL, PCR products were electrophoresed and detected in an ABI
20
Prism
377 DNA Sequencer (PE Applied Biosystems, Forest City, CA). ABI
collection software, version 1.1, was used for raw data collection. Microsatellite
fragments were analyzed using GENEScAN' analysis software version 2.1 as
described in the user's manuals.
Table 2.1 Primer sequences and annealing temperatures for SSR loci in chromosome
regions that were significantly associated with spike morphology characteristics a
Locus
Primers
Tm(°C)
XgwmlI-IB
GGATAGTCAGACAATTCTTGTG
GTGAATTGTGTCTTGTATGCTTCC
TGGCGCCATGATTGCATTATCTTC
GGTTGCTGAAGAACCTTATTTAGG
AATCCCCACCGATTCTTCTC
AGTTCGTGGGTCTCTGATGG
TTCAATTCAGTCTTGGCTTGG
CTGCAGGAAAAAAAGTACACCC
ACCACTGCAGAGAACACATACG
GTGCTCTGCTCTAAGTGTGGG
TGCATCAAGAATAGTGTGGAAG
TGAGAGGAAGGCTCACACCT
CTCCCTGTACGCCTAAGGC
CTCGCGCTACTAGCCATTG
GAGAAACATGCCGAACAACA
GCATGCATGAGAATAGGAACTG
ATTGGACGGACAGATGCTTT
AGCAGTGAGGAAGGGGATC
AATTCAACCTACCAATCTCTG
GCCTAATAAACTGAAAACGAG
TGTCATGGATTATTTGGTCGG
CTGCACTCTCGGTATACCAGC
50
Xgwm /8-lB
Xgwm/3 I-/B
Xgwml6O-4A
Xgwm/69-6A
Xgwm2 1 O-2D
Xgwm26I-2D
Xgwm264-1B
Xgwm2 73-lB
Xgwm296-2D
Xgwm397-4A
50
60
60
60
60
55
60
55
55
55
(continued)
21
Table 2.1 (continued)
Locus
Primers
Tm (°C)
Xgwm4] 3-lB
TGCTTGTCTAGATTGCTTGGG
GATCGTCTCGTCCTTGGCA
ATTCGGTTCGCTAGCTACCA
ACGGAGAGCAACCTGCC
ACATCGCTCTTCACAAACCC
AGTTCCGGTCATGGCTAGG
ATTGAACAGGAAGACATCAGGG
TTCCTGGAGCTGTCTGGC
GGTGGTATGGACTATGGACACT
TTTGCATGGAGGCACATACT
TCGCCTTTTACAGTCGGC
ATGGGTAGCTGAGAGCCAAA
CTGCCTTCTCCATGGTTTGT
AATGGCCAAAGGTTATGAAGG
GATCTTGGCGCTGAGAGAGA
CTCCGATGGATTACTCGCAC
AAAGAGGTCTGCCGCTAACA
TATACGGTTTTGTGAGGGGG
CTAGCCAGAAGGTTACTTTG
CAACATTAACATTAACGCAC
CCTGCTCTGCCCTAGATACG
ATGTGAATGTGATGCATGCA
AGCAACAAACGCGAGAGC
TGACACCCGGTTGTTGG
60
Xgwm455-2D
Xgwm484-2D
Xgwm494-6A
Xgwm498-JB
Xgwm57O-6A
Xgwm6lO-4A
Xgwm6l7-6A
Xgwm637-4A
Xgdm5-2D
Xgdm35-2D
XgdmlO7-2D
a
Reported byRoder et al. (1998) and Pestsova et al. (2000)
55
55
60
55
60
60
60
60
50
55
55
22
RESULTS
Analysis of phenotypic data
The spike morphology characteristics such as spike length, number of spikelets
per spike, and rachis internode length were normally distributed (Figure 2.1)
suggesting that they were controlled by multiple genetic determinants as is typical of
quantitatively inherited traits. Analysis of variance showed significant differences
between recombinant inbred lines for each trait in all three data sets. Mean trait values
of the parental lines, W-7984, Opata 85, and Altar 84, and 110 recombinant inbred
lines (RILs) plus standard deviations, minima, and maxima for three environments are
presented in Table 2.2.
Spike length was not significantly different between W-7984 and Opata 85 in
two environments (1-lyslop farm and East farm). Opata 85 had significantly shorter
spikes than W-7984 when grown in the greenhouse (Table 2.2 and Figure 2.2). Altar
84, a parent of W-7984, had significantly shorter spikes than W-7984 and Opata 85 in
two environments (ITyslop farm and greenhouse). Spike length was not significantly
different between Altar 84 and Opata 85 when grown at East farm (Table 2.2). In this
environment, W-7984 had significantly longer spikes than Altar 84 (Table 2.2). The
ITMI RILs had an average spike length of 9.61 cm at Hyslop farm, 8.61 cm at East
farm and 9.29 cm in material grown in the greenhouse. Spike lengths for the RILs
ranged from 5.6 to 12.6 cm at Hyslop farm, from 5.8 to 11.9 cm at East farm, and
from 6.0 to 12.5 cm at greenhouse (Table 2.2).
23
Significant differences in spikelet number between W-7984 and Opata 85 were
observed in two environments (Hyslop farm and East farm). In these environments,
Opata 85 had a significantly larger number of spikelets per spike than W-7984 and
Altar 84. At East farm, Altar 84 also had significantly larger number of spikelets per
spike than W-7984 (Table 2.2 and Figure 2.3). In the greenhouse, there were no
significant differences for spikelet number between Opata 85 and W-7984 (Table 2.2).
Altar 84 had an intermediate number of spikelets per spike to that of Opata 85 and W7984 (Table 2.2). The RILs had an average number of spikelets per spike of 15.2 at
Hyslop farm, 15.5 at East farm, and 19.0 at greenhouse. Spikelet number for the ITivil
population ranged from 10.8 to 19.8 at Hyslop farm, from 12.0 to 20.0 at East farm,
and from 11.5 to 27.3 at the greenhouse (Table 2.2).
Rachis internode length, a measure of spike compactness, was significantly
different between the parental lines in all three environments. Opata 85 had
consistently shorter rachis internode lengths than W-7984 (Table 2.2 and Figure 2.4).
The rachis internode length of Altar 84 was significantly shorter than those of W-7984
and Opata 85 in two environments (Hyslop farm and greenhouse). At East farm, 2000,
Altar 84 had significantly shorter rachis internodes than W-7984 but they were not
significantly different from those of Opata 85 (Table 2.2). The RILs had an average
rachis internode length of 0.68 cm at Hyslop farm, 0.60 cm at East farm, and 0.51 cm
at the greenhouse. Rachis internode length for the ITIvIT RILs ranged from 0.44 to 0.88
cm at Hyslop farm, from 0.45 to 0.88 cm at East farm, and from 0.34 to 0.67 cm at the
greenhouse (Table 2.2).
24
The estimated heritability for spike length was 74.5 % at East farm and 93.9 %
at the greenhouse. The estimated heritability for spikelet number was 92.4 % at East
farm and 94.8 % at the greenhouse. The estimated herittability for rachis internode
length was 72.6 % at East farm and 95.3 % at the greenhouse.
Trait correlations
Spike length and spikelet number were positively correlated in all three
environments (Table 2.3). Spike length and rachis internode length were also
positively correlated in all three environments (Table 2.3). On the other hand, numbers
of spikelets per spike and rachis internode length were negatively correlated (Table
2.3) in all three environments.
25
Figure 2.1 Phenotypic frequency distributions for three spike morphology traits.
Spike length (A), number of spikelets per spike (B), and ear compactness (C) for
Hyslop farm, 1999, East farm, 2000, and greenhouse, 2000. Parental values for
W-7984 (W) and Opata 85 (0) are indicated with arrows.
East Farm
Hyslop Farm
Greenhouse
w
o,w
[I]
A
No.
of
RILs
7
8
9
13
11
12
13
7
8
9
1)
11
12
13
7891811213
Spike length (cm)
w
B
No.
of
RILs
2
14
2
E
14
18
18
14
18
Spikelet number
C
No.
of
RILs
044
060
076
092
044
060
076
092
Rachis internode length (cm)
Figure 2.1
044
060
076
092
Hyslop farm
1999
W-7984
Opata 85
Altar 84
RILs
mean
range
SDb
East farm
2000
W-7984
Opata 85
Altar 84
RILs
mean
range
SDb
Greenhouse
2000
W-7984
Opata 85
Altar 84
RILs
mean
range
SDb
9.02a
9.04a
5.56b
13.lb
17.la
5.60-12.6
1.07
10.8-19.8
1.47
8.95a
8.00ab
5.75b
13.2c
18.2a
14.6b
5.80-11.9
12.0-20.0
9.61
8.61
1.06
10.55a
8.70b
5.25c
9.29
6.00-12.5
1.4
13.9b
15.2
15.5
1.55
17.9ab
21.2a
16.9b
19.0
11.5-27.3
2.70
0.75a
0.56b
0.43c
0.68
0.44-0.88
0.07
0.77a
0.46b
0.41b
0.60
0.45-0.88
0.07
0.62a
0.43b
0.33c
0.51
0.34-0.67
0.08
Mean traits values for the parental lines W-7984, Opata 85, and Altar 84 followed by the same letter are not significantly different
at the 0.05 probability level using F-protected LSD in SAS Version 6.12.
b
SD stands for standard deviation.
a
30
QTL detection
Spike length
Two QTLs were detected for spike length (Table 2.4 and Figure 2.5). The QTL
on chromosome lB was flanked by Xcdoll73
and Xbcdl2,
and the QTL on
chromosome 4A was flanked by Xmwg549 and Xbcdl67O. These QTLs were detected
in two environments (Hyslop farm and East farm). Spike length was increased by the
alleles from Opata 85 at these QTL loci (Table 2.4). Each individual QTL accounted
for 15 to 22% of the phenotypic variance in each environment. Together, these QTLs
accounted for 36% (Hyslop farm) and 44% (East farm) of the phenotypic variance. All
QTLs were confirmed in the analysis across environments (Figure 2.5). QTL x
environment interactions were not significant. No significant two-locus epistatic
interactions between these QTLs were found.
Number of spikelets per spike
One QTL was detected for spikelet number (Table 2.4 and Figure 2.6). This
QTL on chromosome 4A was flanked by Xmwg549 and Xbcdl67O. This QTL was
detected in two environments (Hyslop farm and East farm). This QTL explained 24%
of the phenotypic variance at Hyslop farm, and 17% of the phenotypic variance at East
farm. Spikelet number was increased by alleles of Opata 85 at this QTL (Table 2.4).
This QTL was confirmed in the QTL analysis across environments (Figure 2.6). QTL
x environment interactions were not significant.
31
Rachis internode 1enth
A QTL on chromosome ÔA was detected in all three environments. This QTL
was flanked by Xgwrn494 and Xcdo1428. A W-7984 allele at this QTL locus increased
rachis internode length (Table 2.4 and Figure 2.7). This QTL accounted for 18%, 14%,
and 18% of the phenotypic variance in different environments.
Table 2.4 Quantitative trait locus significance, effect, and percentage of phenotypic variation accounted for for spike
morphology based on simple interval mapping analysis of 110 recombinant inbred lines (RILs)
Trait
Environment
Spike length (cm)
Hyslop farm
East farm
Chromosome Marker interval
lBS
4AL
lBS
4AL
XcdoII73-XbcdI2
Xmwg549-XbcdI67O
[multi-locus model]b
XcdoIl73-XbcdI2
Xmwg549-XbcdI67O
[multi-locus model]"
Test statistic
R2
Additive effecta
18.50
25.93
0.15
0.21
0.36
-0.81
-0.95
16.60
24.67
0.15
0.22
0.44
-0.74
-0.89
Number of spikelets Hyslop farm
per spike
East farm
4AL
Xmwg549-XbcdI67O
30.31
0.24
-1.42
4AL
Xmwg549-XbcdI67O
18.86
0.17
-1.23
Rachis internode
Length (cm)
Hyslop farm
6AS
Xgwm494-Xcdo1428
22.03
0.18
0.06
East farm
6AS
Xgwm494-Xcdo1428
14.98
0.14
0.05
Greenhouse
6AS
Xgwm494-Xcdo1428
16.11
0.18
0.06
a
Additive effects indicates an additive SIM main effect of the parent contributing the higher value allele: positive values indicate that higher value alleles
are from W-7984 and the negative values indicate that higher value alleles are from Opata 85.
b
Multi-locus model includes all significant QTLs in the calculation of the proportion of the phenotypic variation accounted for by these QTLs.
33
Chromosome lB
60
50
SIM
C.,
SIMxE
30
sCIM
20
1-
10
0
pill
I
Chromosome 4A
100
.,
U,
80
SIM
60
SIMxE
sCIM
40
20
0
tj
'
-L
Figure 2.5 Scans of test statistic (Y-axis) across environments for simple
interval mapping (SIM, thick solid lines), simplified composite interval
mapping (sCIM, broken lines), and QTL x environment interaction (thin
solid lines) for spike length of the Opata 85 x W-7984 recombinant inbred
lines (RILs). Wheat chromosomes lB and 4A are shown. Horizontal lines
represent thresholds for testing SIM, estimated from 1000 permutations, an
experiment-wise Type I error rate below 5%.
34
Chromosome 4A
60
50
C.)
(a)
In
4(a)
I-
40
SIM
30
SIMxE
20
sCIM
10
0
-
o
"C
C
Figure 2.6 Scans of test statistic (Y-axis) across environments for simple
interval mapping (S1M, thick solid lines), simplified composite interval
mapping (sCIM, broken lines), and QTL x environment interaction (thin
solid lines) for number of spikelets per spike of the Opata 85 x W-7984
recombinant inbred lines (RILs). Wheat chromosomes 4A is shown.
Horizontal lines represent thresholds for testing SIM, estimated from 1000
permutations, an experiment-wise Type I error rate be'ow 5%.
35
chromosome 6A
70
60
50
SIM
44-
40
SIMxE
4-
30
sCIM
C.)
4U)
U)
U)
I-
20
10
0
.4
4
4
C
Figure 2.7 Scans of test statistic (Y-axis) across environments for simple
interval mapping (SIM, thick solid lines), simplified composite interval
mapping (sCIM, broken lines), and QTL x environment interaction (thin
solid lines) for rachis internode length of the Opata 85 x W-7984
recombinant inbred lines (RILs). Wheat chromosome 6A is shown.
Horizontal lines represent thresholds for testing SIM, estimated from 1000
permutations, an experiment-wise Type I error rate below 5%.
36
DISCUSSION
Understanding the genetic control of wheat spike morphology characteristics
including spike length, spikelet number, and rachis internode length has practical
importance because of their direct and indirect effects on the yield potential of wheat.
Theoretically, increasing spike length but keeping spike density constant might be a
way to increase grain yield if floret fertility is also maximized. In addition, knowledge
about the behavior and location of genes that affect spike morphology and the
identification of associated molecular markers may facilitate its manipulation in crop
Improvement context.
In this study, four QTLs that affected spike length, spikelet number, and rachis
internode length, were identified on chromosomes 1B, 4A, and 6A. There was no
linkage between these four QTLs and major genes known to affect spike morphology
such as Q, C, or Si. This suggests that differences in spike morphology were not due
to allelic variation at Q, C, or Si. Ausemus et al. (1967) reported that all chromosomes
except 1B, 5A, and 6D influenced spike length. Our results suggest that a genetic
determinant of spike length is also located on chromosome lB. The QTLs on
chromosomes lB and 4A that affected spike length had higher value alleles from
Opata 85. This is consistent with the fact that Altar 84, a parent of W-7984 has shorter
spikes than Opata 85 (Table 2.2 and Figure 2.2). Thus, variation for spike length in the
ITMI population reflected differences in allelic constitution at these loci. Sourdille et
al. (2000) found five QTLs on chromosome IA, 2B, 2D, 4A, and 5A that affected
spike length. This difference might be due to the genotypic differences between the
mapping populations used. Sourdille et al. (2000) used a doubled-haploid population
37
from a cross between two common wheat lines, Chinese Spring and Courtot. The
population used in this study was derived from a wider cross involving a synthetic
hexaploid (W-7984) and a bread wheat cultivar Opata 85.
We identified one QTL that affected spikelet number on chromosome 4A. This
QTL on chromosome 4A has not been described previously. The location of this QTL
coincidenced with the location of a QTL affecting spike length. This coincidence may
be explained by either the pleiotropic effects of one QTL on both traits or the effects
of two independent but linked QTLs. This QTL had a higher value allele from Opata
85. This is consistent with the observation that Opata 85 had spikes with a
significantly higher number of spikelets per spike than W-7984 in two environments
(Hyslop farm and East farm) (Table 2.2 Figure 2.3).
For rachis internode length, a measure of spike compactness, a QTL on
chromosome 6A was detected in all environments. The Opata 85 allele at this QTL
reduced rachis internode length (increased spike compactness). W-7984 and Opata 85
produce spikes of comparable size but Opata 85 has a larger number of spikelets per
spike (Table 2.2 and Figure 2.4). Consequently, the compactness of spikes of Opata 85
is significantly higher than W-7984. A factor on chromosome 6A of wheat that affects
spike density has also been described in studies of chromosome substitution lines of
three wheat varieties (Thatcher, Hope, and Timstein) in a Chinese Spring background
(Kuspira and Unrau 1957). More recently, Sourdille et al. (2000) identified QTLs that
affect spike compactness on chromosome IA, 2B, 2D, 4A, and 5A. These QTLs
coincided with QTLs that also affected spike length. In this study, the QTL for spike
compactness that we detected on chromosome 6A was independent from QTLs that
affected spike length.
Our study of the ITMI population suggests that spike length, spike let number,
and rachis internode length were controlled by genetic loci on chromosomes IB, 4A,
and 6A. Opata 85 contributed all of the favorable alleles for QTLs that affected the
spike characteristics studied. For this reason, progenies with longer and denser spikes
than Opata 85 were not expected. An increase in spike length without sacrificing spike
density might be possible by combining the QTL on chromosome 6A that affected
spike compactness in this study with complementary QTLs that affected spike length
in other experimental populations (Sourdille et al. 2000).
ACKNOWLEDGEMENTS
We thank Dr. C. J. Peterson for use of field facilities and Ryan Millimaki and
Jumnit Hong for their assistance. A graduate scholarship to C. Jantasunyarat from the
Royal Thai Government and funding from the Oregon Wheat Commission are
gratefully acknowledged.
39
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Basten CJ, Weir BS, Zeng ZB (1999) QTL Cartographer Version 1.13, A reference
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Lim TS, Loh WY (1996) A comparison of tests of equality of variances.
Computational Statistics and Data Analysis 22:287-30 1
Marino CL, Nelson JC, Lu YH, Sorrels ME, Leroy P, Lopes CR, Hart GE (1996)
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Branlard G (1998) Genetic analysis of wheat-extractable arabinoxylans in
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Nelson JC, Sorrels ME, Van Deynze AE, Lu YH, Atkinson M, Bernard M, Leroy P.
Farris JD, Anderson JA (1995a) Molecular mapping of wheat: major genes and
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Nelson JC, Van Deynze AE, Autrique E, Sorrels ME, Lu YH, Merlino M, Atkinson
M, Leroy P (1995b) Molecular mapping of wheat. Homoeologous group 2.
Genome 38:516-524
Nelson JC, Van Deynze AE, Autrique E, Sorrels ME, Lu YH, Negre 5, Bernard M,
Leroy P (1995c) Molecular mapping of wheat. Homoeologous group 3.
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Pestsova E, Ganal MW, ROder MS (2000) Isolation and mapping of microsatellite
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Roder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW
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Sorrells ME, Bernard M (1996) Linkage between RFLP markers and genes
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41
Van Deynze AE, Dubcovsky J, Gill KS, Nelson JC, Sorrels ME, Dvorak J, Gill BS,
Lagudah ES, McCouch SR, Appels R (1995) Molecular-genetic maps for
group 1 chromosomes of Triticeae species and their relation to chromosomes
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Worland AJ, Korzun V. Röder MS, Ganal MW, Law CN (1998) Genetic analysis of
the dwarfing gene Rht8 in wheat. Part II. The distribution and adaptive
significance of allelic variants at the Rht8 locus of wheat as revealed by
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Theor App! Genet 99:1221-1232
Chapter 3
Quantitative Trait Loci Influencing Free-Threshing Habit in Wheat
(Triticum aestivum L.)
C. Jantasuriyarat, M. I. Vales, C. J. W. Watson, and 0. Riera-Lizarazu
43
ABSTRACT
Glume tenacity and threshability of wheat (Triticum aestivum L.) have been
investigated because of their evolutionary significance and their practical importance
in breeding programs. Quantitative trait loci (QTL) that affected the free-threshing
character and glume tenacity were identified in a recombinant inbred line (RIL)
mapping population developed by the International Triticeae Mapping Initiative
(ITMI). The ITMI population was planted in three environments during 1999 and
2000. QTL analyses were performed using simple interval mapping (SJlvI) and
simplified composite interval mapping (sCIM) procedures. Two QTLs, one on
chromosome 2D and one on 4D, affecting threshability were identified. The QTL on
chromosome 4D has not been described previously. A QTL that affected glume
tenacity was also detected on chromosome 2D. Coincident QTLs on chromosome 2D
(Xgwm296 - Xgwm26l interval) that affected both threshability and glume tenacity are
believed to correspond to Tg, a gene for tenacious glumes. In addition, an amplified
fragment length polymorphism (AFLP) marker (XorstP3 74 7207) that was putatively
associated with Tg was identified using bulked segregant analysis. A QTL on
chromosome 5A (Xcdo 1326 Xabg39I interval) affecting glume tenacity was also
identified. The QTL on chromosome 5A is believed to represent Q, a gene known to
affect rachis fragility and glume tenacity. Information on the number, position, and
effect of QTLs determining components of free-threshing habit and their associated
molecular markers may facilitate the use of synthetic hexaploid germplasm.
44
INTRODUCTION
The shift from the primitive to the cultivated forms of hexaploid wheat
includes changes in morphological characters related to seed dispersal. These changes
have affected spike rachis fragility, spikelet disarticulation, awn development,
pubescence, grain size, glume tenacity, and threshability. Genotypes with soft glumes
that require limited mechanical action during the de-hulling process are considered
free-threshing. Based on spike rachis fragility and threshability, hexaploid wheat
(Triticum aestivum L.; 2n=6x=42, AABBDD) has historically been divided into six
subspecies: vulgare (common wheat), sphaerococcum, compactum, spelta, macha, and
vavilovii (Kimber and Sears 1983; Barnes and Beard 1992). Of these subspecies,
sphaerococcum, compactum, and vulgare wheats have a tough rachis and their seeds
are easily de-hulled (they are free-threshing) (Sears 1947; Unrau 1950). On the other
hand, subspecies spelta, vavilovii, and macha have a fragile rachis and they are not
free-threshing (Leighty and Boshnakian 1921; Kabarity 1966). The free-threshing
habit and tough rachis of common wheat are recessive to the non-free-threshing habit
and rachis fragility of spelta and vavilovii wheats (Kerber and Rowland 1974).
Previous genetic studies have revealed that numerous minor and one major
mutation were involved in the evolution of the free-threshing habit in hexaploid wheat.
MacKey (1966) reported a polygenic system scattered throughout all three genomes
that counteracted rachis brittleness and glume tenacity. Another system has been
described where a major gene or complex, Q, located on the long arm of chromosome
5A, affects rachis fragility and glume tenacity as well as the speltoid character (Kerber
and Rowland 1974). The two major effects of the dominant allele of Q are speltoid
45
suppression and squareheadedness. Wheat plants with the Q allele are shorter, their
spikes are more compact, their spike rachises are usually tougher, and their spikes are
free-threshing. All non-free threshing wild wheats are believed to carry the recessive q
allele and all free-threshing tetraploid and hexaploid wheats carry the dominant Q
allele. Muramatsu (1963) presented evidence that the Q allele is a triplication of the q
allele and five doses of the long arm of chromosome 5A of Triticum aestivum ssp.
spelta, which carries
q,
produced the normal squareheaded condition in Triticum
aestivum ssp. vulgare. Muramatsu (1963) suggested that Q might arise from q through
unequal crossing over. Studies on the phenotypic effects of Q in tetraploid and
hexaploid wheats suggested that the expression of the Q gene in speltoid suppression
is different at the tetraploid and at the hexaploid level.
Another genetic system governing threshability is associated with the D
genome. Kerber and Dyck (1969) found that a synthetic hexaploid (2n=6x=42,
AABBDD) produced by combining a free-threshing tetraploid (2n=4x=24, AABB)
with Aegilops tauschii (2n=2x=14, DD) was non-free-threshing despite the expected
homozygocity for the Q factor. Using monosomic and telosomic analysis, Kerber and
Rowland (1974) showed that the non-free-threshing character of synthetic hexaploid
wheat was due to the presence of a partially dominant gene for tenacious glumes, Tg,
on chromosome 2D. This gene, derived from Ae. tauschii, inhibited the expression of
Q.
Thus, a recessive tg allele as well as a dominant Q allele must be present for the
expression of the free-threshing character. In a recent study, Simonetti et al. (1999)
reported the locations of four QTLs associated with the free-threshing character in a
population derived from a cross between a cultivated durum (Triticum turgidum),
Messapia, and a wild tetraploid wheat, Triticum dicoccoides. These QTLs were
present on chromosome 2B, 5A, and 6A. The QTL on chromosome 2B, near Xpsr8O5,
was assumed to be homoeologous to the Tg locus on chromosome 2D and the QTL on
chromosome 5A, near Xpsr3 70, probably represented the Q locus since it associated
marker Xpsr3 70 has been reported by Galiba et al. (1995) to be linked with the Q
locus.
Synthetic wheat hexaploids (2n6x=42, AABBDD) have been shown to have
potentially useful genetic variation for wheat improvement (del Blanco et al. 2001) but
obstacles to their utilization in wheat improvement programs also exist. These
obstacles include the introduction of alleles from the non-adapted tetraploid durum
wheat parent (Cox 1998) and the low proportion of threshable progeny from wheat x
synthetic crosses (Villareal et al. 1996). Better understanding of the genetic basis of
threshability in synthetic hexaploid germplasm and the ability to use molecular
markers to select for threshable progeny from larger wheat x synthetic populations
might alleviate these problems.
A molecular marker linkage map of wheat composed of restriction fragment
length polymorphism (RFLP) (Nelson et al. 1995a; Nelson et al. 1995b; Nelson et al;
1995c; Marino et al. 1996; Van Deynze et al. 1995) and simple sequence repeat (SSR)
(ROder et al. 1998; Pestsova et al. 2000) markers was constructed using a recombinant
inbred line population derived from a cross between a CIMMYT wheat cultivar, Opata
85, and a synthetic hexaploid wheat, W-7984, by the collaborative genome mapping
project of the International Triticeae Mapping Initiative (ITMT). The ITMI mapping
population and the DNA-based marker information used in the construction of the
47
linkage map are publicly available (GrainGenes database-http://wheat.pw.usda. govi).
This valuable reference mapping population has already been used to unveil the
genetic determinants of a number of traits including kernel hardness (Sourdille et al.
1996), chlorosis resistance caused by Pyrenophora fritici-repentis (Fans et al. 1997),
Karnal bunt resistance (Nelson et al. 1998) leaf and stripe rust resistance (Nelson et al.
1997; Singh et al. 2000), and pentosan content (Martinant et aT. 1998). The ITMI
population is highly variable for other traits including threshability. Thus, the
objective of this study was to identify quantitative trait loci (QTL) that affected
threshability in this population. In addition, we report the identification of an
amplified fragment length polymorphism (AFLP) that was associated with a QTL on
chromosome 2D.
MATERIALS AND METHODS
Mapping population
The ITMI mapping population consists of recombinant inbred lines (RILs)
developed from a cross between the hard red spring wheat cultivar Opata 85 (Triticum
aestivum L. 2n=6x=42, AABBDD genomes) and a synthetic hexaploid wheat, W-7984
(Nelson et al. 1995a; Nelson et al. 1995b; Nelson et a!; 1995c; Marino et al. 1996; Van
Deynze et al. 1995). For this experiment, seeds for the ITMI population, the synthetic
hexaploid (W-7984), and Opata 85 were provided by Dr. C. Qualset, University of
California, Davis. Seeds for the durum wheat cultivar Altar 84 (Triticum turgidum L.
2n=4x=28, AABB) were provided by Dr. C. J. Peterson, Oregon State University.
48
Experimental design and phenotypic assessment
In April 1999, one-hundred and ten recombinant inbred lines (RILs), their
parents (Opata 85 and W-7984), and the durum cultivar Altar 84 (a parent of W-7984)
were planted at Hyslop Farm Field Laboratory, Corvallis, Oregon (referred to as
Hyslop farm in the rest of this paper). Opata 85, W-7984, and Altar 84 were planted in
three replications. Due to the limited seed available in 1999, the RILs were planted in
unreplicated 15-foot (4.6 m) rows. In April 2000, the mapping population, their
parents (Opata 85 and W-7984), and the durum cultivar Altar 84 were planted at the
University East Farm, Corvallis, Oregon (referred to as East farm in the rest of this
paper). Each line was planted in four-row plots (4
m2)
in a randomized complete block
design with two replications. The mapping population and parental lines were also
planted in the greenhouse at Oregon State University in February 2000 (referred to as
greenhouse in the rest of this paper). Each line was represented by one pot containing
four plants arranged in a randomized complete block design with two replications.
The RILs were evaluated for free-threshing habit by measuring percent
threshability and glume tenacity. In 1999, five randomly chosen mature spikes of each
line were selected for evaluation. In 2000, eight and four randomly chosen mature
spikes of each line were selected from plants grown at the East farm and at the
greenhouse, respectively. To measure percent threshability, the selected spikes were
processed through a gasoline-powered plant head thresher, collecting both threshed
and unthreshed seeds. Threshability was calculated as the percentage of completely
threshed seeds from all seeds harvested. To measure glume tenacity, a Hunter force
gauge [Model LKG-1, from AMETEK, Inc., Hatfield, PAl was used to measure the
force (N = Newton) necessary to disarticulate or break glumes at their base in four
randomly selected spikelets per spike. Glume tenacity data was not collected for
materials grown at Hyslop farm in 1999 because the force gauge was not available.
Statistical analysis
Normality and homoscedasticitytests were performed on all phenotypic data
sets. The test of normality was performed using PROC UNI VARIATE in SAS
Version 6.12 (Statistical Analysis System, SAS Institute 1996) and the test of
homogeneity of variance was performed using the software program hov Version 1.0
(Lim and Loh 1996). The three threshability and two glume tenacity data sets showed
a significant deviation from normal distribution and they were heterogeneous for their
variances. Accordingly, the analysis of variance was performed with each data set
separately using the General Linear Model procedure of SAS Version 6.12 (SAS
Institute 1996). Because normality was not significantly improved with
transformation, untransformed values were used for the analysis of variance.
Considering the effects of the genotypes random, estimates of variance components
were computed by equating mean squares to their expectations. Estimates of
heritability were calculated as:
recombinant inbred lines,
2e
h2 = 02g1 (02g +
c2elr) where
ü2g
is the variance among
is the error variance among recombinant inbred lines,
and r is the number of replications.
50
QTL analysis
To identify QTLs controlling the free threshing characters, phenotypic
evaluations were used in combination with public DNA marker data for the ITMI
population (GrainGenes database-http://wheat. pw. usda. gov/). QTL analyses were
performed using the Simple Interval Mapping (SIM) and simplified Composite
Interval Mapping (sCIM) procedures in the software package MQTL (Tinker and
Mather 1995) and in the software package QTL Cartographer (Basten et al. 1999).
Each linkage group was scanned at 5-cM intervals with 1,000 permutations and a
Type I error rate of 5%. A forward backward stepwise regression procedure in the
software package QTL Cartographer (Basten et al. 1999) was used to identify the co-
factors for composite interval mapping. Based on this analysis, a maximum of three
background markers per linkage group were used as co-factors. The significance of
QTL regions is reported as a test statistic (Tinker and Mather 1995). A primary QTL
was declared at position where both SIM and sCIM gave evidence for the presence of
a QTL. Individual and joint additive effects of QTLs were used to estimate the
proportion of phenotypic variation (R2) accounted for by significant QTLs. Detected
QTLs were paired and tested for epistatic interaction using analysis of variance to test
for differences between means of each genotypic combinations PROC GLM
procedure in SAS Version 6.12.
After QTL detection, the entire ITMI population was genotyped with simple
sequence repeat (SSR) markers (ROder et al. 1998; Pestsova et al. 2000) in regions
where QTLs had been detected (Table 3.1). Eighteen SSR markers were used to
genotype the entire ITM1 population and subjected to linkage analysis using GMendel
51
3.0 (Hollaway and Knapp 1994). Linkage groups were calculated using LOD 4.0 and
rmax 0.22. Marker order was checked by Monte Carlo Bootstrap simulation, using
annealing temperatures of 300 for the inner loop and 200 for the outer ioop. The
augmented genetic linkage map was combined with phenotypic data and reanalyzed as
described earlier.
DNA extraction and microsatellite genotyping
Genomic DNA was extracted from 5 g of young fresh leaf tissue, ground in
liquid nitrogen. Powdered leaf tissue was transferred to a 35 ml centrifuge tube on ice
and mixed with 10 ml of lysis buffer [8 ml of extraction buffer (0.03 M Tris HC1 pH
9.5, 0.03 M EDTA, 0.3 M KC1, 1.5 M Sucrose, 3 jiM spermidine, and 12 tM
spermine), 2 ml of 10% sarkosyl], plus 1
J.Ll
of 0.1% -mercaptoethanol. About 5 ml of
1:1 mixture of phenol and chloroform was added and mixed vigorously and then
centrifuged at 10,1 OOg for 10 mm in a J2-HC BEKMAN ultracentrifuge. The aqueous
layer was transferred to a clean centrifuge tube, and re-extracted with 1:1
phenol/chloroform as described above. The aqueous layer was transferred to a clean
centrifuge tube and 1 ml of 3 M sodium acetate (pH 5.0) plus 25 ml of ice-cold
absolute ethanol were added and mixed well. Precipitated DNA was spooled out with
a hooked pasteur pipette. The isolated DNA was rinsed three times with 70% ice-cold
ethanol, and allowed to dry for 10 mm. Finally, the DNA was resuspended in 200 p1 of
TE pH 8.0 (10 mM Tris HC1 pH 8.0 and 1 mM EDTA pH 8.0) containing 10 jig/mi of
RNase.
52
Table 3.1 Wheat microsatellite loci, primer sequences, and annealing temperatures
Locus
Primers
Tm (°C)
XgwmI26-5A
CACACGCTCCACCATGAC
GTTGAGTTGATGCGGGAGG
AAGTTGAGTTGATGCGGGAG
CCATGACCAGCATCCACTC
GATCTGCTCTACTCTCCTCC
CGACGCAGAACTTAAACAAG
TGCATCAAGAATAGTGTGGAAG
TGAGAGGAAGGCTCACACCT
CTCCCTGTACGCCTAAGGC
CTCGCGCTACTAGCCATTG
CATCCCTACGCCACTCTGC
AATGGTATCTATTCCGACCCG
AATTCAACCTACCAATCTCTG
GCCTAATAAACTGAAAACGAG
ATTCGGTTCGCTAGCTACCA
ACGGAGAGCAACCTGCC
ACATCGCTCTTCACAAACCC
AGTTCCGGTCATGGCTAGG
GCATAGCATCGCATATGCAT
GCCACGCTTGGACAAGATAT
GCGACATGACCATTTTGTTG
GATATTAAATCTCTCTATGTGTG
TTGATATTAAATCTCTCTATGTG
AATTTTATTTGAGCTATGCG
CTAGCCAGAAGGTTACTTTG
CAACATTAACATTAACGCAC
CCTGCTCTGCCCTAGATACG
ATGTGAATGTGATGCATGCA
TTCTTTGCGTGTGTGCGT
CGCACTTTTTACTCGGGGTC
AGCAACAAACGCGAGAGC
TGACACCCGGTTGTTGG
GCAGGCGTGTTACTCCAAGT
CCGAGGTGGATAGGAGGAAA
GAGCAGGCAGCAGCTAGC
TGCATCATCATCGGTCAAGT
60
Xgwml79-5A
XgwmJ94-4D
Xgwm2lO-2D
Xgwm26l-2D
Xgwm29l-5A
Xgwm296-2D
Xgwm455-2D
Xgwm484-2D
Xgwm595-5A
Xgwm6O9-4D
Xgwm624-4D
Xgdm5-2D
Xgdm35-2D
Xgdm6l-4D
XgdmlO7-2D
XgdmI25-4D
XgdmI29-4D
a
Reported by ROder et al. (1998) and Pestsova Ct al. (2000).
55
5
60
55
60
55
55
55
60
50
50
50
55
60
55
60
60
a
53
The polymerase chain reaction (PCR) amplification of SSRs were performed
in a MJ Research, Inc (Watertown, MA) thermocycler using conditions described by
ROder et al. (1998). Fluorescent detection of PCR amplification products was achieved
by using reverse primers labeled with either 5-carboxy-fluorescein (5-FAM) or
4,7,2' ,4' ,5 ' ,7' -hexachloro-6-carboxyrhodamin (HEX). PCR amplification products
were prepared and sent to the Central Service Laboratory (CSL) at the Center for Gene
Research & Biotechnology (CGRB), Oregon State University. At the CSL, PCR
products were eletrophoresed and detected in an ABI Prism
377 DNA Sequencer
(PE Applied Biosystems). ABI collection software version 1.1 was used for raw data
collection. Microsatellite fragments were analyzed using GENEScAN
analysis
software version 2.1 as described in the user's manuals.
Bulked segregant analysis
At least two major QTLs that affected threshability, one on chromosome 2D
and one on chromosome 4D were detected. An additional objective of this study was
to find additional markers that were linked to a major QTL affecting threshability in
the ITMI population. To achieve this goal, a bulked segregant analysis (Michelmore et
al. 1991) procedure was used. The QTL on chromosome 2D was targeted for bulked
segregant analysis taking into account the QTL on chromosome 4D. First, RILs were
divided into two groups based on the source of the QTL on chromosome 4D (Opata 85
vs. W-7984). Subsequently, Fisher's least significant difference (F-LSD) was used to
differentiate genotypes that were threshable from those with lower threshability andlor
glume tenacity in each group. To achieve this, the mean for each genotype was
54
compared to the means of both parents (Opata 85 and W-7984) of the population.
Three data sets for threshability and two data sets for glume tenacity were evaluated.
In the first group, all genotypes had the W-7984 allele at the chromosome 4D QTL. If
a RIL had a mean that was not significantly different from the mean of W-7984, it was
assigned to the non-threshable group and the rest were assigned to the threshable
group. The same method was used to subdivide the second group that contained the
Opata 85 allele at the chromosome 4D QTL. These four groups of genotypes and the
parents of the ITMI population were then used for bulked segregant analysis with
AFLP markers.
AFLP analysis
The AFLP method developed by Vos et al. (1995) was followed with some
modifications. Genomic DNA (1.5 j.tg) was digested with 3.75 units of MseI (New
England Biolabs Inc., Beverly, MD) in 50 p.1 of restriction buffer (50 mM NaC1, 10
mM Tris-HC1, 10 mM MgCl2, 1 mM DTT, and 100 p.g/ml BSA, pH 7.9) at 37°C for 5
hours. About 1 p.! of 2.5 M NaC1 was added to the reaction with 3.75 units of PstI
(Amersham Pharmacia Biotech, Piscataway, NJ). This solution was then incubated at
37°C for 12 hours or overnight. About 5 p.! of restriction and digestion products were
electrophoresed on a 1% agarose gel to ensure that the DNA had been completely
digested. Adaptors were prepared by combining equimolar quantities of single
stranded oligonulceotides, 5 pmol/ml for PstI and 50 pmol/ml for
MseI,
and annealed
by incubating at 95°C for 5 mm, 65°C for 10 mm, 37°C for 10 mm, and followed by
gradual return to room temperature. Ligation cocktail [lx T4 DNA ligase buffer, 1
55
Unit of T4 DNA ligase (Promega, Madiso, WI), 5 pmol PstI adaptor, and 50 pmol
MseI
adaptor, 10 j.il total volume] was added to the digestion reaction and incubated at
room temperature for 2 hours.
Pre-amplification was performed using primers specific for the PstI and MseI
adaptors, including one selective nucleotide, followed by selective amplification using
similar primers with three selective nucleotides. A total of six PstI and sixteen MseI
primers were used, giving a total of 96 possible primer combinations (Table 3.2). Five
j.tL of digestion and ligation products were added to the pre-amplification PCR
cocktail [4 1iL of lOx PCR buffer (Promega), 50 ng MseI +N, 50 ng PstI + N, 0.2 mM
each dNTP, 1.5mM MgC12, and 1.5 units Taq polymerase (Promega); 35 tl total
volume] and subjected to PCR using a MJ Research PTC-l00 (MJ Research, Inc.,
Watertown, MA) thermocycler. The PCR protocol included a step of 94°C for 2 mm
followed by 40 cycles of 94°C for 30 sec, 50°C for 30 sec, and 72°C for 60 sec with a
final step of 72°C for 5 mm. Five microliters of pre-amplification PCR products were
electrophoresed on 2% agarose gel to determine that DNA pre-amplification was
successful. The pre-amplification PCR products were then diluted 1:5 in TE pH 8.0
(10 mM Tris HCI pH 8.0 and 1 mM EDTA) before being used as template in the
selective amplification step.
Five microliters of diluted pre-amplifi cation PCR products were combined
with selective amplification PCR cocktail [4 gi of lOx PCR buffer (Promega), 30 ng
MseI
+NNN, 5 ngFstl + NNN, 0.2mM each dNTP, 1.5mM MgC12, and 1.5 units Taq
polymerase (Promega); 15 tI total volume], where the MseI+NNN primer was labeled
with y-P33 using T4 polynucleotide kinase (Promega). A touch down PCR cycle profile
56
was performed with the first step at 94°C for 2 mm, followed by the first PCR cycle at
94°C for 30 sec, 65°C for 30 sec, and 60°C for 60 sec, followed by 12 cycles in which
the annealing temperature decreased 0.7°C per cycle, followed by 23 cycles with the
annealing temperature at 56°C with a final step of 72°C for 10 mm.
The selective amplification PCR products were mixed with 4 p.1 loading buffer
(98% formamide, 10 mM EDTA, 0.15% Bromophenol blue, 0.15% Xylene Cyanol),
denatured at 95°C for 3 mm and quickly put on ice. Four microliters of the mixture
were loaded onto a 0.4 mm 6% denaturing polyacrylamide gel in lx TBE running
buffer. The gel was run at constant power (60 W) for 3 hours, transferred to two layers
of chromatography paper (3MM) (Fisher Scientific, Pittsburgh, PA, USA), and dried
on a gel dryer (Fisher Biotech, Fisher Scientific, Pittsburgh, PA, USA) under vacuum
at 80°C for 2 hours. X-ray film was exposed to the dry gel with the help of Kodak
BioMax MS (Eastman Kodak Company, Rochester, NY) intensifying screen in a
Kodak bioMax cassette. The cassette and its contents were kept in a -80°C freezer for
24 hours before the X-ray film was developed. AFLP bands were visually scored from
autoradiographs.
57
Table 3.2 Oligonucleotide sequences used for amplified fragment length
polymorphism (AFLP) analysis.
Name
LvfseI
adaptor (Forward)
MseI adaptor (Reverse)
PstI adaptor (Forward)
PstI adaptor (Reverse)
MseI + I primer
PstI +1 primer
MseI +3 primers
FstI+3 primers
Sequence
5' -GACGATGAGTCCTGAG-3'
5' -TACTCAGGACTCAT-3'
5 '-CTCGTAGACTGCGTACATGCA-3'
5 '-TGTACGCAGTCTAC-3'
5' -GACGATGAGTCCTGAGTAA/C-3'
5' -GACTGCGTACATGCAG/A-3'
5' -GACGATGAGTCCTGAGTAA/CGG-3'
5' -GACGATGAGTCCTGAGTAA/CGA-3'
5 '-GACGATGAGTCCTGAGTAAICGT-3'
5' -GACGATGAGTCCTGAGTAA!CGC-3'
5' -GACGATGAGTCCTGAGTAA/CAG-3'
5' -GACGATGAGTCCTGAGTAAICAA-3'
5' -GACGATGAGTCCTGAGTAAICAT-3'
5 '-GACGATGAGTCCTGAGTAA/CAC-3'
5' -GACGATGAGTCCTGAGTAA/CTG-3'
5' -GACGATGAGTCCTGAGTAA!CTA-3'
5' -GACGATGAGTCCTGAGTAA!CTT-3'
5' -GACGATGAGTCCTGAGTAAICTC-3'
5' -GACGATGAGTCCTGAGTAAICCG-3'
5 '-GACGATGAGTCCTGAGTAAICCA-3'
5' -GACGATGAGTCCTGAGTAA/CCT-3'
5' -GACGATGAGTCCTGAGTAA!CCC-3'
5' -GACTGCGTACATGCAG/ACG-3'
5' -GACTGCGTACATGCAG/ACA-3'
5' -GACTGCGTACATGCAG/ACT-3'
5' -GACTOCGTACATGCAOIACC-3'
5 '.-GACTGCGTACATGCAG/AGG-3'
5 '-GACTGCGTACATGCAG/AGC-3'
58
RESULTS
Analysis of phenotypic data
The distribution of percent threshability in the ITMI population was skewed
toward the more threshable group and deviated significantly from a normal
distribution (Figure 3. 1A). The distribution of glume tenacity in the ITMI population
was also skewed but toward the softer glume group and deviated significantly from a
normal distribution (Figure 3. IB).
Glume tenacity was significantly different between the parental lines in two
environments (East farm and greenhouse). W-7984 had significantly tougher glumes
than Opata 85 and Altar 84. Altar 84, a parent of W-7984, did not significantly differ
from Opata 85 with respect to glume tenacity (Table 3.3). The average force required
to break a glume from spikelets of the ITMI RILs was 0.16 N for materials grown at
East farm, and 0.11 N for materials from the greenhouse. The force required to break
glumes ranged from 0.03 to 0.51 N in materials from East Farm and from 0.02 to 0.44
N in materials grown in the greenhouse (Table 3.3).
W-7984 and Opata 85 differed significantly for percent threshability
measurements in materials from all three environments. Altar 84, a parent of W-7984,
did not differ significantly from Opata 85 in two environments (East farm, and
greenhouse). Altar 84 had significantly lower threshability than Opata 85 in materials
grown at Hyslop farm (Table 3.3). The RILs had an average percent threshability of
70.6 % when grown at Hyslop farm, 73.2 % when grown at East farm, and 89.9 %
59
when grown at the greenhouse. Percent threshability for the RILs ranged from 18.2 to
100 %, 28.6 to 100 %, and 33.3 to 100 % in materials grown at Hyslop farm, East
farm, and the greenhouse, respectively (Table 3.3).
The estimated heritability for glume tenacity was 96.4 % at East farm and 98.4
% at the greenhouse. The estimated heritability of percent threshability was 91.7 % at
East farm and 88.8 % at the greenhouse. Percent threshability and glume tenacity were
negatively correlated (P < 0.001) with correlation coefficients of 0.76 and 0.66 for
data from East farm and the greenhouse, respectively.
QTL detection
Glume Tenacity
Glume tenacity was evaluated in only two environments (East farm and
greenhouse). Two QTLs were detected for glume tenacity, one on chromosome 2D
and one on chromosome SA (Table 3.4). The QTL on chromosome 2D, flanked by
Xgwm296 and Xgwm26l, was detected in the two environments tested (East farm and
greenhouse). W-7984 contributed the higher value allele. This QTL on chromosome
2D explained 15% of the phenotypic variance at East farm and 24% of the phenotypic
variance in the greenhouse. Another QTL on chromosome 5A, flanked by Xcdo 1326
and Xabg39l, was detected in one environment (East farm). W-7984 also contributed
the higher value allele. This QTL explained 16% of the phenotypic variance. These
two QTLs accounted for 33% of the phenotypic variance at East farm. QTL x
environment interactions were not significant. No significant epistatic interactions
were found.
Threshability
Two QTLs that affected percent threshability were found (Table 3.4). The QTL
on the short arm of chromosome 2D was flanked by Xgwm296 and Xgwm26l. Percent
threshability increased with the Opata 85 allele at this locus. The QTL accounted for
33 to 53% of the phenotypic variance. At Flysiop farm, one QTL was detected on
chromosome 4D, close to XJbb226. W-7984 contributed the higher value allele at this
locus. This QTL accounted for 16% of the phenotypic variance. QTLs on
chromosomes 2D and 4D accounted for 49% (Hyslop farm), 53% (East farm), and
33% (greenhouse) of the phenotypic variance. Both QTLs were confirmed in the QTL
analysis across environments (Figure 3.4). Significant QTL x environment interactions
were found for the QTL on chromosome 2D. The significant QTLs were also tested
for two-locus epistatic interactions. No significant epistatic interactions were found.
61
Table 3.3 Mean trait values of the parental lines W-7984, Opata 85, and Altar 84, and
110 recombinant inbred lines (RILs) derived from the cross between W-7984 and Opata
85, plus standard deviations (SD), minima (Mm.), and maxima (Max.) for phenotypic
values from three environmentsa
Environments
Lines
1-lyslop farm
1999
W-7984
Glume Tenacityb
(N)
85.lb
mean
range
70.6
18.2-100.0
19.7
SD
East farm
2000
W-7984
Opata 85
Altar 84
RILs
mean
range
SD
Greenhouse
2000
W-7984
Opata 85
Altar 84
RILs
(%)
26.6c
97.7a
Opata85
Altar 84
RILs
Threshabilityc
mean
range
SD
0.59a
0.04b
0.05b
0.16
0.03-0.51
0.10
32.7b
98.5a
87.8a
73.2
28.6-100.0
0.61a
0.03b
0.05b
41.6b
99.3a
94.6a
89.9
33.3-100.0
0.11
0.02-0.44
0.07
19.0
12.2
Mean traits values for the parental lines W-7984, Opata 85, and Altar 84 followed by
the same letter are not significantly different at the 0.05 probability level.
b
Glume tenacity is expressed as the force (N = Newton) necessary to disarticulate a
glume in a spikelet.
Threshability is expressed as the proportion of threshed seeds from all seeds obtained
after threshing.
a
62
Hyslop Farm
East Farm
Greenhouse
A
No.
of
RILs
a)
I
n
D
2)
0
D
a)
W
0
5
.111
"I'I
1)3)
)
2)
:
a)
)
t_.Ii
1)3)
a)
)
Percent threshability
B
No.
of
RILs
w
w
010
03)
03)
04)
010
03)
03)
04)
Force (N) necessary to break glumes
Figure 3.1 Phenotypic frequency distributions for threshability (A) and glume
tenacity (B) for Hyslop Farm, East Farm, and greenhouse. Parental values for W7984 (W) and Opata 85 (0) are indicated with arrows.
Table 3.4 Quantitative trait locus significance, effect, and percentage of phenotypic variation accounted for for free-threshing
habit based on simple interval mapping analysis of 110 recombinant inbred lines (RILs).
Additive effecta
Trait
Environment
Chromosome Nearest locus
Glume tenacity (N)
East farm, 2000
2DS
5AL
Xgwm296-Xgwm26l
Xcdo1326-Xabg39l
[multi-locus modellb
16.03
17.54
0.15
0.16
0.33
0.077
0.102
Greenhouse, 2000
2DS
Xgwm296-Xgwm26l
22.98
0.24
0.073
Hyslop farm, 1999
2DS
4DL
Xgwm296-Xgwm26I
XJb/,226-Xcdo949
[multi-locus modellb
53.99
19.05
0.39
0.16
0.49
-24.52
18.83
East farm, 2000
2DS
Xgwm296-Xgwm26I
76.53
0.53
-27.21
Greenhouse, 2000
2DS
Xgwm296-Xgwm26l
32.49
0.33
-13.22
Threshability (%)
Test statistic
R2
Additive effects indicates an additive SIM main effect of the parent contributing the higher value allele: positive values indicate that higher value alleles
are from W-7984 and the negative values indicate that higher value alleles are from Opata 85.
b
Multi-locus model includes all significant QTL5 in the calculation of the proportion of the phenotypic variation accounted for by these QTLs.
a
65
Chromosome 2D
A
C.)
U)
i;;
C,
25
SIM
20
SIMxE
15
sCIM
10
5
kIiIITIIiIIIIiI
Chromosome 5A
35
30
C.)
SIM
U)
SIMxE
sCIM
10
"C
-11
'-J
-L
t.
C
-'
C
Figure 33 Scans of test statistic (Y-axis) for simple interval mapping (SIM,
thick solid lines), simplified composite interval mapping (sCIM, broken
lines), and QTL x environment interaction (thin solid lines) for glume
tenacity of the full population of Opata 85 x W-7984 recombinant inbred
lines (RILs). Wheat chromosomes 2D and 5A are shown. Horizontal lines
represent thresholds for testing SIM, estimated from 1000 permutations, an
experiment-wise Type I error rate below 5%.
Chromosome 20
200
150
SIM
U,
SIMxE
100
U)
@3
i-
sCIM
50
LIJ
-
30
cJ
-"C
"C O
'J
-
'.-.
30
Chromosome 4D
o
U)
40
SM
30
SIMxE
20
sCIM
@3
10
30
'
.Jl
Figure 3.4 Scans of test statistic (Y-axis) for simple interval mapping (SIM,
thick solid lines), simplified composite interval mapping (sCIM, broken
lines), and QTL x environment interaction (thin solid lines) for threshability
of the full population of Opata 85 x W-7984 recombinant inbred lines
(RIiLs). Wheat chromosomes 2D and 5A are shown. Horizontal lines
represent thresholds for testing SIM, estimated from 1000 permutations, an
experiment-wise Type I error rate below 5%.
67
Bulked segregant analysis
To identify additional markers linked to a QTL region on chromosome 2D, a
bulked segregant analysis was conducted. A total of four bulks were created.
Individuals in bulk number one and bulk number two contained the W-7984 allele for
the QTL on chromosome 4D. Individuals in bulk number three and bulk number four
contained the Opata 85 allele. Individuals in bulk number one and three had percent
threshability and glume tenacity values not significantly different from the W-7984.
Individuals in bulk number two and four had percent threshability and glume tenacity
values not significantly different from Opata 85. There were twelve individuals in bulk
one, eleven individuals in bulk two, nine individuals in bulk three, and eight
individuals in bulk four.
AFLP analysis
Ninety-six AFLP primer combinations were used to screen parents of the ITMI
mapping population, Opata 85 and W-7984, and bulks one, two, three, and four. Each
primer combination generated from 24 to 96 scorable bands, of which between 1 to 26
were polymorphic between the parents of the ITMIT mapping population. Overall,
15.5% of the AFLP markers were polymorphic between the Opata 85 and W-7984. Of
the 96 primer combinations tested, two primer combinations (PstI ACG
and PstI AGC
+ MseI
+ MseI
CAA
CGC) produced amplification products that were present in bulk
one, bulk three, and W-7984 (Figure 3.5). Linkage between the QTL region on
chromosome 2D and the amplified products was confirmed by mapping these AFLP
markers. These AFLP markers were mapped using G-MENDEL, as described earlier.
Two AFLP loci, XorstP4O56I24 and XorstP374 7207, were linked to markers on
chromosome 2D. The addition of XorstP4O56l24 to the linkage map resulted in
excessive map expansion. Thus, XorstP4O56I24 was not added to the linkage map on
chromosome 2D. The addition of XorstP3 74 7207 to the linkage map of chromosome
2D had no detrimental effects. XorstP3 74 7207 was localized 4.2 cM proximal to
Xgwm296 (Figure 3.6).
DISCUSSION
Previous studies have suggested that the free-threshing habit in wheat is
controlled by at least two major genes, the Q locus on chromosome 5A and the Tg
locus on chromosome 2D (Kerber and Rowland 1974; Kerber and Dyck 1969). In this
study, the ITMI mapping population was used to identify QTLs that affected free-
threshing habit and glume tenacity in wheat. Two QTLs, one on chromosome 2D and
one on chromosome 4D, were detected for threshability and two QTLs, one on
chromosome 2D and one on chromosome 5A, were detected for glume tenacity. The
coincident QTLs on chromosome 2D detected with measurements of threshability and
glume tenacity probably represent the same QTL (Table 3.4, Figure 3.3 and 3.4). A
W-7984 allele at the chromosome 2D QTL decreased threshability and increased
glume tenacity. Ae.
tauschii
was the donor of chromosome 2D in W-7984. Thus, the
QTL in the Xgwm296 - Xgwm26I interval that affected both threshability and glume
tenacity on chromosome 2D probably correspond to Tg, a gene for tenacious glumes.
This interpretation is also consistent with the report of Simonetti and co-workers
,Ii
2DS
cM
Xbcdl8
26.6
Xgwm455
9.8
Xgwm296
4.2
XorstP3 747207
20.6
Xcdol 376
15.9
Xgwm484
18.6
XgwmlO2
20.0
Centromere
Xbcd26O
2DL
Figure 3.6 Genetic map of the chromosome 2D showing location of
AFLP marker: XorstP3 74 7207.
71
(1999), who found two major QTLs, one on chromosome 2B and one on 5A, that
affected threshability in a RIL population derived from a cross between a durum wheat
cultivar and T. dicoccoides. The QTL on chromosome 2B was believed to represent a
homoeologue of Tg (named Tg2). Based on map comparisons (see Roder et al. 1998
and Korzun et al. 1999), the location of our QTL on chromosome 2D and Simonetti et
al's QTL on chromosome 2B are homoeologous. Based on this evidence, our study
represents the first report where Tg on chromosome 2D has been tagged with SSR
markers (Xgwm296 and Xgwm26l). In addition, we used bulked segregant analysis to
identify two DNA-based marker (AFLPs) associated with Tg (Figure 3.5). One of
these AFLP markers (XorstP4056124) caused map expansion and could not be
reliably mapped. The other AFLP marker (XorstP3 74 7207) was linked to Xgwm296
(Figure 3.6).
A QTL on chromosome 5A (Xcdo1326 Xabg39I interval) also affected
glume tenacity. An Opata 85 allele of this QTL reduced glume tenacity. The location
of our QTL on chromosome 5A and that of Simonetti et al. (1999) are virtually the
same. Thus, both of these QTLs probably represent Q. In contrast to the report of
Simonetti et al. (1999), we were not able to detect the effect of Q on threshability but
we did detect its effect on glume tenacity. This may be due to differences in the effect
of various Q alleles in the populations studied. In our case, W-7984, the synthetic
hexaploid parent of the ITMII population, is believed to possess a dominant Q allele
from its free-threshing durum parent, Altar 84. Likewise, Opata 85, the common
wheat parent of the ITMII population, possesses a dominant Q allele. In theory, the
ITIvil RILs were homozygous for Q. Thus, variation in the effect of Q on threshability
72
and glume tenacity was not expected in the ITMI population. On the other hand,
differences in the effect of Q alleles between hexaploid and tetraploid wheats have
been reported (Muramatsu 1963). Our results suggest that in the ITMI population, the
Q allele donated by the tetraploid wheat, Altar 84, and the Q allele from the hexaploid
wheat, Opata 85, have a different effects only in glume tenacity but their effect on
threshability per se might have been diluted by the overpowering effect of Tg (on
chromosome 2D). Messapia, the durum wheat parent of the RIL population used by
Simonetti and others (1999), possesses a dominant Q allele. However, the T.
dicoccoides
parent (MG4343) of the population is believed to possess a recessive q
allele. In this case, a significant effect of Q on both threshability and glume tenacity
might be expected.
A new QTL on chromosome 4D that affected threshability was also detected in
this study. A W-7984 allele at this QTL locus increased threshability. This result was
unexpected since W-7984 and its Ae.
tauschii
parent are non-free threshing genotypes.
However, QTLs with favorable effects that have originated from wild relatives have
been reported in other interspecific crosses (DeVincente and Tanksley 1993). If Opata
85 and W-7984 had complementary favorable alleles that control threshability, then
one might expect to identify positive trangressive segregrants. However, transgressive
segregants were not observed (Figure 3.1). Tests for epistatic interactions between
QTLs on chromosomes 2D and 4D were also not significant. Thus, the effects of these
QTLs were not complementary. The nature of the QTL on chromosme 4D cannot be
explained at this time.
73
Our study suggests that Tg and Q are major loci controlling threshability and
glume tenacity in the ITMI population. This conclusion is in agreement with reports
suggesting that a minimum of two genes control the free-threshing character in crosses
involving synthetic wheats (Kerber and Dyck 1969, Kerber and Rowland 1974,
Villareal et al. 1996). An additional QTL on chromosome 4D has also been identified.
n addition, DNA-based markers that were significantly associated with the pertinent
genetic loci were also identified. Since synthetic wheats represent potential new
sources of genetic variation for crop improvement, our results
will
hopefully facilitate
their utilization.
ACKNOWLEDGEMENTS
We thank Dr. C. J. Peterson for use of field facilities and Ryan Millimaki and
Jumnit Hong for their assistance. A graduate scholarship to C. Jantasuriyarat from the
Royal Thai Government and funding from the Oregon Wheat Commission are
gratefully acknowledged.
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77
Chapter 4
Conclusion
The objective of the first part of this research was to use quantitative trait
mapping to identify regions of the wheat genome associated with spike compactness
components (Chapter 2). For this purpose, a wheat mapping population developed by
the International Triticeae Mapping Initiative (ITMI) was used. This population is
composed of recombinant inbred lines (RILs) derived from a cross between the hard
red spring wheat cultivar Opata 85 and a synthetic hexaploid wheat, W-7984 (Nelson
et al. 1995a; Nelson et al. 1995b; Nelson et al; 1995c; Manno et al. 1996; Van Deynze
et al. 1995). Our results showed that spike morphology (spike length, number of
spikelets per spike, and rachis internode length) was controlled by multiple genetic
loci on chromosomes 1B, 4A, and 6A. Two quantitative trait loci (QTLs), one on
chromosome lB and one on 4A, affected spike length. One QTL controlling spikelet
number was also detected on chromosome 4A. This QTL coincided in location with
the QTL on chromosome 4A that affected spike length. One QTL on chromosome 6A
affected spike compactness. There was no linkage between these four QTLs and major
genes (i.e. Q, C, Si, Ppdi, and Ppd2; Worland et la. 1987; McIntosh et al. 1998)
known to affect spike morphology. These results suggested that differences in spike
morphology were not due to allelic variation at these loci.
Genetic loci on chromosomes 4A and 6A that affected spike length and spike
compactness, respectively, have been described previously (Sourdille et al. 2000;
Kuspira and Unrau 1957). A QTL on chromosome lB that affected spike length has
78
not been described previously. Only one of the parents of the ITIvil population, Opata
85, contributed all of the favorable alleles for QTLs that affected the spike
characteristics studied. For this reason, progenies with longer and denser spikes than
Opata 85 were not expected. An increase in spike length without sacrificing spike
density might be possible by combining the QTL on chromosome 6A that affected
spike compactness in this study with complementary QTLs that affected spike length
in other experimental populations (Sourdille et al. 2000).
The second objective of this research was to identify regions of the wheat
genome that were associated with the free-threshing habit in wheat (Chapter 3). Again,
the ITMI mapping population was used. Threshability and glume tenacity was
controlled by multiple genetic loci on chromosomes 2D, 4D, and 5A. Two QTLs, one
on chromosome 2D and one on 4D, affected threshability. A QTL that affected glume
tenacity was also found on chromosome 2D. Coincident QTLs on chromosome 2D
that affected both threshability and glume tenacity are believed to correspond to Tg, a
gene for tenacious glumes. A QTL on chromosome 5A affected glume tenacity. This
QTL on chromosome 5A is believed to represent Q, a gene known to affect rachis
fragility and glume tenacity.
The third objective of this thesis was to find additional molecular markers that
were linked to a major QTL on chromosome 2D affecting threshability in wheat
(Chapter 3). Bulked segregant analysis was used to identify two DNA-based marker
(AFLPs) associated with the QTL on chromosome 2D. One of these AFLP markers
79
(XorstP4O56l24) caused map expansion and could not be reliably mapped. The other
AFLP marker (XorstP3 747207) was linked to Xgwm296 (Figure 3.6), a marker that
was found to be significantly associated with both threshability and glume tenacity.
Studies on the free-threshing habit suggest that Tg and Q are major loci
controlling threshability and glume tenacity in the ITMI population. This conclusion is
in agreement with reports suggesting that a minimum of two genes control the freethreshing character in crosses involving synthetic wheats (Kerber and Dyck 1969,
Kerber and Rowland 1974, Villareal et al. 1996). An additional QTL on chromosome
4D has also been identified. In addition, DNA-based markers that were significantly
associated with the pertinent genetic loci were also identified. Since synthetic wheats
represent potential new sources of genetic variation for crop improvement, our results
will hopefully facilitate their utilization.
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