Genetic Analysis of Quality Traits for Food-Grade Soybean (Glycine max L. Merr.) in a
Breeding Population
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in
the Graduate School of The Ohio State University
By
Mao Huang
Graduate Program in Horticulture and Crop Science
The Ohio State University
2012
Master's Examination Committee:
Dr. Leah K. McHale, Advisor
Dr. David Francis
Dr. M. A. Rouf Mian
Dr. Gonul Kaletunc
Copyrighted by
Mao Huang
2012
Abstract
Food-grade soybeans are a specialty crop with unique chemical and
physical seed quality requirements. The tofu production market requires large,
round soybean seed with a clear hilum and high protein content. The aim of
this project was to detect QTL responsible for traits important in food-grade
soybeans, and to provide information for the selection of food-grade soybeans
in a breeding program. This study assessed seed shape, size, density, weight,
protein content, and oil content in two independent populations of breeding
lines and cultivars as well as textural traits of the tofu produced from a subset
of those. By identifying the relationship between seed traits and tofu texture,
the previously established positive correlation between seed protein content
and the stiffness and gel strength of tofu were confirmed. Little to no
correlations between seed size and shape measurements and the texture of tofu
were detected, indicating that this consumer preference for large, round seeds
may not relate to tofu quality. The effects of selection for specialty traits on the
genetic differentiation within a breeding program were assessed, results shown
that both parental and progeny selection contributed to genetic differentiation
and population structure. Association mapping was conducted with 504
markers in a population of 242 breeding lines and cultivars, leading to the
identification of 50 significant marker-trait associations. Of the 27 significant
ii
associations tested in another independent confirmation population consisting
of 152 lines or cultivars, 12 were confirmed. These results can be directly
applied to increase selection efficiency in a breeding program.
iii
Dedication
To all who I love and who have been supporting me and pouring their love into
my life!
iv
Acknowledgments
I want to sincerely first thank my adviser Dr. Leah K McHale for her teaching and
guidance. I am very grateful that I have joined in her lab for the past over two
years. Her profound knowledge in plant breeding has helped me gradually develop
my interests in this area. I have learnt from Dr. McHale not only the knowledge
and skills, but also the optimistic attitude towards science research. Most
importantly, her good personal characters have affected me, as well as all other lab
members, in enjoying cheerful lab work atmosphere. Dr. McHale has always been
very supportive and encouraging to her students. I feel honored to be her student
and lucky to share a valuable path of life with Dr. McHale.
I want to thank all my committee teachers: Dr. David Francis, Dr. Gonul Kaletunc,
and Dr. Rouf Mian. They have all been very patiently teaching me and supporting
this project. Dr. David Francis especially has invested his time in showing me to
learn how to be a better researcher. I am thankful that they have been providing me
valuable comments and suggestions for this project and have been generously
spending their time for this dissertation.
I thank Dr. Steve St. Martin wholeheartedly for his dedicated teaching. Dr. Steve
St. Martin has been providing patient guidance to help me better understand this
v
project. I want to also thank all my lab members and the student workers that have
been supporting and encouraging me with this project: Dr. Veena Ganeshan,
Jeesica Schwartz, Qianli Shen, Kamila Rezende Dazio de Souza, Brad Snyder and
our previous lab member Sumin Lee are good friends to work with; I have learnt
different things from each of them. I also want to thank Rhiannon Schneider,
Elizabeth Baskin, Christine Dubler, Angela Parker, and our lab manager Amanda
Gutek for spending their time helping me with my experiments. Besides, I want to
thank Dr. Asela Wijeratne, Jody Whittier from MCIC, and Dr. Stephen Opiyo for
their help with the genotyping process.
I thank my beloved mum, dad and all my other family members. They have been
strongly supportive to me far cross the ocean from China. Their unconditional love
is the strongest source of my strength!
I appreciate the short internship experience in Pioneer to allow me to learn and
broaden my experience towards industrial production; I want to thank my
internship boss and colleagues who have been encouraging me and teaching me:
Joseph Stull, Ryan Morrison, John Woods, Paula Burkholder, Goran Synic, and
David Whitaker.
Many thanks also go to my wonderful friends and classmates that have been
providing me the support in the past over two years: Bruce and Karen Messenger,
vi
Tim and LaRonda Stauffer, Mark and Amy Newmeyer, Karen Oliva, Rich
Mendola, Daniel Thomas, Leighton Buntain, Andika Gunadi, Jose Mendoza, Lisa
Friedberg, Xuan Zhu, Xueqing Geng, Ruiqiang Liu, Yanru Liang, Dan Liu,
Lingzhi Li, Beizhen Hu, Zhifen Zhang, Yuting Chen, and Lin Jin.
Also, thank Ohio Soybean Council for funding support!
vii
Vita
March, 1988 ...................................................Born: Hunan Province, China
2010................................................................B.S. Agronomy, China Agricultural
University
2012................................................................Internship, Pioneer Hi-Bred, Plain
City, Ohio
2012 to present ...............................................Graduate Research Associate,
Department of Horticulture and Crop
Science, The Ohio State University
Fields of Study
Major Field: Horticulture and Crop Science
Minor Field: Statistics
viii
Table of Contents
Abstract ..................................................................................................................... ii
Acknowledgments..................................................................................................... v
Vita......................................................................................................................... viii
List of Tables .......................................................................................................... xii
List of Figures ........................................................................................................ xiii
Literature review ....................................................................................................... 1
Introduction ........................................................................................................... 1
The importance of specific traits in food-grade soybeans ................................. 2
Heritabilities and loci for food-grade traits ....................................................... 3
Methods and tools for genetic analysis of traits in soybean .............................. 5
Association mapping ......................................................................................... 6
Objectives .............................................................................................................. 8
Tables and figures ............................................................................................... 10
References ........................................................................................................... 11
Chapter 2: Correlations of seed traits with tofu characteristics in 49 soybean
cultivars and breeding lines .................................................................................... 16
Introduction ......................................................................................................... 17
Materials and Methods ........................................................................................ 20
Seed material ................................................................................................... 20
Seed measurements.......................................................................................... 21
Tofu production ............................................................................................... 21
Textural analysis of tofu .................................................................................. 22
Statistical Analyses .......................................................................................... 23
Results ................................................................................................................. 24
Seed measurements.......................................................................................... 24
Textural qualities of tofu ................................................................................. 25
ix
Correlations between textural traits of tofu and seed measurements .............. 26
Discussion ........................................................................................................... 27
Tables and Figures .............................................................................................. 31
References ........................................................................................................... 37
Chapter 3: Analysis of population structure in a soybean breeding program for
commodity and specialty types ............................................................................... 41
Introduction ......................................................................................................... 42
Materials and Methods ........................................................................................ 45
Plant Populations and DNA Isolation .............................................................. 45
Collection of Genotypic Data .......................................................................... 47
Collection of Phenotypic Data ......................................................................... 47
Statistical Analysis of Phenotypic Data........................................................... 48
Analysis of Population Substructure ............................................................... 49
Results ................................................................................................................. 50
Genotypic data ................................................................................................. 50
Population structure ......................................................................................... 50
Effect of pedigree on population structure ...................................................... 51
Effect of phenotypic selection on population structure ................................... 52
Differentiation of phenotypes among populations and groups ........................ 53
Discussion ........................................................................................................... 54
Tables and figures ............................................................................................... 58
References ........................................................................................................... 70
Chapter 4: Association mapping of food-grade quality traits in a soybean
breeding program for commodity and food-grade cultivars ................................... 74
Introduction ......................................................................................................... 76
Materials and Methods ........................................................................................ 79
Initial population.............................................................................................. 79
Confirmation population.................................................................................. 79
Phenotypic data collection ............................................................................... 79
Statistical analysis of phenotypes .................................................................... 80
Genotypic data collection ................................................................................ 80
x
Association mapping ....................................................................................... 81
Results ................................................................................................................. 82
Discussion ........................................................................................................... 84
Tables and Figures .............................................................................................. 87
References ........................................................................................................... 96
Bibliography ......................................................................................................... 100
xi
List of Tables
Table 1.1. Positional information for oil, protein content and seed size QTL. ...... 10
Table 2.1. Phenotyped cultivars and breeding lines. .............................................. 31
Table 2.2. Variance for tofu textural traits. ............................................................ 32
Table 2.3. Pearson’s correlation coefficient between textural traits of tofu. ......... 32
Table 2.4. Pearson’s correlation coefficient between tofu textural measurements
and seed measurements. .......................................................................................... 33
Table 3.1. Pedigrees, locations, groups and population membership for cultivars
and breeding lines. .................................................................................................. 58
Table 3.2. Correlation coefficients for subpopulations and PCs and mean PC
values for groups. .......................... ………………………………………………..63
Table 3.3. Eigenvectors for all of the phenotypic variables contributing to
phenotypic PC1 and PC2. ....................................................................................... 64
Table 4.1. Confirmation population breeding lines and their pedigrees as well as
lines used as checks. ............................................................................................... 87
Table 4.2. Proportion of the observed phenotypic variance (σp2) explained by
genetic variance for BLUP values of seed traits in the initial and confirmation
populations. ............................................................................................................. 90
Table 4.3. Significant marker-trait associations. .................................................... 91
xii
List of Figures
Figure 2.1. Workflow of tofu production. .............................................................. 33
Figure 2.2. Force-deformation plot of tofu penetration analysis ........................... 34
Figure 2.3. Histograms of LSmeans for tofu texture traits and seed traits. ........... 35
Figure 2.4. Scatter plots comparing LSmeans of protein and oil content. ............. 35
Figure 2.5. Scatter plots comparing LSmeans of tofu textural traits. .................... 36
Figure 2.6. Scatter plots comparing LSmeans of tofu textural traits versus seed
traits......................................................................................................................... 36
Figure 3.1. Plot of ∆K. ........................................................................................... 65
Figure 3.2. Bayesian admixture proportion for individual soybean lines with the
K = 2 population model. ......................................................................................... 66
Figure 3.3. PCA plot of genotypic data. ................................................................ 67
Figure 3.4. Bar graph displaying the percentage of alleles attributed to the
major subpopulation for each group defined on the basis of pedigree or
phenotype. ............................................................................................................... 68
Figure 3.5. PCA plots of phenotypic data. ............................................................. 69
Figure 4.1 LD decay for the initial mapping population........................................ 93
Figure 4.2. Manhattan plots of the MLM result for marker associations with
seed traits. ............................................................................................................... 94
Figure 4.3. Display of LD selected chromosomes. ................................................ 95
xiii
CHAPTER 1
LITERATURE REVIEW
INTRODUCTION
Soybean (Glycine max L. Merr.) is the second largest crop in the U.S (Zhang et al.,
2010). In 2009, soybean had an estimated planting area of 77.45 million acres, which is
the highest recorded in the U.S since 1960 (National Agricultural Statistics Service,
2010). As a commodity seed crop with high oil and protein content, soybean is primarily
used as a source of vegetable oil and protein meal. A small but increasing portion of
soybean acreage is devoted to food-grade soybeans, the raw material for making tofu,
miso, edamame, soymilk, soy sauce, natto and tempeh. The successful breeding of foodgrade soybeans will benefit farmers and consumers, as well as the Ohio economy, where
soybean production largely contributor (Ohio Soybean Council, 2011).
Soy-food demand is growing, partly due to the nutraceutical value of soybean
(Rao et al., 2002), as well as its value as inexpensive source of protein (Cheng et al., 1990;
Hong et al., 2004). The identification of molecular markers associated with food grade
traits can enhance the selection of qualified soybean cultivars. However, genetic
information on many food-grade soybean traits is currently limited (Shi et al., 2010).
Seed traits for which quantitative trait loci (QTL) analyses have been conducted have
1
been primarily limited to seed protein, oil content, and seed weight. A few QTL have
been identified for seed shape (Salas et al., 2006) and no QTL have been detected for the
quality of produced tofu or the water up take efficiency of seed.
The importance of specific traits in food-grade soybeans
Traits important for food-grade soybean cultivars differ from that of commodity
soybean cultivars where breeders are ultimately selecting for yield above all other traits
(Nichols et al., 2006). Traits important in food-grade soybean cultivars include a
collection of specific features, such as seed shape, size, color, efficiency of water uptake,
and protein and oil content (Poysa et al., 2002). The total content and composition of seed
protein largely determine tofu yields and texture. Harovinton, the Canadian quality
standard tofu-type soybean cultivar, contains approximately 44% of protein content
(Poysa et al., 2002; Poysa et al., 2006). The texture of tofu can be enhanced by A3
glycinin subunits, which also contributes to tofu firmness (Poysa et al., 2006). Large,
round-shaped seeds are suitable for making tofu, edamame, miso and soymilk, while
smaller seeds are used in the production of natto (Poysa et al., 2002; Zhang et al., 2010).
A low surface-to-volume ratio reduces the amount of materials lost as residuals during
tofu processing, thus large round seeds are desirable for tofu producing soybeans (Poysa
et al., 2002). In contrast, small seeds with high water uptake capacity, high carbohydrate
content and low oil content are conducive to the fermentation process of natto type
soybeans (Mullin and Xu, 2001; Wei and Chang, 2004). For tofu production, a colorless
seed coat and hilum is preferred; however, a yellow seed coat and hilum is acceptable,
2
based on the characteristics of Harovinton (Poysa et al., 2002; Poysa et al., 2006). For
natto production, pale yellow and smooth seed coat is preferred (Wei and Chang, 2004).
Large seeds are desirable for miso and edamame production (Salas et al., 2006).
Heritabilities and loci for food-grade traits
Soybean oil and protein content are important and heritable traits, which have
been extensively studied (Diers et al., 1992; Chung et al., 2003; Clemente and Cahoon,
2009; Bolon et al., 2010). The broad-sense heritability for protein and for oil content has
been reported as 0.84 and 0.91 in a recombinant inbred line (RIL) population with 131
F6-dereived lines (Hyten et al., 2004). There is a well-established negative correlation
between oil and protein content in soybean seed (Liang et al., 2010).
In contrast to protein and oil content and seed weight, knowledge of the
heritabilities of other traits such as soybean seed shape, water uptake and firmness of tofu
is lacking. Soybean product quality such as tofu firmness is related to and can be
influenced by seed protein content (Poysa et al., 2006). Seed shape is important trait for
food-grade soybean (Salas et al., 2006; Xu et al., 2011). Seed shape is determined by seed
length (SL), seed width (SW) and seed height (SH). The estimated heritability for SL:SW
and SL:SH ratios range from 0.59 to 0.79. There is no correlation between SL:SW,
SL:SH ratios and cross sectional area (Cober et al., 1997). The narrow-sense heritability
in three recombinant inbred line (RIL) populations range from moderate to high with
heritabilities of 0.72 to 0.83 for SH, 0.42 to 0.88 for SW, 0.58 to 0.85 for SL, and 0.44 to
0.88 for calculated seed volume SL × SW × SH (Salas et al., 2006). Broad-sense
3
heritability in three bi-parental populations for seed weight is high, with values ranging
from 0.76 to 0.93 (Hoeck et al., 2003). The estimated heritability for water uptake at 16h
and the water uptake best fit curve have been reported to be moderate at 0.36 and 0.42,
respectively (Cober et al., 2006) indicating the potential to select for improvement in high
final water uptake in natto soybean (Cober et al., 2006). Broad sense heritability for
firmness of tofu has been reported as 0.37 and 0.61 in two different experiments
(Aziadekey et al., 2002).
QTL for protein, oil content, and seed weight, have been widely detected (Diers et
al., 1992; Chung et al., 2003; Jun et al., 2008; Teng et al., 2008; Bolon et al., 2010). Up to
2004, the position and effects of previously published QTL responsible for seed protein
and oil content and seed weight have been summarized by Hyten et al. (2004). QTL for
protein and oil content after 2004 were summarized in this proposal (Table 1; Panthee et
al., 2005; Liang et al., 2010; Shi et al., 2010). To date, 124 QTL for oil, 108 QTL for
protein, 2 QTL for Oil/protein, and 119 QTL for seed weight have been published in
Soybase (Soybase, 2012).
QTL for other traits like seed shape and water uptake efficiency have been less
studied (Salas et al., 2006; Xu et al., 2011). In a single study, 26 QTL were detected for
seed shape in three densely mapped RIL populations grown in two environments (Salas
et al., 2006). There have been no QTL published for water uptake efficiency.
In the present study, detection of QTL included seed weight, protein and oil
content, but also focused on seed shape, seed size (volume), water uptake efficiency and
firmness of tofu. Among all the traits, the firmness of tofu is depended on a large number
4
of factors, such as total protein content and the relative abundance of protein subunits
(Poysa et al., 2006). While these factors may result in a lack of statistical power to
identify marker-trait associations the resulting data will be useful to identifying lines
suitable for food-grade cultivars as well as identifying extreme lines for tofu firmness
which may be candidates for a future more thorough evaluation of seed and protein
composition.
Methods and tools for genetic analysis of traits in soybean
The exploration of quantitative traits has been a major area of genetic study for
over a century. Knowledge of which genes and/or quantitative trait loci (QTL) are
responsible for food-grade soybean traits will facilitate the efficient combination of traits
in the development of new cultivars. This process is assisted by the use of molecular
markers to construct linkage maps and genetic analysis of complex quantitative traits.
Marker assisted selection together with traditional breeding methods facilitate new
cultivar development. Recently, large numbers of molecular markers have been
developed and made available for soybean researchers. An integrated genetic linkage
map of soybean was released in 1999 with a total of 606 simple sequence repeat (SSR)
markers, mapped in one or more of three populations (Cregan et al., 1999). A new
integrated genetic linkage map of soybean was published in 2004 with the addition of 420
new SSRs to the previous map and with total of 1015 SSR markers (Song et al., 2004).
The addition of 1141 transcript genes were mapped by single nucleotide polymorphism
(SNP) markers provided a valuable resources to soybean breeders with increased number
5
of sequence-based markers (Choi et al., 2007). The most current version of the soybean
integrated linkage map was developed by adding 2500 SNP markers with the Illumina
GoldenGate Assay, which has proven to be a successful tool in the high-throughput SNP
genotyping of soybean (Hyten et al., 2008; Hyten et al., 2010). A total of 5500 SNP
markers were included in the current consensus map of soybean (Hyten et al., 2010).
This most recent addition of Illumina GoldenGate SNP markers have been
organized into a panel of 1536 markers selected to be informative for genetic analyses on
a wide variety of soybean populations (Hyten et al., 2010). These markers were identified
by sequencing amplicons from five parental lines from which 2072 bi-allelic SNPs were
selected. Adding another 500 SNPs by Choi et al. (2007), a total of 3072 SNP markers
were completed for two set of 1536 custom GoldenGate Assay (Hyten et al., 2010).
These two panels were assayed on a diverse array soybean accessions in order select a
single, optimally informative set of 1536 SNP markers. The published Universal Soy
Linkage Panel of 1536 SNPs by Hyten et al. (2010) was used in the proposed research.
Association mapping
As described above, marker-trait associations are commonly identified via QTL
mapping in a bi-parental population. The populations studied for soybean linkage
mapping have predominantly been bi-parental RIL populations derived from crosses
between Minsoy, Noir 1, and Archer (Mansur et al., 1996; Cregan et al., 1999; Song et al.,
2004; Hyten et al., 2008; Hyten et al., 2010). However, to use conventional bi-parental
mapping, the genetic resolution can be limited by the restricted opportunities for
6
recombination during population development. In addition, bi-parental mapping can be
limited by the time and effort required to create and advance large populations; as a result,
many published bi-parental mapping populations have relatively small population sizes
(Yu and Buckler, 2006; Zhu et al., 2008; Jun et al., 2008). In relation to a breeding
program, the primary limitation of bi-parental mapping populations is that the values of
QTL tend to be contextual, relying solely on the two alleles present in the parents.
However, association mapping can be conducted within a much broader germplasm
context, taking advantage of existing individuals, historical recombination events, and the
evaluation of a large number of alleles in a single population (Yu and Buckler, 2006; Zhu
et al., 2008). The use of association mapping to identify QTL responsible for complex
traits has been of great interest in plant breeding research (Zhu et al., 2008). The
proposed research will conduct association mapping in a multi-parent inter-crossed
breeding population; therefore, the results will be immediately applicable to the Ohio
public soybean breeding.
In recent years, association mapping has been successfully used to detect QTL in
soybean and other crops. Two new QTL for protein were detected by using a
combination of selective genotyping and association mapping with 150 simple sequence
repeat (SSR) markers in a soybean population of accessions from Korea, Japan and China
(Jun et al., 2008). By using association mapping, two QTL were confirmed for iron
deficiency chlorosis in soybean in two independent populations with 139 lines and 115
advanced breeding lines (Wang et al., 2008). In other crops such as lettuce, association
mapping has been used in a population representative of the diversity in cultivated lettuce
7
to assist in pinpointing the resistance gene Tvr1 (Simko et al., 2009). Association
mapping was also used to identify previously known flowering time and pathogen
resistance loci in a population representative of the diversity within the model plant
Arabidopsis (Aranzana et al., 2005). To detect QTL for kernel size and milling quality in
wheat, association mapping was used in a population representative of elite soft winter
wheat cultivars grown in the eastern U.S (Breseghello and Sorrells, 2006). In rice,
association mapping was performed in a population of core rice cultivars collected by
USDA to detect genes for stigma and spikelet characteristics (Yan et al., 2009). In maize,
association mapping has been conducted in the nested association mapping population to
detect genes responsible for leaf architecture traits (Tian et al., 2011). These studies used
populations ranging in size from 68 accessions to ~5,000 lines. Markers were either SSRs
or SNPs, or a combination of SSRs with indels; the number of markers in each study
ranged from 62 to 1.6 million. Due to the nature of the populations that can be used,
association mapping is a technique which can identify maker-trait associations that can be
immediately applicable to breeding programs.
OBJECTIVES
The aim of this project was to detect QTL responsible for traits important in foodgrade soybeans, and to provide information for the selection of food-grade soybeans in a
breeding program. The objectives included: 1) determining the relationship between the
8
quality of tofu produced and seed characters, including shape, size, density, weight,
protein content, and oil content; 2) determining the effect of selection for multiple
specialty types on the population structure of breeding population; 3) determining the
values, variation, and estimate the heritability for soybean seed quality traits including
seed oil and protein content, seed weight, seed shape and firmness of produced tofu; 4)
identifying marker trait associations through association mapping.
9
TABLES AND FIGURES
QTL
Seed size
(weight)
Oil
content
Oil
content
Protein
content
Oil
content
Oil
content
Oil
content
Oil
content
Oil
content
Oil
content
Oil
content
Oil
content
Oil
content
Protein
content
Oil
content
Protein
content
Map
Linkage
Marker
Chr.
position
group
(cM)
LOD
No. of
environ.
17.52
11.3
3.50
6
157.08
NR†
5.13
2
157.21
NR
5.01
2
Parent 1 Parent 2
1
A1
5
A1
5
C2
6
Satt281
40.3
0.42
NR
2
C2
6
Satt363-
91.87
NR
2.17
2
C2
6
Satt277
93.92
NR
2.25
2
A2
8
Satt162-
14.18
NR
12.61
2
A2
8
BSC
14.21
NR
12.52
2
A2
8
Satt187-
23.24
NR
11.81
2
A2
8
Satt129
25.31
NR
11.02
2
O
10
Satt358
5.44
0.24
NR
2
O
10
Satt479
54.2
12
3.10
6
B1
11
Satt453
123.96
0.14
NR
2
B1
11
Satt453
123.96
0.23
NR
2
F
13
Satt146
1.92
0.4
NR
2
F
13
Satt146
1.92
0.36
NR
2
Protein
content
F
13
Satt586
3.63
0.19
NR
2
54 US & 51 Asian
breeding lines
Oil
content
B2
14
Satt063-
45.14
NR
3.69
2
Jindou 23
B2
14
Satt070
45.21
NR
3.62
2
Jindou 23
D2
17
Satt002
47.73
10
2.90
6
G
18
Satt570
12.21
20.2
3.50
6
Satt225Satt599
Satt225Satt599
References
N87-984Panthee et al.,
TN93-99
16
2005
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
54 US & 51 Asian
Shi et al., 2010
breeding lines
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
Huibu
Liang et al.,
Jindou 23
zhi
2010
54 US & 51 Asian
Shi et al., 2010
breeding lines
N87-984Panthee et al.,
TN93-99
16
2005
54 US & 51 Asian
Shi et al., 2010
breeding lines
54 US & 51 Asian
Shi et al., 2010
breeding lines
54 US & 51 Asian
Shi et al., 2010
breeding lines
54 US & 51 Asian
Shi et al., 2010
breeding lines
D1a
Oil
content
Seed size
(weight)
Protein
content
Satt184
r2
Huibu
zhi
Shi et al., 2010
Liang et al.,
2010
Huibu
zhi
Liang et al.,
2010
N87-984Panthee et al.,
TN93-99
16
2005
N87-984Panthee et al.,
TN93-99
16
2005
Table 1.1. Positional information for oil, protein content and seed size QTL not
summarized elsewhere (Hyten et al., 2004).
†NR: not reported
10
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15
CHAPTER 2
CORRELATIONS OF SEED TRAITS WITH TOFU CHARACTERISTICS IN 48
SOYBEAN CULTIVARS AND BREEDING LINES
Abstract
In comparison to commodity or field-grade soybeans [Glycine max (L.) Merr], foodgrade soybeans, used to produce tofu and other soy-food products, have specific seed
composition, shape, size and color requirements. Many of these seed qualities, such as
protein content, have been correlated with the tofu texture. To study these correlations,
tofu was produced from 48 high protein or food-grade type soybean cultivars and
breeding lines grown in two locations. Four tofu textural traits were assessed: work to
break, brittleness, stiffness, and gel strength. Seed traits measured included seed protein
content, oil content, weight, volume, density, and shape. Correlation analysis was
conducted between tofu texture quality and soybean seed traits. Seed protein and oil are
both detected to be significantly correlated with the above tofu textural traits with the
exception of brittleness. No significant correlations between tofu textural traits and seed
volume or shape were detected, implying that the preference for large, round seeds by
tofu producers is unrelated to texture of the tofu produced under these conditions.
16
INTRODUCTION
As an inexpensive high protein source, tofu demands are increasing (Dimitri and
Greene, 2002; Chianu et al., 2010; Yamaura, 2011). The textural qualities of tofu are
important for consumer acceptance as well as the marketing classification or type of tofu
(Golbitz et al., 2006). Many factors can affect tofu texture, these include external factors
associated with the tofu making process as well as factors which are intrinsic to the seed
and may be associated with the soybean cultivar or seed production environment (Min et
al., 2005; Kumar et al., 2006). Instrumental measurements of tofu textural properties have
been correlated with the descriptive sensory scores of tofu quality by trained panelists
(Yuan and Chang, 2007). Thus, instrumental texture units have become a standard
method of reporting the textural quality of tofu (e.g. Poysa and Woodrow, 2002; Mujoo
et al., 2003; Liu and Chang, 2004).
External factors affecting tofu quality include the soymilk processing, coagulant
properties, other additives, and the pressure applied to form the tofu curd (Gandhi and
Bourne, 1988; Sun and Breene, 1991; Johnson and Wilson, 1984; Cai et al., 1997; Hou et
al., 1997; Cai and Chang, 1998; Cai and Chang, 1999; Liu and Chang, 2004). In terms of
processing the soymilk, tofu yield and texture are affected by the temperature and length
of time used for soaking and grinding the soybean seeds, as well as the heating method of
the soymilk (Johnson and Wilson, 1984; Cai and Chang, 1999; Liu and Chang, 2004).
The coagulation of soymilk into curd is one of the most critical processes in tofu
production. The type of coagulant used, its concentration, and the method and timing of
17
its incorporation all influence the texture of tofu produced (Sun and Breene, 1991; Cai et
al., 1997; Hou et al., 1997; Cai and Chang, 1998; Liu and Chang, 2004). Increased
pressure applied to form tofu curd results in lower tofu moisture and yield (Gandhi and
Bourne, 1988). However, springiness, cohesiveness, adhesiveness, and stringiness are
only minimally affected by the processing pressure (Gandhi and Bourne, 1998). In
addition, soymilk additives, such as transglutaminase can increase tofu firmness (Kwan
and Easa, 2003; Yasir et al., 2006).
Factors intrinsic to the seed which may affect tofu yield and/or texture include,
among a number of factors, the seed protein content and composition (Cai et al., 1997;
Cai and Chang, 1999; Poysa and Woodrow, 2002; Shih et al., 2002; Mujoo et al., 2003;
Poysa et al., 2006). Tofu is produced from what is known as food-grade soybeans, which
are characterized by having a clear or yellow hilum, high protein content, and a large,
round seed shape (Graef and Specht, 1989; Griffis and Wiederman, 1990). Food-grade
soybeans are distinct from the commodity or field-grade seed, which is generally smaller,
with lower protein content and displays a dark hilum and oblong shape. Higher protein
content in soymilk results in a springier tofu texture, in addition to being positively
correlated with tofu yield and firmness (Shih et al,. 2002; Cai et al., 1997; Poysa and
Woodrow, 2002). The relative concentration of the 11S and 7S protein subunits affects
tofu yield and texture; however, the affect is dependent on the tofu processing method
and coagulant used (Cai and Chang, 1999; Mujoo et al., 2003; Poysa et al., 2006).
Although the tofu market prefers large, round seeds (Graef and Specht, 1989), it
remains unclear if seed size or shape of soybean has a functional impact on tofu quality
18
or if the preference for large, pearl-like soybean seed is related solely to tofu yield or is
sociological rather than functional in origin. Reports on this subject have been
inconsistent, possibly due to the many factors which may affect the texture of tofu.
Studies which have used seed weight as an estimate for seed size, have concluded and
found that seed size has no effects on the physicochemical properties, yield, and firmness
of tofu (Lim et al., 1990; Wang and Chang, 1995; Cai et al., 1997). Alternatively, seed
size, calculated from seed straight length and width, has been positively correlated to tofu
yield, as well as soymilk concentration, which is directly related to tofu firmness (Shih et
al., 1997; Shih et al., 2002).
The aims of the present study are to determine if 1) there is a correlation between
seed and shape characteristics and the textural quality of the tofu produced as well as
confirm, in the present population, the known correlation between protein and tofu
texture shown in previous studies, 2) to determine if the textural qualities of tofu are
heritable in the present population, and 3) to determine if the genetic basis of tofu texture
can be improved through indirect selection on correlated traits. To address these aims, we
measured seed volume, shape, weight, and density as well as protein and oil content for
seed from 48 soybean cultivars or breeding lines grown in two different environments in
Ohio. Tofu was produced using a bench top methodology (Liu, 1997; Evans et al., 1997;
Mullin et al., 2001) for each cultivar or breeding line from both locations. Textural
quality measurements of our tofu (work to break, brittleness, stiffness and, gel strength)
were correlated with the seed measurements. Genetic variation was also estimated for
these traits and genetic values was estimated by best linear unbiased predictors (BLUPs)
19
were correlated. Our findings reproduce the well-established correlation between seed
protein content and the textural qualities of the tofu produced, but identify little to no
correlation between textural qualities and the seed size, weight, shape, and density.
MATERIALS AND METHODS
Seed material
A collection of 250 soybean breeding lines and check cultivars were grown at two
locations in Ohio, Hoytville and Wooster, in 2010. Lines were grown in trials for either
maturity group (MG) II or III and in either a preliminary or advanced trial. Preliminary
trials were composed of breeding lines in F4:5 generation; advanced trials were composed
of breeding lines in the F4:6-8 generations. Lines for each trial were grown in a randomized
complete block design with two replicates for the preliminary lines or three replicates for
the advanced lines.
The 25 lines with the highest protein content (see ‘Seed measurements’ section
below) were selected for analysis of seed traits and for tofu production and textural
analysis. In addition to these lines, check cultivars used for the food-grade market were
included in the analysis, as well as breeding lines with a food-grade cultivar in their
pedigree. In total, 48 lines were selected for analysis of seed traits and tofu production
and textural analysis (Table 2.1).
20
Seed measurements
Seed oil and protein concentration were measured from all replicates at the National
Center for Agricultural Utilization Research in Peoria, IL using Near Infrared (NIR)
technology with an Infratec 1255 Food & Feed Analyzer (UltraTec Manufacturing Inc.,
Santa Ana, CA). Seed volume was measured from a single replicate by water
displacement by placing fifty dry seeds into a 50 ml graduated cylinder with 25 ml of
water. Average volume per seed was determined by dividing the displaced volume by 50.
Seed weight was measured with 100 seeds from each replicate with an OHAUS
Adventurer TM Pro, Model AV212 scale. Average weight per seed was determined by
dividing 100-seed weight by 100. The average seed weight divided by average seed
volume was recorded as seed density. Seed straight length, straight width and length to
width ratio measurements were conducted by scanning ten seeds from a single replicate
followed by image analysis with WinSEEDLE software (Regent Instruments Inc.,
Canada).
Tofu production
Tofu firmness data were collected from the 48 selected lines (Table 2.1) from a
single replicate from each location. The method of making tofu is adapted from Liu
(1997), Evans et al. (1997), and Mullin et al. (2001) and is described in Figure 2.1. Seeds
(100 g) from the selected lines were soaked in 250 ml water at 20-22 °C for 16 hours to
uptake water to approximately 2.1 times the original seeds weight. Soaked seeds were
further combined with 400 ml distilled water and blended for 3 minutes at high speed
21
(blender model 908-2, Hamilton Beach Co., Washington, NC). The bean slurry was
filtered through two layers of cheese cloth and squeezed manually to obtain soymilk. The
okara, or bean residual, was washed and mixed with 150 ml more water so that the final
volume ratio between raw beans and total water used was estimated to be 1:8. Total
soymilk obtained from the slurry (300 ml) was heated to 95°C and maintained for five
minutes while vigorously stirred with a magnetic stirring bar. Heated soymilk was poured
into a glass jar and allowed to cool to 87 °C at which point calcium sulfate (CaSO4
·2H2O) was added to a concentration of 3.49 mM to the produced soymilk. The mixture
was stirred vigorously for about three seconds and incubated at room temperature for 30
minutes to allow the formation of tofu curd. In order to account for the variation in curd
production, two separate tofu curds were made for each cultivar from each growing
environment.
Textural analysis of tofu
Measurements of tofu firmness were made from the formed curd within the jar with a
Texture Analyzer Measuring System (Model TA.XT2, Texture Technologies Corp.,
Scarsdale, NY/Stable Micro Systems, Godalming, Surrey, England) employing a
penetration test with a spherical stainless probe with a diameter of 1.905 cm (TA 18 A
3/4" dia ball probe; Figure 2.1) (Mullin et al., 2001). The test speed was 1.00 mm/s, with
200 data points collected per second. The trigger force to detect sample surface is 5 gf.
For each measurement with the Texture Analyzer Measuring System, force was
plotted against time. Textural traits calculated from this plot included work to break,
22
brittleness, stiffness, and gel strength. Gel strength, sometimes referred to as hardness,
was defined as the peak value of the force applied at the breaking point of the tofu sample
(b in Figure 2.2). Brittleness is the distance from initial contact to the break point (c
×1.00 mm/s in Figure 2.2). Stiffness, sometimes referred to as firmness, is the slope from
the initial contact point to the break point ([(b - a) / c] in Figure 2.2). Work to break is the
definite integral, or area under the curve, from the initial contact point (0, a) to the break
point (c, b) (Figure 2.2).
Statistical Analyses
In order to obtain the phenotypic value for each sample (48 cultivars grown in two
locations), measurements of tofu texture and seeds were analyzed in SAS (SAS Institute
Inc., Cary, NC) by calculating least-squares means (LSmeans) using PROC MIXED. The
model for LSmeans was: Yik = μ + Gi(Lk) + Lk + εik, where Yik is the observed value for a
given trait, μ is the overall mean, Gi(Lk) is the ith cultivar or breeding line in the kth
environment, representing each sample effect; Lk is the effect of the kth environment.
Gi(Lk) is treated as fixed effect; Lk is a random effect. Using LSmeans, Pearson’s
correlation coefficients were calculated between tofu textural traits; between eight seed
traits and four tofu textural traits; and also between seed protein content and oil content.
Significance levels of correlations were corrected for the 39 multiple comparisons by
Bonferroni’s method. Histograms of LSmeans for all traits and scatter plots for all
pairwise LSmeans comparisons were obtained using R. Coefficient of variation for tofu
textural traits was calculated in R.
23
In order to address the questions of heritability and the value of indirect selection for
improvement of tofu texture, the data was further decomposed into the specific sources of
variance and best linear unbiased prediction (BLUP) values calculated with PROC
MIXED in SAS using the model Yijkl = μ + Gi + Lk + Tl(Lk) + Lk × Gi + εijkl, where μ
represents the ground mean, Gi is the effect of the ith cultivar or breeding line, Lk is the
effect of the kth environment, Tl(Lk) is the effect of the lth trial within the kth
environment, and Lk × Gi is the interaction effect of the kth environment with the ith
cultivar or breeding line, εijkl is the error associated with the observation. Pearson’s
correlation coefficients between BLUP values were conducted in R. Significance levels
of correlations were corrected for the 24 multiple comparisons by Bonferroni’s method.
Variance components were estimated using the REML method. For tofu texture traits, the
proportion of genetic variance to total observed variance was calculated with the
combined data set for both environments.
RESULTS
Seed measurements
Seed measurements collected for each of 48 cultivars or breeding lines included
seed weight, volume, density, straight length, straight width, and the length to width ratio
as well as protein and oil content. Q-Q plots and Anderson Darling normality tests were
conducted as an exploratory step (results not shown); with the exception of seed density
24
and seed oil, LSmeans for all traits are significantly different from a normal distribution,
pictorially displayed in Figure 2.3. LSmeans for density ranged from 1.00 to 1.47 g·cm-3
and for the seed oil content ranged from 16.85 to 21.73% (Figure 2.3). LSmeans for other
seed traits with non-normal distributions ranged from 11.12 to 21.18 g for seed weight,
0.092 to 0.179 cm3 for volume, 5.81 to 8.45 mm for straight length, 5.22 to 7.17 mm for
straight width, 1.06 to 1.33 for length to width ratio, and 37.89 to 47.46% for protein
content (Figure 2.3). There was a significant negative correlation between the LSmeans
for seed protein and oil content (r= -0.82, adjusted p = 8.6 x 10-15; Figure 2.4).
Textural qualities of tofu
The tofu texture was studied for curds produced from the same set of 48 breeding
lines or cultivars included in this experiment. The textural measurements included work
to break, brittleness, stiffness, and gel strength. LSmeans were calculated for each sample
(breeding line or cultivar from each environment). For all four tofu texture traits, the
LSmeans were normally distributed according to Anderson Darling normality tests
(results not shown) with ranges from 303.36 to 1284.18 gf·s for work to break, 9.18 to
14.56 gf·s-1 for stiffness, 5.37 to 16.48 mm for brittleness, and 4.19 to 5.44 gf for gel
strength (Figure 2.3). With the exception of brittleness which is not significantly
correlated with stiffness, the other textural traits are highly correlated with each other
(Figure 2.5; Table 2.3).
To estimate the genotypic value of a trait for each breeding line or cultivar, a general
linear model which included the genotype by environment interactions was applied to
25
combined data from both locations. The BLUP values for all tofu textural traits lacked
significant genetic variance and possessed significant genotype by environment
interaction effects (Table 2.2). The proportion of total variance explained by genetic
variance for work to break, stiffness, and gel strength are low (0.10 to 0.17), indicating
only a minimal possibility for improvement through direct phenotypic selection.
Correlations between textural traits of tofu and seed measurements
The correlations between textural traits of tofu and the seed measurements were
calculated both using LSmeans and BLUPs values. LSmeans for tofu textural traits and
seed traits were correlated to explore the physical relationship between seed traits and
tofu texture (Table 2.4). Scatter plots for the LSmeans correlations are displayed in
Figure 2.6. LSmeans for work to break, stiffness, and gel strength of tofu are
significantly positively correlated with LSmeans for seed protein content (adjusted p =
1.4 x 10-3, 7.8 x 10-6, 5.7 x 10-4, respectively; Table 2.4). LSmeans for stiffness and gel
strength were significantly negatively correlated with seed oil content (adjusted p = 1.7 x
10-3, 3.8 x 10-2, respectively; Figure 2.6, Table 2.4). Other tofu traits were not detected to
be significantly or highly correlated with any of the seed size or seed shape traits (Figure
2.6 and Table 2.4).
BLUP values for tofu textural traits and seed traits were correlated to determine the
feasibility of indirect selection for improvement of tofu texture (Table 2.4). No BLUP
values were calculated for brittleness which had an estimated genetic variance of 0 (Table
2.3). BLUP values for work to break, stiffness, and gel strength of tofu are significantly
26
positively correlated with BLUP values for seed protein content (adjusted p = 2.4x 10-3,
1.6 x 10-3, 3.8 x 10-3, respectively; Table 2.4). BLUP values for stiffness and gel strength
had a significant negative correlation with BLUP values for seed oil content (adjusted p =
9.3x 10-3, 2.6 x 10-2, respectively; Table 2.4).
DISCUSSION
Except that brittleness calculated in this study is not significantly correlated to
stiffness, each of the tofu textural traits is highly correlated with the other textural traits.
This is largely due to the mathematical relationship among the measurements, with force,
time, and distance to the fracture point being the critical numbers (Figure 2.2). As
examined from two locations combined, work to break, stiffness, and gel strength, exhibit
non-significant genetic variance. While few studies have examined the genetic variance
in textural traits of tofu; significant genetic variance of the gel strength (hardness) has
been reported (Mullin et al., 2001; Poysa et al., 2006). Mullin et al. (2001) reported
significant genetic variance of gel strength by compression and penetration and of
firmness by penetration based on four soybean varieties grown in a single environment.
In a study with a series of glycinin and β-conglycinin subunits null lines in the
‘Harovinton’ (Buzzell et al., 1991) genetic background, Poysa et al (2006) reported
significant genetic variance of gel strength and firmness by compression and penetration
among genotypes grown in two environments and across two years.
27
It has frequently been reported that the firmness and/or gel strength of tofu are
positively correlated with the protein content of soymilk, which is highly correlated to
soybean seeds’ protein (Lim et al., 1990; Shen et al., 1991; Cai et al., 1997; Poysa and
Woodrow, 2002; Shih et al., 2002). This finding has been further verified in the present
study, where stiffness and hardness were significantly correlated to seed protein content.
In confirmation of results reported by Poysa and Woodrow (2002), these textural
qualities of tofu are also negatively correlated to seed oil content. This is not unexpected
as there is a long established negative correlation between oil and protein content in
soybean seed (Johnson and Bernard, 1962) with correlation coefficients ranging from 0.18 to -0.62 (Yaklich et al., 2002). This study reported a similar negative correlation of 0.82 between seed oil and protein content.
The positive correlation between seed protein content and work to break has not been
reported before; however, it is expected that work to break will follow trends similar to
tofu stiffness and gel strength because, as discussed above, these measurements are
highly correlated and dependent on the force and distance required to reach the fracture
point.
The significant correlation of BLUPs values between seed protein content and other
tofu texture traits indicate that selection of higher protein in soybean seeds will result in
greater firmness, gel strength and stiffness in tofu. Given the large amount of time and
labor involved and the relatively large amount of seed required for tofu production, it
follows that high protein content should be and is a breeding goal (Panthee et al., 2005) in
food-grade cultivar development.
28
We report that brittleness exhibits no significant genetic variance. While significant
differences for brittleness as measured by compression have been reported with the use of
different coagulants (Hou et al., 1997), in concordance with the present study, it has also
previously been reported that there was little to no variation in brittleness measurements
from tofu produced from three different cultivars (Shih et al., 2002).
In addition to high protein, large and round seed are important characteristics
considered by tofu producers (Poysa et al., 2002; Salas et al., 2006); however, this study
did not detect evidence that seed size and shape significantly affect the textural quality of
the tofu. It should be noted that the present study looked at only one tofu production
system and did not measure the yield of soymilk or tofu. While seed size and shape may
have importance in other production methods, to soymilk or tofu yield, or have
sociological importance in the food-grade soybean market, selection for these traits is
unlikely to improve the textural quality of tofu as measured in this study. Work to break,
stiffness, and gel strength all presented a low proportion of genetic variance contributing
to total variance, indicating that improvement in textural quality of tofu could be limited
through direct selection on these traits and methods of indirect selection should be further
explored.
Credits: Seed protein and oil measurements were conducted by NCAUR. All other work
was conducted by Mao Huang with assistance from Amanda Gutek with making tofu
samples, and assistance from additional members of the McHale lab with seed shape and
29
size measurements. Dr. Kaletunc assisted with textural measurements. Dr. Steve St.
Martin assisted with statistical data analysis.
30
TABLES AND FIGURES
Line or cv. name
Dennison
‡
HC95-15MB‡
HC95-24MB
IA3024‡
IA3027
‡
LD00-3309
‡
Ohio FG1
Ohio FG3
Ohio FG4
Ohio FG5
Prohio
‡
Williams‡
Wyandot
H05-221
H09-106
‡
H09-121‡
H09-179
H09-260‡
H09-264
H09-277
‡
H09-4
‡
H09-6‡
H09-9
HS5-3519
HS7W-190
HS7W-191
HS7W-94
‡
HS8-3284‡
HS8-3331
‡
HS8-3451
HS8-3538
HS8-3713
HS8W-102
HS8W-103
HS8W-178
HS8W-179
HS8W-514
HS8W-517
HS8W-520
HS8W-521
Pedigree
‘Athow’ x HS94-4533
‘Maverick' x 'Dwight'
LS301 x HS84-6247
HS89-8843 x Ohio FG1
‘Ohio FG1’ x HS89-3078
HC94-81PR x Asgrow2506
‘Wayne' x L57-0034
OXR-96243 x Ohio FG1
HCS95-15MB x Williams
SF114 x Wyandot
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Wyandot
SF114 x Wyandot
SF114 x Wyandot
HS99-4577 x PI133293
HS1-3641 x HS1-3907
HS1-3641 x HS1-3907
HS1-5870 x Ohio FG4
‘Kottman’ x HS1-7267
HS3-25233 x (Williams x PI 424354)
HS3-25233 x (Williams x PI 424354)
Dennison2 x HFPR4
C2033 x HS1-423
HS2-8086 x HS1-7267
HS2-8086 x HS1-7267
HS1-424 x Ohio FG4
HS1-424 x Ohio FG4
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Reference†
St. Martin et al., 2008
Cooper & Hammond, 1999
Cooper & Hammond, 1999
Iowa State Univ.
Iowa State Univ.
Diers et al., 2006
St. Martin et al., 1994
St. Martin et al., 2004
OSU-OARDC
St. Martin et al., 2006
Mian et al., 2008
Bernard & Lindhal, 1972
OSU-OARDC
Continued
Table 2.1. Phenotyped cultivars and breeding lines.
† A reference is provided for released cultivars.
‡ Phenotypic data were only included from one location (either Wooster or Hoytville).
31
Table 2.1 continued
OHS 306
M09-W033
M09-W039
M09-W043
M09-W095
M09-W104
M09-W105
Wyandot Rg2
Trait
Work to break
Brittleness
Stiffness
Gel Strength
HS98-3409 x HS99-5021
OHS 304 x LG00-3372
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
Wyandot x PI 243540
σ2G
σ2G×E
―― P-value ――
0.18
0.0085‡
n.e.§ 0.087
0.11
0.0024
0.11
0.0032
OSU-OARDC
σ2G/ σ2T
CV%†
0.10
0
0.16
0.17
37.02
14.73
29.76
6.89
Table 2.2. Variance for tofu textural traits. The proportion of the observed total variance
(σ2T) explained by genetic variance (σ2 G) for BLUPs values of tofu textural traits and the
coefficient of variation (CV%) are presented. Significance levels of genetic variance (σ2G)
and genotype × environment variance (σ2G×E) were determined by Wald z-test in SAS.
† CV% is calculated across all samples for each trait with raw data from each genotype
and location.
‡ Significant values (α = 0.05) for σ2G and σ2G×E are italicized for emphasis.
§Not estimable.
Brittleness Stiffness Gel strength
0.84*** 0.93***
Area 0.53***
0.060
0.36**
Brittleness
0.93***
Stiffness
Table 2.3. Pearson’s correlation coefficient between textural traits of tofu. Correlation
analysis conducted based on LSmeans of each trait. Significance of Pearson’s correlation
coefficient is tested by t-test and corrected for multiple comparisons using Bonferonni’s
method; indicated by * for adjusted p ≤ 0.05, ** for adjusted p ≤ 0.01 level, and *** for
adjusted p ≤ 0.001.
32
Trait
a.
Work to break
Brittleness
Stiffness
Gel Strength
b.
Work to break
Stiffness
Gel Strength
Weight Volume Density
Straight Straight Length Protein
length
width :width
0.065
-0.0067
0.081
0.072
0.065
0.084
0.025
0.050
0.0010
-0.21
0.14
0.059
-0.058
0.25
-0.17
-0.043
0.033
0.25
-0.088
0.0093
0.014
0.072
0.061
-0.022
-0.0077
0.017
0.12
0.18
0.11
-0.069
-0.084
-0.019
-0.008
-0.25
-0.059
-0.25
0.0054 -0.18
-0.17
0.091
-0.20
-0.14
Oil
0.45**
0.0049
0.55***
0.47***
-0.34
-0.027
-0.44**
-0.36*
0.53**
0.60***
0.52**
-0.46*
-0.49*
-0.41
Table 2.4. Pearson’s correlation coefficient between tofu textural measurements (left side)
and seed measurements (top). Significance levels are as described in Table 2.3. (a)
Correlation analysis conducted based on LSmeans of each trait. (b) Correlation analysis
conducted based on BLUP values.
Figure 2.1. Workflow of tofu production. Methods adapted from Liu (1997), Evans et al.
(1997), and Mullin et al. (2001).
33
Figure 2.2. Force-deformation plot of tofu penetration analysis. The zero time point is
equal to the initial contact of the probe to the surface of the tofu. Dashed line indicates
break point, (a) force at the initial contact point, (b) peak force at break point (c) distance
from initial contact to breakpoint.
34
Seed protein content (%)
Figure 2.3. Histograms of LSmeans for (a) tofu texture traits and (b) seed traits. The yaxis represents the count of breeding lines and cultivars at each location. The mean value
for each trait is illustrated with a vertical red line.
Seed oil content (%)
Figure 2.4. Scatter plots comparing LSmeans of protein and oil content.
35
Work to break
(gf∙s)
Brittleness
(mm)
Stiffness
(gf∙s-1 )
Gel strength
(gf)
Figure 2.5. Scatter plots comparing LSmeans of tofu textural traits.
Figure 2.6. Scatter plots comparing LSmeans of tofu textural traits (y-axis) versus seed
traits (x-axis).
36
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40
CHAPTER 3
ANALYSIS OF POPULATION STRUCTURE IN A SOYBEAN BREEDING
PROGRAM OF COMMODITY AND SPECIALTY TYPES
Abstract
Many soybean breeding programs are now focusing on the development of specialty
soybean types for niche markets. These specialty soybean cultivars require modified fatty
acid profiles for the vegetable oil market or high protein, large seed size, and clear hilum
for the food-grade soybean market. The selection for different specialty types within a
single breeding program may result in the structuring or genetic differentiation of the
population. Population structure and the phenotypes important to commodity, modified
fatty acid, and food-grade soybean markets were evaluated in a breeding population of
242 lines comprised of both commodity and specialty type soybeans. Two subpopulations
associated with the phenotypes of specialty soybean types were identified on the basis of
504 single nucleotide polymorphism markers assayed on the breeding population. Our
results indicate that selection of parents as well as the directional selection among
progeny in a single selection event can both contribute to the structuring of this breeding
population.
41
INTRODUCTION
Increasing soybean yields has been an important breeding goal (Kim et al., 2012). In
2011, more than 9.2 billion bushels of soybeans were produced worldwide, with 33% of
that coming from the United States (The American Soybean Association, 2012). Over the
past 80 years, soybean breeding programs in North America have been successful in
increasing yields, with genetic gains in soybean yield across maturity groups and regions
ranging from 0.87% to 3.49% (Lange and Fedrizzi, 2009). In addition to increasing yield,
soybean breeding programs have further diversified their goals with recent emphasis
being placed on various specialty traits. This diversification of breeding goals may result
in genetic differentiation, or population structure, within a breeding population.
Understanding population structure can be important in efficiently introducing and
utilizing genetic diversity for a breeding program (Glaszmann et al., 2010).
Being the world’s major seed crop for vegetable oil production, modified fatty acid
composition of soybean oil has become an important trait for which specialty soybean
types have been selected and developed (Lee et al., 2007). In particular, high oleic acid,
low saturated fatty acids, and low linolenic acid content are important for human health
and oil stability (Miller et al., 1987; Grundy et al., 1988). Mutations in specific genes in
the fatty acid biosynthesis pathway have been exploited for the development of specialty
soybeans with modified oil profiles (e.g., Burton et al., 2004; Burton et al., 2006;
Shannon et al., 2005). The genetic background and loci of small effect have also been
shown to be important to the expression of the modified fatty acid traits. Thus, selection
42
for specific modified fatty acid profiles can involve several to many loci (Burton et al.,
2006; Panthee et al., 2006; Han et al., 2011).
High seed protein content is an important specialty trait selected for in soybean
breeding programs. Soybeans contribute to more than 70% of the protein consumed by
humans; its utilization as a protein source is demonstrated in its widespread use as a feed
ingredient for livestock and poultry production (Krishnan, 2005). Additionally, high
protein content is one of the desired traits in food-grade soybeans, soybeans produced
specifically for tofu and soymilk (Wang et al., 1983). As a result of an increased demand
for soy foods, the development of food-grade soybean cultivars is becoming an important
focus of many breeding programs (Poysa et al., 2006). Food-grade soybeans have a suite
of requisite characters, which include clear hilum, large seed size, and a high protein
content, requiring selection at numerous loci for improvement of this set of traits (Wang
et al., 1983).
Within a given breeding program, parental and progeny selection for these specialty
traits may result in structuring of the breeding population and the creation of genetically
distinct subpopulations. Previous studies on soybean population structure have been
conducted in regional populations of cultivated and wild soybeans collected from Japan,
China, and Korea (Hirata et al., 1999; Yan et al., 2003; Kuroda et al., 2006) or
populations of one specialty soybean type from multiple regions (Shi et al., 2010). These
studies have shown the presence of subpopulations associated with the geographic origin
of the soybean genotypes. However, these studies do not address population structure
within a single breeding program or between soybean specialty types. As a result of
43
selection for different specialty types, a population of soybean lines from a single
breeding program may exhibit genetic differentiation resulting in subpopulations.
Identifying population structure is a requisite first step in conducting linkage
disequilibrium mapping, a useful tool in understanding and utilizing the genetic diversity
within a breeding program (Cardon and Palmer, 2003).
The software STRUCTURE has been useful in objectively detecting population
structure using genotypic data (Falush et al., 2003). STRUCTURE implements a
Bayesian model-based clustering method. Compared to purely distance-based methods, it
overcomes the somewhat arbitrary selection of a distance threshold or selection of
clusters “by-eye” from a graphical display (Pritchard et al., 2000). Through the inclusion
of a vector representing the individuals’ admixture proportion in the model, the methods
implemented in STRUCTURE are also able to estimate the proportion of membership to
a population for each individual.
In this study, population structure is examined in a collection of 242 lines from a
breeding program which aims to develop both food-grade and modified fatty acid
specialty soybean cultivars, as well as commodity (high yield) cultivars (for examples: St.
Martin et al., 1996; St. Martin et al., 2004; St. Martin et al., 2006a; St. Martin et al.,
2006b; McHale et al., 2012). The objectives were to 1) determine if genotypic data
provides evidence of subpopulations within a soybean breeding program for both
specialty and commodity soybean cultivars, 2) explore the relationship of subpopulations
with phenotypic data, and 3) determine whether population structuring is related to
pedigree and/or progeny selection. The results indicate that there are two distinct
44
subpopulations within this breeding population. One of the subpopulations consists
primarily of lines with food-grade cultivars in their pedigree. The observed phenotypic
differences are associated with these genotypically determined subpopulations. The data
also supports that the detected genetic substructure is associated with the pedigree history
and, to a lesser extent, directional selection on progeny occurring from a single selection
event.
MATERIALS AND METHODS
Plant Populations and DNA Isolation
The population, grown in 2010, consisted of 242 F4 lines derived from crosses
between numerous maturity group (MG) II and III public varieties and plant introductions
with additional cultivars used as trait, maturity, and yield checks (Table 3.1). Lines were
grown in separate yield tests for early or late maturity and advanced or preliminary lines
dependent on their maturity group (MGII or MGIII) and their generation (F4:7-9 or F4:6),
respectively. Lines for each test were planted in a randomized complete block design
with two replicates for the preliminary lines (F4:6) or three replicates for the advanced
lines (F4:7-9) at four Ohio locations, South Charleston, Plain City, Hoytville, and Wooster.
The early maturing advanced lines (ALTA), the late maturing advanced lines (ALTB),
and the large seeded advanced lines (LST) were planted in six row plots at four locations
with a plot size of 4 m x 2.5 m in South Charleston and Plain City, and 4.9 m x 3 m in
45
Hoytville and Wooster. The preliminary tests for the early maturing lines (OPTA1,
OPTA2) and the late maturing lines (OPTB) were planted at three sites (South Charleston,
Hoytville, and Wooster). In South Charleston, OPT tests were planted in three row plots
with a plot size of 4 m x 1.3 m. In Hoytville and Wooster, tests were planted in two row
plots with a plot size of 4.9 m x 0.6 m. Seeds were harvested from each location during
the fall of 2010. Check cultivars were included in multiple yield tests and used to
normalize data.
For each line, leaf tissue from the first two true leaves of nine seedlings was
collected in liquid nitrogen and lyophilized. DNA was extracted from each line following
a protocol from Keim et al. (1988) adapted for use in a 96-well plate.
Breeding lines were placed into three groups on the basis of their parental
pedigree: commodity, modified fatty acid, or food-grade/high protein. Individuals with at
least one parent with modified fatty acid or food-grade characteristics (high protein or
large seed with clear or yellow hilum) were placed into the pedigree-based modified fatty
acid group or the food-grade/high protein group, respectively. All other individuals were
placed in the commodity group.
Individuals were also placed into groups on the basis of observed phenotypes.
Individuals with low combined saturated fatty acids, high oleic acid, and/or low linolenic
acid were placed into the phenotype-based modified fatty acid group. Individuals with
high seed protein content and/or large seed size with a clear hilum were placed into the
phenotype-based food-grade/high protein group. Individuals with yields exceeding the
check cultivars were selected for the commodity group. These sub-groupings correspond
46
to the individuals which would be selected for continuation in the breeding program. All
other individuals were placed into the non-selected group.
Collection of Genotypic Data
Ninety six individuals with representative lines from public breeding programs in
Ohio and parents of mapping populations were genotyped with the Universal Soy
Linkage Panel 1.0 comprised of 1,536 SNP markers (Hyten et al., 2010) using the
GoldenGate assay (Illumina Inc, San Diego, CA). From this screen for polymorphic
markers, an initial set of 384 markers was selected with a high polymorphism information
content (PIC) and even genome distribution. An additional set of 384 markers were added
in order to fill in gaps between markers in the first set (Hyten et al., 2010). These 768
markers were assayed on all 242 lines and genotypes were assigned using the
GenomeStudio software (Illumina Inc.). Uninformative markers were removed, including
markers which were monomorphic, consisted of poor data with > 24% missing scores,
markers with a minor allele frequency < 3.05% for the first set of markers, or markers
with a minor allele frequency < 0.4% for the second set of markers.
Collection of Phenotypic Data
Seed oil and protein concentrations and fatty acid composition were collected
from all lines at each location and each block replicate. Seed oil and protein
concentration was measured at the National Center for Agricultural Utilization Research
(NCAUR) in Peoria, IL using Near Infrared (NIR) technology with an Infratec 1255 Food
47
& Feed Analyzer (UltraTec Manufacturing Inc., Santa Ana, CA). Fatty acid composition
data were collected by gas chromatography of fatty acid methyl esters at NCAUR using
an Agilent Technologies 6890 GC equipped with an autosampler (Agilent Technologies,
Santa Clara, CA).
Seed weight, volume, density, and shape measurements were collected from 242
lines at each location with a single replicate. One-hundred seed weight was measured by
scale (OHAUS Adventurer TM Pro, Model AV212). Seed volume was measured by water
displacement of 50 dry seeds in 25 ml water in a 50 ml graduated cylinder. Displaced
volume was divided by 50 to obtain the volume per seed. Seed density data calculated as
seed weight divided by seed volume. Seed straight length, straight width and length to
width ratio measurements were made via scanning of seeds and subsequent image
analysis with WinSEEDLE software (Regent Instruments Inc., Canada).
Statistical Analysis of Phenotypic Data
For each trait the genotypic effect of each individual was estimated with Best Linear
Unbiased Prediction (BLUP) values calculated with SAS software v. 9.2 (SAS Institute
Inc., Cary, NC). The model used was: Yijkl = μ + Cj + Gi(Cj) + Lk + Tl(Lk) + Lk × Gi(Cj) +
εijkl, where Yijkl is the observed value for a give trait, μ is the overall mean, Cj is the effect
of the jth class of cultivar or breeding line in which j is equal to a number one to nineteen
for the check cultivars and twenty for all experimental breeding lines, Gi(Cj) is the effect
of the ith check or experimental breeding line within jth class, Lk is the effect of the kth
location, Tl(Lk) is the effect of the lth trial within the kth location, Lk × Gi(Cj) is the
48
interaction effect of the kth location with the ith cultivar or breeding line within a class
and εijkl is the error associated with the observation. All effects are treated as random
except Cj is fixed.
Analysis of Population Substructure
The software STRUCTURE was used to cluster the population into
subpopulations and to generate a Q-matrix indicating each individual’s proportion
membership to a subpopulation (Qw; Pritchard et al., 2000). Starting with a small Burn-in
(100,000) and Markov chain Monte Carlo (MCMC) (100,000), the number of
subpopulations (K) was advanced from K = 1 to K = 15 with 20 replications. StrAuto was
used to automate STRUCTURE runs (Chhatre, 2012). The output of STRUCTURE runs
were summarized using the Evanno method via STRUCTURE HARVESTER (Dent and
Bridgett, 2012) in which the true K is selected as the peak of ∆K, which is
mean(|L’’(K)|)/s[L(K)] where L”(K) is the second order rate of change of the log
likelihood of K (L(K)) divided by standard deviation of L(K) (Evanno et al., 2005). An
increased Burn-in (500,000) and MCMC (500,000) with K fixed at 2 was carried out to
determine population membership for each line. The number of subpopulations was
further confirmed and the population structure further explored by conducting Principal
Component Analysis (PCA) in R software. For PCA of genotypic data, the imputed
marker data file consisting of 242 breeding lines or cultivars and 504 markers was used.
Markers were scored as: “2” for the homozygous common allele, “0” for the homozygous
rare allele, and “1” for heterozygote. For the PCA of phenotypic data, all 242 breeding
49
lines or cultivars were included and a total of 14 traits being assessed, including yield,
seed protein and oil content, oil palmitic, stearic, oleic, linoleic and linolenic content, and
seed length, width, length to width ratio, weight, volume, and density. For both genotypic
and phenotypic PCA, a correlation matrix was used and analysis was conducted in R
using prcomp() command. A scree diagram plotting eigenvalues of each component
against the component number was examined to assist in selecting an appropriate number
of PCs (results not shown). Three PCs were chosen and plotted for genotypic data and
two PCs were chosen and plotted for phenotypic data.
RESULTS
Genotypic data
A total of 504 markers were included in the genotypic analysis. According to the
soybean consensus map v4.0 (Hyten et al., 2010), markers were evenly distributed across
all chromosomes with the average gap distance 4.3 cM. The largest gap distance between
markers is 34.2 cM.
Population structure
The data set consisting of 504 markers assayed on 242 individuals was used to
estimate population structure using the Bayesian model-based methods implemented in
STRUCTURE. To obtain an estimate of the best fitting number of clusters (K), values of
50
K ranging from 1 to 15 were tested using the ΔK method to reveal a peak of ΔK at K = 2
(Figure 3.1) (Evanno et al., 2005). However, as it is not possible to plot ΔK while K = 1,
further investigation into whether K was equal to 1 or 2 was required. Clustering of
individuals based on genotypic data was observed from PCA. The first three principal
components, describing 19.9% of the genotypic variance, indicate that the individuals do
loosely cluster into two subpopulations as defined by STRUCTURE at K = 2 (Figures 3.2
and 3.3). The two subpopulations defined by STRUCTURE are hereafter referred to as
the major and minor subpopulations where the major subpopulation represents the larger
of the two subpopulations with 65% of the alleles in the full population attributed to this
subpopulation (Figure 3.2a).
The majority of the lines exhibited high levels of admixture (Figure 3.2). For this
study, an admixed individual was defined as an individual having less than 90% of the
alleles attributed to a single population. By this criteria, over half (50.2%) of the lines in
the population were admixed (Figure 3.2). Eighty-five of 242 lines (35.1%) belonged
primarily to the major subpopulation with little to no admixture, represented as more than
90% of the alleles of a single line attributed to the major subpopulation (Figure 3.2). In
contrast, only 34 of 242 lines (14%) were members of the minor subpopulation with little
or no admixture (Figure 3.2).
Effect of pedigree on population structure
The pedigree-based food-grade/high protein group had 94% of their alleles attributed
to the minor subpopulation (Figure 3.2a) and was significantly different from all other
51
groups (Figure 3.4). In contrast to the food-grade/high protein group, the allele
distributions of the pedigree-based modified fatty acid and commodity groups were not
significantly different from each other nor from the allele distribution of the entire
population (Figure 3.4)
Effect of phenotypic selection on population structure
The phenotype-based groups represent how lines would be selected in a breeding
program as compared to the pedigree-based group. The alleles represented in the
phenotype-based food-grade/high protein group become less dominated by the minor
subpopulation alleles and closer to the mean subpopulation membership observed in the
population as a whole (Figures 3.2 and 3.4). In concordance with the mean subpopulation
membership for the pedigree and phenotype-based food-grade/high protein groups, the
genotypic PCs also differentiated the pedigree-based food-grade/high protein group from
the entire population more than these PCs differentiated the phenotype-based foodgrade/high protein group from the entire population (Table 3.2). Ten lines from the
pedigree-based commodity group (lacking a food-grade/high protein line in the pedigrees)
were categorized in the food-grade/high protein group based on phenotype (Table 3.1).
This “reclassification” of lines from the pedigree-based commodity group to the
phenotype-based food-grade/high protein group contributed to the mean subpopulation
membership of this group becoming closer to the mean subpopulation membership of the
entire population.
52
In contrast to the effects of selection on the food-grade/high protein groups, the mean
subpopulation membership of the phenotype-based modified fatty acid group was further
from the mean subpopulation membership of the whole population (Figures 3.2 and 3.4).
This was in concordance with the genotypic PCs which differentiated the phenotypebased modified fatty acid group, more so than the pedigree-based modified fatty acid
group, from the entire population (Table 3.2). The mean subpopulation membership of
the phenotype-based modified fatty acid and food grade/high protein groups were
significantly different from the phenotype-based commodity group (Figure 3.4).
Differentiation of phenotypes among populations and groups
PCA was conducted using phenotypic data (Figure 3.5). The first two PCs describe
44% of the phenotypic variance. However, in contrast to the distinct population structure
apparent from PCA with genotypic data, there was no obvious clustering from PCA with
phenotypic data (Figures 3.3 and 3.5). The phenotypic variables which had a high
contribution to PC1 were related seed size and included seed straight length, straight
width, weight and volume (Table 3.3). The phenotypic variables which were the primary
contributors to PC2 were primarily related to seed composition and included seed protein
content, seed oil content, oil stearic and oleic acid content, seed density, and, interestingly,
seed length to width ratio (Table 3.3). The phenotypic PCA separates individuals
according to both the pedigree and phenotype based groups. The food-grade/high protein
groups were significantly different than other groups and had the highest average
absolute values for PC1 (seed size; Table 3.2). The modified fatty acid groups were
53
significantly different that other groups and had the highest absolute values for PC2 (seed
composition; Table 3.2).
DISCUSSION
There were at least two subpopulations detected within this studied soybean breeding
population. This is in concordance with other studies indicating that population structure
appears to be common both between and within breeding programs. A barley population
from eight breeding programs was separated into subpopulations, with examples of
subpopulations being breeding program specific, of lines from a single breeding program
divided into multiple subpopulations , and of phenotype specific subpopulations (tworow versus six-row barley) (Wang et al., 2012b). In a maize breeding program, four
subpopulations reliably identified the four different heterotic groups in the breeding
program (Van Inghelandt et al., 2010). Both the barley and maize studies on population
structure showed that the results from the Bayesian model-based methods implemented
by STRUCTURE were consistent with PCA (Van Inghelandt et al., 2010; Wang et al.,
2012b).
Within this studied soybean breeding program, both the choice of parental lines and
the selection among progeny were related to population structure. Given that this study
focused on a single population, it is unclear whether these results will be applicable to a
range of populations and selection criteria. However, the effect of pedigree, or parental
selection, in this population was obviated by the extreme bias towards alleles from the
54
minor subpopulation in the food-grade/high protein group defined on the basis of
pedigree. The present study only evaluated a single selection event. Yet, in the modified
fatty acid group, there was evidence that in a single cycle of selection, a breeding
population could become structured. While we are aware of no other studies which have
looked at the effects of a single selection event on population structure, previous studies
have explored the effects of long term selection on population structure. Jones et al (2011)
studied 651 barley landraces with both genotypic and phenotypic data and clustered these
lines into nine groups associated with geographic distribution and, to some degree,
phenotypes, suggesting human selection and environmental adaptations were factors in
affecting the population structure of barley landraces. Wang et al (2012a) identified two
subpopulations, representing Chinese wheat landraces or modern wheat varieties, in a
wheat mini-core collection consisting of 262 accessions. A significant difference in
kernel weights and allele frequencies of loci associated with kernel weight were detected
between the two subpopulations, implying that artificial selection has improved wheat
kernel weight and contributed to genetic differentiation of the subpopulations over the
past six decades of the breeding program.
Likewise, the extent of population structure is greater in crops where subtypes have
been long selected for and are firmly defined by the industry. Genetic subpopulations of
corn distinguished sweet corn and popcorn germplasm (Liu et al., 2003). Japonica and
indica rice were also clearly separated into distinct subpopulations (Garris et al., 2005).
Subpopulations within elite wheat germplasm clearly aligned with market classification
and geographic origin for soft and hard wheat (Zhang et al., 2010). Similarly,
55
subpopulations of tomato were in concordance with defined market classes (Sim et al.,
2011). In these cases, subpopulations have a clear association with subtype or industry
classification of the crop. The population structure observed in these studies was likely
the result of long term parental and progeny selection.
Soybean was introduced as a small crop in North America in 1765 and, following
World War II, its production greatly expanded for its use in animal feed (Hymowitz and
Harlan, 1983). The introduction and subsequent breeding of soybean for North America
was associated with an intense bottleneck, such that 75% of the genetic diversity in North
American modern cultivars can be represented by only 17 founding lines (Gizlice et al.,
1994). While the breeding history of soybean for adaptation to North America is
relatively short, the breeding history for specialty types is extremely recent. The first
registered cultivar developed for tofu purposes was released in 1981(Fehr et al., 1984),
and the food-grade market has been emphasized since the early 1990s (Lim et al., 1990).
In 2007, the US updated the trans-fat labeling laws, creating an emphasis on fats in
relation to human health and industrial need for methods of oil stabilization other than
hydrogenation, which notably creates trans-fats (American Public Health Association,
2008). This was the impetus for modified fatty acid soybean breeding programs in North
America. As is seen in other crops, soybean may be trending towards the establishment of
distinct subpopulations for specialty types (Garris et al., 2005; Sim et al., 2011; Wang et
al., 2012b).
It is clear that over many cycles of selection in a breeding population, selection for
differing phenotypic groups within the population can result in genetic differentiation to
56
the extent that subpopulations are developed. The number of cycles of selection required
for this to occur is likely dependent on the level of selection, the heritability of the trait,
and the number of loci involved in the trait. However, the population membership of the
modified fatty acid groups in the current study provides evidence that genetic
differentiation can begin in a single cycle of selection.
Understanding how selection affects population structure in a breeding program can
provide valuable information on the selection of parental lines and new sources of alleles
for a breeding program. The contribution of lines from the pedigree-based commodity
group to the phenotype-based food-grade/high protein group provide evidence that, in
this breeding population, valuable alleles can be contributed to a specialty type from
cultivars outside of that specialty type. Further analysis within additional populations
may provide evidence of haplotypes important to specific specialty types.
Credits: Genotyping was conducted by MCIC. Seed protein, oil, and fatty acid
measurements were conducted by NCAUR. All other work was conducted by Mao
Huang with assistance from members of the McHale lab with seed traits measurements.
Dr. David Francis and Dr. Steve St. Martin assisted with statistical data analysis.
57
TABLES AND FIGURES
Breeding line
or cv. name
Pedigree
Locations†
Dennison¶
H09-106
H09-260
H09-266
H09-277
H09-4
HS0-3243¶
HS5-3519
HS6-3705A
HS6-3705B
HS6-3705C
HS6-3705D
HS6-3705E
HS6-3705F
HS6-3705G
HS6-3705-R
HS6-3967A
HS6-3967B
HS6-3967C
HS6-3967D
HS6-3967-R
HS6-3971-R
HS6-3973A
HS6-3973B
HS6-3973C
HS6-3973D
HS6-3973-R
HS7-4176
HS7-4314
HS7-4437
HS7W-127
HS7W-136
HS7W-190
HS7W-191
HS7W-194
HS7W-29
HS7W-82
HS7W-94
HS8-3284
Athow x HS94-4533
SF114 x Wyandot
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Ohio FG5
SF114 x Wyandot
HS93-4118 x Kottman
HS99-4577 x PI133293
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS99-4256 x Dilworth
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS98-78262 x PI399073
HS1-3641 x H2885
HS1-3641 x HS1-7116
HS1-3661 x IA3017
Kottman x Dilworth
Kottman x Dilworth
HS1-3641 x HS1-3907
HS1-3641 x HS1-3907
HS1-3641 x HS1-3907
H2885 x HF99-019
HS1-3641 x HS1-7116
HS1-5870 x Ohio FG4
Kottman x HS1-7267
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, S, W
H, P, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, P, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, P, S, W
H, P, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
Pedigreebased
groups‡
YLD
FG/HP
FG/HP
FG/HP
FG/HP
FG/HP
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
FA
YLD
YLD
YLD
YLD
YLD
YLD
YLD
FG/HP
YLD
Phenotypebased
groups‡
YLD
FG/HP
FG/HP
FG/HP
FG/HP
FG/HP
YLD
NS
NS
NS
NS
NS
NS
NS
NS
NS
FG/HP
YLD
NS
YLD
NS
NS
NS
NS
NS
NS
NS
NS
YLD
NS
YLD
NS
FG/HP
NS
FG/HP
YLD
YLD
NS
NS
Structure§
0.003
0.948
0.982
0.996
0.986
0.913
0.03
0.724
0.475
0.579
0.558
0.49
0.547
0.492
0.481
0.516
0.003
0.002
0.002
0.002
0.002
0.002
0.71
0.002
0.002
0.003
0.002
0.748
0.412
0.496
0.009
0.005
0.165
0.154
0.171
0.611
0.26
0.924
0.241
Continued
Table 3.1. Pedigrees, locations, groups and population membership for cultivars and
breeding lines.
† Locations in Ohio where field trials were conducted for each breeding line or cultivar;
H: Hoyville, P: Plain city, S: South Charleston, W: Wooster.
‡ Predefined groups are abbreviated as follows: FA, fatty acid lines; FG/HP, food
grade/High protein lines, YLD, Commodity soybean lines; All, All soybean lines in the
population, NS: Non-selected individuals.
§Proportion membership to the major subpopulation assigned by STRUCTURE.
¶Check cultivars grown in multiple trials.
58
Table 3.1 continued
HS8-3289
HS8-3317
HS8-3331
HS8-3334
HS8-3341
HS8-3362
HS8-3451
HS8-3459
HS8-3463
HS8-3486
HS8-3538
HS8-3582
HS8-3657
HS8-3664
HS8-3667
HS8-3672
HS8-3713
HS8W-1
HS8W-102
HS8W-103
HS8W-106
HS8W-115
HS8W-156
HS8W-177
HS8W-178
HS8W-179
HS8W-183
HS8W-184
HS8W-185
HS8W-23
HS8W-3
HS8W-30
HS8W-503
HS8W-504
HS8W-507
HS8W-510
HS8W-514
HS8W-515
HS8W-517
HS8W-518
HS8W-520
HS8W-521
HS8W-54
HS8W-56
HS8W-58
HS8W-68
HS8W-69
HS8W-8
HS8W-82
HS8W-83
HS8W-93
HS8W-96
M09-A003
M09-A037
M09-A044
M09-A045
M09-A047
M09-A059
M09-A060
M09-A061
Kottman x HS1-7267
HS3-25233 x (Williams x PI
424354)
HS3-25233 x (Williams x PI
424354)
OHS 3033 x (Williams x
PI424354)
Dennison2 x HFPR4
Dennison2 x HFPR4
HS3-25233 x (Williams x PI
424354)
HS3-25233 x (Williams x PI
424354)
OHS 3033 x (Williams x
PI424354)
Dennison2 x HFPR4
Dennison2 x HFPR4
HS3-25233 x (Williams x PI
424354)
HS1-3661 x LG00-3372
HS1-3661 x LG00-3372
HS1-3661 x LG00-3372
HS1-3661 x LG00-3372
C2033 x HS1-423
HS0-3243 x LG00-3372
HS2-8086 x HS1-7267
HS2-8086 x HS1-7267
HF01-0821 x Kottman
HF01-0821 x Kottman
HS1-6811 x IA 3017
HS1-3641 x HS1-7116
HS1-424 x Ohio FG4
HS1-424 x Ohio FG4
HS1-3641 x HS1-3907
HS0-8435 x HS1-3907
HS0-8435 x HS1-3907
HS1-3661 x HF01-0821
HS0-3243 x LG00-3372
HS1-3661 x HF01-0821
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
Dennison x N98-4445A
HS1-3661 x HF01-0821
HS1-3661 x HF01-0821
HS1-3661 x HF01-0821
HS1-3661 x HF01-0821
HS1-3661 x HF01-0821
HS0-3243 x LG00-3372
HS1-3661 x HF01-0821
HS1-3661 x HF01-0821
HS2-8086 x HS1-7267
HS2-8086 x HS1-7267
Dennison x HF03-546
HFPR-4 x LS01-1987
HFPR-4 x LS01-1987
HFPR-4 x LS01-1987
HFPR-4 x LS01-1987
HS0-3243 x HF03-546
HS0-3243 x HF03-546
HS0-3243 x HF03-546
H, P, S, W
YLD
NS
0.283
H, P, S, W
YLD
YLD
0.003
H, P, S, W
YLD
NS
0.004
H, P, S, W
YLD
NS
0.66
H, P, S, W
H, P, S, W
YLD
YLD
NS
YLD
0.002
0.002
H, P, S, W
YLD
FG/HP
0.016
H, P, S, W
YLD
NS
0.021
H, P, S, W
YLD
YLD
0.674
H, P, S, W
H, P, S, W
YLD
YLD
NS
NS
0.143
0.002
H, P, S, W
YLD
NS
0.004
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, S, W
H, S, W
H, S, W
H, P, S, W
H, S, W
H, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
FG/HP
FG/HP
YLD
YLD
YLD
YLD
YLD
YLD
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
NS
YLD
NS
NS
FG/HP
FG/HP
YLD
NS
NS
NS
NS
FG/HP
NS
NS
NS
NS
YLD
YLD
NS
YLD
NS
YLD
FG/HP
NS
FG/HP
FG/HP
NS
NS
YLD
NS
NS
NS
NS
YLD
NS
NS
NS
YLD
NS
NS
NS
NS
NS
NS
YLD
YLD
0.75
0.965
0.769
0.462
0.874
0.296
0.791
0.789
0.133
0.091
0.112
0.248
0.995
0.994
0.124
0.519
0.366
0.544
0.33
0.575
0.045
0.02
0.027
0.024
0.03
0.006
0.251
0.108
0.082
0.219
0.502
0.59
0.512
0.593
0.69
0.201
0.533
0.464
0.008
0.089
0.382
0.16
0.128
0.249
0.367
0.296
0.191
0.159
Continued
59
Table 3.1 continued
M09-A063
M09-A075
M09-A076
M09-A077
M09-A078
M09-A079
M09-A086
M09-A088
M09-B001
M09-B002
M09-B004
M09-B005
M09-B010
M09-B012
M09-B013
M09-B015
M09-B016
M09-B017
M09-B019
M09-B020
M09-B021
M09-B023
M09-B024
M09-B025
M09-B026
M09-B027
M09-B028
M09-B030
M09-B031
M09-B032
M09-B033
M09-B034
M09-B036
M09-B037
M09-B038
M09-W031
M09-W032
M09-W033
M09-W034
M09-W035
M09-W036
M09-W037
M09-W038
M09-W039
M09-W041
M09-W042
M09-W043
M09-W045
M09-W047
M09-W049
M09-W050
M09-W051
M09-W052
M09-W053
M09-W054
M09-W055
M09-W056
HS0-3243 x HF03-546
OHS 201 x IA3017
OHS 201 x IA3017
OHS 201 x IA3017
OHS 201 x IA3017
OHS 201 x IA3017
HS1-3661 x HS1-7531
HS1-3661 x HS1-7531
HS1-3661 x HS1-7531
HS1-3661 x HS1-7531
HS1-3661 x HS1-7531
HS1-3661 x HS1-7531
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
HS1-3661 x (OHS 201 x
Md99-173-11-17)
OHS 304 x LG00-3372
OHS 304 x LG00-3372
OHS 304 x LG00-3372
OHS 304 x LG00-3372
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
YLD
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
NS
FA
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
YLD
FA
NS
NS
NS
YLD
NS
0.182
0.654
0.582
0.624
0.679
0.704
0.506
0.369
0.519
0.664
0.666
0.69
0.035
0.004
0.182
0.012
0.005
0.02
0.004
0.007
0.01
0.005
0.413
0.005
0.01
H, S, W
FA
NS
0.934
H, S, W
FA
NS
0.645
H, S, W
FA
NS
0.856
H, S, W
FA
FA
0.99
H, S, W
FA
NS
0.98
H, S, W
FA
NS
0.952
H, S, W
FA
NS
0.37
H, S, W
FA
NS
0.917
H, S, W
FA
NS
0.912
H, S, W
FA
YLD
0.989
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
NS
FG/HP
YLD
NS
YLD
YLD
YLD
NS
YLD
YLD
NS
FG/HP
YLD
NS
NS
NS
YLD
YLD
YLD
YLD
NS
NS
0.017
0.011
0.03
0.008
0.003
0.005
0.002
0.002
0.12
0.003
0.007
0.005
0.002
0.202
0.002
0.224
0.004
0.004
0.005
0.241
0.269
0.298
Continued
60
Table 3.1 continued
M09-W060
M09-W061
M09-W063
M09-W065
M09-W066
M09-W084
M09-W085
M09-W086
M09-W087
M09-W088
M09-W089
M09-W095
M09-W096
M09-W099
M09-W104
M09-W105
M09-W106
M09-W109
M09-W110
M09-W111
M09-W117
M09-W118
M09-W122
M09-W124
M09-W125
M09-W126
M09-W127
M09-W128
M09-W129
M09-W130
M09-W131
M09-W132
M09-W133
M09-W142
M09-W143
M09-W144
M09-W145
M09-W146
M09-W147
M09-W148
M09-W149
M09-W150
M09-W151
M09-W152
M09-W153
M09-W154
M09-W155
M09-W156
M09-W157
M09-W158
M09-W159
M09-W160
M09-W161
M09-W162
M09-W163
M09-W166
M09-W169
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
LG00-3372 x HS3-2523
Dennison x HF03-546
Dennison x HF03-546
Dennison x HF03-546
Dennison x HF03-546
Dennison x HF03-546
Dennison x HF03-546
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x Dennison
HS0-3243 x U01-390489
HS0-3243 x U01-390489
HS0-3243 x U01-390489
HS0-3243 x U01-390489
HS0-3243 x U01-390489
HS0-3243 x U01-390489
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
IA3017 x Dennison
HS0-3243 x IA 3017
HS0-3243 x IA 3017
HS0-3243 x IA 3017
HS0-3243 x IA 3017
HS0-3243 x IA 3017
HS0-3243 x IA 3017
HS0-3243 x IA 3017
(HS1-3661 x Wyandot) x
Md99-173-11-17
(HS1-3661 x Wyandot) x
Md99-173-11-18
(HS1-3661 x Wyandot) x
Md99-173-11-19
(HS1-3661 x Wyandot) x
Md99-173-11-20
(HS1-3661 x Wyandot) x
Md99-173-11-21
(HS1-3661 x Wyandot) x
Md99-173-11-22
(HS1-3661 x Wyandot) x
Md99-173-11-23
(HS1-3661 x Wyandot) x
Md99-173-11-24
(HS1-3661 x Wyandot) x
Md99-173-11-25
(HS1-3661 x Wyandot) x
Md99-173-11-26
(HS1-3661 x Wyandot) x
Md99-173-11-27
Dennison x IA2065
Dennison x IA2065
Dennison x IA2065
Dennison x IA2065
Dennison x IA2065
Dennison x IA2065
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
YLD
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
NS
NS
YLD
YLD
YLD
YLD
NS
NS
YLD
NS
NS
FG/HP
NS
NS
FG/HP
YLD
YLD
YLD
NS
NS
YLD
NS
YLD
NS
NS
FA
NS
NS
FG/HP
NS
NS
NS
FA
NS
FA
FA
NS
FA
NS
FA
0.323
0.308
0.188
0.275
0.318
0.276
0.173
0.004
0.115
0.243
0.014
0.004
0.009
0.005
0.01
0.033
0.019
0.578
0.578
0.509
0.434
0.415
0.523
0.199
0.008
0.027
0.014
0.017
0.02
0.016
0.059
0.069
0.015
0.008
0.008
0.007
0.009
0.009
0.004
0.014
H, S, W
FA
NS
0.991
H, S, W
FA
FA
0.993
H, S, W
FA
NS
0.993
H, S, W
FA
NS
0.992
H, S, W
FA
FG/HP
0.992
H, S, W
FA
FA
0.995
H, S, W
FA
YLD
0.983
H, S, W
FA
FA
0.99
H, S, W
FA
NS
0.993
H, S, W
FA
FA
0.989
H, S, W
FA
FA
0.992
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
FA
FA
FA
FA
FA
FA
YLD
NS
NS
NS
NS
NS
0.144
0.189
0.237
0.177
0.462
0.008
Continued
61
Table 3.1 continued
M09-W171
M09-W172
M09-W173
M09-W174
M09-W175
M09-W176
M09-W177
M09-W179
M09-W180
M09-W183
M09-W184
M09-W195
M09-W196
M09-W197
M09-W199
M09-W201
M09-W202
Ohio FG1
Ohio FG3
Ohio FG4¶
Ohio FG5¶
OHS 202¶
OHS 305
OHS 306
Prohio¶
Streeter¶
Wyandot¶
Wyandot Rg2
Wyandot Rg2
Pm
Dennison x IA2065
HFPR-4 x IA2065
HFPR-4 x IA2065
HFPR-4 x IA2065
HFPR-4 x IA2065
Dennison x HS1-7531
Dennison x HS1-7531
Dennison x HS1-7531
Dennison x HS1-7531
Dennison x HS1-7531
Dennison x HS1-7531
HS1-3661 x IA3017
HS1-3661 x IA3017
HS1-3661 x IA3017
HS1-3661 x IA3017
HS1-3661 x IA3017
HS1-3661 x IA3017
LS301 x HS84-6247
HS89-8843 x Ohio FG1
Ohio FG1 x HS89-3078
A95-581028 x PI592926
A98-980047 x Kottman
HS98-3409 x HS99-5021
HC94-81PR x Asgrow2506
U97-3114 x HS97-5261
OXR-96243 x Ohio FG1
Wyandot x PI87
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, S, W
H, P, S, W
H, P, S, W
H, P, S, W
H,W
H, S, W
H, S, W
H, P, S, W
H, P, S, W
H, S, W
H, P, S, W
H, P, S, W
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FG/HP
FG/HP
FG/HP
FG/HP
YLD
YLD
FG/HP
FG/HP
YLD
FG/HP
FG/HP
NS
NS
FA
FA
NS
NS
NS
NS
YLD
NS
NS
NS
FA
YLD
NS
YLD
NS
FG/HP
FG/HP
FG/HP
FG/HP
YLD
YLD
FG/HP
FG/HP
YLD
FG/HP
FG/HP
0.134
0.16
0.254
0.272
0.214
0.234
0.214
0.279
0.234
0.218
0.235
0.525
0.553
0.33
0.58
0.566
0.51
0.997
0.897
0.961
0.998
0.525
0.151
0.95
0.554
0.002
0.995
0.998
Wyandot x PI87
H, P, S, W
FG/HP
FG/HP
0.996
62
Phenotypic Phenotypic Genotypic
PC1
PC2
PC1
Genotypic
PC2
Genotypic
PC3
a. Correlation coefficients
Major population
membership
-0.29***
-0.37***
0.86***
0.41***
0.15
b. Mean PC values
All
0cd
0bc
0ab
0b
0b
Pedigree- FA†
Pedigree- FG/HP
Pedigree- YLD
-0.44d
4.5a
-0.26cd
0.92a
-0.30bcd
-0.65cd
0.31ab
-19d
2.2a
-2.6c
6.3a
1.1b
2.7a
-0.56b
-2.0b
Phenotypic- All selected
Phenotypic- FA
Phenotypic- FG/HP
Phenotypic- YLD
Phenotypic- Non-selected
0.60c
-0.62d
2.6b
-0.11cd
-0.44d
-0.21bcd
1.2a
-0.73d
-0.37bcd
0.16b
-1.4b
-1.1b
-8.7c
2.5a
1.0ab
0.57b
-5.3d
4.2a
0.41b
-0.42bc
-0.066b
4.5a
-0.65b
-1.1b
0.049b
Table 3.2. Correlation coefficients for subpopulations and PCs and mean PC values for
groups. (a) Pearson’s correlation coefficient between the major population membership
and PC values for the first two PCs from phenotypic data and the first three PCs from the
genotypic data. Significance of Pearson’s correlation coefficient was tested by t-test and
corrected for multiple comparisons using Bonferonni’s method; indicated by * for
adjusted p ≤ 0.05, ** for adjusted p ≤ 0.01, *** for adjusted p ≤ 0.001. (b) Mean PC
values of each predefined group for the aforementioned PCs. Different superscript letters
indicate significant differences (p ≤ 0.05; Duncan's test for multiple comparisons)
between group means for an individual PC. Group means which are significantly
different from the entire population (All) are in bold.
†Abbreviations are as described in Table 3.1.
63
Seed straight length
Seed volume
Seed weight
Seed straight width
Seed protein content
Oil palmitic acid
Seed oil content
Seed density
Oil linoleic acid
Seed length:width
Oil stearic acid
Yield
Oil oleic acid
Oil linolenic acid
Phenotypic
PC1
0.46
0.46
0.46
0.45
0.27
-0.17
-0.12
-0.12
-0.10
0.07
0.07
-0.07
0.06
0.04
Phenotypic
PC2
0.15
0.08
0.01
0.05
-0.42
-0.17
0.39
-0.29
0.21
0.41
-0.38
-0.09
0.35
-0.28
Table 3.3. Eigenvectors for all of the phenotypic variables contributing to phenotypic
PC1 and PC2. Absolute values of eigenvectors which are > 70% of the absolute value of
the highest eigenvector are shown in bold and contribute highly to the PC (Daultry et al.,
1976; Mardia et al., 1979).
64
Figure 3.1. Plot of ∆K. ∆K is equal to the mean of the absolute value of L’’(K) divided
by the standard deviation of L(K), where L(K) is the log likelihood function of true
number of subpopulations (K) and L’’(K) is the second order rate of change of L(K).
65
Figure 3.2. Bayesian admixture proportion for individual soybean lines with the K = 2
population model. Vertical bars represent individual lines which are partitioned into 2 (K)
colored segments according to their subpopulation membership. Vertical black lines
separate soybean lines based on predefined groups. Number within predefined group
areas indicates the percent of alleles attributed to the major subpopulation (red). (a)
Commodity, modified fatty acid, and food grade/high protein groups are defined
according to their pedigree history. (b) Commodity, modified fatty acid, food grade-high
protein, and non-selected groups are defined according to the observed phenotypes.
66
Figure 3.3. PCA plot of genotypic data. Individuals are colored in accordance with the
STRUCTURE output displayed in figure 3.2, where individuals with over 90% of alleles
being contributed from the major subpopulation are colored red, individuals with over 90%
of alleles being contributed from the minor subpopulation are colored green, and admixed
individuals are colored black. Abbreviations are as defined in table 3.1. (a) Groups were
predefined based on pedigree information. (b) Groups were predefined based on observed
phenotypes.
67
Figure 3.4. Bar graph displaying the percentage of alleles attributed to the major
subpopulation for each group defined on the basis of pedigree or phenotype.
Abbreviations are as described in Table 3.1. All: All soybean lines in the population.
Different letters indicate significant difference (p ≤ 0.05; Duncan's test of multiple
comparisons).
68
Figure 3.5. PCA plots of phenotypic data. Individuals are colored in accordance with the
STRUCTURE output displayed in figure 3.2, where individuals with over 90% of alleles
being contributed from the major subpopulation are colored red, individuals with over 90%
of alleles being contributed from the minor subpopulation are colored green, and admixed
individuals are colored black. Abbreviations for groups are as described in table 3.1. (a)
Groups were predefined based on pedigree information. (b) Groups were predefined
based on observed phenotypes.
69
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73
CHAPTER 4
ASSOCIATION MAPPING OF FOOD-GRADE QUALITY TRAITS IN A
SOYBEAN BREEDING PROGRAM FOR COMMODITY AND FOOD-GRADE
CULTIVARS
Abstract
Soybean is primarily used as a vegetable oil for human consumption and a high
protein feed for livestock. Food-grade soybeans, used to produce tofu, miso, edamame,
soymilk, soy sauce, natto and tempeh, are a specialty crop with unique chemical and
physical seed quality requirements. We have estimated the proportion of phenotypic
variance explained by genetic variance for various soybean seed quality traits and
detected significant marker-trait associations through association mapping. Genotypic
and phenotypic analyses were conducted on an initial mapping population of 242
breeding lines and cultivars from OSU soybean germplasm. Soybean traits analyzed
included seed oil and protein content, volume, weight, density, and seed shape (straight
length, straight width, and length to width ratio). Fifty significant marker-trait
associations were detected in the initial mapping population using a mixed linear model.
Twenty-seven of the significant associations were assessed by one-way ANOVA in an
independent confirmation population consisting of 152 breeding lines; 12 were confirmed.
74
Confirmed marker-trait associations included novel loci for seed shape. As a result of
conducting association mapping in a breeding population, these marker trait associations
can be directly applied in marker assisted selection.
75
INTRODUCTION
Soybeans are widely sold as a commodity and processed into vegetable oil and
meal (Panthee et al., 2005). In contrast to commodity soybeans, food-grade soybeans,
used to produce tofu, miso, edamame, soymilk, soy sauce, natto and tempeh, are a
specialty crop sold at a premium and possessing a unique suite of seed quality
requirements (Poysa et al., 2002; Zhang et al., 2010). These requirements are specifically
tailored to the targeted end product and include seed qualities such as seed protein, oil,
sugar, and secondary metabolite content and composition as well as seed coat color,
hilum color, seed size, and seed shape (Poysa et al., 2006; Shi et al., 2010). The present
study has focused on several well-studied traits which are crucial to food-grade soybeans
and include seed weight, protein and oil, as well as several less studied traits, including
seed volume, density, shape.
Soybean protein and oil content are important, not only for food-grade soybean,
but also for commodity soybean, and have been extensively studied (Diers et al., 1992;
Chung et al., 2003; Hyten et al., 2004; Panthee et al., 2005; Clemente and Cahoon, 2009;
Bolon et al., 2010; Liang et al. 2010; Shi et al., 2010). Seed protein and oil contents are
generally negatively correlated (Liang et al., 2010) and highly heritable (Hyten et al.,
2004). To date, 124 quantitative trait loci (QTL) for oil and 108 QTL for protein have
been detected (Hyten et al., 2004; Panthee et al., 2005; Liang et al. 2010; Shi et al., 2010).
Seed weight has also been widely studied (Teng et al., 2008). Broad-sense
heritability, as estimated from three bi-parental populations, is high (Hoeck et al., 2003).
76
A total of 120 QTLs have been reported for seed weight (Soybase, 2012). Seed volume
and seed density have been less studied; however, there is little variation for seed density,
thus seed weight is highly correlated with seed volume (Cai et al., 1997). The heritability
of seed volume, estimated from seed length, width, and height in three mapping
populations, has been estimated as moderate to high (Salas et al., 2006).
Seed shape traits, including seed length, width and length to width ratio, are
considered to be critical elements of seed quality by tofu processors (Poysa et al., 2006;
Salas et al., 2006). The heritabilities of these traits range from moderate to high (Salas et
al., 2006). Only a few studies have conducted QTL analysis on seed shape in a small
number of bi-parental populations (Salas et al., 2006).
With few exceptions (e.g. Shi et al., 2010), the QTL identified for seed quality
traits have been identified in bi-parental mapping populations, which, by design, are
limited in possible recombination events and the number of haplotypes represented.
Compared to linkage mapping in a conventional bi-parental population, association
mapping exploits historical recombination events and increased haplotype diversity; thus,
leading to a relatively higher mapping resolution (Zhu et al., 2008). The creation of an
inbred bi-parental mapping population requires multiple generations of single seed
descent; however, in the context of a breeding program, association mapping can
efficiently utilize research resources and time by conducting mapping on existing
individuals which are part of the breeding population. In addition, results of association
mapping can be more directly applied to the breeding program as it can be conducted in
wider germplasm than bi-parental population, whose results tend to be contextual and
77
only applicable in the mapping population or individuals with a similar genetic base (Zhu
et al., 2008).
To date, many studies have reported using association mapping techniques in
breeding programs to identify loci controlling complex traits with moderate to high
heritability. Association mapping studies conducted in a soft winter wheat (Triticum
aestivum L.) breeding population identified loci associated with kernel size (Breseghello
and Sorrells, 2006). In a maize (Zea mays L.) breeding population, loci were identified
for grain yield and moisture (Liu et al., 2011). Loci contributing to resistance of Fusarium
head blight were confirmed and mapped more precisely in an association mapping study
conducted in a barley (Hordeum vulgare L.) breeding population (Massman et al., 2009).
In soybean, association mapping in breeding populations has been used to identify loci
associated with iron deficiency chlorosis (Wang et al., 2008). The identification of alleles
which will be useful for selection and improvements of the associated traits can be
predicted by the frequency of the allele in the population, the contribution of the locus to
the total variance, and the robustness of marker-trait association as determined by the
confirmation of the association in multiple populations and environments (Wang et al.,
2008). The aforementioned studies and others have shown that association mapping
conducted in a breeding population can result in the direct utilization of marker-trait
linkage in a marker assisted selection program.
78
MATERIALS AND METHODS
Initial population
Genotypic and phenotypic analyses have been being conducted on breeding lines
and cultivars from OSU soybean germplasm. The first mapping population consisted of
242 lines and is described in Chapter 3.
Confirmation population
An independent confirmation population of 152 breeding lines was also selected
from the OSU soybean germplasm (Table 4.1). This confirmation population consisted of
a set of 152 F4:6 lines which were non-overlapping with the initial mapping population.
Breeding lines were grown with check cultivars in three field tests (OPTA, OPTB1, and
OPTB2) in the summer of 2011 with the same field design described in Chapter 3.
Phenotypic data collection
Phenotypic data collected included seed protein and oil concentration, seed
weight, volume, density, length, width, and length to width ratio. The collection of this
data from the initial mapping population is described in Chapter 3.
For the confirmation population, phenotypic data was collected in the same manner
as the initial population. Minor changes were that block replicates were included for the
seed shape measurements and the near-infrared spectroscopy for protein and oil
79
measurements was performed using a Perten DA7200 Feed Analyzer (Perten Instruments,
Stockholm, Sweden).
Statistical analysis of phenotypes
For each trait the genotypic effect of each line was estimated Best Linear
Unbiased Prediction (BLUP) values, calculated using SAS v. 9.2 (SAS Institute Inc.,
Cary, NC) as described in Chapter 3. Genetic variance and proportion of genetic variance
to total phenotypic variance ratio was estimated for each trait. Genotype and locations
were considered random in SAS PROC MIXED: h2 = σ2G / [ σ2G + (σ2GL / L) + (σ2e /
Rep*L)], where h2 is the heritability, σ2G represents the genetic variance, σ2GL is the
variance of genotype by location, σ2e is the variance of error, Rep is the number of field
block replicates, L is the number of locations (Nyquist, 1991).
Genotypic data collection
A total of 504 markers were used for the first mapping population (Chapter 3).
Briefly, these markers were a subset of markers from 768 Illumina GoldenGate markers
which were assayed on the BeadXpress (Chapter3). Uninformative markers were
removed to achieve the final data set of 504 markers genotyped on 242 individuals
(Chapter 3). Missing values were imputed using fastPHASE (Stephens and Donnely,
2003; Scheet and Stephens, 2006). Markers were evenly distributed across all
chromosomes with an average gap distance of 4.3 cM (Chapter 3).
80
The confirmation population was genotyped with a set of 384 GoldenGate markers
(Illumina Inc., San Diego, CA), described in chapter 3 as the second set of markers. The
set of 384 GoldenGate markers included 14 markers found to have significant
associations with traits in the initial mapping population. Genotyping of the confirmation
with GoldenGate markers was conducted using a BeadXpress (Illumina Inc.) at the
Molecular and Cellular Imaging Center at Ohio Agricultural Research and Development
Center at the Ohio State University (Chapter 3).
Association mapping
For the initial mapping population, the software STRUCTURE was used to cluster
the population into sub-groups and generate the Q-matrix (Chapter 3). The software
TASSEL (Bradbury et al., 2007) was used to conduct association mapping with the
unified mix model (Yu and Buckler, 2005): Y = Xβ + Mα + Qw + Ku + e, where Y is
phenotypic score, Xβ represents fixed effects other than SNP markers and population
structure, Mα is marker effects, Qw represents population structure, Ku represents
familial relatedness and e is the error term. The Kinship matrix required for association
mapping was directly generated in TASSEL (Bradbury et al, 2007). For the confirmation
population, single marker one-way ANOVA was conducted for a subset of 27 markertrait associations which were detected to be significant in the initial population.
81
RESULTS
The ratio of genetic variance to phenotypic variance (σ2G/ σ2P) for all traits ranged
from moderate to high; exceptions were seed density and seed straight length to width
ratio (Table 4.2). For traits with moderate to high σ2G/ σ2P, σ2G/ σ2P was consistent
between the initial and confirmation populations (Table 4.2). A total of 52 significant
marker-trait associations were detected for 30 markers. The number of markers found to
be significantly associated with the trait varied for each trait and ranged from 1 to 11
(Table 4.3). With a moderate σ2G/ σ2P ratio in the initial mapping population (Table 4.2),
seed straight length had 11, the largest total number, of significant marker-trait
associations detected in the initial mapping population (Table 4.3). Marker-trait
associations were found on 14 of the 20 chromosomes and were distributed into 22
genetic regions or clusters (Table 4.3; Figure 4.3). These regions were named according
to their linkage group to facilitate discussion within this study (Table 4.3). Limiting the
familywise Type I error rate to 0.1% resulted in 50 significant marker-trait associations
(Table 4.3; Benjamini and Hochberg, 1995).
The proportion of total variance explained by each significant marker (R2) ranges
from 5.4% to 20.0%. Markers with the largest effects include BARC-016485-02069 on
chr. 3 at 61.5 cM and associated with seed length, width, volume, and
Gmax7x61_2757538 on chr. 20 at 19 cM and associated with seed protein and oil
concentrations (Table 4.3; Hyten et al. 2008; Hyten et al., 2010). It should be noted that
the MLM used for the association mapping estimates R2 independently for each marker
82
(Bradbury et al., 2007). Thus, as a result of linkage disequilibrium, the sum of R2 for a
trait is often greater than one.
Twenty-seven of the 50 significant marker-trait associations were tested using singlemarker ANOVA in the confirmation population; 11 of them were at monomorphic or low
minor allele frequency. In total, 12 out of 16 polymorphic significant marker-trait loci
that were tested in the confirmation population were confirmed (Table 4.3). The
confirmed marker-trait associations included seed length, width, length to width ratio,
volume, and weight. No markers which had a significant association for seed oil or
protein concentration were tested in the confirmation population (Table 4.3)
Although LD decayed rapidly in this population (Figure 4.1), there is evidence of LD
at both linked and unlinked markers (e.g. Figure 4.3). There were five regions of markertrait associations which were in LD with one or two other regions which were physically
unlinked (Figure 4.3). Analysis of a subset of markers in an independent confirmation
population allowed further interpretation of the validity of these marker-trait associations.
There exists LD between genetic regions N-1 (seed length, width, volume, weight), and
C1-1 (seed length, width, volume, weight) (Table 4.3; Figure 4.3). Marker BARC016485-02069 in region N-1; markers BARC-016519-02081, BARC-021219-04011, and
Gmax7x194_694393 in region C1-1 were tested in the confirmation population. BARC016519-02081 and BARC-021219-04011 were confirmed to be significantly associated
with seed length, width, and weight; BARC-021219-04011 was also confirmed to be
associated with seed volume. LD was also detected between genetic regions K-1 and O-3,
both of which were associated with seed length to width ratio in the initial population
83
(Table 4.3; Figure 4.3). Both of these markers were tested in the confirmation population;
the association of seed length to width ration was confirmed for the marker in region K-1,
the association was not confirmed for the marker in region O-3 (Table 4.3, Figure 4.2).
LD was detected between genetic regions J-1 and D2-1, but both markers were not tested
in the confirmation population (Table 4.3, Figures 4.1 and 4.2).
DISCUSSION
In this study, an independent confirmation population was used to evaluate 27 out of
50 significantly detected marker-trait associations and 12 of them were confirmed. Two
significant marker-trait associations for seed density were tested in the confirmation
population and none were confirmed (Table 4.2). These “spurious” associations detected
in the initial mapping population may be attributed to the low contribution of genetic
variance to phenotypic variance (Table 4.1) and serve to emphasize the need to confirm
QTL in an independent study.
On chromosome 4 at 15.6 cM and 20.3 cM, markers were confirmed to be
significantly associated with seed straight length, straight width and seed weight (Table
4.3). The marker at 20.3 cM was also confirmed to be significantly associated with seed
volume (Table 4.3). These loci correspond to known QTL for length, volume, and weight
(Mian et al., 1996; Salas et al., 2006). While no QTL have been reported for seed width
84
in this region, the significant association to width by these two markers is likely due to
the contribution of the gene(s) to overall seed size and not seed width, per se.
A total of five QTL for length to width ratio were detected and confirmed to be
significant in both populations and four of them were newly detected in this present study
(Table 4.3). These loci are located on chromosomes 4, 9, 12, 13, 14; those on
chromosome 4 were close to a region of previously published QTL for length to width
ratio (Salas et al., 2006) (Table 4.1).
Significant markers at 25.7 cM on chromosome 9 and 95.9 cM on chromosome 10
were significantly associated with length to width ratio and were in LD in the initial
population (Figure 4.2). Only the marker on chromosome 9 was confirmed to be
significantly associated with length to width ratio in the confirmation population. Given
that soybean lines within a breeding population can be in admixture (Lam et al., 2010;
Chapter 3), it is likely that the inter-chromosomal LD patterns differs in two different
populations.
The QTL for protein and oil on chromosome 20 (I) has been previously widely
detected and was verified in the initial mapping population (Chung et al., 2003; Nichols
et al., 2006). However, the markers associated with this chromosome 20 QTL for protein
were not assayed in the confirmation population (Figure 4.3).
In this study, LD decays to the critical r2 of smaller than 0.1 (Figure 4.1), the genetic
distances is approximately 1cM, which is a more rapid decay than previously reported at >
2.5 cM (Zhu et al., 2003). Thus, an increased marker density might be required to further
capture all functional sites. Though association mapping can effectively detect
85
phenotypic associations with common alleles, it is less efficient in detecting rare alleles
(Yu and Buckler, 2006). As such, even with complete marker saturation, association
mapping in this population will not likely detect all large effect QTL and there is a need
to investigate traits using larger association mapping panels or both bi-parental as well as
association mapping panels. However, the markers-trait associations which have been
detected in both the initial mapping population and the confirmation population are
expected to be robust and directly applicable for use in marker assisted selection.
Credits: Genotyping was conducted by MCIC. Seed protein and oil measurements were
conducted by NCAUR for the initial mapping population and by Mr. Scott McIntyre for
the confirmation population. All other work was conducted by Mao Huang with
assistance from members of the McHale lab for seed size and shape measurements. Dr.
David Francis and Dr. Steve St. Martin assisted with statistical data analysis.
86
TABLES AND FIGURES
Breeding line or cv.
‘Dennison’‡
HS0-3243‡
‘IA3024’‡
‘OHS 202’‡
OHS 307
‘Prohio’‡
‘Streeter’‡
M10-A010
M10-A012
M10-A021
M10-A033
M10-A034
M10-A054
M10-A078
M10-B018
M10-B025
M10-B031
M10-B032
M10-B034
M10-B035
M10-B036
M10-B037
M10-B054
M10-B082
M10-B090
M10-C001
M10-C003
M10-C005
M10-C006
M10-C007
M10-C011
M10-C012
M10-C016
M10-C063
M10-C069
M10-C072
M10-C073
M10-C077
M10-C081
M10-D011
M10-D017
M10-D025
Pedigree
Reference†
St. Martin et al., 2008
St. Martin et al., 2006
Iowa State Univ.
OSU-OARDC
Mian et al., 2008
OSU-OARDC
Dennison x HS4-2973
Dennison x HS4-2973
LG00-3372 x Wyandot
LG00-3372 x Wyandot
LG00-3372 x Wyandot
Dennison x HS3-2669
LD00-3309 x HS4-2973
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
Dennison x HS3-2669
OHS 202 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS4-2973 x HS4-9864
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS4-2915 x HS3-2669
HS4-2915 x HS3-2669
HS4-2915 x HS3-2669
Continued
Table 4.1. Confirmation population breeding lines and their pedigrees as well as lines
used as checks.
† Lines that have been released as a cultivar have references listed.
‡ Checks cultivars and breeding lines which were grown in multiple trials, but not used as
part of the independent confirmation population.
87
Table 4.1 continued
M10-D028
M10-D049
M10-D050
M10-D054
M10-D061
M10-D064
M10-D065
M10-D067
M10-D071
M10-D073
M10-D074
M10-D075
M10-D076
M10-D078
M10-D079
M10-D080
M10-D081
M10-D084
M10-D085
M10-D086
M10-E003
M10-E030
M10-E032
M10-E033
M10-E034
M10-E035
M10-E036
M10-E037
M10-E038
M10-E059
M10-E061
M10-E062
M10-E063
M10-E064
M10-E065
M10-E066
M10-E069
M10-F002
M10-F003
M10-F009
M10-F043
M10-F048
M10-F050
M10-F061
M10-F078
M10-F081
M10-F083
M10-F087
M10-F089
M10-W056
M10-W059
M10-W081
M10-W083
M10-W100
M10-W102
Wyandot x HS3-2669
HS2-4225 x HS4-2973
HS2-4225 x HS4-2973
HS2-4225 x HS4-2973
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
HS3-2669 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
OHS 303 x HS4-9232
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
IA2065 x OHS 303
HS1-36612 x N98-4445A
HS1-36612 x N98-4445A
HS1-36612 x N98-4445A
Dennison x HS5-1089-11
Dennison x HS5-1089-11
Dennison x HS5-1089-11
Dennison x HS5-1089-14
Dennison x HS3-2669
IA2065 x OHS 303
HS4-2973 x HS4-9864
LD00-3309 x HS4-2973
HS3-2523 x Dennison
U01-390489 x HS4-9908
U01-390489 x HS4-9908
Dennison x HS4-9864
Dennison x HS4-9864
HS4-9864 x HS3-2669
HS4-9864 x HS3-2669
Continued
88
Table 4.1 continued
M10-W106
M10-W107
M10-W108
M10-W109
M10-W111
M10-W114
M10-W115
M10-W116
M10-W117
M10-W118
M10-W119
M10-W120
M10-W121
M10-W125
M10-W127
M10-W130
M10-W166
M10-W168
M10-W169
M10-W170
M10-W171
M10-W174
M10-W175
M10-W185
M10-W225
M10-W226
M10-W227
M10-W228
M10-W232
M10-W237
M10-W241
M10-W242
M10-W244
M10-W268
M10-W269
M10-W295
M10-W297
M10-W299
M10-W303
M10-W312
M10-W314
M10-W335
M10-W336
M10-W337
M10-W344
M10-W345
M10-W346
M10-W348
M10-W354
M10-W357
M10-W369
M10-W370
M10-W371
M10-W372
M10-W373
HS4-9864 x HS3-2669
HS4-9864 x HS3-2669
HS4-9864 x HS3-2669
HS4-9864 x HS3-2669
HS4-9864 x HS3-2669
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS4-5450 x OHS 303
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2669 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
HS3-2523 x Dennison
Dennison x HS4-5426
Dennison x HS4-5426
IA2065 x HS3-2669
IA2065 x HS3-2669
IA2065 x HS3-2669
IA2065 x HS3-2669
IA2065 x HS3-2669
IA2065 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HS5-1134-21 x HS3-2669
HF03-534 x IA3024
HF03-534 x IA3024
OHS 202 x HS5-1112-44
OHS 202 x HS5-1112-44
OHS 202 x HS5-1112-44
OHS 202 x HS5-1112-44
OHS 202 x HS5-1112-44
89
Trait
Protein
Oil
Weight
Volume
Density
Length
Width
Length:width
Initial population σ2G/ σ2P
0.96
0.93
0.90
0.81
0.09
0.62
0.53
0.22
Confirmation population σ2G/ σ2P
0.83
0.89
0.85
0.78
0.15
0.56
0.36
0.42
Table 4.2. Proportion of the observed phenotypic variance (σ2p) explained by genetic
variance for BLUP values of seed traits in the initial and confirmation populations.
90
Chr.
(LG)
PositionConsensus
4.0 map
(cM)
PositionComposite Genetic
Marker
2003 map region
†
(cM)
2(D1b)
116.5
116.3- 121.0 D1b-1
BARC-028373-05856
3(N)
61.3
75.3-78.9
N-1
BARC-028205-05791
3(N)
61.5
75.3
N-1
BARC-016485-02069
4(C1)
15.6
28.4
C1-1
BARC-016519-02081
4(C1)
19.5
30.7
C1-1
BARC-031733-07217
4(C1)
20.3
30.7
C1-1
BARC-021219-04011
4(C1)
27.7
42.9- 44.5
C1-1
BARC-014361-01331
4(C1)
31.1
27.7
C1-1
Gmax7x194_694393
4(C1)
43.5
59.9- 67.0
C1-2
BARC-024445-04886
4(C1)
63.9
C1-3
BARC-044523-08716
5(A1)
2.5
0-2.0
A1-1
BARC-040651-07808
5(A1)
7(M)
7(M)
30.9
24.5
68.7
31.1-31.4 A1-2
23.4- 33.2 M-1
71.7-77.23 M-2
BARC-053559-11912
BARC-054347-12492
BARC-047995-10452
9(K)
25.7
10(O)
10(O)
10(O)
29.4
54.4
82.5
40.7-44.6
24.6-33.2
51.9-57.0
85.7
10(O)
95.9
100.4
87.3
K-1
Gmax7x85_2848025
O-1
O-2
O-3
BARC-065789-19751
BARC-022175-04293
Gmax7x141_1711038
O-3
Gmax7x300_135386
Trait
density
length
volume
weight
length
width
volume
weight
length
width
volume
weight
length
width
volume
weight
length
width
volume
weight
length
weight
length
width
volume
weight
length
length:
width
density
length
oil
density
length
length:
width
density
oil
protein
length:
width
IP‡ MLM
(p-value)
2.90E-05
3.78E-08
8.48E-04
7.87E-06
1.86E-12
2.00E-07
4.97E-08
2.44E-08
3.93E-10
2.72E-05
1.96E-06
3.94E-08
3.56E-09
1.52E-04
7.57E-06
3.31E-06
3.56E-09
1.52E-04
7.57E-06
3.31E-06
9.90E-06
3.35E-04
6.62E-09
3.17E-04
2.90E-05
5.86E-06
2.00E-04
QTL
CP§
positions¶ R2 for IP ANOVA
Composite
MLM
(p2003 map
value)
(cM)
0.084
n.d. None
0.132
n.a. 84.6-102
0.057
n.a. None
0.093
n.a. 78.5-84.5
0.200
n.d. 84.6-102
0.119
n.d. None
0.131
n.d. None
0.136
n.d. 78.51-84.51
0.164
0.0006 21.0-33.3
0.083
0.0015 None
0.104
0.077 10.3-65.1
0.132
0.042 17.6-19.6
0.149
n.a. 21.0-33.3
0.070
n.a. None
0.094
n.a. 10.3-65.1
0.100
n.a. 32.3-34.3
0.149
0.0288 21.0-33.3
0.070
0.0253 None
0.094
0.01 10.3-65.1
0.100
0.005 32.3-34.3
0.091
n.a. 21.0-33.3
0.064
n.a. 32.3-34.3
0.144
n.d. 21.0-33.3
0.064
n.d. None
0.083
n.d. 10.3-65.1
0.095
n.d. 32.3-34.3
0.068
10.3-65.1
2.12E-04
0.068
2.25E-04
3.86E-04
2.05E-04
2.53E-06
2.48E-05
0.068
0.063
0.069
0.103
0.084
2.30E-04
0.055
7.20E-06
5.71E-04
6.48E-06
0.095
0.061
0.081
3.35E-05
0.082
Reference
Present study
Salas et al., 2006
Present study
Chen et al., 2007
Salas et al., 2006
Present study
Present study
Chen et al., 2007
Salas et al., 2006
Present study
Salas et al., 2006
Mian et al., 1996b
Salas et al., 2006
Present study
Salas et al., 2006
Orf et al., 1999a
Salas et al., 2006
Present study
Salas et al., 2006
Orf et al., 1999a
Salas et al., 2006
Orf et al., 1999a
Salas et al., 2006
Present study
Salas et al., 2006
Orf et al., 1999a
Salas et al., 2006
0.0024 90.72-122.62 Salas et al., 2006
n.a.
n.a.
n.d.
n.a.
n.a.
None
None
29.3-31.3
None
62.3-71.7
0.0032 None
n.a.
n.a.
n.d.
None
49.7-54.2
58.4-106
0.1287 None
Present study
Present study
Mansur et al., 1996
Present study
Salas et al., 2006
Present study
Present study
Panthee et al., 2005
Chen et al., 2007
Present study
Continued
Table 4.3. Significant marker-trait associations.
† Position may be estimated according to neighboring markers.
‡ IP, initial population. Bold values are significantly associated with markers () with
limitation of the familywise error rate to 0.1% using the Benjamini-Holm method.
§CP, confirmation population. Bold values are significantly associated with phenotypes
in the confirmation population according to single marker ANOVA (α = 0.05).
¶Previously published QTL.
n.a not assayed in confirmation population
n.d monomorphic or low minor allele frequency
91
Table 4.3 continued
12(H)
26.9
12(H)
62
12(H)
62.1
12(H)
101.1
27.6-28.8-
H-1
BARC-016807-02334
H-2
BARC-018973-03046
62.6-67.8
H-2
BARC-061985-17608
108.2
H-3
BARC-039237-07479
62.6
13(F)
64.1
77.7- 82.8
F-2
BARC-061189-17109
14(B2)
12.6
14.7
B2-1
BARC-061557-17270
16(J)
16(J)
8.2
25.4
3.81- 11.74 J-1
22.1
J-1
BARC-063377-18348
BARC-020505-04644
17(D2)
33.4
34.1- 39.3
D2-1
BARC-054249-12398
18(G)
70.6
72.8
G-1
BARC-024489-04936
length:
width
length:
width
length:
width
protein
length:
width
density
length:
width
density
length
protein
density
width
8.70E-06
0.092
5.54E-07
0.113
8.05E-05
0.076
1.67E-04
0.070
9.89E-05
0.074
5.03E-07
0.115
2.30E-04
0.055
1.45E-08
1.38E-04
3.58E-04
6.13E-07
5.95E-04
0.141
0.071
0.064
0.113
0.059
0.8598 None
n.a.
None
Present study
0.0029 None
Present study
n.a.
123-125
0.0008 None
0.67
Present study
Present study
n.a.
n.a.
n.a.
n.a.
n.a.
None
None
None
None
None
27.0-39.0
20(I)
19
22.9-37.6
I-1
0.116
n.a.
Gmax7x61_2757538
30.6-34.7
31.4-33.4
34.2-36.2
36.0-49.3
36.4-36.9
37.1-39.1
21.0-23.0
31.4-33.4
31.4-33.4
protein
8.30E-12
0.191
n.a.
31.4-33.4
35.9-37.9
35.9-37.9
36.4-36.9
37.1-39.1
92
Present study
None
21.8-23.8
4.14E-07
Qiu et al., 1999
0.0025 None
21.0-23.0
oil
Present study
Present study
Present study
Present study
Present study
Present study
Reinprect et al.,
2006
Csanadi et al., 2001
Reinprect et al.,
2006
Qi et al., 2011
Sebolt et al., 2000
Specht et al., 2001
Qi et al., 2011
Chung et al., 2003
Diers et al., 1992
Reinprect et al.,
2006
Sebolt et al., 2000
Brummer et al.,
1997
Diers et al., 1992
Tajuddin et al.,
2003
Tajuddin et al.,
2003
Chung et al., 2003
Diers et al., 1992
Figure 4.1 LD decay for the initial mapping population.
93
Figure 4.2. Manhattan plots of the MLM result for marker associations with seed traits.
The dashed lines indicate a p-value threshold of 0.001. Markers are in genetic order (cM)
across the x-axis; vertical bars separate chromosomes.
94
Figure 4.3. Display of LD selected chromosomes. Markers are organized in genetic order;
pairwise LD, the squared correlation coefficient (r2) is shown by the level of shading in
each diamond. Black diamonds are loci between which r2 = 1; grey diamonds indicate 0 <
r2 < 1; white diamonds indicate r2 = 0. Red lines highlight all unlinked marker pairs with
r2 > 0.1 and a significant association to a trait in the initial mapping population. Image
was generated with Haploview 4.2 (Barrett et al., 2005).
95
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