QTL AND TRANSCRIPTOMIC ANALYSIS BETWEEN RED WHEAT AND WHITE
WHEAT DURING PRE-HARVEST SPROUTING INDUCTION STAGE

BY

Yuanjie Su

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

Plant Breeding, Genetics and Biotechnology-Crop and Soil Sciences - Doctor of Philosophy

2013

ABSTRACT

QTL AND TRANSCRIPTOMIC ANALYSIS BETWEEN RED WHEAT AND WHITE
WHEAT DURING PRE-HARVEST SPROUTING INDUCTION STAGE

By
Yuanjie Su

Wheat pre-harvest sprouting (PHS) is a precocious germination of seed in the head when
there are prolonged wet conditions occurs during the harvest period. Recent damage caused by
PHS occurred in 2008, 2009 and 2011, resulting in severe losses to the Michigan wheat industry.
Direct annual losses caused by PHS worldwide can reach up to US $1 billion. Breeding for PHS
resistant wheat cultivars is critical for securing soft white wheat production and reducing the
economic loss to Michigan farmers, food processors and millers. In general, white wheat is more
susceptible to PHS in comparison to red wheat. However, the underlying mechanism connecting
seed coat color and PHS resistance has not been clearly described. In this study, a recombinant
inbred line population segregating for seed coat color alleles was evaluated for seed coat color
and α-amylase activity in three years with two treatments. The genotyping results enabled us to
group individuals by the specific red allele combinations and allowed us to examine the allelic
contribution of each color loci to both seed coat color and α-amylase activity.
A high-density genetic map based upon Infinium 9K SNP array was generated to locate
QTL in relatively narrow regions. A total of 38 Quantitative Trait Loci (QTL) for seed coat color
and α-amylase activity were identified from this population and mapped on eleven chromosomes
(1B, 2A, 2B, 3A, 3B, 3D, 4B, 5A, 5D, 6B and 7B) from three years and two post-harvest
treatments. Most QTL explained 6-15% of the phenotypic variance while a major QTL on

chromosome 2B explained up to 37.6% of phenotypic variance of α-amylase activity in 2012
non-mist condition. Significant QTL × QTL interactions were also found between and within
color and enzyme related traits.
Next generation sequencing (NGS) technology was used in current study to generate
wheat transcriptome using Trinity with two methods: de novo assembly and Genome Guided
assembly. Quality assessment of the two assemblies was conducted based on their concordance,
completeness and contiguity. Three assembly scenarios were evaluated in order to find a balance
between sample specificity and transcriptome completeness. Red wheat and white wheat lines
from previous QTL population were collected under mist and non-mist conditions and their
expression profiles were compared to identify differentially expressed (DE) genes. At non-mist
condition, only around 1% of the genes were differentially expressed between physiologically
matured red wheat and white wheat while the rate had a 10-fold increase after 48 hr misting
treatment. Annotation of the DE genes showed signature genes involved in germination process,
such as late embryogenesis abundant protein, peroxidase, hydrolase, and several transcription
factors. They can be potential key players involved in the underlying genetic networks related to
the PHS induction process. Gene Ontology (GO) terms enriched in DE genes were also
summarized for each comparison and germination related molecular function and biological
process were retrieved.
In conclusion, with the population segregating for seed coat color loci, the relationship
between seed coat color and α-amylase activity were examined using biochemical methods, QTL
analysis, and transcriptome profiling. The variation of seed coat color do closely linked with
PHS resistance level at all three levels. DE genes and enriched GO terms identified were
discussed for their potential role in bridging the gap between seed coat color and PHS resistance.

Copyright by
YUANJIE SU
2013

ACKNOWLEDGEMENTS

This is it! At the end of my Ph.D. study, I finally got a chance to document my sincere
gratitude for all the help I received during the last four years at Michigan State University.
Without their generous help, I can never go this far. First, I would like to thank my previous
advisor, Dr. Janet Lewis, who first brought me to the field of wheat breeding. Her passion in
plant breeding and agriculture truly inspired me to work in this exciting field. Then, I would like
to express my greatest appreciation to my major advisor, Dr. David Douches, for his insightful
suggestion and guidance on my dissertation study and career development, for his constant
support and encouragement throughout my doctoral study and especially in the preparation of
this dissertation. I would also like to thank my committee members, Dr. Mitch McGrath, Dr.
Shinhan Shiu, and Dr. Dechun Wang, who spent significant amount of time discuss, critique,
challenge, and encourage me through my dissertation work and the final dissertation editing.
Discussions with them were always a pleasure and a great learning experience.
When it comes to research, I would like to first thank Dr. Robin Buell’s group, especially
John Johnston, for generously sharing with me their insights and experiences in bioinformatics.
Then I would like to thank Dave Main, who helped me freeze dried my last two year’s wheat
samples in a timely manner. I would also like to thank MSU High Performance Computing
Center, where all my bioinformatics analysis was done, and all the laboratories sharing
equipment with me: USDA-ARS sugarbeet and bean research unit, soybean research lab, Dr.
Ning Jiang’s lab and Dr. Randy Beaudry’s lab.

v

The last two years with potato breeding group was a great experience: Nathan Butler, Dr.
Norma Manrique-Carpintero, Joe Coombs, Dr. Kim Felcher, Donna Kells, Dr. Alicia Massa, Dr.
Dan Zarka, Kelly Zarka, … Working with them really made me feel like living with a big family,
which is even more meaningful for an international student like me.
I am deeply indebted to Dr. Russ Freed for his leadership during the wheat lab transition
period and his continuous support for the last years of my doctoral study. I am also grateful to
my cohort in the wheat lab: graduate students, Esteban Falconi, Swasti Mishra, Chong Yu;
technicians, Lee Siler, Sue Hammer, Randy Lorenz, and all the hardworking undergraduate
crews, who offered a lot of help during my phenotype collection stage both in the field and in the
lab. I would also like to thank Monsanto Fellows in Plant Breeding program for supporting my
Ph.D. study.
Last but not the least, my mom, Xueqing Zhang, and my dad, Qinghe Su, whose
unconditional love and support made me through the toughest time of the process. I also would
like to thank my long-time friend, Ya Liu and all my friends here in the department: Valerio
Hoyos, Guangxi Wu, Zixiang Wen, Jiazheng Yuan, Veronica Vallejo, Dongyan Zhao, Carmille
Bales. Their companion, support, and friendship made my doctoral life exciting and memorable!

vi

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... ix

LIST OF FIGURES ....................................................................................................................... xi

CHAPTER 1 Literature review ....................................................................................................... 1
1.1 White wheat as a commodity crop ............................................................................................ 2
1.1.1 Wheat market division and usage ................................................................................... 2
1.1.2 White wheat in Michigan ............................................................................................... 3
1.2 Pre-harvest sprouting damage in wheat production .................................................................. 3
1.3 Seed dormancy .......................................................................................................................... 4
1.3.1 Dormancy during seed development .............................................................................. 5
1.3.2 Genetic control of seed dormancy .................................................................................. 6
1.4 Genetic control of seed coat color ............................................................................................ 8
1.5 Germination and α-amylase ...................................................................................................... 9
1.6 PHS in wheat........................................................................................................................... 11
1.6.1 Evaluation of pre-harvest sprouting ............................................................................. 11
1.6.2 Improving PHS resistance in wheat ............................................................................. 13
1.7 Project objectives .................................................................................................................... 15
REFERENCES ............................................................................................................................. 17
CHAPTER 2 QTL analysis for seed color components and α-amylase activity in a spring wheat
recombinant inbred line population segregating for seed color .................................................... 27
Abstract ......................................................................................................................................... 28
Introduction ................................................................................................................................... 29
Materials and Methods .................................................................................................................. 31
Plant Materials ....................................................................................................................... 31
Field Design and Greenhouse Misting .................................................................................. 31
Seed coat color measurement ................................................................................................ 32
Determination of α-amylase activity ..................................................................................... 33
Statistical analyses ................................................................................................................. 34
DNA isolation and SNP genotyping...................................................................................... 35
SNP calling, filtering, map construction ............................................................................... 36
QTL mapping for color measurements and α-amylase activity ............................................ 37
Results ........................................................................................................................................... 38
Map construction with Infinium 9k wheat SNP array ........................................................... 38
Phenotype distribution of seed color components and α-amylase activity ........................... 38
Correlation between seed coat color and α-amylase activity ................................................ 42
Genotypic effects on color components and α-amylase activity ........................................... 42
QTL analysis for seed color measurements and α-amylase activity ..................................... 47
Discussions ................................................................................................................................... 52
vii

Map construction and QTL mapping with high-density markers ......................................... 52
Correlations of phenotypic traits ........................................................................................... 53
Dosage effect of homeologs for grain color and α-amylase activity..................................... 54
QTL identified for PHS related traits .................................................................................... 55
QTL by QTL interaction ....................................................................................................... 57
APPENDIX ................................................................................................................................... 58
REFERENCES ........................................................................................................................... 109

CHAPTER 3 RNA-seq analysis of wheat transcriptome during pre-harvest sprouting induction
..................................................................................................................................................... 114
Introduction ................................................................................................................................. 115
Materials and methods ................................................................................................................ 119
Plant materials ..................................................................................................................... 119
RNA extraction, library construction and Illumina sequencing .......................................... 121
Quality trimming of short reads .......................................................................................... 121
Short read assembly and quality evaluation ........................................................................ 122
Comparison of assembly strategy........................................................................................ 123
Results and discussions ............................................................................................................... 125
Global characteristics of transcript assemblies.................................................................... 125
Concordance analysis of de novo and Genome Guided assemblies .................................... 129
Transcripts representation and assembly completeness ...................................................... 131
Transcripts contiguity .......................................................................................................... 136
Evaluation of de novo assembly strategy ............................................................................ 138
RNA-seq transcriptome profiles and DE calling ................................................................. 145
Transcriptome profiling during PHS induction stage .......................................................... 145
Annotation of differentially expressed genes ...................................................................... 148
GO enrichment of differentially expressed genes ............................................................... 150
DE transcripts without annotations ..................................................................................... 158
Conclusions ................................................................................................................................. 159
APPENDIX ................................................................................................................................. 162
REFERENCES ........................................................................................................................... 199

CHAPTER 4 Future directions ................................................................................................... 206
REFERENCES ........................................................................................................................... 210

viii

LIST OF TABLES

Table 2.1 Mapping population used in this study with red homeolog combinations determined by
SSR marker linked to color loci .................................................................................................... 36

Table 2.2 Summary of linkage map constructed with 1695 markers for ‘Vida’ × MTHW0471
population ..................................................................................................................................... 39

Table 2.3 Phenotypic correlations of traits within each environment .......................................... 43
Table 2.4 Analysis of variance of color components and α-amylase activity evaluated in three
years with two post-harvest treatments in Mason, MI for ‘Vida’×MTHW0471 population…….44
Table 2.5 Least square means of α-amylase activity for different dosage groups across different
environments……………………………………………………………………………………..46

Table 2.6 Summary of QTL identified in ‘Vida’ × MTHW0471 from 2010 to 2012 .................. 48

Table 2.7 QTL identified by MIM having Additive × Additive interaction and the heritability of
the trait .......................................................................................................................................... 51

Table A.1 Growth stage of ‘Vida’ × MTHW0471 population and recipitation from physiological
maturity to the end of harvest from 2010 to 2012……………………………………...……......59
Table A.2 SNP filtering pipeline (from raw genotype call to Joinmap input)..............................60
Table A.3 1695 markers mapped by ‘Vida’ × MTHW0471population…………….………..….61
Table A.4 Significant correlations between phenotypic traits within and across environments.105

Table 3.1 Summary for RNA-seq comparisons .......................................................................... 120

ix

Table 3.2 Summary of sample raw reads (million pairs) for comparison of assembly strategy. 124
Table 3.3 Summary of Trinity assembled transcripts compared with public databases ............. 126
Table 3.4 Clustering results of Trinity assembled transcripts based on CD-HIT-EST at different
sequence similarities ................................................................................................................... 132
Table 3.5 MegaBLAST results of Trinity assembled transcripts vs. Unigene cDNA library .... 133
Table 3.6 Percentage of Trinity assembled transcripts in public wheat EST databases ............. 135
Table 3.7 Bowtie2 alignment for single samples to Trinity assemblies with different assembly
strategies ..................................................................................................................................... 142
Table 3.8 MegaBLAST results for single sample assembly against bulk (1-rrr, 1-RRR) and total
assembly (1-All).......................................................................................................................... 143
Table 3.9 Sample correlations between biological replicates used for differential expression...147
Table 3.10 Gene Ontology (GO) categories significantly enriched in Up-regulated wheat genes
in four comparisons..................................................................................................................... 151
Table 3.11 Gene Ontology (GO) categories significantly enriched in Down-regulated wheat
genes in four comparisons .......................................................................................................... 154

Table B.1 BLASTn results of 28 Trinity assembled transcripts longer than 10,000bp………...163

Table B.2 Differentially expressed transcripts with BLASTp and KEGG annotations…….….164
Table B.3 Differential expressed transfrags based on Cuffdiff categorization……………..…..168
Table B.4 GO terms that match proteins at differentially expressed loci……………….……...169

x

LIST OF FIGURES

Figure 2.1 Distribution of color components and α-amylase activity for ‘Vida’ × MTHW0471
population from 2010 to 2012....................................................................................................... 40

Figure 3.1 Length distribution of Trinity assembly compared against public databases............ 128

Figure 3.2 The distribution of length coverage of EST by the Trinity assembled transcripts in
cDNA library made from ABA-treated seed. ............................................................................. 134

Figure 3.3 The distribution of length coverage of full length cDNA (flcDNA) by the Trinity
transcripts. ................................................................................................................................... 137

Figure 3.4 Correlations between input read pairs and other assembly statistics. ....................... 139

xi

CHAPTER 1 Literature review

1

1.1 White wheat as a commodity crop
1.1.1 Wheat market division and usage
Wheat (Triticum aestivum L.) is a staple crop after its domestication 9000 years ago
(Peng et al., 2011). Wheat provides 20% of calories consumed by more than 40% of the world’s
population (Gill et al., 2004). Wheat is widely grown in all the continents and traded
internationally as a commodity. In the US, wheat ranks third among field crops in both planted
acreage and gross farm receipts (National Agricultural Statistics Service, 2012). Wheat can be
divided into six categories based on vernalization requirement (winter, spring), seed coat color
(red, white), and kernel texture (hard, soft) (McFall and Fowler, 2009). Winter wheat requires
vernalization, a prolonged exposure to cold temperature, in order to flower and spring wheat can
flower without vernalization. Red seed coat color is reddish brown and white seed coat color is
yellowish and tan. For downstream usage, hard wheat is generally used for bread-making while
soft wheat is used for pastry, snacks and breakfast cereals. The usage difference is due to the
protein content. Major protein type in wheat is gluten, which holds carbon dioxide during
fermentation and gives a soft texture. Thus, the higher protein content in hard wheat, when
compared with soft wheat, makes it a preferred material for bread-making.
Recent years, whole-grain products are in high demand for its beneficial effects in
prevention of diet-related disorders and cancers (Seal and Brownlee, 2010). White wheat is the
preferred material for whole-grain products due to its higher flour yield and better tasting quality.
However, the market demand for high quality white wheat is not fulfilled, which may due to the
competition for acreage from genetically modified crops and farmers’ concern about weatherinduced quality issues, such as pre-harvest sprouting. White wheat production has declined, from

2

352 million bushels to 221 million bushels during last 15 years (Economic research services,
United States department of agriculture, 2013).
1.1.2 White wheat in Michigan
Wheat is Michigan’s third largest crop, grown on approximately half a million acres, with
an economic impact of $2.7 billion (National Agricultural Statistics Service, 2012). Soft red (60%
of acreage) and soft white (40% of acreage) winter wheats produced in Michigan are used as the
primary ingredient of breakfast cereal and bakery goods (Peterson et al., 2006). During the past
15 years, white wheat production has declined rapidly in the eastern US, which has led to
Michigan being the leading white wheat producer (78%) in this region of the US (Sutherland,
2011). This rapid decrease in white wheat acreage increased the production cost of major cereal
and milling companies, such as Kellogg, Post, and Star of the West milling company, resulting in
a premium pricing for high quality white wheat. Therefore, growing soft white wheat is a
profitable and growing market for Michigan farmers.
1.2 Pre-harvest sprouting damage in wheat production
Pre-harvest sprouting (PHS) is the precocious germination of seed induced by prolonged
wet conditions prior to harvest. Sprout damage is mainly three-fold: loss in yield, reduction in
test weight, and low-quality downstream products. The first two directly affect farmers profit and
the last one is the biggest concern for milling and cereal companies. Sprouted wheat produces
excess amounts of α-amylase. The final products are off-color with weak texture even after
blending with batches containing low α-amylase activity. This causes substantial economic loss
to food processors, therefore farmers get discounted price at grain receivers if grains have
unacceptable sprout damage. Direct annual losses caused by PHS worldwide can reach up to US
$1 billion (Black et al., 2006).

3

Based on the trend of climate change, Michigan is expected to experience more extreme
weather and variable precipitation (Doll and Baranski, 2011), which poses a higher risk of PHS
for wheat growers. However, few precautionary management practices has been established to
avoid PHS except for a timely harvest, while an early harvest can increase drying cost and lower
yield. On the other hand, swathing, a commonly used farm practice that cuts and windrows the
wheat crop, can significantly increase PHS risk when rainfall is present (Derera, 1989).
Moreover, a recent phenotypic screen at Michigan State University showed that few Michigan
soft wheat varieties have PHS resistance (Yu, 2012). Therefore, enhancing PHS resistance in
wheat varieties adapted to Michigan is in urgent need and is one of the most effective ways to
reduce wheat growers’ risk of PHS.
1.3 Seed dormancy
Pre-harvest sprouting is seed germination prior to seed harvest that is attributed to a lack
of dormancy. Dormancy induction, maintenance and release in seed are closely related to PHS
process. Dormancy can be defined as temporary growth arrest when environmental conditions
are sub-optimal for germination. It is a strategy adopted by many species to survive adverse
environmental conditions (Footitt and Cohn, 2001). It can be affected by internal balance of
hormone levels and sensitivity, while the environmental signals can also modify the expression
of related enzyme in a feedback-regulated fashion (Finkelstein et al., 2008; Footitt and Cohn,
2001). Seed dormancy has been previously reviewed from different aspects (Bentsink et al., 2007;
Finkelstein et al., 2008; Foley, 2001; Graeber et al., 2012; Koornneef et al., 2002; Penfield and
King, 2009).

4

1.3.1 Dormancy during seed development
Seed development initiates from double fertilization. A cereal seed includes embryo,
endosperm and testa and dormancy occurs in both embryo and testa during seed development.
The embryo development can be divided into two stages: 1. embryo morphogenesis, which is the
formation of embryo and acquisition of polarity, and 2. embryo maturation, the stage that the
embryo accumulates nutrients to prepare for the stress caused by desiccation (Ohto et al., 2007).
The embryo gains the ability to germinate as soon as the embryo is fully developed. In order to
continue the nutrient deposition and finish embryo maturation, abscisic acid (ABA) content
increases to induce dormancy and stay high before grain desiccation to ensure the germination
does not occur (Finkelstein et al., 2008). Evidences proved that ABA controls and coordinates
various processes through multiple transcription factors and seed dormancy is closely related to
the ABA content in the grain (Bentsink et al., 2007). Embryo sensitivity to ABA played an
equally important role as ABA content in the induction of dormancy (Walker-Simmons, 1987).
ABA insensitive mutants from Arabidopsis showed reduced seed dormancy (Koornneef et al.,
1984), while ABA hyper-sensitive mutants showed enhanced seed dormancy (Cutler et al., 1996).
In wheat, when ABA responsiveness of wheat embryo was restored by introducing a functional
VP1 ortholog from oat, the seed dormancy, or PHS resistance, increased (McKibbin et al., 2002).
Seed dormancy peaks when seed reach physiological maturity, which is characterized by
the maximum dry weight. Then seed enters desiccation process, or dry-down, during which
dormancy is slowly released. Several events happen during this period including a shift in the
internal hormone balance from ABA to gibberellin acid (GA) which promotes seed germinability,
a decrease in ABA content and sensitivity (Ali-Rachedi et al., 2004; Grappin et al., 2000), and an
increased sensitivity to GA and light (Hilhorst, 2007). Alteration of membranes and protein

5

degradation also improves germination vigor (Angelovici et al. 2010). Hence, the longer the seed
enters into dry-down process, the less dormant and more germinable the seed is, and the higher
risk of PHS the seed has. When internal seed dormancy level is low, seed can quickly pass the
dormancy threshold during the early stage of desiccation and PHS can occur when
environmental conditions are favorable (Obroucheva and Antipova, 2000).
1.3.2 Genetic control of seed dormancy
Dormancy in plants is jointly regulated by embryo- and seed coat-imposed pathways,
which are independently controlled by separate genetic systems. The embryo-imposed
dormancy is controlled by both maternal and paternal parents while coat-imposed dormancy is
adopted from female parents (Flintham, 2000; Himi et al. 2002).
Potential mechanisms of embryo-imposed dormancy include the balance of ABA and GA
content, embryo sensitivity to these hormones, small molecules such as nitric oxide, and
environmental cues, such as moisture, temperature and light (Walker-Simmons, 1987; Ohto et al.,
2007; Bethke et al. 2007). After physiological maturity, balance between ABA and GA content
shifts to GA after physiological maturity and germination occurs when outside conditions are
optimal. Arabidopsis mutants identified in ABA and GA pathways, such as ABA-insensitive 3
(ABI3) (Giraudat et al., 1992) and GA-insensitive (GAI) (Peng et al., 1997) are key players
during the germination process (Koornneef et al., 2002). Their wheat orthologs, such as
viviparous 1 (VP1), ABI3 orthologs, (Flintham and Gale, 1982) and reduced height 3 (RHT3),
and other GAI orthologs (Peng et al., 1999) are also reported with similar functions. Recently
cloned genes related to dormancy and germination are delay of germination 1 (DOG1) in
Arabidopsis, seed dormancy 4 (SDR4) in rice and TaPHS1, a wheat homeolog of mother of
flowering time (MFT) on short arm of Chromosome 3A (Bentsink et al., 2006; Liu et al., 2013;

6

Sugimoto et al., 2010). Functional studies of the causative mutations within these genes and
allele mining from a diverse germplasm can be an effective approach to recover functional
alleles and introduce into elite lines with genetic defects. One example lies in VP1. Common
wheat, including ancestral varieties, are prone to PHS due to its mis-spliced VP1 locus
(McKibbin et al., 2002). After screening a diverse wheat germplasm, several functional alleles
were found and proven to offer improved dormancy in wheat cultivars (Sun et al., 2012).
Seed-coat imposed dormancy can affect seed germination in three major ways:
mechanical restriction to radical protrusion, testa permeability to water and gas exchange, and
supply of germination inhibitor such as flavonoids (Debeaujon et al., 2007). Seed germination
requires optimal environments. Water and oxygen are two major components. During
germination, testa permeability interferes with seed imbibition and leaching of germination
inhibitors, such as ABA (Bewley and Black, 1994). Several studies done in Arabidopsis showed
that the more permeable the testa, the easier germination occurred (Debeaujon and Koornneef,
2000; Nesi et al., 2001). Testa permeability was also found to be inversely proportional to the
phenolic content and their degree of oxidation (Debeaujon et al., 2007). Germination also
involved active respiration process. Hence the limited oxygen supply to the embryo caused by
the testa can slow down the germination process. This barrier to oxygen diffusion increases with
increasing temperature and decreases during dry periods of after-ripening (Lenoir et al., 1986). In
summary, seed coat imposed dormancy can come from the mechanical restriction provided by
seed coat and by germination inhibitors, which are mainly phenolic compounds deposited in the
seed coat.

7

1.4 Genetic control of seed coat color
Classical work on wheat kernel color by Nilsson-Ehle (1909) showed that grain color is
controlled by three loci, termed R genes, with partial dominance. Each locus resides on the long
arm of chromosome group 3 of hexaploid wheat (Sears, 1944; Metzger and Silbangh, 1970; Himi
et al., 2011). However, the expression of color is more complex because of additional minor
genes (Freed et al., 1976; Reitan, 1980), genotype x environment (G x E) interactions (MatusCadiz et al., 2003) which includes location effects (Wu et al., 1999), and soil nitrogen content
(Kettlewell, 1999).
Wheat seed coat color is mainly composed of phelobaphene and proanthocyanidin (PAs).
Phlobaphene is the flavonoid that provides reddish color in wheat seed coats, which is a
derivative of catechin and catechin tannin and also is endogenous germination inhibitors
(Miyamoto and Everson, 1958). These polyphenol compounds are synthesized through the
flavonoid biosynthesis pathway (Debeaujon et al., 2007). A wheat R gene was cloned recently
and found to be a Myb-type transcription factor (Himi and Noda, 2005), which is involved in
activation of several flavonoid biosynthesis genes (CHS, CHI, F3H, DFR) and in turn controls
phlobaphene biosynthesis (Himi et al., 2005).
Proanthocyanidins (PAs) are colorless phenolic oligomers or polymers synthesized
during early stage of seed development and accumulate in the seed coat. During the dry-down
period, PAs are oxidized to give brown derivatives that confer mature seed color (Koornneef et
al., 2002). PAs can increase seed-coat dormancy by increasing the testa thickness and its
mechanical strength (Meredith and Pomeranz 1985). During oxidation, PAs have a tendency to
crosslink with proteins and carbohydrates in cell walls, thus reinforcing testa structure and also
modifying its permeability properties (Marles et al., 2003; Marles and Gruber, 2004). Both

8

exogenous and endogenous PAs can inhibit seed germination by promoting de novo synthesis of
ABA in Arabidopsis (Jia et al., 2012). PA-deficient Arabidopsis mutants were also found to have
reduced dormancy (Winkel-Shirley, 2001), while in barley, HvMYB10, a key regulator for PAs
accumulation, were found to be positively correlated with grain dormancy (Himi et al., 2012).
In summary, grain color is controlled by the presence and amount of phenolic compounds,
and it is quantitatively expressed with a GxE interaction. Its relationship with seed dormancy is
through phenolic compounds, which are known germination inhibitors.
1.5 Germination and α-amylase
Germination occurs when the environment, mainly moisture and temperature, is optimal
and internal seed dormancy has been reduced to a low level during grain dry-down. Internal seed
dormancy is generally controlled by the balance of ABA and GA. During later stages of drydown, hormone balance shifts to GA. Excessive GA stimulates the degradation of DELLA
proteins, a class of GA signaling repressor, via a ubiquitin-proteasome pathway (Silverstone, et
al., 2001). This de-repression of GA responsiveness in seed further stimulates the downstream
events of germination (Steber, 2007). Germination begins with a rapid increase in water uptake
(imbibition), then follows with a lag phase that has reduced water uptake but more active
metabolism and ends when the radicle protrudes from the pericarp (Davies et al., 2011). During
germination, GA is released from embryo to aleurone layer, where hydrolytic enzymes are
synthesized and secreted into the endosperm, causing reserve mobilization. These enzymes can
be categorized as carbohydrate- and protein-degrading enzymes based on their targets (Kruger,
1989).
One of the major carbohydrate-degrading enzymes is α-amylase. It is synthesized in
response to GA and further regulated by GA during germination process. The first element of α-

9

amylase promoter is part of a GA response element (Skriver et al., 1991). GA also activates a
Myb transcription factor, GAMyb, which in turn activates α-amylase expression (Gubler et al.,
1995). Considered as a signature of germination, α-amylase has been extensively studied over
the past few decades (Sun and Gruber, 2004). In wheat, there are two major types of α-amylase,
α-AMY-1 and α-AMY-2, each of which includes multiple forms (isozymes) (Kruger, 1989). The
α-AMY-1 isozyme is the high iso-electric point (pI) group that is present mainly in the aleurone
layer and/or scutellum of germinating grain. It is the primary α-amylase form in seed during
germination. The other group, α-AMY-2, is the low pI group found in pericarp of immature grain.
During seed development, it is the major form detected. It degrades continuously during
maturation and its activity is low during germination (Kruger, 1989; Lunn et al., 2001).
Due to its deleterious effects in starch degradation, α-amylase content in sprouted grain is
a concern for cereal companies. In a sprouted wheat grain, α-amylase cleaves α-(1→4) Dglucosidic linkages in starch components, which contributes to dextrin production during baking
and forms a sticky crumb structure in the final product (Buchanan and Nicholas, 1980). In order
to measure the sprouted damage caused by α-amylase, a number of quantitative methods have
been developed, including viscometric, turbidometric, fluorometric, colorimetric, gel-diffusion,
and reducing sugar assays (Kruger, 1989). In commercial practice, the falling number test has
been widely accepted by elevators and mills (Hagberg, 1960). This method gives an indication of
the amount of sprout damage that has occurred within a wheat sample by testing flour viscosity.
Generally, a falling number value of 350 seconds or longer indicates a low α-amylase activity
and sound grain quality. As the amount of α-amylase activity increases, the falling number
decreases. Values below 200 seconds indicate an excessive α-amylase activity in the grain.
2

Falling number test was found to be highly correlated (R =0.975) with direct measurement of α-

10

amylase activity (Moot and Every, 1990; Verity et al., 1999; Perten 1964). The falling number
test has three major disadvantages when adapted to a cultivar development program: 1. It has a
relatively narrow detection range; 2. The measurement variation can be larger on the lower end
of the detection range, which is due to the impact of base time, which includes the time for flour
gelatinization and free-fall of the stirrer on the falling number value [The lower the falling
number value, the larger the base time will impact the result]; and 3. The large sample size
required by the test can be a limitation for early generation testing (Verity et al. 1999). In recent
years, with the optimization of an enzyme-linked immunosorbent assay (ELISA) method, a
direct measurement of flour α-amylase activity was adopted by regional wheat quality labs for
PHS resistance screening (Dr. Edward Souza, Bayer CropSciences, per comm.).
In general, synthesis of α-amylase as a direct response to GA induction during
germination is a phenotypic marker for grain germination. Hence, the measurement of α-amylase,
either directly or indirectly, to evaluate PHS resistance in cereals is routine (Masojć et al., 2011;
Ullrich et al., 2012; Yang et al., 2012; Zanetti, et al., 2000).
1.6 PHS in wheat
1.6.1 Evaluation of pre-harvest sprouting
In cereal crops, physiological maturity (PM) is defined as the time when seed reaches its
maximum dry weight (Hanft and Wych, 1982). It is a transition point from seed maturation to
seed dry-down. During dry-down, seed dormancy drops dramatically and seed is vulnerable to
PHS. If seed moisture is low during this period, seed will enter dormancy as expected. However,
if moisture remains high at this stage, PHS can happen, especially in varieties with low PHS
resistance (King, 1976; Derera, 1989). Therefore, selection of wheat varieties with PHS
resistance is usually conducted with materials at three to seven days after PM instead of at

11

harvest maturity. Complete loss of green pigments from glume and peduncle was found to be
closely related with PM in hard red spring wheat (Hanft and Wych, 1982), and has been adopted
as a consistent visual indicator of PM for wheat and barley, with little cultivar bias (Clarke, 1983;
Copeland and Crookston, 1985). It is now widely used in wheat PHS research programs to
reduce sample variation in plant maturity (Humphreys and Noll, 2002; Kulwal et al., 2012; Liu et
al., 2008).
PHS resistance is quantitative and can be affected by multiple factors besides inherent
dormancy level. Morphological traits that enable slower water-uptake have been studied for their
potential impacts on PHS resistance such as awn or awnless, erectness of spike, openness of
florets, tenacity of glumes, and germination inhibitors in the bracts (Gatford et al., 2002; King,
1984; Paterson et al., 1989). Conflicting results were shown between these studies.
Due to its complexity, PHS is phenotyped indirectly using different metrics. Four
phenotypes, namely sprouting count (%), germination rate (%), α-amylase activity (units/gram
flour), and falling number test (seconds), have been used in PHS related studies. Sprouting count
is a direct measure of germination with the whole spike, which mimics field observations (Liu et
al., 2008; Kulwal et al., 2012). Germination rate is a measure of seed dormancy per se (Chen et
al., 2008). Both measurements are based on the visual evidence of sprouting, however
information about the extent of internal enzyme damage to the starch integrity, which starts
before visual sprouting, is missed. Thus, α-amylase activity (Mccleary and Seehan, 1987; Singh
et al., 2008) and falling number (Perten, 1964; Rasul 2009) are often used to examine early
stages of PHS.
QTL mapping and association mapping of PHS related traits has been done in multiple
populations with different phenotypes (Imtiaz et al. 2008; Zanetti et al., 2000; Kulwal et al., 2005,

12

2012; Mares et al., 2005). QTL have been mapped on all 21 chromosomes of wheat (Kulwal et
al., 2012), and can be divided into two categories: QTL do and do not collocate with color alleles
on Chromosome Group 3. For QTL not linked to color, α-amylase is one of the traits of special
interest. Wheat α-amylase has two major groups, α-AMY1 and α-AMY2. An earlier study
mapped α-amylase to Chromosomes 6 and 7 in wheat (Gale et al., 1983). Recent QTL mapping
and association methods have mapped α-amylase isozymes on the long arm of chromosome 6B
in wheat (Mrva and Mares, 1999; Netsvetaev et al., 2012). Recently, studies in rye to evaluate
the relationship between α-amylase and PHS (Masojć and Milczarski, 2005; Masojć et al., 2011;
Masojć and Milczarski, 2009) showed QTL controlling α-amylase were found on all
chromosomes. Some QTL can promote PHS while some can inhibit PHS (Masojć et al., 2011).
These results indicate a complex genetic linkage between α-amylase and PHS. Based on synteny
relationships between rye and wheat (Devos et al., 1993), a similar genetic structure linking PHS
and α-amylase activity in wheat may be expected.
1.6.2 Improving PHS resistance in wheat
Pre-harvest sprouting (PHS) in cereals has been recognized as an international problem
since 1973. International meetings focusing on PHS have been hosted every three to four years
around the world since then (Nyachiro, 2012). The direct economic loss worldwide due to PHS
can reach up to US $1 billion annually (Black et al., 2006). Cultivar development for PHS
resistance is challenging due to limited genetic resources, genotype x environment interactions,
laborious sampling procedures, and inconsistent funding resources.
The intuitive way of breeding for PHS resistance is to improve seed dormancy. However,
during crop domestication, dormancy is a trait that breeders strongly select against. In wheat, the
mis-splicing of viviparous 1 (VP1) in ancestral and modern varieties caused an even narrower

13

variation in seed dormancy (McKibbin et al., 2002). When compared with red wheat, the genetic
variation of PHS resistance in white wheat is even narrower, which might be due to a lack of
coat-imposed dormancy offered by red phenolic compounds in seed coat.
Phenotyping for PHS resistance requires careful sampling at physiological maturity and
extra greenhouse space is required if artificial misting is conducted to provide high moisture
conditions conducive for PHS. The timing, extra labor, and resources required by PHS breeding
can significantly impact the harvest season of a breeding program and a lack of dedicated
funding will limit the progress in this area. Moreover, PHS induction is highly dependent on
surrounding environments. Known factors that can affect PHS include cold temperature during
growth, moisture stress, and interaction between maturity and stress (Mares and Mrva, 2008;
Biddulph et al., 2007). Therefore, the complex interactions between genotypes and surrounding
environment further complicate the selection for PHS resistance (Joosen et al., 2013; Rasul et al.,
2012).
With all these challenges, breeding for PHS is not an easy task. However, several
powerful tools for genetic study have been developed during the last couple years, such as nextgeneration sequencing, high-throughput SNP genotyping, and genotyping-by-sequencing
(Cavanagh et al., 2013; Elshire et al., 2011). In the meantime, the understanding of the wheat
genome has been improved extensively while more and more genomic resources are available
for the public, such as Cereals Data Base (http://www.cerealsdb.uk.net) and International Wheat
Genome Consortium (http://www.wheatgenome.org). These tools will help identify candidate
regions for complex trait discovery at a higher resolution than currently afforded while the
annotation of the wheat genome of will facilitate identification of the underlying genes.

14

Some breeding schemes for selecting white seed color were suggested without a specific
consideration for PHS resistance (Cooper and Sorrells, 1984; Knott et al., 2008). Recently,
genomic selection (GS), a breeding scheme that uses all marker information to predict breeding
value of each line, has been tested for efficiency in PHS breeding. Due to its low heritability,
PHS using GS does not outperform traditional phenotypic selection significantly (Mark Sorrells,
unpublished data). Except for conventional breeding, there are other ways to breed PHS
resistance into wheat. Recently, a EMS mutated wheat cultivar increased seed dormancy by
reducing ABA sensitivity (Schramm et al., 2013). Microarrays using after-ripened seed helped to
identify genetic networks during the dormancy release period (Liu et al., 2013). A combination
of candidate gene improvement with genome wide marker value prediction may help improve
the efficiency of phenotypic selection, while multiple-environment screening is also critical to
understand the genotype x environment interaction for PHS.
1.7 Project objectives
Breeding for PHS resistant wheat cultivars is critical for securing soft white wheat
production and reducing the economic loss to Michigan wheat growers, food processors and
millers. In Chapter 2, the allelic contributions of seed coat color and α-amylase activity were
examined in a RIL population segregating for seed coat color (‘Vida’ × MTHW0471). The
Infinium 9K SNP array was used for QTL mapping. QTL related to seed coat color and αamylase activity were identified based on data collected over three years with two post-harvest
treatments. Additive × additive gene interactions within and between traits were also identified in
current study, which further demonstrated the genetic complexity of PHS resistance in wheat. In
Chapter 3, wheat transcriptome data were generated for red and white wheat during misting
process. The resulting transcripts assembly would be a valuable resource for future genetic

15

studies and genome annotation. Differential expression analysis conducted between red wheat
and white wheat under mist and non-mist conditions for seeds at physiological maturity showed
the similarity and differences of red and white wheat in response to misting treatment. GO
enrichment test also showed multiple germination related GO terms. Both of them can be
potential candidates for future analysis. In Chapter 4, the future directions related to current
research were discussed.

16

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17

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25

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26

CHAPTER 2 QTL analysis for seed color components and α-amylase activity in a spring
wheat recombinant inbred line population segregating for seed color

27

Abstract
Pre-harvest sprouting (PHS) in wheat (Triticum aestivum L.) is precocious grain
germination induced by prolonged wet conditions during the harvest period. Sprouted kernels
significantly downgrade flour quality and cause significant financial losses to farmers and
downstream processors. It is commonly known that red wheat is more resistant to PHS than
white wheat. The objective of this study is to 1) Assess the allelic contribution of seed coat color
loci on Chromosome Group 3 to seed coat color and α-amylase activity, and 2) Identify QTL for
these phenotypes using a high-throughput genotyping platform. An F5-derived recombinant
inbred line population of 165 individuals from spring wheat cross ‘Vida’ × MTHW0471 was
used as the mapping population. A 9K single nucleotide polymorphism (SNP) array was used to
construct a high-density genetic map with 1,692 SNPs and three simple sequence repeats (SSR).
Strong correlations were found between color components and α-amylase activity within and
across environments while a strong year effect was found in α-amylase activity from 2010 to
2012. The different combinations of color alleles and the dosage of dominant alleles at color loci
*

significantly affected both the seed color components (b, L ) and α-amylase activity. The
dominant color allele on Chromosomes 3A and 3B showed a significant effect in reducing αamylase activity. A total of 38 quantitative trait loci (QTL) were identified on eleven
chromosomes (1B, 2A, 2B, 3A, 3B, 3D, 4B, 5A, 5D, 6B and 7B) from three years with two postharvest treatments in this population. Most QTL explained 6-15% of the phenotypic variation
while a major QTL on Chromosome 2B explained up to 37.6% of phenotypic variation of αamylase activity in 2012 non-misted conditions. A total of 21 significant QTL × QTL
interactions were also found in both the color and PHS resistance traits while Chromosome
Group 3 were identified as a hotspot for QTL × QTL interactions. In conclusion, the population
28

segregating for color allele is critical for the evaluation of allelic contribution to phenotype. QTL
identified in current study showed a pleiotropic effect between seed color loci and α-amylase
activity.
Introduction
Wheat (Triticum aestivum L.) is a staple crop, first domesticated 9,000 years ago (Peng et
al., 2011). Wheat provides 20% of calories consumed for more than 40% of the world’s
population (Gill et al. 2004). In the US, wheat ranks third among field crops in both planted
acreage and gross farm receipts (National Agricultural Statistics Service, 2012). Wheat is
categorized as red wheat and white wheat based on seed coat color. In recent years, industry
demand for white wheat has increased rapidly due to the popularity of whole grain products and
white wheat is the preferred material due to its higher flour yield and better end-use quality.
However, due to the susceptibility to pre-harvest sprouting, white wheat is not favored for
commercial production. Sprouted wheat has an extensive amount of α-amylase, which degrades
the endosperm reserve and severely limits grain end-use. Growers receive discounted price for
sprouted wheat while food processors also need to make adjustments when processing with
batches contain sprouted wheat. The direct annual losses caused by PHS can reach up to US $1
billion worldwide (Black et al., 2006).
Pre-harvest sprouting (PHS) is the precocious seed germination induced by prolonged
wet conditions during harvest season. It has been documented as a worldwide issue since the
1970s (Derera, 1989). Red wheat is known to be categorically more resistant to PHS than white
wheat (Gale and Lenton, 1987). However, the debate of either the relationship is a causal
pleotropic or a close linkage between red allele and seed dormancy still await further study (Gale,
1989). Recent evidence showed that the red gene was a Myb type transcription factor in

29

flavonoid biosynthesis pathway (Himi and Noda, 2005), but the mapping resolution is still not
enough to differentiate the R genes from surrounding genes.
Both phenotypic evaluation and QTL analysis has been done on PHS related traits.
Phenotypic evaluations can be divided into two categories: measurement of single seed
dormancy after regular harvest, and measurement of in-head sprouting resistance at physiological
maturity (Bewley, 1997; Mares et al., 2005). The latter category is done by the measurement of
sprout damage mainly caused by α-amylase. Therefore, a direct measure of α-amylase activity or
an indirect measure of flour viscosity through falling number test are commonly used (Rasul et
al., 2009). QTL studies have been done with germination index, sprout index, α-amylase activity,
and falling number test to represent PHS resistance level in bi-parental populations and QTL
were identified from different genetic background on all 21 chromosomes (Imtiaz et al. 2008;
Rasul et al. 2009; Kulwal et al. 2012). Recently, association mapping has been adapted to
identify genetic factors providing PHS resistance specifically within white wheat germplasm
(Kulwal et al., 2012). All these studies identified PHS related QTL in various regions of wheat
genome, which might offer us a way to pyramid different PHS resistance resources in the near
future.
Seed coat color is known to be closely related to seed dormancy and red wheat is
generally more resistant in PHS than white wheat. Proanthocynidin (PA) is the red pigment in
wheat seed coat (Himi et al., 2005). Studies in Arabidopsis found that exogenous PA enhances
abscisic acid (ABA) biosynthesis, which inhibits germination, and PA-deficient mutants had
reduced dormancy (Himi et al., 2005; Winkel-Shirley, 2001). Grain color is mainly controlled by
three loci on Chromosome Group 3. Red is dominant to white and the degree of redness can be
affected by environment and management practices. However, the dosage effect of the three

30

alleles has not been examined before, which may be due to the availability of allelic specific
markers.
In this study, a recombinant inbred line population derived from a red by white spring
wheat cross was used to examine the dosage effect of specific color allele and their combinations
to seed coat color and α-amylase activity. QTL identification was conducted with a high-density
linkage map and QTL by QTL interaction were explored. This study provided us more details for
dissecting the relationship between seed coat color and PHS resistance.
Materials and Methods
Plant Materials
A recombinant inbred line (RIL) population consisting of 165 lines, kindly shared by Dr.
Jamie Sherman at Montana State University, was developed from a cross between two elite
spring wheat lines that segregated for seed coat color homeologs on Chromosome Group 3. The
female parent, ‘Vida’, is a hard red spring wheat cultivar with all three color homeologs
dominant. The male parent, MTHW0471, is an elite hard white spring wheat breeding line with
all three color homeologs recessive. F2 population was genotyped with simple sequence repeats
(SSR) markers closely linked to the color loci. F2 individuals that were homozygous (dominant
or recessive) at all three seed color loci were selected and inbred until F5 using single-seeddescent (Sherman et al., 2008). Seed increase was done in the greenhouse in 2009. Field
evaluations were conducted on F5:6, F5:7 and F5:8 seeds in 2010, 2011, and 2012, respectively.
Field Design and Greenhouse Misting
Field trials were conducted in three consecutive years (2010, 2011, and 2012) at
Michigan State University Mason Research Farm, Mason, MI. Soil type is sandy loam. Within
each experimental field, the RIL population was planted in 1.5-meter twin rows (2010 was single

31

row) with 1.2-meter alley in a randomized complete block design with three replications, each
contains 165 RIL and two parents. Growth-related dates and precipitation information are
summarized in Table A.1. Physiological maturity was visually determined by the date when 80%
heads within the plot were loss-green at peduncle. Due to differences in maturity, lines were
hand-harvested individually at 3 days after physiological maturity. The wheat heads with its 20
cm stem were transported to the greenhouse for artificial misting treatments.
Harvested wheat heads were divided into two subsamples, one mist group, the other nonmist group. Two groups were kept in the same greenhouse with a temperature ranging from 2530°C, and processed in an identical manner except for the misting treatment received. The plants
were arranged in plastic tubes (5.3 cm in diameter and 25 cm in depth), which were placed
upright in racks to simulate PHS field conditions. The misted group was placed on the
greenhouse bench under a misting system made up of brass seedling nozzles (1.4 meter interval)
attached to the pipelines 1.5 meter above the bench. The misting system was controlled by a
timer (Model 15079, General Electric), which set to mist for 45 min every 6 h. Both groups were
collected after 48 hr misting and stored at -20 °C freezer prior to freeze-drying. The freeze-dried
spikes were hand-threshed and stored at -20 °C to keep enzyme activity.
Seed coat color measurement
Grain color of each recombinant inbred line and two parents, from the mist and non-mist
groups, was measured using a chroma meter (Knoica Minolta, CR-400) with a 20 g grain sample.
* *

This equipment decomposes color into a 3-dimension color space, L a b (Smith and Guild,
*

*

1931). ‘L ’ measures black (0) to white (100), ‘a ’ measures green when negative and red when
positive, and ‘b’ measures blue when negative and yellow when positive. The measurement was
done by pressing the chroma meter onto the cover of a 60 × 15 mm petri plate (Falcon 351007)
32

filled with grains from one genotype. Then three readings were taken from different regions of
the plate and averaged to represent each sample. Three biological replicates were measured and
averaged to represent that genotype.
Determination of α-amylase activity
Whole grain flour was milled from each RIL and the two parents using a UDY
Laboratory Mill with a 0.5 mm sieve. Flours were analyzed for α-amylase activity based on
American Association of Cereal Chemists International (AACCI) approved method 22-02.01,
Ceralpha method (AACC International, 2002). The enzyme extraction and assay was performed
using the Megazyme kit (K-CERA 08/05, Megazyme International Ltd., Ireland) with a modified
protocol established by the USDA Soft White Wheat Quality Lab, Wooster, Ohio and was
described in details below (Dr. Edward Souza, pers. comm.).
Three grams of whole grain flour and 20 mL of extraction buffer (1 M sodium malate, 1
M sodium chloride, 40 mM calcium chloride, 0.1% sodium azide , provided by Megazyme kit)
were added into a 50 mL centrifuge tube followed by vigorous shaking and incubated at 42°C in
a water bath (Fisher Scientific, Pittsburgh, PA) for 20 minutes. Samples were taken out of the
water bath and were shaken vigorously every 5 min. The samples were then centrifuged at 3100
rpm for 10 minutes (SORVALL RT7, Kendro Laboratory Products, Newton, CT). At the same
time, twenty microliter (µL) aliquots of Ceralpha substrate (non-reducing-end blocked
pnitrophenyl maltoheptaoside, BPNPG7, provided by Megazyme kit) solution were dispensed
into each well of a 96-well plate based on sample layout and pre-incubated at 42°C for 5 minutes.
After centrifuge, twenty microliter α-amylase extract from the supernatant of each sample was
added to the bottom of each well and mixed with the pre-heated Ceralpha substrate. The enzyme
reaction took place for precisely 20 minutes at 42°C. For processing efficiency, three replicates

33

per sample were assigned to the same column next to each other on a 96-well plate and a 30second interval was kept between each triplicate aliquots pipetted. A 300 µL Stopping Reagent
(1% (w/v) Trizma base, provided by Megazyme kit), was added to the well at the end of the 20minute period for each triplicate. Sample controls were prepared by adding the substrate after the
stopping reagent while plate controls were 340 µL of distilled water. The absorbance of each
well was read using a spectrophotometer (Synergy HT Multi-Mode Microplate Reader, BioTek,
Winooski, VT) at 400 nm. If the absorbance value was greater than 1.2, the enzyme extract was
diluted to a proper range due to the possible saturation of limited amount substrate and a second
run for the sample was done. The standard curve was generated based on the dilution series of
pnitrophenol standard solution (Sigma, 104-1) in 1% tri-sodium phosphate. The extinction
coefficient (EmM = 14.1) was obtained following instructions in Megazyme manual. The αamylase activity (Unit/g) was then calculated based on Formula 2.1.
1
Extraction Volume
Absorbance (reaction-blank) Total Volume in Cell
×
×
×
(2.1)
Aliquot Assayed
EmM
Sample Weight
Incubation Time
Statistical analyses

Data was analyzed using SAS statistical software 9.2 (SAS Institute, Cary, NC). Due to
the large variation within the population, α-amylase activity was transformed using Box-Cox
transformation with SAS procedure, PROC TRANSREG, recorded as EnzBC, to restore residual
normality (Box and Cox, 1964). Pearson’s correlations were calculated for phenotypes within
each environment and between environments. Significance level was chosen (0.05 or smaller)
and p-value was adjusted by Tukey’s procedure.
*

*

For seed color components (L , a , b) and transformed α-amylase activity (EnzBC),
analysis of variance (ANOVA) was carried out using PROC MIXED to check the effect of fixed

34

factors (genotype group, dosage group, allele group, treatment) and to calculate least square
means between different genotype groups, dosage groups or allele groups. The genotype groups
of the RIL population were provided by Dr. Sherman (Table 2.1) and dosage groups were
generated by combining genotype groups with same number of dominant red alleles: 0R (group
1), 2R (group 2, 3, 4), 4R (group 5, 6, 7), 6R (group 8). For each allele (R-3A, R-3B, R-3D),
allele groups divided the population into two groups, one contains the specific allele as dominant,
the other contains the same allele as recessive.
Within each environment (2010 mist; 2011 mist, non-mist; 2012 mist, non-mist),
contrasts were established between either dosage groups or allele groups to measure the dosage
effects or effects of specific allele on color components and α-amylase activity by comparing.
Pairwise comparisons were adjusted with Tukey’s procedure. Dunnett’s tests were also used to
compare the significance of difference between selected groups and 0R (white) or 6R (red)
groups. Mist treatment effects on α-amylase activity were measured by comparing groups having
the same R allele combinations between mist and non-mist conditions. Results across
environments were recorded and compared over years to see if there were Genotype × Year
interactions.
DNA isolation and SNP genotyping
DNA from the parents (two replicates each) and 165 RILs were extracted from young
leaf tissues of greenhouse plants using Wizard Genomic DNA Purification Kit (Promega,
A1120), quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, San Diego, CA)
and adjusted to a concentration of 50 ng/ul before SNP genotyping. SNP genotyping was
conducted on an Illumina iScan Reader utilizing the Infinium HD Assay Ultra (Illumina, Inc.,
San Diego, CA) and the Infinium 9k Wheat array (Cavanagh et al., 2013).

35

Table 2.1 Mapping population used in this study with red homeolog combinations determined by
SSR marker linked to color loci
Number of
Red
#
Group
R-3B
R-3D
* Grain color
R-3A
dosage
individuals
Parents
‘Vida’
1
Red
6
b
b
b
MTHW0471 1
White
0
a
a
a
RILs
1
16
White
0
a
a
a
2
24
Red
2
b
a
a
3
18
Red
2
a
b
a
4
16
Red
2
a
a
b
5
20
Red
4
b
b
a
6
22
Red
4
b
a
b
7
25
Red
4
a
b
b
8
24
Red
6
b
b
b
*

These are the individuals having full set of genotyping and phenotyping data and used for QTL
analysis;
#
Upper case letter represents the chromosome where the SSR marker is located and lower case
letter were named following the recommended rules for gene symbolization in wheat to represent
dominant (b) and recessive (a) color alleles according to McIntosh et al. (2008).

36

SNP calling, filtering, map construction
Raw SNP genotypes obtained from Illumina GenomeStudio software (Illumina, San
Diego, CA) were first categorized based on the custom cluster file to filter out low quality SNP
calls (Cavanagh et al., 2013). Prior to mapping, SNPs were further filtered to remove noninformative SNPs or SNPs with a call rate less than 90% (greater than 15 progeny with missing
genotypes) were removed (Table A.2). The three SSR markers linked to color loci (Sherman et al.
2008) were added to the 1692 SNP markers for map construction. The linkage analysis was
performed using Joinmap 4.1 (Van Ooijen 2006). The genotype data was coded as RIL
population type. Markers were first grouped into linkage groups based on a minimum logarithm
of odds (LOD) score of three. The grouping result was validated by custom defined chromosome
assignment for each SNP (Cavanagh et al., 2013). Then each linkage group was ordered by
regression method with Kosambi function.
QTL mapping for color measurements and α-amylase activity
QTL mapping was conducted by MapQTL6 (Van Ooijen 2004) using Multiple QTL
Mapping (MQM) method and validated by WinQTL Cartographer,V2.5 (Wang et al. 2008) with
composite interval mapping (CIM) using standard model Zampqtl6. A permutation test of 1000
runs was conducted to identify genome-wide threshold of LOD at 5% significance level for
declaring significant QTLs (Doerge and Chuchill 1996). QTL by QTL interaction (additive
effects only) were explored with Multiple Interval Mapping (MIM) method using WinQTL
Cartographer V2.5. Broad sense heritability was obtained from MIM results in WinQTL
Cartographer V2.5.

37

Results
Map construction with Infinium 9k wheat SNP array
A genetic map, based upon 1692 SNP markers and three SSR markers, was constructed
for all 21 linkage groups with a total map size of 1992.6 cM (Table 2.2; Table A.3). The
chromosome assignment of mapped SNPs was based on the consensus map released by the 9K
SNP array design team (Cacanagh et al. 2013). The A genome had the largest subgenome size
(931.5 cM) out of the three sub-genomes with a relatively even distribution of chromosome map
size and marker density. The B genome had a linkage map size of 749 cM while Chromosome
3B was potentially under-represented with 26 markers spanning on a 42.1cM map. On average,
the A genome had a SNP density of 0.95 SNPs per cM, while B genome had an average of 1.01
SNPs per cM. Both A and B genomes were significantly better represented than D genome,
which had the smallest linkage map size (312.1 cM) and fewest markers (81). However, due to a
lack of linkage, all subgenomes had some linkage groups were segmented. However, with the
chromosome assignment information released by the 9k SNP chip design team, we were able to
retain all the linkage groups for later QTL study.
Phenotype distribution of seed color components and α-amylase activity
Based on the SSR markers linked to the red seed color locus, the population was further
2

divided into eight genotype groups (χ = 0.7145) according to the combination of three
*

*

homeologs (Table 2.1). Figure 2.1 shows the distribution of color components (L , a , b) and
Box-Cox transformed α-amylase activity (EnzBC) for three years with two post-harvest

38

Table 2.2 Summary of linkage map constructed with 1695 markers for ‘Vida’ × MTHW0471
population
Chromosome
A genome
B genome
D genome
Group
SNP
cM
SNP
cM
SNP
cM
1
134
128.4
109
140.7
17
75.3
2
97
123.7
156
113.7
15
81.5
3
133
124.1
26
42.1
9
33.5
4
141
104.3
59
114
9
39.5
5
132
148.1
214
153.6
12
41.5
6
87
127
130
83.2
4
0.3
7
127
175.9
69
101.7
15
40.5
Sum
851
931.5
763
749
81
312.1

39

Percentage of RIL (%)

L* Distribution (2010-2012)
70
60
50
40
30
20
10
0
50

52

54

56

58

L* value
2010 Mist

2011 Non-Mist

2011 Mist

2012 Non-mist

2012 Mist

Percentage of RIL (%)

a* Distribution (2010-2012)
70
60
50
40
30
20
10
0
4.2

4.5

4.8

5.1

5.4

5.7

6

6.3

a* value
2010 Mist

2011 Non-mist

2011 Mist

2012 Non-mist

2012 Mist

Figure 2.1 Distribution of color components and α-amylase activity for ‘Vida’ × MTHW0471
population from 2010 to 2012.
For interpretation of the references to color in this and all other figures, the reader is referred to
the electronic version of this dissertation.
^
EnzBC is the Box-Cox transformed value of α-amylase activity.

40

Figure 2.1 (cont’d)

Percentage of RIL (%)

b Distribution (2010-2012)
60
50
40
30
20
10
0
13
2010 Mist

14

15

2011 Non-Mist

16
b value
2011 Mist

17

18

2012 Non-mist

19
2012 Mist

Percentage of RIL (%)

EnzBC Distribution (2010-2012)
80
70
60
50
40
30
20
10
0
-3
2010 Mist

-2
2011 Non-mist

-1

0

1

EnzBC^
2011 Mist 2012 Non-mist

41

2
2012 Mist

treatments. All the traits are quantitatively distributed and the year effect was significant to affect
the value of all three color components and α-amylase activity (p< 0.05). Moreover, across the
three years, a trend was shown that the more precipitation the plant received during the harvest
period, the higher α-amylase activity of the population would have for that year (Table A.1).
This trend corresponds well with the fact that in a humid year, the plants are more susceptible to
PHS damage.
Correlation between seed coat color and α-amylase activity
Color measurements and α-amylase activity were conducted on both mist and non-mist
material and correlations of these traits within each environment were summarized in Table 2.3.
*

For all five conditions, the highest correlation were identified between L and b, ranging from
*

*

78.1-85.8%. Both L and a were highly correlated with EnzBC in four out of five conditions.
*

While the white color, represented by L , was positively correlated with the α-amylase activity;
*

red color, represented by a , was in negative correlation (p < 0.01). On the other hand, when
considering a single trait across environments, EnzBC in 2012 mist condition was significantly
correlated with EnzBC from all other environments, while strong correlations between mist and
non-mist materials were also found between traits across environments (Table A.4).
Genotypic effects on color components and α-amylase activity
Within each environment, genotypic effects on color components and α-amylase activity
were measured at genotype group, dosage group, and allele group levels. Table 2.4 summarized
the ANOVA results for all five conditions. The genotype group, or the eight different allele
combinations, showed a significant difference (p<0.001) for seed whiteness (L*) and yellowness

42

Table 2.3 Phenotypic correlations of traits within each environment
2010 Mist
a

*

*

L

0.251

b

0.855

EnzBC

#

2011 Mist
a

^

*

0.460

*

*

b

L

***
***

*

0.450

EnzBC

0.422

2012 Mist

L

*

-0.092
a

***
***

*

0.449

*

0.858

EnzBC

0.280

b

*

b

***
*

b

2011 Non-mist L
a

0.125
-0.398
a

***

*

0.539
b

-0.100

b

*

***

-0.079
0.840

a

**

L

b

a

a

***
**

***

**

-0.402

***

0.132

-0.082

b

0.781

EnzBC

0.291

***
**

*

2012 Non-mist L
a

0.280

*

a

*

0.326

***

-0.256
a

**

*

0.299

**

b

-0.181

b

0.856

EnzBC

0.234

***

0.229
-0.301

***

0.052

^

Mist/Non-mist represents the treatment each group received; 2010/2011/2012 represented the
year experiment conducted;
#

EnzBC is the Box-Cox transformed value of α-amylase activity;

**, ***

significant at 0.01 level and 0.001 level respectively, all the p-values were Tukey-adjusted.

43

Table 2.4 Analysis of variance of color components and α-amylase activity evaluated in three
years with two post-harvest treatments in Mason, MI for ‘Vida’× MTHW0471population
Environment
Source
2011
2012
2010 Mist
Non-mist
2011 Mist
Non-mist
2012 Mist
Genotype group
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Dosage group
0.028
<0.0001
<0.0001
<0.0001
<0.0001
*
R-3A
<0.01
<0.01
<0.01
<0.01
<0.01
L
R-3B
<0.01
<0.01
<0.01
<0.01
<0.01
R-3D
<0.01
<0.01
<0.01
<0.01
<0.01
^
Genotype group
NS
0.038
0.002
NS
NS
Dosage group
NS
0.043
0.028
<0.0001
NS
*
a
R-3A
NS
NS
NS
<0.05
NS
R-3B
<0.05
<0.05
NS
<0.05
NS
R-3D
NS
NS
NS
<0.05
NS
Genotype group
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Dosage group
0.006
<0.0001
<0.0001
<0.0001
<0.0001
b
R-3A
<0.01
<0.01
<0.01
<0.01
<0.01
R-3B
<0.01
<0.01
<0.01
<0.01
<0.01
R-3D
<0.01
<0.01
<0.01
<0.01
<0.01
Genotype group
<0.0001
<0.0001
<0.0001
<0.0001
0.003
Dosage group
<0.0001
<0.0001
<0.0001
NS
0.016
#
R-3A
<0.01
<0.01
<0.01
<0.01
<0.01
EnzBC
R-3B
<0.01
<0.01
<0.01
<0.01
<0.01
R-3D
<0.01
NS
<0.01
NS
NS
^
#

NS: non-significant, p > 0.05;
EnzBC: the Box-Cox transformed value of α-amylase activity.

44

*

(b), while seed redness (a ) only differed in two out of five conditions (p<0.05). At dosage group
*

level, L and b, again showed significant differences for all five conditions; while color
*

component a was significantly impacted in three out of five conditions. For each specific allele
(R-3A, R-3B, R-3D), when comparing individuals containing a specific allele as dominant and
*

individuals containing that same allele as recessive, both whiteness (L ) and yellowness (b) of
*

the seed coat showed significant differences for all three alleles. For grain redness (a ), groups
contained R-3B as dominant showed a significant difference with groups containing R-3B as
recessive for three out of five conditions. While the R-3A and R-3D allele only showed
significant effect in 2012 non-mist condition.
For α-amylase activity, the genotypic effect was significant for all environments at
genotype group level (p < 0.01). At dosage group level, the different dosage can significantly
impact the α-amylase activity in all environments except for 2012 mist condition (p < 0.02). At
allele group level, a dominant red allele at R-3A or R-3B can significantly reduce α-amylase
activity when compared with a recessive allele at the same loci in all environments, while R-3D
red allele were only effective in 2010 and 2011 mist conditions in terms of reducing α-amylase
activity when compared between individuals contained R-3D allele as dominant versus recessive.
On the other hand, α-amylase activity was significantly different between white wheat
(0R) and red wheat (2R, 4R, 6R) except for 2012 non-mist environment, while within red wheat
groups, the dosage difference didn’t show a significant effect except for 2011 mist condition,
which significant differences for all different dosages (Table 2.5).

45

Table 2.5 Least square means of α-amylase activity for different dosage groups across different
environments
*
#
2011 Mist
2011 Non-mist
2012 Mist
2012 Non-mist
EnzBC
2010 Mist
0R
2R
4R
6R
*
#

^

-0.536a
-1.593b
-1.668b
-1.676b

0.722a

-0.611a

-2.742a

-2.772a

-0.855b
-1.297c
-2.017d

-0.949b
-0.943b
-0.905b

-3.001b
-3.045b
-3.035b

-2.761a
-2.783a
-2.685a

EnzBC is the Box-Cox transformed value of α-amylase activity;

Mist/Non-mist represents the Mist or Non-mist treatment each group received; 2010, 2011,
2012 represents the year the experiment conducted;
^
LS Means followed by different letters are significantly different according to Fisher’s
protected LSD (p = 0.05).

46

QTL analysis for seed color measurements and α-amylase activity
A total of 38 QTL were identified from three years with two post-harvest treatments for
color components and α-amylase activity using MQM method in MapQTL6. All the significant
QTL were confirmed by CIM and MIM methods in WinQTLCartographer V2.5 (Table 2.6).
Three QTL on Chromosome 3A, 3B and 3D were co-located with the three SSR markers
representing color loci. They were shown consistently in multiple environments for different
color components and α-amylase activity traits which indicated a potential pleiotropic effects.
*

The QTL on Chromosome 3A explains 11-13.4% of phenotypic variation for L , 12.9-15.1% for
b, and 10.4-14.7% for EnzBC, respectively. However, it was not identified as a significant QTL
*

for a in any environment. The QTL on Chromosome 3B was always shown together with QTL
*

on 3A while it explains a range of 8.0-8.8% for L and 9.5-16.0% for b.
*

The QTL directly related to grain redness, a , were only detected in 2012. There were
three QTL on Chromosome 5A, 5D, and 6B that were identified in both mist and non-mist
conditions with similar QTL effects, while a QTL on Chromosome 2A was only detected in non*

mist environment. When all the QTL related to a were added together, they can explained a
total of 24.6 and 33.8% of phenotypic variation in non-mist and mist conditions, respectively.
The QTL related to α-amylase activity (EnzBC) were detected in all five conditions. In
2012 non-mist condition, one major QTL on 2B can explains up to 37.6% phenotypic variation
for α-amylase activity. In most environments, except for 2011 mist condition, multiple QTL
were identified for EnzBC in each environment. When added together, these QTL explain 32.261.3% of phenotypic variation within a specific environment. Furthermore, all the QTL

47

Table 2.6 Summary of QTL identified in ‘Vida’ × MTHW0471 from 2010 to 2012
Location
Variance
#
Environment Trait
Chromosome
(cM)
LOD PT
%
Additive effects
2010
Mist

*

L
b

EnzBC
2011
Non-mist

*

L
b

EnzBC
2011
Mist

*

L
b

EnzBC
2012
Non-mist

*

L
a

*

b

EnzBC

2012
Mist

*

L
a

*

b

EnzBC

^

3A2
3A2
3B3
1B
3A2

0.0
0.0
11.3
41.6
0.0

5.2
5.8
4.2
6.9
5.7

3.4
3.2

3A2
3A2
3B3
1B
2B1

0.0
2.0
3.7
41.2
73.1

5.0
7.1
6.4
9.5
4.2

3.4
3.3

3A2
3B3
3A2
3B3
3A2

0.0
3.7
0.0
3.7
0.0

5.5
3.4
6.3
5.6
3.8

3.3

3A2

0.0

5A1
5D2
2A
6B1
3A2
3B3
3D1
1B
2B1
4B
7B2

13.2
13.2
9.5
17.5
14.7

-0.57
-0.47
-0.40
-0.57
-0.52

12.2
15.1
13.6
23.8
10.8

-0.45
-0.43
-0.39
-0.25
0.17

3.3

13.4
8.0
13.5
11.8
10.4

-0.47
-0.37
-0.36
-0.34
-0.42

5.1

3.3

12.5

-0.56

46.6
0.0
103.6
62.8
0.0
3.7
25.5
41.2
66.2
43.1
33.8

5.9
4.1
3.8
3.3
7.6
5.5
4.3
3.5
16.5
4.4
4.4

3.2

11.9
8.0
7.4
6.5
14.1
10.0
7.5
6.2
37.6
11.9
11.8

0.10
-0.08
-0.08
0.07
-0.53
-0.42
-0.47
-0.33
0.02
0.01
-0.01

3B3
3A2

11.3
0.0

4.2
5.2

3.2

8.8
11

-0.38
-0.46

5D2
6B1
5A1
3B3
3A2
3D1
1B
2B1
7B2

4.0
62.8
47.6
4.0
0.0
25.5
40.3
66.2
33.8

4.0
3.5
3.4
7.6
6.2
3.6
3.4
11.2
4.4

3.3

9.0
7.9
7.7
16.0
12.9
7.3
9.2
27.4
11.7

-0.10
0.09
0.09
-0.50
-0.44
-0.40
-0.35
0.63
-0.41

48

3.2

3.2

3.3

3.2

3.3

3.3

3.3

Table 2.6 (cont’d)
#

Permutation threshold, empirical likelihood of odds (LOD) threshold based on 1000
permutations at 5% significance level;
^

The number behind letter represented the specific linkage group assigned to that chromosome
based on Cavanagh et al. (2013).

49

were identified more than once in both mist and non-mist conditions, except for one QTL on
Chromosome 4B which identified only in 2012 non-mist condition.
The QTL related to α-amylase activity (EnzBC) were detected in all five conditions. In
2012 non-mist condition, one major QTL on 2B can explains up to 37.6% phenotypic variation
for α-amylase activity. In most environments, except for 2011 mist condition, multiple QTL
were identified for EnzBC in each environment. When added together, these QTL explain 32.261.3% of phenotypic variation within a specific environment. Furthermore, all the QTL were
identified more than once in both mist and non-mist conditions, except for one QTL on
Chromosome 4B which identified only in 2012 non-mist condition.
All the QTL identified by CIM method were also validated by MIM method in
WinQTLCartographer. In addition, novel QTL were identified by MIM also with minor effects
(not listed here). Additive by additive interactions (A × A) were found for multiple traits across
environments (Table 2.7). It can occur between QTL identified only in CIM, QTL identified only
in MIM, or one from each. For each trait, these interactions can explain 1.5-22% of phenotypic
variation. One of the hotspots containing Additive × Additive interactions is the QTL on 3A,
which interacted with not only its homeolog (3B, 3D), but also QTL on other chromosomes (5B
and 7A). The major QTL identified on 2B for EnzBC were also found to interact with 3B and 7B,
which explains an additional 5% of the phenotypic variation. The broad-sense heritability was
calculated for each trait when considering all the QTL and their interactions. The heritability for
*

*

L , a , b ranged from 0.14-0.39, 0.13-0.33, 0.33-0.64, while EnzBC was only calculated in 2012
mist condition as 0.4.

50

Table 2.7 QTL identified by MIM having Additive × Additive interaction and the heritability of
the trait
Additive × Additive interaction
Variance
2#
Environment
Trait
h
%
QTL-CIM
QTL-MIM
2010 Mist

2011
Non-mist

*

L
b
a

^

Chr 3A2
Chr 3A2 and Chr 3B3

*

b
2011 Mist

Chr 3A2 and Chr 3B3
Chr 3A2

2012 Mist

0.14

Chr 3D1

1.9
7.2

0.48

Chr 1B

2.0

0.22

Chr 7A1

7.4
3.0

0.38

3.1

0.29

*

Chr 3A2 and Chr 3B3

*

Chr 7B2
Chr 3A2
Chr 3A2 and Chr 3B3

Chr 4A1
Chr 6B2

4.2
4.2
1.2

0.13
0.33

L

*

Chr 3A2

Chr 7A1

2.2

0.29

b

Chr 3A2 and Chr 3B3
Chr 3A2 and Chr 3D1
Chr 3A2 and Chr 5B

9.5
6.4
6.1

0.64

Chr 3A2 and Chr 3B3
Chr 3A2 and Chr 7A1
Chr 7A1
*
Chr 5D2 and Chr 6B1
a
b
Chr 3A2 and Chr 3D1
EnzBC Chr 2B1 and Chr 7B2

2.8
2.8
0.2
1.9
9.7
3.6

*

L

Chr 2B1 and Chr 3B3
# 2

h stands for broad-sense heritability of the trait;

^

1.5

L
a
b

2012
Non-mist

Chr 3B3

Chr 5B and Chr 7D

Chr stands for chromosome.

51

Chr 3D1

1.2

0.39
0.33
0.39
0.4

Discussions
Map construction and QTL mapping with high-density markers
In this study, Infinium 9k SNP array was used to develop a high density genetic map.
Due to large linkage gaps in current RIL population, more than 21 linkage groups were initially
formed. Based on the consensus map released by Cavanagh et al. (2013), we were able to merge
and assign groups into specific chromosomes and the orders of the markers were also validated.
Due to the lack of genetic diversity in D genome (Cavanagh et al. 2013), SNP markers for D
genome were under-represented in current map. Furthermore, the fact that only two QTL from D
genome were mapped on the current map with a relatively large marker interval may also due to
this low map resolution for D genome. More D genome markers are needed to map D genome
QTL at a better resolution for future studies.
Recent years, new technologies such as single nucleotide polymorphism (SNP), Diversity
Arrays Technology (DaRT, (Jing et al., 2009), Insertion site based polymorphism (ISBP, Paux et
al., 2010) and Genotyping-by-Sequencing (GBS, Elshire et al., 2011) make marker discovery a
high-throughput process. However, the use of DaRT markers was limited due to the business
model of this technology while only 2000 markers with sequences information are available at
this time (http://www.diversityarrays.com/sequences.html, accessed on July 10th, 2013).
Compared with DaRT’s low, ISBP and GBS had a much higher marker density but most genetic
markers were identified from repeats or intergenic region, while the low coverage caused
genotype calling issued in GBS make the downstream analysis even more complicated in
polyploid species. On the contrary, Infinium SNP array technology, which was recently made
available to the wheat community, was designed to capture major genetic diversity at exonic
regions (Cavanagh et al., 2013). The SNP genotype data output is easily managed and the

52

marker quality is confirmed. The high-throughput genotyping protocol of Infinium 9k SNP array
allows 192 samples to be genotyped in a 3-day period along with user-friendly genotype calling
process makes it an attractive marker set for breeding programs. At the same time, the second
generation of Infinum wheat array with around 90,000 SNPs was under development by the
same group, which is aimed to significantly enrich the marker density (Dr. Akhunov, pers.
comm.). Moreover, a core set of exonic SNPs might shorten the candidate gene mapping process
for QTL mapping projects when QTL were identified in a marker dense region. In this study,
most QTL regions defined by two flanking SNP markers were less than 2 cM. The SNPs that
were developed from exonic regions will also help us identify potential candidate genes more
effectively. Furthermore, the Infinium wheat arrays are a standard marker set that makes genetic
studies across different populations more comparable.
Correlations of phenotypic traits
Various strong correlations between α-amylase activity and color components within
each environment were identified (Table 2.3). The positive correlation between color component
*

*

L and α-amylase activity (EnzBC) and negative correlation between color component a and
EnzBC fitted perfectly with the observation that white wheat are generally more PHS susceptible,
which was represented by an elevated level of α-amylase activity.
The strong correlations between non-mist and mist groups in all the environments
suggested the possibility of breeding for PHS resistance through selection under various
environments (Table A.3). EnzBC in 2012 mist condition was significantly correlated with
EnzBC from all other environments, which indicated the potential of breeding for PHS resistance
using artificial misting treatment, especially in a dry year.

53

Dosage effect of homeologs for grain color and α-amylase activity
In this study, dosage grouping of color loci was based on the SSR genotyping results
*

shared by Dr. Sherman (pers. comm.). L and b showed a significant difference between white
*

(0R) and red (2R, 4R, 6R) wheat across all five conditions. However, a , the direct indicator of
grain redness, has not shown a consistent dosage effect across environments. This might be due
to the complexity of seed coat color, which is a combination of red (phlobaphenes) and brown
(oxidized proanthocyanidin). Similar results were presented in Groos et al. (2002), who used a
*

*

ratio of a /L to represent grain redness. Historically, there are some debates about using
colorimeter to measure grain color (Matus-Cadiz et al., 2003; Peterson et al., 2001). This might
be explained by the genotype x environment effect of seed color (Wu et al., 1999). In current
study, our samples were grown in the same location which might reduce this effect to some
extent.
Moreover, the significant differences on grain redness between groups having R-3B allele
as dominant or recessive suggested an unequal contribution of specific alleles to the seed coat
color might also contribute to the inconsistency. The potentially larger contribution by R-3B
allele was also supported by Groos et al. (2002) who reported a stronger influence coming from
QTL on Chromosome 3B. The additive × additive interactions among Chromosome Group 3
identified within or between traits also suggest a interaction between color loci homeologs
(Table 2.7). Recent studies on polyploidy evolution also provide evidence about an unbalanced
usage of alleles among subgenomes (Feldman and Levy, 2012; Page et al., 2013).

54

QTL identified for PHS related traits
Most QTL identified in this study were also found by previous studies related to preharvest sprouting. In this study, all SSR markers, representing color loci, were positioned on the
SNP map at expected location, the long arm of Chromosome Group 3. Hence, we divided the
QTL identified into two groups: QTL linked to color loci on Chromosome Group 3 and QTL
identified on other chromosome. The majority of the first group of QTL were QTL for color
components while the 3A QTL were also found to explain 10-15% of the phenotypic variations
of α-amylase activity in 2010 and 2011 mist conditions. A close linkage between color loci and
PHS related QTL were previously documented by Groos et al. (2002). Gu et al. (2011) also
claimed that the relationship between pericarp color and seed dormancy in weedy rice was due to
pleiotropy. Rasul et al. (2009) also identified QTL on 3A and 3D that are related to sprouting
index. Moreover, Viviparous 1 (VP1) is also mapped on the long arm of Chromosome Group 3
but in loose linkage with R gene loci (Bailey et al., 1999). Recently, Liu et al. (2013) cloned a
gene, TaPHS1, proposed to controlling PHS on Chrmosome 3AS, which is independent from
grain color. They proposed that while TaPHS1 controlled PHS resistance qualitatively, seed
color may modify PHS resistance in a quantitative manner. The accumulation of PHS related
genes on Chromosome 3A and color homeologs on Chromosome Group 3 may help to explain
the close relationship between seed coat color and PHS resistance while further study is required
to explore this relationship at a higher resolution. The recent study using genotyping-bysequencing technology provided massive amount of markers near a color loci region, which
might help us to dissect that region in the near future (Saintenac et al. 2013). On the other hand,
the Chromosome 3R of rye is syntenic to wheat Chromosome Group 3. Masojć and Milczarski

55

(2009) found two QTL loci on Chromosome 3R had overlapping effects for both α-amylase
activity and PHS.
For QTL not mapped to Chromosome Group 3, there were various types of candidate
genes related to them. In a recent genome wide association study conducted in white winter
wheat association panel, Kulwal et al. (2012) identified a significant correlation between a
marker on Chromosome 1B and PHS related traits. This marker may be related to the QTL we
identified on Chromosome 1B for EnzBC under 2011 non-mist and 2012 mist conditions. Groos
et al. (2002) also found a QTL on Chromosome 5A which was in close linkage to grain color and
*

this was also validated in our population for color component, a , in 2012 environments. A
recent study in tetraploid wheat found genes related to seed dormancy and PHS resistance on
Chromosomes 2A, 2B, 3A, 4A and 7B may help explain several QTL identified in current study
for α-amylase activity (Chao et al., 2010). A QTL identified on Chromosome 4B for EnzBC may
be co-located with QTL for seed dormancy previously located on wheat Chromosome Group 4
(Kato et al. 2001). Furthermore, this QTL may be orthologous to the cloned seed dormancy gene,
sdr4 in weedy rice (Gu et al. 2011). The 4B QTL was also identified by Rasul et al. (2009, 2012)
controlling falling number, germination index and sprouting index. The QTL identified on
*

Chromosome 6B for color component, a , in both conditions of 2012 may related to a QTL
identified for sprouting score described by Roy et al. (1999). In the same paper, a QTL on
Chromosome 7D was also related to PHS resistance, which might be homeolog to the 7B QTL
we identified for EnzBC under 2012 conditions. Chromosome Group 6, especially 6B, and group
7 were also known to be where wheat α-amylase gene mapped (Emebiri et al., 2010; Gale et al.,
1983; Mrva and Mares, 1999).

56

QTL by QTL interaction
Groos et al. (2002) claimed no significant interaction among QTLs that were identified;
hence the effects of the red dominant alleles seem to act essentially as additive factors. However,
several additive by additive interactions were found in current study using MIM method for both
color traits and α-amylase activity (Table 2.8). As mentioned earlier, Chromosome Group 3 was
confirmed to be a hotspot for the interactions among Chromosomes 1B, 2B, 5B and 7A.
However, a combined effect for grain dormancy between 3A and group 4, which was found by
Mori et al. (2005), were not identified by current study.
*

As for QTL effects, the multiple QTL identified for each trait, such as a , usually contain
conflicting additive effects and the sum is around zero. This might indicate the need of
pyramiding correct QTL alleles for future PHS resistance breeding. In a recent study evaluating
epistatic effects of QTL controlling α-amylase activity (Yang and Ham, 2012), a strong QTL by
environment interaction was identified. The epistatic effect and various types of QTL
interactions can really complicate the expression of PHS resistance even within red wheat and
might be able to explain why it is common to identify different QTL across environments for
EnzBC.
In conclusion, by using a high density genotyping platform and a population segregating
with all combinations of seed color alleles helped us to dissect the dosage and allelic effect of
each color allele and enabled us to identify QTL × QTL interactions between traits. However, a
dissection of color loci and PHS related traits was not accomplished even with improved
mapping resolution, while the identification of Chromosome Group 3 as a hotspot for QTL
interactions might complicate the dissection.

57

APPENDIX

58

Table A.1 Growth stage of ‘Vida’ × MTHW0471 population and precipitation from
physiological maturity to the end of harvest from 2010 to 2012
Year

2010
2011
2012

Planting
Date
April
12th
May
9th
March
29th

50% Flowering
Interval

Physiological
Maturity Interval

Precipitation
Harvest Interval

(cm)

*

June 13 - July 3

July 16 - July 29

July 19 - Aug.1

2.1

July 1 – July 21

July 28 - Aug.9

July 31 - Aug. 12

11.3

June 10 - June
17

July 7 - July 15

*

July 10 –18-Jul

0.5

Precipitation was recorded from July 16 to August 1 for 2010, July 28 to August 12 for 2011,
July 7 to July 18 for 2012.

59

Table A.2 SNP filtering pipeline (from raw genotype calling to Joinmap input)
SNP
Filtering criteria
Kept
Removed
8632
0 Began with 8632 SNP from the Infinium SNP array
7921
711 Removed SNPs deemed bad or null (Feb 2012, Dr. Chao)
7304
617 Removed SNPs with greater than 20% (29 progeny) no-call
7013
291 Removed SNPs having genotype calling error
6801
212 Removed SNPs with low-confidence genotype-calls
1849
4952 Removed monomorphic SNPs
1852
Added 3 SSR markers

60

Table A.3 1695 markers mapped by ‘Vida’ x MTHW0471population
SNP-ID
Chromosome
cM
4163
1A1
0
4008
1A1
0.047
4164
1A1
0.131
1387
1A1
0.631
4754
1A1
18.997
268
1A1
19.428
459
1A1
19.518
4126
1A1
19.923
7021
1A1
21.2
4327
1A1
21.226
4326
1A1
21.305
6338
1A1
23.496
5268
1A1
23.881
2982
1A1
23.892
7779
1A1
24.033
7557
1A1
24.101
4661
1A1
24.168
3533
1A1
24.292
7945
1A1
24.297
5083
1A1
24.356
4265
1A1
24.401
5084
1A1
24.447
3528
1A1
24.47
5009
1A1
24.477
3957
1A1
24.539
3955
1A1
24.547
7104
1A1
24.548
3666
1A1
24.589
3665
1A1
24.597
6972
1A1
24.618
5031
1A1
24.619
3695
1A1
24.621
2981
1A1
24.623
6971
1A1
24.624
6031
1A1
24.633
1279
1A1
24.638
42
1A1
24.638
4577
1A1
24.641
3956
1A1
24.696
61

Table A.3 (cont’d)
SNP-ID
Chromosome
2744
1A1
8198
1A1
6984
1A1
6985
1A1
2762
1A1
1806
1A1
2630
1A1
3538
1A1
1807
1A1
3536
1A1
1952
1A1
4797
1A1
1785
1A1
2629
1A1
4852
1A1
3144
1A1
3867
1A1
3472
1A1
3882
1A1
3473
1A1
5140
1A1
3883
1A1
3499
1A1
3451
1A1
4302
1A1
6044
1A1
3160
1A1
4301
1A1
6636
1A1
498
1A1
3884
1A1
4283
1A1
7868
1A1
7869
1A1
605
1A1
8101
1A1
2490
1A1
5776
1A1
2314
1A1

cM
24.861
24.946
24.948
24.948
24.95
24.952
24.953
24.959
24.961
24.963
24.967
24.975
24.985
25.01
25.016
25.074
25.129
25.222
25.247
25.313
25.334
25.341
25.382
25.415
25.442
25.497
25.531
25.56
25.716
26.053
26.218
32.569
34.25
34.446
36.834
37.092
41.774
41.866
45.997
62

Table A.3 (cont’d)
SNP-ID
Chromosome
3980
1A1
2541
1A1
3340
1A1
5125
1A1
530
1A1
4080
1A1
7639
1A1
6934
1A1
8334
1A1
3060
1A1
691
1A1
1195
1A1
5754
1A1
3146
1A1
3145
1A1
4931
1A1
5505
1A1
475
1A1
4934
1A1
8135
1A1
7924
1A1
1618
1A1
3486
1A1
1619
1A1
1119
1A1
1118
1A1
5910
1A1
2405
1A1
2450
1A1
3409
1A1
1368
1A1
6530
1A1
1560
1A1
3089
1A1
2035
1A1
4271
1A1
4123
1A1
3977
1A1
3661
1A1

cM
49.496
50.327
50.38
50.763
50.942
51.108
51.632
55.061
62.784
62.941
63.62
63.651
69.02
70.809
70.809
73.071
80.913
81.695
81.74
83.439
83.501
83.513
83.529
83.575
83.661
83.805
86.815
87.008
98.465
98.669
100.196
107.914
110.603
110.879
111.173
114.804
115.666
115.838
116.055
63

Table A.3 (cont’d)
SNP-ID
Chromosome
4122
1A1
4120
1A1
3799
1A1
7290
1A1
5734
1A1
7488
1A2
4119
1A2
2326
1A2
2325
1A2
8351
1A2
8213
1A2
8214
1A2
5483
1A2
5484
1A2
7034
1A2
7477
1A2
1063
1A2
7067
1B
1480
1B
5976
1B
6449
1B
1578
1B
361
1B
7480
1B
4504
1B
2998
1B
4975
1B
7737
1B
5301
1B
6448
1B
7703
1B
6062
1B
6611
1B
6610
1B
4093
1B
1566
1B
8392
1B
5681
1B
7219
1B

cM
116.149
116.156
116.476
116.71
117.463
0
0.615
0.636
0.641
0.665
0.676
0.677
0.694
0.697
0.698
0.768
10.924
0
1.584
1.599
2.462
2.473
2.501
2.51
2.56
2.57
4.496
4.892
37.928
38.775
38.78
39.289
39.607
39.77
39.811
39.943
40.279
40.597
40.614
64

Table A.3 (cont’d)
SNP-ID
Chromosome
5592
1B
8338
1B
6259
1B
6891
1B
3631
1B
6890
1B
7343
1B
139
1B
4402
1B
7234
1B
6581
1B
5546
1B
5304
1B
3295
1B
107
1B
106
1B
44
1B
5779
1B
3057
1B
4556
1B
188
1B
4198
1B
141
1B
189
1B
5278
1B
890
1B
6073
1B
4197
1B
7594
1B
6674
1B
2554
1B
4987
1B
3307
1B
7017
1B
4557
1B
3945
1B
270
1B
4316
1B
491
1B

cM
40.652
40.717
41.166
41.483
41.642
41.642
42.106
42.166
42.166
42.206
42.213
42.219
42.226
42.549
44.419
44.663
44.808
47.7
48.441
48.46
48.539
48.604
48.628
48.769
48.887
48.907
48.91
48.937
48.959
48.987
49.016
49.033
49.198
49.27
49.472
50.284
50.485
50.8
50.883
65

Table A.3 (cont’d)
SNP-ID
Chromosome
7700
1B
7836
1B
269
1B
573
1B
2315
1B
2040
1B
734
1B
2790
1B
1729
1B
2788
1B
4939
1B
2588
1B
5159
1B
4940
1B
2586
1B
2041
1B
2890
1B
7040
1B
4702
1B
4703
1B
1313
1B
7527
1B
7811
1B
2889
1B
6479
1B
4488
1B
146
1B
7178
1B
7179
1B
3341
1B
8379
1B
255
1B
367
1B
2989
1B
5186
1B
5749
1B
696
1B
695
1B
1092
1B

cM
51.001
51.032
51.063
52.509
56.129
63.215
63.333
63.551
63.609
63.615
63.719
63.73
63.739
63.897
63.904
63.925
64.04
64.04
64.289
64.325
64.361
64.502
64.542
64.644
64.69
67.514
70.402
73.737
74.306
74.374
74.402
74.528
74.633
74.853
75.314
82.782
97.355
98.551
112.798
66

Table A.3 (cont’d)
SNP-ID
Chromosome
3998
1B
1791
1B
724
1B
6512
1B
6647
1B
2928
1B
2077
1B
1504
1B
4935
1B
3125
1D
1397
1D
7797
1D
1787
1D
8551
1D
5020
1D
5018
1D
5019
1D
57
1D
362
1D
830
1D
1192
1D
1193
1D
642
1D
7425
1D
165
1D
7154
1D
1512
2A
6922
2A
1562
2A
1563
2A
4989
2A
2425
2A
5423
2A
681
2A
3047
2A
2696
2A
2059
2A
3235
2A
5762
2A

cM
117.106
130.584
132.138
139.458
139.64
140.282
140.284
140.708
140.752
0
7.95
7.967
7.993
31.133
56.888
56.929
56.936
57.522
57.704
59.045
59.236
59.267
59.427
59.693
61.593
75.291
0
0.275
0.364
0.365
0.424
0.662
1.17
13.231
26.872
33.918
34.235
35.179
36.571
67

Table A.3 (cont’d)
SNP-ID
Chromosome
841
2A
1242
2A
1166
2A
4441
2A
4830
2A
5495
2A
901
2A
991
2A
5824
2A
562
2A
5793
2A
2007
2A
2005
2A
2006
2A
2948
2A
887
2A
411
2A
72
2A
32
2A
70
2A
33
2A
71
2A
69
2A
812
2A
120
2A
3151
2A
111
2A
7947
2A
488
2A
1174
2A
5243
2A
1960
2A
8157
2A
7864
2A
4373
2A
4375
2A
5215
2A
5733
2A
5463
2A

cM
36.705
36.713
36.736
37.02
48.684
62.17
62.235
62.335
62.338
62.371
62.412
62.422
62.425
62.459
67.766
68.783
68.827
68.88
69.661
69.708
69.753
69.786
69.867
69.945
70.187
70.273
70.88
73.059
73.207
73.212
82.394
83.682
84.772
85.164
85.308
85.386
86.347
86.48
86.509
68

Table A.3 (cont’d)
SNP-ID
Chromosome
7433
2A
6499
2A
5214
2A
5216
2A
7593
2A
6307
2A
2884
2A
6844
2A
6503
2A
7540
2A
5856
2A
5855
2A
3576
2A
5244
2A
7998
2A
7876
2A
2612
2A
8040
2A
5686
2A
8041
2A
5685
2A
6549
2A
5840
2A
227
2A
7761
2A
7412
2A
4870
2A
4228
2A
6840
2A
7142
2A
4229
2A
1347
2A
6841
2A
1351
2A
6839
2A
6620
2A
5588
2A
3743
2A
702
2A

cM
86.686
86.7
86.762
86.768
86.846
86.873
86.984
87.049
87.084
87.131
87.309
87.461
87.972
89.716
89.966
91.35
92.008
103.218
103.241
103.284
103.326
103.573
112.146
112.556
112.974
114.296
114.818
115.08
115.082
115.086
115.099
115.105
115.114
115.125
115.127
115.159
115.162
115.168
115.189
69

Table A.3 (cont’d)
SNP-ID
Chromosome
1350
2A
5072
2A
2601
2A
5759
2A
4491
2A
4493
2A
2846
2B1
7936
2B1
5137
2B1
2407
2B1
749
2B1
6767
2B1
6768
2B1
7697
2B1
1929
2B1
5708
2B1
6085
2B1
2117
2B1
7799
2B1
1930
2B1
888
2B1
2116
2B1
2115
2B1
2440
2B1
7120
2B1
5721
2B1
2442
2B1
2443
2B1
2441
2B1
4285
2B1
4284
2B1
4554
2B1
6739
2B1
7069
2B1
6740
2B1
4421
2B1
8083
2B1
4420
2B1
5392
2B1

cM
115.326
115.343
116.024
117.464
123.473
123.734
0
0.423
0.809
1.375
1.707
3.354
3.565
19.265
25.602
25.758
26.014
26.078
26.292
26.61
26.616
26.626
26.657
26.935
26.974
26.995
26.995
27.185
27.286
27.742
28.307
41.709
41.74
41.866
41.875
41.927
41.943
41.964
52.326
70

Table A.3 (cont’d)
SNP-ID
Chromosome
8381
2B1
6364
2B1
607
2B1
7030
2B1
7029
2B1
8182
2B1
7567
2B1
608
2B1
5753
2B1
5560
2B1
3081
2B1
2557
2B1
762
2B1
2887
2B1
3329
2B1
5916
2B1
3080
2B1
295
2B1
3824
2B1
4532
2B1
1114
2B1
4531
2B1
5246
2B1
6554
2B1
7263
2B1
5038
2B1
5464
2B1
6462
2B1
4102
2B1
3428
2B1
5811
2B1
6136
2B1
1059
2B1
5149
2B1
3657
2B1
439
2B1
3213
2B1
2977
2B1
7951
2B1

cM
53.276
53.836
54.785
54.899
54.957
55.259
55.26
55.272
55.275
55.28
55.284
55.285
55.286
55.291
55.294
55.304
55.309
55.396
55.922
56.284
56.338
56.366
64.324
66.244
66.688
67.016
67.107
67.111
67.113
67.117
67.12
67.136
67.338
67.397
67.4
67.409
67.438
67.453
67.456
71

Table A.3 (cont’d)
SNP-ID
Chromosome
6476
2B1
1938
2B1
4984
2B1
4983
2B1
6664
2B1
6830
2B1
5059
2B1
6875
2B1
3656
2B1
5724
2B1
8517
2B1
1656
2B1
6430
2B1
2290
2B1
6209
2B1
4541
2B1
5128
2B1
1036
2B1
6969
2B1
8359
2B1
5547
2B1
4853
2B1
1690
2B1
5525
2B1
5008
2B1
6175
2B1
2924
2B1
4956
2B1
6093
2B1
2131
2B1
2903
2B1
2261
2B1
5414
2B1
469
2B1
5415
2B1
1389
2B1
3935
2B1
4890
2B1
4948
2B1

cM
67.473
67.477
67.484
67.485
67.486
67.499
67.61
67.719
67.949
68.434
71.723
71.95
72.112
72.134
73.881
78.432
81.007
81.219
81.3
81.321
81.341
81.343
81.359
81.404
81.944
82.04
94.431
94.604
94.676
94.782
94.986
95.191
95.236
95.248
95.482
95.969
96.072
96.325
96.335
72

Table A.3 (cont’d)
SNP-ID
Chromosome
3395
2B1
4357
2B1
4358
2B1
4356
2B1
933
2B1
8195
2B1
1273
2B1
6122
2B1
8362
2B1
6121
2B1
7371
2B1
2701
2B1
4909
2B1
4097
2B1
2678
2B1
2676
2B1
4095
2B1
4098
2B1
2158
2B1
3176
2B1
7955
2B1
1765
2B1
2459
2B1
2502
2B1
4130
2B1
3010
2B1
5460
2B1
8555
2B1
8406
2B1
8449
2B1
1599
2B1
1822
2B1
5810
2B1
5081
2B1
5694
2B2
6852
2B2
4619
2B2
2046
2B2
1667
2B2

cM
97.713
97.752
97.783
97.861
98.319
99.91
102.619
102.889
103.096
103.1
103.191
103.233
103.477
103.49
103.502
103.509
103.625
103.781
103.927
105.302
106.072
106.145
106.18
106.634
106.936
107.599
107.613
107.617
107.617
107.668
107.671
107.674
108.286
109.856
0
0.238
1.589
1.701
1.821
73

Table A.3 (cont’d)
SNP-ID
Chromosome
1668
2B2
2551
2B2
3252
2B2
3773
2B2
4118
2B2
5442
2B2
7273
2D
4496
2D
2722
2D
5637
2D
5252
2D
2631
2D
4666
2D
3849
2D
4108
2D
230
2D
229
2D
3976
2D
1440
2D
552
2D
6813
2D
2174
3A1
6387
3A1
447
3A1
3939
3A1
8105
3A1
8106
3A1
5050
3A1
4257
3A1
2048
3A1
2047
3A1
7086
3A1
2049
3A1
7085
3A1
5641
3A1
3448
3A1
443
3A1
6413
3A1
4676
3A1

cM
1.875
2.126
3.069
3.408
3.441
3.847
0
0.544
24.947
34.951
49.347
74.037
76.347
76.429
76.536
76.588
76.652
76.928
77.225
81.151
81.495
0
1.003
2.136
24.57
24.735
24.785
29.771
29.833
37.22
37.345
37.435
37.596
37.744
44.685
44.686
53.6
54.261
54.273
74

Table A.3 (cont’d)
SNP-ID
Chromosome
1972
3A1
2019
3A1
2985
3A1
7662
3A1
3166
3A1
1812
3A1
7501
3A1
5399
3A1
4009
3A1
335
3A1
720
3A1
6306
3A1
3498
3A1
1614
3A1
143
3A1
5006
3A1
4913
3A1
2156
3A1
5005
3A1
1922
3A1
4912
3A1
1422
3A1
4381
3A1
3156
3A1
8577
3A1
5786
3A1
3794
3A1
1699
3A1
1019
3A1
7114
3A1
7355
3A1
5332
3A1
1887
3A1
5632
3A1
4883
3A1
249
3A1
234
3A1
133
3A1
743
3A1

cM
55.096
57.025
57.412
57.722
57.964
58.009
58.415
59.241
59.254
59.825
61.713
62.252
63.705
63.89
64.297
64.55
64.909
64.921
64.921
64.924
64.925
64.93
64.93
64.934
64.944
64.944
66.159
66.475
68.715
69.002
69.194
69.222
69.224
69.26
69.332
69.342
70.418
70.805
70.805
75

Table A.3 (cont’d)
SNP-ID
Chromosome
8465
3A1
5124
3A1
7891
3A1
7970
3A1
3600
3A1
1507
3A1
7817
3A1
3512
3A1
3772
3A1
1701
3A1
2944
3A1
5994
3A1
6907
3A1
8621
3A1
4001
3A1
4930
3A1
8558
3A1
4110
3A1
1983
3A1
1605
3A1
3250
3A1
2925
3A1
5579
3A1
1700
3A1
4707
3A1
1762
3A1
2332
3A1
7150
3A1
5315
3A1
3771
3A1
7541
3A1
5286
3A1
1678
3A1
5285
3A1
5284
3A1
7159
3A1
5650
3A1
7877
3A1
5651
3A1

cM
70.857
70.86
71.043
71.049
71.065
71.087
71.13
71.141
71.149
71.152
71.156
71.156
71.164
71.176
71.212
71.215
71.249
71.257
71.305
71.306
71.314
71.315
71.318
71.323
71.323
71.367
71.388
71.392
71.398
71.482
71.482
72.028
72.03
72.035
72.067
72.075
72.079
72.081
72.084
76

Table A.3 (cont’d)
SNP-ID
Chromosome
6750
3A1
3198
3A1
3093
3A1
5649
3A1
7073
3A1
2649
3A1
2291
3A1
1462
3A1
2774
3A1
1463
3A1
4851
3A1
6652
3A1
R
3A2
1207
3A2
7297
3A2
2372
3A2
4259
3A2
2949
3A2
6173
3A2
4258
3A2
7835
3A2
3559
3A2
5111
3A2
2396
3A2
6716
3A2
8000
3A2
2397
3A2
7157
3A2
1457
3A2
3178
3A2
2870
3A2
602
3A2
4850
3A2
3177
3A2
7158
3A2
3949
3A2
4796
3B1
6471
3B1
3103
3B1

cM
72.131
72.259
72.398
72.524
80.018
84.492
84.619
84.734
84.884
84.895
90.115
90.564
0
5.39
5.667
6.119
20.275
20.387
20.521
20.545
20.675
21.262
21.483
24.921
25.093
25.324
25.52
32.819
32.864
32.877
32.944
32.952
33.092
33.164
33.219
33.543
0
0.264
0.91
77

Table A.3 (cont’d)
SNP-ID
Chromosome
5202
3B1
2177
3B1
4801
3B1
5106
3B1
4800
3B1
5426
3B1
7342
3B2
280
3B2
8460
3B2
6464
3B2
2360
3B3
6056
3B3
4324
3B3
5013
3B3
2462
3B3
8053
3B3
3331
3B3
8054
3B3
R
3B3
7542
3B3
939
3B3
8058
3B3
1814
3D1
R
3D1
4559
3D2
1321
3D2
7468
3D2
5695
3D2
5224
3D3
6485
3D3
811
4A1
3061
4A1
3698
4A1
5968
4A1
3068
4A1
7322
4A1
7653
4A1
2761
4A1
3774
4A1

cM
1.708
2.145
2.145
5.915
5.921
6.069
0
2.081
5.743
6.916
0
3.165
3.281
3.714
4.053
5.659
5.847
5.891
11.25
21.994
28.662
29.054
0
25.517
0
0.395
0.399
1.49
0
6.504
0
2.495
2.502
3.113
4.154
7.769
18.864
19.701
21.289
78

Table A.3 (cont’d)
SNP-ID
Chromosome
3756
4A1
6035
4A1
6906
4A1
6733
4A1
5200
4A1
1720
4A1
2123
4A1
1505
4A1
4651
4A1
7264
4A1
7265
4A1
6696
4A1
6884
4A1
6882
4A1
6883
4A1
7442
4A1
4023
4A1
2460
4A1
7077
4A1
2606
4A1
6193
4A1
5123
4A1
2900
4A1
3191
4A1
7699
4A1
2585
4A1
2901
4A1
7604
4A1
730
4A1
1400
4A1
4319
4A1
4709
4A1
1727
4A1
385
4A1
4067
4A1
1639
4A1
3826
4A1
6743
4A1
1329
4A1

cM
25.957
29.489
33.051
36.151
42.283
42.316
46.367
46.762
50.121
50.266
50.361
52.854
57.334
57.372
57.5
61.493
62.135
62.467
62.608
62.872
63.033
70.567
72.638
72.801
72.908
73.06
73.155
74.089
74.117
74.161
74.164
74.17
74.362
75.15
75.327
75.437
75.463
75.464
75.465
79

Table A.3 (cont’d)
SNP-ID
Chromosome
40
4A1
493
4A1
6025
4A1
157
4A1
142
4A1
160
4A1
1141
4A1
4876
4A1
1327
4A1
192
4A1
6103
4A1
4079
4A1
112
4A1
2334
4A1
1060
4A1
750
4A1
7082
4A1
6659
4A1
6392
4A1
1824
4A1
5652
4A1
5069
4A1
7657
4A1
3541
4A1
7271
4A1
115
4A1
5975
4A1
3311
4A1
7134
4A1
8296
4A1
8220
4A1
6678
4A1
5196
4A1
912
4A1
3818
4A1
1969
4A1
7081
4A1
8265
4A1
5127
4A1

cM
75.466
75.525
75.559
75.6
75.687
75.743
75.748
75.783
75.825
75.925
76.178
77.524
77.57
77.676
77.76
77.899
77.932
77.991
78.147
78.15
78.159
78.2
78.204
78.213
78.215
78.216
78.217
78.266
78.274
78.278
78.284
78.308
78.326
78.327
78.331
78.331
78.333
78.339
78.339
80

Table A.3 (cont’d)
SNP-ID
Chromosome
3119
4A1
826
4A1
1178
4A1
5705
4A1
3763
4A1
3671
4A1
6702
4A1
5237
4A1
5897
4A1
7859
4A1
1341
4A1
4772
4A1
3845
4A1
3028
4A1
7270
4A1
5865
4A1
4405
4A1
4700
4A1
6540
4A1
3582
4A1
2000
4A1
3565
4A1
110
4A1
6873
4A1
2781
4A1
8414
4A1
6597
4A1
7133
4A1
3027
4A1
1919
4A1
109
4A1
3326
4A1
3088
4A1
1694
4A1
7395
4A1
7092
4A1
3361
4A1
172
4A1
3542
4A1

cM
78.352
78.352
78.352
78.356
78.357
78.357
78.367
78.368
78.37
78.441
78.441
78.442
78.448
78.449
78.45
78.455
78.457
78.457
78.457
78.458
78.459
78.46
78.463
78.464
78.464
78.465
78.465
78.471
78.474
78.479
78.481
78.489
78.499
78.502
78.53
78.537
78.588
78.599
78.612
81

Table A.3 (cont’d)
SNP-ID
Chromosome
4560
4A1
1693
4A1
6911
4A1
3029
4A1
7522
4A1
1320
4A1
7382
4A1
8150
4A1
7124
4A1
7632
4A1
108
4A1
3749
4A2
3751
4A2
3747
4A2
8425
4A2
6457
4B
2298
4B
8108
4B
4569
4B
3290
4B
102
4B
4854
4B
8263
4B
75
4B
76
4B
453
4B
327
4B
2591
4B
113
4B
6011
4B
1405
4B
3846
4B
4070
4B
1344
4B
7437
4B
1007
4B
1818
4B
1006
4B
2732
4B

cM
78.621
78.628
78.635
78.671
78.674
78.858
79.013
79.08
79.973
103.551
103.978
0
0.317
0.317
0.318
0
19.565
19.822
23.725
26.148
36.391
43.066
43.546
43.648
43.697
43.918
44.302
45.364
46.193
51.855
52.002
52.057
52.53
52.602
52.681
52.747
52.772
52.774
52.814
82

Table A.3 (cont’d)
SNP-ID
Chromosome
4330
4B
1105
4B
7167
4B
2532
4B
2155
4B
6898
4B
2745
4B
5195
4B
8019
4B
1961
4B
2666
4B
2683
4B
7752
4B
1035
4B
3396
4B
6635
4B
7313
4B
2754
4B
2755
4B
1382
4B
2171
4B
1028
4B
908
4B
892
4B
3038
4B
3042
4B
3039
4B
3697
4B
2031
4B
5358
4B
564
4B
7299
4B
4618
4B
1798
4B
2087
4B
752
4D
5381
4D
4044
4D
161
4D

cM
52.819
52.824
52.826
52.829
52.842
52.861
52.867
52.884
53.064
53.172
53.215
54.231
54.538
54.58
54.592
54.679
55.324
57.703
57.937
58.035
58.214
59.74
59.764
61.588
69.53
69.654
69.918
85.423
86.441
92.637
95.045
113.556
113.725
113.876
114.044
0
15.651
21.853
22.478
83

Table A.3 (cont’d)
SNP-ID
Chromosome
430
4D
465
4D
3555
4D
3815
4D
7482
4D
2558
5A1
7880
5A1
5002
5A1
5003
5A1
3334
5A1
649
5A1
648
5A1
3083
5A1
2802
5A1
2828
5A1
7162
5A1
1670
5A1
2897
5A1
6641
5A1
3323
5A1
5154
5A1
2282
5A1
2163
5A1
3623
5A1
2959
5A1
675
5A1
674
5A1
7044
5A1
4744
5A1
4468
5A2
6683
5A2
6682
5A2
3625
5A2
5838
5A2
1439
5A2
7553
5A2
687
5A3
8093
5A3
1120
5A3

cM
23.448
33.126
34.75
35.851
39.452
0
6.022
11.248
11.612
12.197
12.698
13.11
14.527
17.383
20.556
20.564
21.342
22.372
22.437
28.734
28.83
46.602
50.528
54.343
55.169
57.305
57.411
57.413
60.355
0
0.001
0.046
0.092
0.188
0.188
0.799
0
0.156
0.284
84

Table A.3 (cont’d)
SNP-ID
Chromosome
1201
5A3
4687
5A3
2429
5A3
1236
5A3
4299
5A3
4051
5A3
5118
5A3
4052
5A3
7529
5A3
6573
5A3
6949
5A3
4629
5A3
8559
5A3
3313
5A3
6515
5A3
66
5A3
3413
5A3
740
5A3
738
5A3
6255
5A3
6523
5A3
7436
5A3
7220
5A3
2447
5A3
5329
5A3
5567
5A3
121
5A3
122
5A3
4734
5A3
300
5A3
3873
5A3
5529
5A3
5528
5A3
5884
5A3
5032
5A3
5034
5A3
5033
5A3
7130
5A3
7565
5A3

cM
0.417
0.439
0.587
3.828
5.538
5.684
5.742
5.777
5.811
7.096
8.465
10.558
10.668
11.489
11.522
11.882
15.254
15.485
15.516
15.844
15.859
17.464
23.892
24.114
25.433
31.394
33.299
33.456
33.69
33.88
35.873
37.05
37.132
37.709
38.675
38.886
39.313
43.657
44.313
85

Table A.3 (cont’d)
SNP-ID
Chromosome
3099
5A3
1301
5A3
4149
5A3
4454
5A3
5395
5A3
3100
5A3
2480
5A3
1988
5A3
315
5A3
278
5A3
3445
5A3
5496
5A3
7351
5A3
1951
5A3
5521
5A3
3349
5A3
4736
5A3
2120
5A3
8563
5A3
2467
5A3
87
5A3
6415
5A3
1950
5A3
7961
5A3
7960
5A3
1943
5A3
7926
5A3
7925
5A3
7691
5A3
7690
5A3
114
5A3
1253
5A3
5105
5A3
7129
5A3
7109
5A3
3263
5A3
3190
5A3
8154
5A3
7061
5A3

cM
44.547
44.552
44.86
45.071
45.093
45.275
45.471
45.747
45.937
45.973
46.031
46.112
46.195
46.318
46.416
46.49
46.558
46.564
46.709
46.75
46.763
46.765
47.079
47.111
47.133
47.182
47.226
47.241
47.303
47.315
47.395
47.431
47.574
47.874
48.445
49.24
49.878
50.103
53.682
86

Table A.3 (cont’d)
SNP-ID
Chromosome
3530
5A3
4465
5A3
5728
5A3
2378
5A3
1062
5A3
3811
5A3
4767
5A3
4765
5A3
5614
5A3
4069
5A3
330
5A3
331
5A3
3365
5A3
6859
5A3
5368
5A3
3196
5A3
3197
5A3
14
5A3
4932
5A3
7361
5A3
23
5B
4635
5B
4748
5B
5454
5B
6211
5B
22
5B
197
5B
1564
5B
7340
5B
3972
5B
3214
5B
8031
5B
6393
5B
3984
5B
6416
5B
8444
5B
7020
5B
8433
5B
6097
5B

cM
53.733
62.518
62.566
63.751
63.754
63.779
63.834
63.841
63.868
63.868
64.025
64.1
65.277
65.925
69.001
83.04
83.119
83.64
83.98
86.906
0
1.25
1.265
1.492
1.568
2.648
6.187
9.555
11.67
12.089
12.138
12.142
12.148
12.294
12.719
17.059
17.093
17.225
17.229
87

Table A.3 (cont’d)
SNP-ID
Chromosome
2827
5B
7585
5B
2388
5B
7965
5B
6782
5B
6148
5B
6779
5B
6147
5B
7494
5B
1433
5B
7493
5B
4184
5B
7963
5B
8262
5B
7668
5B
5179
5B
3800
5B
7910
5B
1577
5B
7733
5B
4057
5B
5836
5B
5835
5B
3226
5B
3432
5B
2565
5B
2255
5B
3479
5B
6024
5B
1775
5B
1780
5B
8508
5B
3002
5B
6894
5B
6895
5B
1755
5B
6905
5B
6112
5B
6111
5B

cM
17.369
27.969
28.326
28.386
30.169
30.693
30.775
30.929
30.93
31.009
31.371
31.528
41.823
41.889
41.892
41.893
42.546
52.823
53.37
53.4
55.442
56.266
56.609
56.651
57.343
57.359
57.41
57.457
57.505
57.84
59.342
60.469
61.413
70.674
70.775
71.536
79.79
80.652
81.082
88

Table A.3 (cont’d)
SNP-ID
Chromosome
7815
5B
4280
5B
6516
5B
8069
5B
7175
5B
337
5B
5488
5B
4632
5B
8187
5B
2694
5B
2335
5B
4571
5B
7471
5B
3009
5B
2336
5B
952
5B
7470
5B
6171
5B
3044
5B
3008
5B
7776
5B
5139
5B
2697
5B
2430
5B
7844
5B
5482
5B
3719
5B
951
5B
3964
5B
5947
5B
6366
5B
6125
5B
6766
5B
7446
5B
5487
5B
6235
5B
5795
5B
1446
5B
987
5B

cM
81.172
81.637
81.638
81.887
82.062
82.194
82.312
82.354
82.399
82.414
82.417
82.424
82.427
82.441
82.459
82.474
82.475
82.475
82.481
82.481
82.482
82.511
82.538
82.539
82.545
82.546
82.548
82.554
82.56
82.56
82.578
82.607
82.699
82.712
82.726
82.732
82.74
82.75
82.869
89

Table A.3 (cont’d)
SNP-ID
Chromosome
2133
5B
338
5B
1772
5B
3165
5B
4832
5B
1445
5B
265
5B
4074
5B
3985
5B
6383
5B
6867
5B
5283
5B
1018
5B
5217
5B
6291
5B
4622
5B
2934
5B
2432
5B
2536
5B
1401
5B
1402
5B
6638
5B
5331
5B
3436
5B
1471
5B
6992
5B
7944
5B
5742
5B
8603
5B
5289
5B
1584
5B
301
5B
7227
5B
1776
5B
6344
5B
1777
5B
5280
5B
6526
5B
4158
5B

cM
82.882
82.905
82.992
82.993
83.103
83.137
83.842
86.28
86.336
86.758
86.95
87.632
89.07
89.558
89.658
89.793
89.901
89.975
91.391
92.295
92.307
93.996
94.2
94.636
102.834
102.865
103.248
105.367
106.902
110.921
111.165
111.4
112.28
112.344
112.527
112.712
112.845
112.97
114.251
90

Table A.3 (cont’d)
SNP-ID
Chromosome
4414
5B
8005
5B
3706
5B
3106
5B
2596
5B
4300
5B
396
5B
7272
5B
4494
5B
5166
5B
3630
5B
2597
5B
1705
5B
1706
5B
1994
5B
2071
5B
5764
5B
2742
5B
5497
5B
2032
5B
1057
5B
3209
5B
7608
5B
1965
5B
1176
5B
5079
5B
7609
5B
1394
5B
4377
5B
4281
5B
7300
5B
4282
5B
1342
5B
2910
5B
6568
5B
6447
5B
7217
5B
6909
5B
6910
5B

cM
114.354
114.531
114.732
114.868
114.929
115.096
115.224
115.295
115.344
115.454
115.575
115.622
115.783
115.864
115.873
115.876
116.524
116.607
116.849
116.978
118.616
119.599
119.621
119.797
119.814
119.848
119.923
119.995
120.031
120.07
120.081
120.082
120.1
120.204
120.223
120.229
120.233
120.234
120.239
91

Table A.3 (cont’d)
SNP-ID
Chromosome
894
5B
5494
5B
3870
5B
7988
5B
6567
5B
279
5B
5126
5B
1461
5B
5334
5B
620
5B
3101
5B
6817
5B
2930
5B
1781
5B
2563
5B
6555
5B
6065
5B
6521
5B
4379
5B
5078
5B
5108
5B
6556
5B
6030
5B
2320
5B
2610
5B
2609
5B
7211
5B
4355
5B
7223
5B
3514
5B
7153
5B
5176
5B
1709
5B
4416
5B
6402
5B
7400
5B
4313
5B
5537
5B
4415
5B

cM
120.241
120.275
120.328
120.352
120.364
120.419
120.44
120.48
120.506
120.508
120.584
120.611
120.613
120.629
120.645
120.652
120.697
120.721
120.779
120.781
120.782
120.941
121.126
121.48
123.16
123.459
136.536
142.463
143.574
143.765
148.557
148.691
149.077
152.397
152.443
152.478
152.66
152.774
153.666
92

Table A.3 (cont’d)
SNP-ID
Chromosome
5366
5D1
7517
5D1
4550
5D1
2803
5D1
3429
5D1
1681
5D2
4087
5D2
1427
5D3
1428
5D3
1431
5D3
2878
5D3
2877
5D3
8160
6A
1033
6A
6601
6A
1205
6A
680
6A
8510
6A
612
6A
3321
6A
2018
6A
5930
6A
2017
6A
6337
6A
233
6A
522
6A
6986
6A
7940
6A
5441
6A
1423
6A
1285
6A
2187
6A
7847
6A
4371
6A
4370
6A
2366
6A
5421
6A
7438
6A
1498
6A

cM
0
0.331
0.658
24.844
25.193
0
14.085
0
0.204
1.422
1.422
2.189
0
22.498
26.222
26.941
41.436
48.321
48.452
49.049
53.9
54.285
54.291
54.77
57.778
68.062
71.61
72.035
72.145
72.28
72.38
72.389
72.397
72.398
72.416
72.421
72.423
72.424
72.443
93

Table A.3 (cont’d)
SNP-ID
Chromosome
2421
6A
4607
6A
7492
6A
7458
6A
5656
6A
3529
6A
2805
6A
6095
6A
5057
6A
2186
6A
5712
6A
6560
6A
416
6A
7052
6A
741
6A
651
6A
879
6A
6699
6A
4737
6A
7563
6A
2192
6A
650
6A
6596
6A
5074
6A
6508
6A
5073
6A
3767
6A
1097
6A
4699
6A
1868
6A
1867
6A
7497
6A
2639
6A
7894
6A
1510
6A
2055
6A
2054
6A
5655
6A
4691
6A

cM
72.444
72.482
72.485
72.489
72.492
72.493
72.535
72.612
72.615
72.615
72.644
72.685
73.027
77.222
77.471
77.488
77.516
77.605
77.619
77.63
77.72
77.799
77.907
77.938
77.965
78.101
78.221
87.375
87.392
87.751
87.752
88.026
89.025
89.073
89.134
89.45
89.5
89.663
90.029
94

Table A.3 (cont’d)
SNP-ID
Chromosome
6537
6A
3067
6A
7874
6A
6316
6A
7747
6A
2580
6A
442
6A
1000
6A
1497
6A
3954
6A
4278
6A
214
6A
8568
6A
3488
6A
2705
6A
3487
6A
383
6A
6355
6A
259
6A
7366
6A
4950
6A
4246
6B1
824
6B1
5605
6B1
7116
6B1
4717
6B1
1816
6B1
7056
6B1
1666
6B1
2474
6B1
1268
6B1
219
6B1
4337
6B1
5148
6B1
283
6B1
3327
6B1
2830
6B1
1473
6B1
3636
6B1

cM
90.032
90.039
90.398
90.405
90.414
91.487
91.9
91.961
92.77
95.203
103.242
104.278
105.331
108.066
108.171
108.249
109.042
111.334
126.68
126.945
126.965
0
0.749
2.051
15.362
28.969
29.054
33.588
37.46
38.853
49.831
55.757
55.866
56
56.318
56.455
56.755
56.775
56.782
95

Table A.3 (cont’d)
SNP-ID
Chromosome
4338
6B1
4339
6B1
297
6B1
3967
6B1
4959
6B1
1472
6B1
221
6B1
2773
6B1
8611
6B1
387
6B1
4500
6B1
4503
6B1
5044
6B1
971
6B1
755
6B1
3030
6B1
6825
6B1
3450
6B1
7380
6B1
1743
6B1
3133
6B1
5104
6B1
4487
6B1
4086
6B1
2843
6B1
1839
6B1
6855
6B1
3878
6B1
5102
6B1
1838
6B1
5241
6B1
4599
6B1
2780
6B1
7935
6B1
2342
6B1
5157
6B1
8144
6B1
3632
6B1
3167
6B1

cM
56.789
56.825
56.951
56.965
56.994
57.013
57.167
57.339
58.432
58.786
60.185
60.461
60.659
60.951
60.955
61.971
62.483
62.528
62.536
62.542
62.591
62.592
62.612
62.637
62.642
62.715
62.719
62.741
62.756
62.768
62.773
62.784
62.828
62.844
62.848
62.867
62.903
62.906
62.908
96

Table A.3 (cont’d)
SNP-ID
Chromosome
6142
6B1
8175
6B1
5785
6B1
3131
6B1
1151
6B1
5095
6B1
1742
6B1
4169
6B1
5748
6B1
5029
6B1
4924
6B1
2135
6B1
941
6B1
8189
6B1
617
6B1
8192
6B1
185
6B1
3132
6B1
3652
6B1
4848
6B1
3917
6B1
1545
6B1
5242
6B1
5966
6B1
2090
6B1
613
6B1
6628
6B1
6153
6B1
434
6B1
5225
6B1
7995
6B1
5531
6B1
3459
6B1
4440
6B1
2109
6B1
3677
6B1
5042
6B1
2975
6B1
5043
6B1

cM
62.912
62.919
62.933
62.96
62.963
62.971
62.977
62.981
62.984
62.995
63
63.057
63.067
63.068
63.089
63.095
63.129
63.145
63.146
63.153
63.163
63.197
63.198
63.202
63.205
63.205
63.25
63.258
63.305
63.326
63.427
63.599
63.846
63.847
63.987
67.072
67.087
67.157
67.231
97

Table A.3 (cont’d)
SNP-ID
Chromosome
5056
6B1
6494
6B1
1905
6B1
6953
6B1
7689
6B1
2439
6B1
5055
6B1
8284
6B1
7807
6B1
7809
6B1
7618
6B1
3300
6B1
3501
6B1
3923
6B1
1657
6B1
1640
6B1
6466
6B1
2219
6B1
7937
6B1
7810
6B1
1660
6B1
4408
6B1
7240
6B1
7257
6B1
4010
6B2
4290
6B2
7725
6B2
2098
6B2
969
6B2
6759
6B2
666
6B2
1254
6B2
7070
6B2
860
6B2
203
6D1
4592
6D1
2808
6D2
3624
6D2
3979
7A1

cM
68.563
68.568
68.606
68.607
68.613
68.664
68.694
68.711
68.835
68.875
68.972
68.998
69.313
69.366
69.416
69.623
69.636
69.794
69.945
70.066
71.933
72.139
72.482
74.449
0
3.399
4.19
6.856
7.061
7.197
7.672
7.737
8.212
8.759
0
0.323
0
0
0
98

Table A.3 (cont’d)
SNP-ID
Chromosome
6160
7A1
6519
7A1
7321
7A1
4558
7A1
1921
7A1
7196
7A1
7978
7A1
6642
7A1
2570
7A1
5336
7A1
7197
7A1
3850
7A1
1735
7A1
6127
7A1
557
7A1
7093
7A1
930
7A1
834
7A1
6475
7A1
4614
7A1
8390
7A1
472
7A1
473
7A1
1805
7A1
8161
7A1
7121
7A1
4386
7A1
3351
7A1
947
7A1
796
7A1
486
7A1
222
7A1
5369
7A1
8066
7A1
3863
7A1
2786
7A1
5132
7A1
1456
7A1
3693
7A1

cM
0.393
0.441
0.67
0.693
0.881
0.895
0.907
1.444
1.565
1.719
1.858
2.554
8.905
13.689
17.071
21.619
22.52
22.616
42.414
43.234
47.705
47.774
47.869
49.269
62.274
62.305
62.381
87.68
88.332
88.848
88.861
88.866
89.714
89.743
89.853
89.95
90.201
90.25
90.383
99

Table A.3 (cont’d)
SNP-ID
Chromosome
3727
7A1
3694
7A1
6183
7A1
8171
7A1
2954
7A1
2381
7A1
7990
7A1
3557
7A1
4818
7A1
4072
7A1
4573
7A1
3054
7A1
7975
7A1
7554
7A1
1278
7A1
7855
7A1
4574
7A1
4082
7A1
4638
7A1
2301
7A1
3062
7A1
7193
7A1
4637
7A1
5119
7A1
1833
7A1
3090
7A1
6629
7A1
3091
7A1
2082
7A1
1834
7A1
7798
7A1
4846
7A1
7756
7A1
7755
7A1
4109
7A1
6004
7A1
2011
7A1
2010
7A1
4288
7A1

cM
90.413
90.424
90.432
90.444
90.451
90.453
90.453
90.462
90.529
90.542
90.559
90.653
90.69
90.777
90.825
90.912
91.058
91.47
93.988
94.592
94.812
94.998
95.091
95.091
95.103
95.19
95.197
95.286
95.402
95.418
101.489
101.861
102.093
102.133
102.737
103.648
104.42
104.496
104.553
100

Table A.3 (cont’d)
SNP-ID
Chromosome
8076
7A1
7933
7A1
6562
7A1
4196
7A1
2270
7A1
3562
7A1
5912
7A1
5913
7A1
4910
7A1
4911
7A1
1032
7A1
1031
7A1
7045
7A1
7325
7A1
4483
7A1
7884
7A1
3367
7A1
2725
7A1
6892
7A1
1519
7A1
1515
7A1
1271
7A1
4176
7A1
4137
7A1
4364
7A1
4594
7A1
7184
7A1
4595
7A1
3371
7A1
4175
7A1
2929
7A1
5873
7A1
178
7A1
179
7A1
6576
7A1
5904
7A1
2506
7A2
679
7A2
6331
7A2

cM
108.646
109.019
109.816
119.237
120.024
121.039
122.46
122.545
123.131
123.163
124.005
124.192
126.469
135.132
136.217
138.633
139.012
142.245
142.27
159.258
159.365
159.417
159.463
159.588
159.605
159.99
159.999
160.027
160.245
160.35
160.535
162.706
163.298
164.248
167.979
174.51
0
0.054
0.127
101

Table A.3 (cont’d)
SNP-ID
Chromosome
2735
7A2
2507
7A2
7205
7A2
2820
7A2
4770
7A2
7460
7A2
275
7A2
7161
7A2
1156
7A2
783
7B1
1181
7B1
1526
7B1
2894
7B2
2893
7B2
1241
7B2
4977
7B2
4968
7B2
1437
7B2
6901
7B2
1314
7B2
1436
7B2
5389
7B2
1315
7B2
3915
7B2
4092
7B2
3958
7B2
3572
7B2
3508
7B2
3507
7B2
7232
7B2
7233
7B2
6700
7B2
355
7B2
8469
7B2
4249
7B2
1963
7B2
8387
7B2
5071
7B2
4190
7B2

cM
0.16
0.163
0.893
1.053
1.139
1.24
1.286
1.365
1.421
0
0.18
1.097
0
0.001
1.71
1.896
2.049
2.156
2.164
2.17
2.173
2.183
2.261
6.02
6.022
6.52
8.012
8.709
8.973
9.426
9.457
18.433
27.413
28.088
28.759
29.083
29.201
29.274
29.334
102

Table A.3 (cont’d)
SNP-ID
Chromosome
1420
7B2
3807
7B2
5171
7B2
4191
7B2
832
7B2
7450
7B2
3810
7B2
1964
7B2
3691
7B2
507
7B2
1419
7B2
3655
7B2
8021
7B2
8022
7B2
449
7B2
495
7B2
594
7B2
5706
7B2
4160
7B2
6619
7B2
6618
7B2
3928
7B2
1339
7B2
6608
7B2
3423
7B2
2767
7B2
7403
7B2
4305
7B2
7402
7B2
4306
7B2
5129
7B2
4393
7B2
3387
7B2
2193
7B2
6246
7B2
4749
7B2
4864
7B2
431
7B2
4750
7B2

cM
29.341
29.348
29.361
29.363
29.367
29.387
29.446
29.455
29.479
29.484
29.503
29.611
29.957
29.977
30.626
32.892
33.803
40.889
40.914
41.419
41.448
45.525
45.612
45.65
45.676
48.413
60.335
60.771
60.809
60.954
61.236
73.874
73.877
74.767
74.812
74.836
75
75.031
75.611
103

Table A.3 (cont’d)
SNP-ID
Chromosome
432
7B2
2770
7B2
8448
7B2
1902
7D
1537
7D
604
7D
1257
7D
5557
7D
2772
7D
2208
7D
266
7D
2522
7D
3970
7D
2521
7D
7610
7D
2523
7D
7827
7D
7828
7D

cM
76.107
87.653
101.714
0
7.149
10.988
11.338
11.414
11.558
12.069
33.886
36.929
38.386
38.468
38.565
38.746
40.34
40.498

104

Table A.4 Significant correlations between phenotypic traits within and across environments.
upper
V1
V2
correlation lower 95%
significance
95%
A-MS10
L-MS10
0.2509
0.1011
0.3896
0.0012
B-MS10
L-MS10
0.8549
0.8072
0.8914
<.0001
B-MS10
A-MS10
0.4496
0.3179
0.5643
<.0001
EnzBCMS10
L-MS10
0.4605
0.3301
0.5736
<.0001
EnzBCMS10
B-MS10
0.4485
0.3166
0.5634
<.0001
L-CK11
L-MS10
0.4432
0.3089
0.5602
<.0001
L-CK11
A-MS10
-0.1853
-0.3314
-0.0305
0.0194
L-CK11
B-MS10
0.4409
0.3062
0.5582
<.0001
L-CK11
EnzBCMS10
0.4988
0.3729
0.6067
<.0001
B-CK11
L-MS10
0.5259
0.4032
0.63
<.0001
B-CK11
B-MS10
0.6164
0.5097
0.7045
<.0001
B-CK11
EnzBCMS10
0.5731
0.4591
0.6685
<.0001
B-CK11
L-CK11
0.7814
0.7129
0.8351
<.0001
B-CK11
A-CK11
0.3258
0.1802
0.4574
<.0001
EnzBCCK11
L-MS10
0.2559
0.1044
0.3958
0.0011
EnzBCCK11
B-MS10
0.2454
0.0933
0.3863
0.0018
EnzBCCK11 EnzBCMS10
0.5278
0.4063
0.631
<.0001
EnzBCCK11
L-CK11
0.2909
0.1426
0.4264
0.0002
EnzBCCK11
A-CK11
-0.2556
-0.3947
-0.1051
0.0011
EnzBCCK11
B-CK11
0.299
0.1514
0.4336
0.0001
L-MS11
L-MS10
0.4679
0.3368
0.5812
<.0001
L-MS11
B-MS10
0.5016
0.3752
0.6096
<.0001
L-MS11
EnzBCMS10
0.5326
0.4119
0.635
<.0001
L-MS11
L-CK11
0.6568
0.5589
0.7366
<.0001
L-MS11
B-CK11
0.7339
0.6534
0.798
<.0001
L-MS11
EnzBCCK11
0.3063
0.1591
0.4401
<.0001
A-MS11
L-MS10
-0.1721
-0.3192
-0.0169
0.0301
A-MS11
A-MS10
0.2907
0.1414
0.427
0.0002
A-MS11
EnzBCMS10
-0.2821
-0.4185
-0.1333
0.0003
A-MS11
L-CK11
-0.3609
-0.4883
-0.2184
<.0001
A-MS11
A-CK11
0.2567
0.1062
0.3956
0.001
A-MS11
B-CK11
-0.265
-0.4032
-0.1151
0.0007
A-MS11
EnzBCCK11
-0.2982
-0.4329
-0.1504
0.0001
B-MS11
L-MS10
0.4615
0.3296
0.5758
<.0001
B-MS11
B-MS10
0.5765
0.4623
0.6718
<.0001
B-MS11
EnzBCMS10
0.5176
0.3946
0.6225
<.0001
B-MS11
L-CK11
0.5624
0.4466
0.6597
<.0001

105

Table A.4 (cont’d)
B-MS11
A-CK11
B-MS11
EnzBCCK11
B-MS11
L-MS11
EnzBCMS11
L-MS10
EnzBCMS11
B-MS10
EnzBCMS11 EnzBCMS10
EnzBCMS11
L-CK11
EnzBCMS11
B-CK11
EnzBCMS11 EnzBCCK11
EnzBCMS11
L-MS11
EnzBCMS11
A-MS11
EnzBCMS11
B-MS11
LCK12
L-MS10
LCK12
B-MS10
LCK12
EnzBCMS10
LCK12
L-CK11
LCK12
B-CK11
LCK12
EnzBCCK11
LCK12
L-MS11
LCK12
A-MS11
LCK12
B-MS11
LCK12
EnzBCMS11
ACK12
L-MS10
ACK12
B-MS10
ACK12
A-CK11
ACK12
B-CK11
ACK12
A-MS11
ACK12
B-MS11
ACK12
LCK12
BCK12
L-MS10
BCK12
B-MS10
BCK12
EnzBCMS10
BCK12
L-CK11
BCK12
B-CK11
BCK12
EnzBCCK11
BCK12
L-MS11
BCK12
A-MS11
BCK12
B-MS11
BCK12
EnzBCMS11
BCK12
LCK12

0.2072
0.2381
0.8399
0.3358
0.4258
0.6017
0.5277
0.5577
0.4166
0.4219
-0.3981
0.5386
0.4775
0.5084
0.5198
0.5407
0.6537
0.3748
0.6425
-0.3519
0.5849
0.5663
0.159
0.2227
0.4259
0.2541
0.2126
0.3087
-0.181
0.5622
0.6556
0.6364
0.6395
0.8295
0.3651
0.7074
-0.3134
0.7524
0.6554
0.8558

0.0542
0.0866
0.7875
0.1891
0.2884
0.4922
0.4053
0.4402
0.279
0.285
-0.5214
0.418
0.3481
0.3835
0.3974
0.4212
0.5551
0.2336
0.5417
-0.4804
0.473
0.4504
0.004
0.0699
0.2903
0.1035
0.0599
0.1618
-0.3261
0.4459
0.5571
0.5346
0.538
0.7742
0.223
0.6206
-0.4465
0.6764
0.5565
0.8083
106

0.3506
0.3788
0.8802
0.4678
0.546
0.6925
0.6314
0.6563
0.5374
0.542
-0.2584
0.6405
0.589
0.615
0.624
0.6417
0.7341
0.5004
0.7251
-0.2085
0.6782
0.6635
0.3066
0.3652
0.5447
0.3933
0.3556
0.4423
-0.0275
0.6598
0.7359
0.7199
0.7227
0.8723
0.492
0.7771
-0.1669
0.8125
0.7359
0.8923

0.0084
0.0024
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0446
0.0046
<.0001
0.0011
0.0068
<.0001
0.0212
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001

Table A.4 (cont’d)
EnzBCCK12 EnzBCMS10
EnzBCCK12
A-CK11
EnzBCCK12 EnzBCCK11
EnzBCCK12
LCK12
EnzBCCK12
ACK12
LMS12
L-MS10
LMS12
B-MS10
LMS12
EnzBCMS10
LMS12
L-CK11
LMS12
B-CK11
LMS12
EnzBCCK11
LMS12
L-MS11
LMS12
A-MS11
LMS12
B-MS11
LMS12
EnzBCMS11
LMS12
LCK12
LMS12
BCK12
AMS12
A-CK11
AMS12
EnzBCCK11
AMS12
A-MS11
AMS12
LCK12
AMS12
ACK12
AMS12
EnzBCCK12
BMS12
L-MS10
BMS12
B-MS10
BMS12
EnzBCMS10
BMS12
L-CK11
BMS12
A-CK11
BMS12
B-CK11
BMS12
EnzBCCK11
BMS12
L-MS11
BMS12
A-MS11
BMS12
B-MS11
BMS12
EnzBCMS11
BMS12
LCK12
BMS12
ACK12
BMS12
BCK12
BMS12
LMS12
BMS12
AMS12
EnzBCMS12
B-MS10

0.2131
-0.1883
0.4164
0.2343
-0.3005
0.5291
0.5673
0.5178
0.6059
0.6751
0.243
0.5941
-0.2832
0.6121
0.5591
0.7453
0.7789
0.3611
-0.2153
0.2645
-0.2987
0.5522
-0.3221
0.5498
0.64
0.5179
0.593
0.179
0.7782
0.1822
0.6171
-0.1899
0.7192
0.5642
0.6193
0.3758
0.8312
0.8582
0.2803
0.2341

0.0609
-0.3333
0.2798
0.0831
-0.4346
0.4074
0.4519
0.3952
0.4979
0.5811
0.0917
0.4839
-0.4195
0.5052
0.442
0.6679
0.7099
0.2186
-0.358
0.1145
-0.433
0.4351
-0.4537
0.4314
0.5384
0.3953
0.4826
0.025
0.7089
0.0283
0.5113
-0.3348
0.6352
0.4479
0.5142
0.2353
0.7766
0.8114
0.1318
0.0819
107

0.3556
-0.0347
0.5366
0.375
-0.1535
0.6324
0.664
0.6223
0.6954
0.7513
0.3832
0.6858
-0.1344
0.7004
0.6576
0.8068
0.833
0.4885
-0.0627
0.4027
-0.1515
0.651
-0.1766
0.6495
0.7234
0.6224
0.6848
0.3246
0.8327
0.3276
0.7046
-0.0363
0.7864
0.6617
0.7061
0.501
0.8735
0.8941
0.4165
0.3756

0.0065
0.0167
<.0001
0.0027
0.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0019
<.0001
0.0003
<.0001
<.0001
<.0001
<.0001
<.0001
0.0061
0.0007
0.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0231
<.0001
0.0207
<.0001
0.0158
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0003
0.0029

Table A.4 (cont’d)
EnzBCMS12 EnzBCMS10
EnzBCMS12
L-CK11
EnzBCMS12
B-CK11
EnzBCMS12 EnzBCCK11
EnzBCMS12
L-MS11
EnzBCMS12
A-MS11
EnzBCMS12
B-MS11
EnzBCMS12 EnzBCMS11
EnzBCMS12
LCK12
EnzBCMS12
ACK12
EnzBCMS12
BCK12
EnzBCMS12 EnzBCCK12
EnzBCMS12
LMS12
EnzBCMS12
AMS12

0.2743
0.2752
0.258
0.3315
0.2317
-0.1892
0.1974
0.3262
0.3235
-0.2021
0.263
0.6089
0.2799
-0.4017

0.1254
0.1259
0.1076
0.1864
0.0799
-0.3341
0.044
0.1797
0.1782
-0.3455
0.1134
0.5019
0.1314
-0.5234

108

0.4111
0.4124
0.3968
0.4625
0.373
-0.0356
0.3416
0.4586
0.455
-0.0494
0.4009
0.6976
0.4161
-0.2638

0.0004
0.0004
0.001
<.0001
0.0031
0.0162
0.0121
<.0001
<.0001
0.0099
0.0007
<.0001
0.0003
<.0001

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109

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113

CHAPTER 3 RNA-seq analysis of wheat transcriptome during pre-harvest sprouting
induction

114

Introduction
Pre-harvest sprouting (PHS) is the precocious germination of seed in the head before
harvesting. It can be induced by prolonged wet conditions. Sprouting can significantly
downgrade the storage and processing quality of the seed. Direct annual losses caused by PHS
can reach up to US $1 billion worldwide (Black et al., 2006). Due to this reason, the percentage
of sprouted kernel has become an important criterion in grain grading and has been closely
inspected by processors. Sprouting resistance is a critical quality trait for wheat breeding
programs, especially for white wheat, which is more susceptible to PHS than red wheat.
PHS is a process that can be affected by both internal factors such as dormancy, hormone
balance at different developmental stages, and external environments, such as humidity during
physiological maturity or cold shock during seed development (Barrero et al., 2013; Derera,
1989; DePauw et al., 2012). Given its complexity, PHS resistance has been examined from
different approaches ranging from physiology to biochemistry and genetics (Derera, 1989). For
example, QTL analysis and association mapping have been utilized to identify QTL regions or
significant markers associated with various traits related to PHS, such as α-amylase activity and
falling number test of milled flour, germination rate of detached seeds, or sprouting index of
intact spikes (Imtiaz et al., 2008; Kulwal et al., 2005, 2012; Mares et al., 2005; Zanetti et al.,
2000). These studies indicated a complex genetic structure of PHS. Some QTL were identified in
close linkage with color loci on Chromosome Group 3 while other QTL were scattered in diverse
regions of wheat genome (Flintham et al., 2000). The latter group might offer breeders an
opportunity to pyramid different PHS resistance sources into elite germplasm, especially for
white wheat.

115

Red wheat is categorically more resistant to PHS compared with white wheat (Gale and
Lenton, 1987). Pigments in wheat seed coat are mainly phlobaphene and proanthocynidin
(Debeaujon et al., 2007). Both compounds were found to contribute to seed dormancy by either
serving as a germination inhibitor or interacting with hormone pathways (Miyamoto and Everson,
1958; Winkel-Shirley, 2001). In wheat, red grain color is controlled by three major loci on
chromosome group 3 and is influenced by minor genes (Freed, 1976). Red is dominant to white,
and the degree of redness can be affected by the environment (Wu et al. 1999). The phenotypic
correlation between seed coat color and PHS resistance were reported by Groos et al. (2002), but
either this relationship is caused by a close linkage or a pleiotrophy still need more study.
Previous studies on PHS mainly focused on various phenotypic screening methods, such
as germination rate or sprouting count, to measure the sprouting resistance based on the
downstream events during germination process. However, these methods do not capture the
initial change of PHS, which usually happens in the absence of visual symptoms. Therefore, the
measurement at transcription level would be critical to identify potential candidates involved in
the induction of PHS. Transcriptional events in seeds are arrested after seeds reach physiological
maturity and will resume when environmental conditions are suitable for germination
(Dobrzanska et al., 1973). During dry-down process, a small amount of RNA is carried over
during seed development and may be expressed at a low level (Leubner-Metzger, 2005). During
germination, seed RNA expression is activated in a cascading manner in the order of mRNA,
rRNA, and tRNA (Dobrzanska et al., 1973). Messenger RNAs are quickly mobilized at the
beginning of imbibition to support de novo protein synthesis and prepare for germination when
large scale transcription initiates (Nonogaki et al. 2010). However, little information has been
reported for the expression patterns during PHS process for which the seed started dry-down

116

process after physiological maturity. Therefore, information on expression levels at the initial
stage of PHS may help us to generate a more complete picture of PHS process, and enable us to
compare PHS with germination, for which seed had gone through dry-down. Moreover, a
comparison of the expression pattern between red wheat and white wheat at the initial stage of
PHS can also help us to understand how red and white wheat respond to PHS induction and what
the relationship is between seed coat color and PHS resistance.
Traditionally, microarrays have been used for measuring expression pattern differences at
a global scale. The probe design requires previous knowledge about the transcripts sequences
(Dalma-Weiszhausz, 2006). So far, the most popular microarray platform for wheat, Affymetrix
®

GeneChip (http://www.affymetrix.com/estore/browse/products.jsp?productId=131517), is
based on wheat unigene dataset built in 2004, which is outdated with limited gene representation.
Extra issues related to microarray technology, such as high background noise caused by nonspecific hybridization and data repeatability (Wang et al., 2009), also made it an obsolete option.
Recent years, RNA-seq, the next generation sequencing (NGS) of cDNA sequences, has
become faster, more accurate, and less expensive (Wang et al., 2009). In a single assay, it can
identify novel genes and splice variants and quantify transcriptome-wide expression levels. Thus,
it has been widely used to identify potential candidate genes underlying various biological
processes in plants, such as development (Kyndt et al., 2012; Teoh et al., 2013), disease infection
(Savory et al., 2012), and different types of abiotic stresses (Zhang et al., 2013; Miller et al.
2010). The transcripts assembled from the RNA-seq study can also be a valuable resource for
single nucleotide polymorphism (SNP) mining, which can be applied in breeding programs
(Trick et al., 2012).

117

The transcript abundance in RNA-seq is measured by counting the reads mapped to
specific loci instead of measuring hybridization signal intensity as seen in microarray studies.
The absolute measurement of transcripts makes RNA-seq more accurate. The measuring range is
also more dynamic when compared to microarray technology which measures differential
expression (DE) based on the relative intensity of florescent hybridization signals (Wang et al.,
2009). RNA-seq based DE would be more user-friendly if a well annotated reference genome is
available. However, with the development of de novo assembly methods, DE estimates based on
a de novo assembled transcriptome is now possible (Mutasa-Gottgens et al., 2012). Assemblers
using de novo methods leverage high sequencing depth and uses a de Bruijn graph method to
construct a reference transcriptome without a draft genome (Martin and Wang, 2011). Several
assemblers have been proven efficient in de novo assembly of complex transcriptomes and the
accuracy of downstream analysis using these assemblies has been confirmed (Grabherr et al.,
2011; Robertson et al., 2010). Another alternative for transcriptome assembly is to align reads to
a phylogenetically close model species (Mayer et al., 2011) or a comprehensive set of ESTs
(Trick et al. 2012). This strategy, called Genome Guided assembly (GG), leverages the available
genomic resources to filter out contamination and sequencing artifacts. The GG strategy can
recover full-length transcripts more easily with available genomic sequences, but can also limit
the gene discovery to the available genomic regions. On the other hand, the DV strategy may
have more fragmented sequences but can recover novel transcripts from regions missing in the
genome assembly (Martin and Wang, 2011).
Due to a large genome size and polyploidy nature, hexploid wheat genome has not been
fully sequenced yet. However, its D genome progenitor, Aegilops tauschii, was sequenced with
NGS technology recently (Jia et al., 2013). Its availability enabled us to evaluate the DV and GG

118

assembly strategies for wheat transcriptome for the first time. Then we assumed three scenarios
in terms of different amount of input reads (single sample, combining biological replicates of one
condition/time or all samples) in order to evaluate transcripts recovery rate. The availability of
annotated Aegilops tauschii genome also simplified the transciptome level comparison. Four
comparisons were made between red wheat and white wheat before and after a 48 hr misting
treatment, in order to identify the effect of seed coat color and misting to PHS induction. This
work demonstrated the feasibility of adapting RNA-seq technology in wheat PHS research and
provided candidates for future dissection of gene networks involved in the PHS process.
Materials and methods
Plant materials
Eight F5:7 recombinant inbred lines were selected from a spring wheat population (‘Vida’
× MTHW0471) segregating for seed coat color (Sherman et al., 2008). Individuals were selected
based on their color loci genotype and α-amylase activity. Out of the eight lines, four lines from
the white wheat group had all three color loci as recessive and consistently high α-amylase
activity in comparison with the rest of the population. Four lines from the red wheat group had
all three color loci as dominant with a consistently low α-amylase activity in comparison with the
rest of the population. The eight lines were grown in the greenhouse until physiological maturity
(loss of green color at peduncle). Plants were transferred to the greenhouse with a misting
schedule of 45 minutes misting every 6 hr for a period of 48 hr (see Chapter 2 for misting
treatment details). Plants were kept in pots and held upright to mimic field conditions. Four lines
within each group (red/white) were considered as biological replicates. Seeds were collected
from single plant at 0 hr, and 48 hr under both mist and non-mist treatments (Table 3.1). Seeds

119

Table 3.1 Summary for RNA-seq comparisons
Comparisons
A
B
Wheat group
White (3) vs
White (3) vs
*
Red (4)
Red (2)
by color
Treatment
Non-mist
Mist
Duration
0 hr
48 hr
*

C
Red (2) vs Red (2)

D
White (3) vs
White (3)

Non mist vs Mist Non-mist vs Mist
0 hr vs 48 hr
0 hr vs 48 hr

Number within parenthesis is the biological replicates actually used for differential expression
analysis within that group.

120

were flash frozen in liquid nitrogen, then transferred to a -80°C freezer for long term storage
until RNA extraction.
RNA extraction, library construction and Illumina sequencing
For RNA extraction, a single seed was used as a sample for each recombinant inbred line
to minimize heterogeneity within a sample. Total RNA was extracted using TRIzol LS reagent
(Invitrogen, 10296-010) following manufacture’s instruction. The quality and quantity of RNA
were inspected on Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) before
library preparation. Sample library preparation and Illumina sequencing were performed by the
Research Technology Supporting Facility at Michigan State University (http://rtsf.msu.edu/).
Samples were sequenced in two rounds: first set of samples were run on the Illumina Genome
Analyzer II system (Illumina, San Diego, CA, USA) generating 75 bp paired-end (PE) reads,
while the second set were sequenced on Illumina HiSeq 2000 system, which generated 100 bp
PE reads. Due to the improved throughput, samples from second set usually had 2-3 times the
number of reads compared with samples in the first set.
Quality trimming of short reads
Reads used for transcriptome assembly were trimmed using Cutadapt version 1.1 (Martin,
2011) with the parameters ‘-f fastq -e 0.01 -m 20’, which trimmed reads to a minimum length of
20 bp and a quality cutoff of 20. Then reads were mapped against the Triticeae Repeat Sequence
Database (TREP, release 10, http://wheat.pw.usda.gov/ITMI/Repeats/), ribosomal RNA database
(SILVA, release 109, www.arb-silva.de), wheat chloroplast genome sequence and wheat
mitochondria genome sequence, sequentially using Tophat 1.4.1 with default parameters
(Trapnell et al., 2009). The reads that mapped onto any of these databases were removed. Only
paired reads were used in transcriptome assembly.

121

Reads used for differential expression were trimmed by Trimmomatic-0.27 (Lohse et al.,
2012) with parameters ‘PE -phred 33 ILLUMINACLIP:TruSeq3-SE.fa:2:30:10 LEADING:13
TRAILING:13 SLIDINGWINDOW:4:20 MINLEN:25’, which removed adapters and scanned
the read within a 4-base sliding window, reads were removed when the average quality per base
dropped below 20, and a minimum length of 25.
Short read assembly and quality evaluation
Trinity v. 20130225 was used for de novo assembly (DV) with paired reads in fasta
format (-seqType fa, –kmer_method meryl) and minimum contig size was set to be 201bp (min_contig_length 200). Genome Guided assembly (GG) was done by first aligning trimmed
paired-end and single-end RNA-seq reads using GMAP/20130331 (Wu and Nacu, 2010) to the
Aegilops tauschii draft genome (Jia et al., 2013). The parameters used for alignment are -N 1 -Q
-B 5 -t 2 and Sequence Alignment/Map (SAM) format was used. The alignment results were then
used by Trinity v. 20130225 to generate GG. Both assemblies were performed with single k-mer
(k=21) due to software configurations. CD-HIT-EST v.4.6.1c (Fu et al. 2012) was used to
measure the similarities of two assembly results. Sequence similarity search were conducted by
Blast+ v. 2.2.25 (BLASTn, BLASTp, BLASTx, MegaBLAST) to identify common transcripts in
different datasets and to assign gene functions to de novo assembled transcripts by identifying
their top match in public databases. Databases used included brachypodium, v1.0
(ftp://ftpmips.helmholtzmuenchen.de/plants/brachypodium/v1.0/brachy1.0_wholegenome_unmasked.mfa.gz), barley,
cultivar ‘Bowman’, (ftp://ftpmips.helmholtzmuenchen.de/plants/barley/public_data/sequences/assembly5_WGSBowman34x_renamed_blast
able_carma.zip) and rice, v 7.0

122

(ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomo
lecules/version_7.0/all.dir/all.seq). The parameter used for defining a top match is based on a
BLAST Expect-value (E-value) cutoff of 1×e

-20

, if not specified otherwise.

Comparison of assembly strategy
In this study eight RNA-seq datasets with 75 bp PE reads (Table 3.2) were used to
generate de novo assemblies in three ways: 1. Single sample assembly; 2. Bulk assembly with
merged reads from four biological replicates; 3. Assembly with reads from all eight samples.
Read alignment rate, via Tophat 1.4.1, as well as transcript representation via reciprocal
BLASTn search, were compared between different assemblies to identify the strength and
weakness of each assembly strategy.
Analysis of differential gene expression
Differential expression was conducted between samples collected under four conditions
(Table 3.1). RNA-seq reads were aligned to Aegilops tauschii draft genome (Jia et al. 2013)
using Tophat 2.0.8b, while Cufflinks 2.1.1 were used to estimate transcripts abundance in
fragments per kilo base pair of target transcript length per million reads mapped (FPKM) and for
differential expression analysis with red and white wheat with different treatment conditions
following the protocol described by Trapnell et al. (2012). Parameters of both software were
adjusted (-I 200000 --min-intron-length 30 -b A.tauschii.fasta) based on annotation files provided
by Dr. Chi Zhang, BGI (pers. comm.). Enrichment of gene ontology (GO) category was
determined by following methods described by Miller et al. (2010). P values from Fisher’s exact
tests were calculated with statistical package R/2.14.1 (R Development Core Team, 2008) and
adjusted with the Benjamini and Hochberg (1995) method to avoid multiple-testing. False
discovery rate (FDR) of 1% was used as the threshold for calling differentially expressed genes.
123

Table 3.2 Summary of sample raw reads (million pairs) for comparison of assembly strategy
*

Sample
Reads amount
*

1-1
7.9

1-2
8.0

1-3
16.1

1-4
10.5

1-5
12.3

White wheat: 1-1, 1-2, 1-7, 1-8; Red wheat: 1-3, 1-4, 1-5, 1-6.

124

1-6
8.8

1-7
8.6

1-8
12.5

Results and discussions
Global characteristics of transcript assemblies
An RNA-seq dataset with 24 million pairs of 100 bp PE reads was used to assess the
quality of Trinity assemblies using de novo (DV) and Genome Guided (GG) methods. The
library was prepared from a single seed of a white wheat line at 4 days past physiological
maturity and the seed was undergone a 48 hr mist treatment.
Both assemblies contained multiple splicing forms for each locus, which were indicated
by the number after “seq” of the transcript name (Haas et al. 2013). In total, DV assembly
contained 105,634 transcripts representing 59,628 gene loci. GG assembly contained 68,365
transcripts representing 44,898 gene loci. To get an overview, two assemblies were compared
between each other and against four wheat transcripts collections: (1) UK454, a de novo wheat
cDNA assembly using 454 reads (Brenchley et al., 2012); (2) Unigene_uniq_v63 with the
longest transcripts of wheat Unigene build 63 clusters
(ftp://ftp.ncbi.nih.gov/repository/UniGene/Triticum_aestivum/); (3) CAAS, a de novo wheat
transcriptome assembly using Illumina reads (Duan et al., 2012), and (4) TriflDB, a collection of
wheat full-length cDNA (Mochida et al., 2009). Key statistics were summarized in Table 3.3.
Unigene_uniq_v63 and CAAS had the largest number of transcripts, which may be due to
their comprehensive collection of treatment conditions and tissue types. GG assembly, DV
assembly and UK454 had similar amount of transcripts while TriflDB, which contained only full
length cDNA, had the least amount of transcripts. N50 size, the length sum of all the contigs that
had at least N50 contig size (see below), is strongly correlated with the number of transcripts
(R2=0.951). N50 contig size is defined as the maximum length whereby at least 50% of the total
assembled sequences reside in contigs of at least that length. Therefore, a length distribution of

125

Table 3.3 Summary of Trinity assembled transcripts compared with public databases
Genome
Unigene_
Database
de novo
Guided UK454
uniq_v63 CAAS
Num_of_transcripts 105,634
68,365
90,174
178,464
223,082
^
Num of loci
59,628
44,898
NA
NA
NA
N50 size (Mb)
44.6
28.5
46.1
54.9
67.5
N50 contig (bp)
1,408
1,392
1,335
821
812
*
16,357
10,382
11,803
10,027
Max_len_trans (bp) 16,351
#

Min_len_trans (bp) 201
References
NA

^
*
#

201
NA

201
(Brenchley
et al.,
2012)

NA: Not available;
Maximum length of transcripts in the dataset;
Minimum length of transcripts in the dataset.

126

103
NCBI
Unigene

201
(Duan et
al., 2012)

TriFLDB
6,137
NA
5.4
1,998
8,930
134
(Mochida
et al.,
2009)

transcript size was used to explain the differences in N50 contig size (Figure. 3.1).
Unigene_uniq_v63 and CAAS had the smallest N50 contig size. Alternately, TriflDB with
flcDNA had the largest N50 contig size. GG assembly, DV assembly and UK454 had nearly the
same N50 contig size while their contig length distribution was also not significantly different
from each other (χ2=0.001, p value =1). The contig length distribution also indicated that the
length was normally distributed for the DV and GG assembled transcripts when compared with
other datasets (Figure 3.1). However, there are 28 transcripts in GG and DV assembly that are
above 10,000 bp (upper limit of public database transcript size). These transcripts were searched
against the NCBI refseq_rna database to determine their identity. All the transcripts were hit by
authentic transcripts from related species (Table B.1).
For both GG and DV assemblies, BLASTn search was conducted against wheat unigene
dataset, Unigene_uniq_v63. 93.4% of GG transcripts and 85.1% of DV transcripts were
confirmed to be putative wheat transcripts based on their BLASTn matches. For transcripts that
didn’t return any matches, BLASTx search was conducted against A. tauschii proteome to check
if they matched protein coding sequences from phylogenetically related species including
brachypodium, barley and rice. No new GG transcripts were further identified from these
databases, while 2,589 DV transcripts had matches. For the transcripts that don’t have a BLAST
match, second round BLASTn search were performed against whole genome sequences of
brachypodium, barley, and rice to check if the assembled transcripts were part of non-coding
genomic sequences. About 40-50% of the remaining sequences from both assemblies were
aligned to the barley genome while only 1-2% of the sequences aligned to the brachypodium and
rice genomes. In total, only 2,092, or 3.1%, of GG transcripts and 7,855, or 7.4%, of DV

127

45.0
40.0
35.0
Percentage of Total

30.0
25.0
20.0
15.0
10.0
5.0
0.0
150 to
250

251 to
500

501 to
750

751 to 1001 to 1251 to 1501 to 2001 to 3001 to 4001 to > 5000
1000
1250
1500
2000
3000
4000
5000
Transcripts size bin (bp)

de novo

Genome Guided

Unigene_uniq_v63

UK454

CAAS

Figure 3.1 Length distribution of Trinity assembly compared against public databases.

128

TriFLDB

transcripts had no hit, which might be novel protein coding transcripts that was not covered by
current databases or sequenced poly-adenylated non-coding mRNA.
Concordance analysis of de novo and Genome Guided assemblies
In order to check the transcripts similarity of the two assemblies, reciprocal BLASTn
search were performed to check for concordance between GG and DV assemblies. In total,
62,535, or 91.5% of GG assembled transcripts and 73,001, or 69.1% of DV assembled transcripts
were shown to have a match in their counterpart’s assemblies. As mentioned earlier, Trinity
assembly can have multiple splicing forms for some loci. DV assembly contained 47,512
singleton loci, which indicated only one splicing form were available for that locus, and 12,116
loci with an average of 4.8 splicing forms while GG assembly contained 35,660 singleton loci
and 9,238 loci with an average of 3.5 splicing forms. The imbalance of transcript match
happened again when compared at loci level: 39,000, or 87% of GG loci matched 31,000, or
52.0% of DV loci. This imbalance between the two assemblies may be due to the potential misassembly of DV transcripts, which might cause one DV transcript to match with multiple GG
transcripts at the same time while one GG transcripts generally hit one DV transcripts. By
reviewing the BLAST results, most loci with splicing forms were covered by both assemblies,
while singleton loci were more specific to their own assemblies. For GG assembly, singleton
contain more novel transcripts might be limited due to the reference genome chosen, while DV
singleton transcripts may either be novel transcripts that do not overlap with current sequence
databases or a potential mis-assembly.
In order to measure the number of transcripts that are common in both assemblies,
reciprocal best BLAST hit (RBBH) were defined by the hits that were common in both
counterparts. 24,055 DV assembled loci (77.5% of DV loci that have hit in GG) and 27,358 GG

129

assembled loci (70.1% of GG loci that have hit in DV) were identified which indicating a
roughly 50% concordance rate based on the number of common transcripts shared by both
methods. These transcripts can be considered as a set of transcripts with high confidence.
In summary, the two methods were sharing approximately 50% of the assembled
transcripts. The difference in the number of assembled transcripts can be due to following
reasons. The align-then-assemble mechanism used by GG only assembled reads that mapped to a
genome. Thus, GG assembly’s completeness and quality can be heavily impacted by the
reference genome. As for DV assembly, its relatively large transcriptome size with more
transcripts can be explained by the fact that the de novo algorithm may split a potential long
transcript into two fragmented ones due to the lack of reads connecting the read parts in between.
This is consistent with the observation that 19144, or 18.1% of DV transcripts had hits from two
or more DV loci. The GG assembly can avoid the fragmentation issue using a draft genome.
Moreover, the GG assembled transcripts may have a higher sequence similarity based on the fact
that fewer reads were mapped uniquely in GG than DV. The use of diploid progenitor genome
as reference may collapse the homeologs of common wheat into one transcript in GG assembly
while the DV assembly can be more accurate in recovering homeologs which maybe indicated
by more novel transcripts shown in the DV assembly. However, further investigation is needed
to confirm current assumptions.
CD-HIT-EST was used to measure the redundancy, which was defined as the number of
transcripts got merged into other transcripts, of the GG and DV assemblies by clustering analysis
based on protein sequence similarities (Fu et al. 2012). A range of 80% to 100% identities were
tested. At 100% identity, the DV assembly had 49 transcripts (0.05%) clustered into larger
transcripts while the GG assembly had 436 transcripts (0.64%) clustered (80% identity). These

130

clustering results were closely related to the clustering of transcripts with splicing forms (Table
3.4).
Transcripts representation and assembly completeness
Transcript completeness is defined in Martin and Wang (2011) as percentage of
expressed transcripts in reference databases covered by the assembled transcripts. The higher the
percentage, the more complete the assembled transcriptome is. The ideal scenario would be
100%, which means all the transcripts in reference database were represented in assembled
transcripts. It can be further divided into two levels: the total number of transcripts represented
by assembled transcripts and the length percentage of each transcript covered by assembled
transcripts. For the first level, GG and DV assemblies were compared with three cDNA libraries
deposited in wheat Unigene databases with similar growth stage or treatment (Table 3.5). From
62.2 to 75.4% of the ESTs in the three datasets were covered by both assemblies. For the second
level, the ABA-treated cDNA library was used to calculate the percentage length coverage of the
ESTs covered by Trinity transcripts, which ranged from 20 to100%. Approximately 50% of the
ESTs overlapped with a single Trinity transcript for more than 80% of their lengths (Figure 3.2).
In addition to the single condition representation, an understanding of the percentage of
transcripts expressed under current condition versus total expressed wheat transcripts may help
us to compare current biological process with other biological processes. Two types of similarity
searches were performed to evaluate this percentage. At nucleotide level, MegaBLAST search
against three comprehensive wheat EST databases (Unigene_uniq_v63, UK454, and CAAS)
returned a result ranging from 16 to 24% of transcripts in reference databases were shown in GG
assembly and 20 to 27% were shown in DV assembly (Table 3.6). At protein level, BLASTx
against A. tauschii protein coding sequences was performed (Jia et al. 2013) against both GG and

131

Table 3.4 Clustering results of Trinity assembled transcripts based on CD-HIT-EST at different
sequence similarities
Sequence similarity
80%
85%
90%
95%
99%
100%
Genome Guided
45,112
47,555
18,111
13,884
6,763
49
*
(69.6)
(73.5)
(79.2)
(89.8)
(99.9)
(66.0)
de novo
68,343
72,250
28,918
22,008
10,757
436
(64.7)
(68.4)
(72.6)
(79.7)
(90.1)
(99.4)
*

Number in parenthesis is the percentage of transcripts that cluster when compared with original
assemblies: Genome Guided assembly contains 68,366 transcripts, de novo assembly contains
105,564.

132

Table 3.5 MegaBLAST results of Trinity assembled transcripts vs. Unigene cDNA library
Unigene
*
#
Hit by GG
Hit by DV
Library ID EST
Brief description
5552
2,173 Mature seed treated with 25 mM
1,547 (71.2%)
1,639 (75.4%)
ABA for 12h at 22°C
12171
1,000 Seeds malted for 55 hr at 22°C
633 (63.3%)
663 (66.3%)
8825
2,954 Embryo from mature dormant seed 1,837 (62.2%)
1,934 (65.5%)
*
#

GG: Genome Guided assembly;
DV: de novo assembly.

133

Percentage of cDNA hit by Trinity in cDNA
library (%)

40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
100

90

80

70

60

50

40

30

20

No Hit

Percentage of EST length covered by Trinity transcripts (%)
de novo

genome guided

Figure 3.2 The distribution of length coverage of EST by the Trinity assembled transcripts in cDNA library made from ABA-treated
seed.

134

Table 3.6 Percentage of Trinity assembled transcripts in public wheat EST databases
MegaBLAST
Unigene_uniq_v63
UK454
CAAS
Genome Guided (GG)
de novo (DV)
References

#

28,623 (16.0)
37,803 (21.2)
NCBI

23,060 (23.7)
26,592 (27.3)
(Brenchley et al., 2012)

#

35,480 (15.9)
45,172 (20.2)
(Duan et al., 2012)

The number in parenthesis is the percentage of the transcripts in target databases with
homology.

135

DV assemblies. A total of 24,900 (55.5%) GG assembled loci, represented by 41,245 GG
transcripts, hit 16,688 (38.7%) predicted protein coding genes in Aegilops tauschii proteome,
while 27,682 (40.5%) DV assembled loci, represented by 53,583 DV transcripts, hit 18,008
(41.7%) predicted protein coding genes. This percentage was in line with a recent transcriptome
study conducted in Arabidopsis, which recorded a range of 7,000 to 14,000 genes, or 28-56%
(assume 25,000 genes in Arabidopsis), expressed during germination process (Dekkers et al.
2013)
In summary, in terms of transcript completeness of Trinity assembly, both GG and DV
assemblies showed a relatively good coverage for this biological stage based on the fact that 75%
of the ESTs in cDNA library were represented in both assemblies while approximately 50% of
these EST were covered by Trinity transcripts for 80% or more of their length (Table 3.5; Figure
3.2). However, when it compared with general wheat EST collections, the relatively low rate of
transcript representation from the general databases showed the diversity and comprehensiveness
of wheat transcriptome (Table 3.6). Therefore, in order to build a comprehensive wheat
transcriptome, materials from multiple biological conditions and different tissues at different
growth stages are required.
Transcripts contiguity
Due to the nature of short read sequencing, transcripts contiguity is another performance
metric for transcriptome assembly. The transcript contiguity is defined in Martin and Wang
(2011) as the percentage of transcripts in reference databases covered by a single, longestassembled transcript. Both assemblies were aligned to TriflDB, the full-length cDNA set, using
BLASTn. The distributions of the percentage of full-length cDNA covered by single Trinity
transcripts were summarized in Figure 3.3. In general, more than 40% of the full length cDNA

136

30

Percentage of Hit in the bin (%)

25

20

15

10

5

0
100

90

80

70

60

50

40

30

20

Percentage of flcDNA length covered by single Trinity transcripts (%)
de novo

genome guided

Figure 3.3 The distribution of length coverage of full length cDNA (flcDNA) by the Trinity transcripts.

137

No Hit

were overlapped with both assemblies for more than 80% of their lengths. The DV assembly had
more full length cDNA covered in total and in all categories compared with GG assembly. In
conclusion, the similar quality of the two assemblies and their comparable quality with the public
wheat ESTs made us confident about the de novo algorithms used by Trinity to assemble full
length transcripts in complex transcriptome like wheat. This is in accordance with previous
results on the efficiency of Trinity to recover full-length transcripts and spliced isoforms in other
species (Grabherr et al., 2011).
Evaluation of de novo assembly strategy
In this study, eight RNA-seq datasets with 75 bp PE reads (Table 3.2) was used to
generate de novo assemblies in three ways: 1) single sample assembly, 2) bulk assembly with
merged reads from four biological replicates, and 3) assembly with reads from all eight samples
together. For single sample assembly, there was an exponential increase in numbers of
2

2

transcripts assembled (R =0.81) and N50 size (R =0.80) and a logarithmic increase for N50
2

contig size (R =0.70) (Figure 3.4, (a-c)) as more reads were included. Similar trends were shown
in merged assemblies. When analyzing all three types together, a linear correlation is shown
between input reads number and assembly statistics (Figure 3.4, (d-f)).
Based on the fact that the more reads gave the larger the de novo transcriptome size, we
would like to determine if the size increase was due to the novel transcripts represented in
different samples or just redundancy. The redundancy of each assembly was measured by CDHIT-EST based on 100% sequence similarity. Each single sample assembly yielded less than 0.3%
redundancy, while the merged assembly was around 0.6%. Two samples with similar input read
numbers and assembly statistics were chosen for further comparisons.
138

Trinity assembled contigs

y = 24379e0.0459x , R² = 0.808
140000
120000
100000
80000
60000
40000
20000
0
0

10

20

30

40

Input read pairs (Million)

N50 transcriptome size
(Mb)

(a)
y = 7.7358e0.0562x , R² = 0.802
60
50
40
30
20
10
0
0

10

20

30

40

Input read pairs (Million)

(b)

y = 253.51ln(x) + 527.78 , R² = 0.703
N50 contig size (bp)

1600
1400
1200
1000
800
0

10

20

30

Input read pairs (Million)

40

(c)

Figure 3.4 Correlations between input read pairs and other assembly statistics.
Figures (a), (b), (c) were based on single sample assembly. Figures (d), (e), (f) were based on
single and merged assemblies.

139

Trinity assembled contigs

Figure 3.4 (cont’d)
y = 1694.6x + 33309 , R² = 0.840

200000
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
0

20

40

60

80

100

Input read pairs (Million)

(d)

N50 transcriptome size
(Mb)

y = 0.8533x + 10.356 , R² = 0.850
100
90
80
70
60
50
40
30
20
10
0
0

20

40

60

80

100

Input read pairs (Million)
(e)

N50 Contig size (bp)

y = 223.86ln(x) + 606.27 , R² = 0.762
1600
1400
1200
1000
800
0

20

40

60

80

Input read pairs (Million)

140

100

(f)

Sample 1-1 was from white wheat. Sample 1-6 are derived from red wheat (Table 3.2).
Bowtie2 (Langmead and Salzberg, 2012) was used to map reads back to their own assembly (11-1, 1-6-6), to each other’s assembly (1-1-6, 1-6-1) and to the bulk or total assembly (1-1-rrr, 11-RRR, 1-1-All; 1-6-rrr, 1-6-RRR, 1-6-All) to see if there are any differences in alignment rate
(Table 3.7). For both samples, the alignment rate increased when mapped to the merged
assembly compared with single assemblies. The smaller alignment difference of sample 1-1 and
1-6 when aligned to assembly of 1-6 versus aligned to assembly of 1-1 may also be explained by
this. Moreover, each sample had a higher alignment rate when aligned to its own bulk (1-1-rrr, 16-RRR), compared with their counterpart’s bulk (1-1-RRR, 1-6-rrr), while no differences were
shown when aligned to the total assembly (1-1-All, 1-6-All). In general, the transcriptome
assembled from a single sample were comparable with the merged assembly in terms of
alignment rates but the merged assembly may capture a broader range of transcripts which is
indicated by the increased alignment rate.
Bowtie2 alignment indicated that around 90% of the reads can be aligned to assembly
without significant bias to a specific sample. This alignment may be biased by highly expressed
transcripts while the sample-specific transcripts may have a lower abundance. In order to
understand what kinds of transcripts tend to be specifically aligned to one sample, the single
sample assemblies were searched against the bulk and total assemblies using BLASTn (Table
3.8). When single sample assembly was searched against total assembly (1-All), around 99% of
their transcripts had a hit in the total assembly. However, not all the transcripts had a unique hit
in the total assembly. This might be explained by the different splicing forms or a potential misassembly of transcripts from different samples. For a single sample, the percentage of its BLAST
matches in total assembly was highly correlated to the number of

141

Table 3.7 Bowtie2 alignment for single samples to Trinity assemblies with different assembly
strategies
Sample
Overall
Properly
Sample
Overall
Properly
mapped (%)
paired (%)
mapped (%)
paired (%)
1-1-1
91.3
70.0
1-6-6
92.1
68.0
1-6-1
89.7
68.0
1-1-6
91.3
68.0
*
#
93.6
66.6
93.4
66.2
1-1-rrr
1-1-RRR
1-6-rrr
92.9
65.5
1-6-RRR
93.8
66.0
^
94.4
65.1
1-6-All
94.3
64.1
1-1-All
*
#
^

rrr was assembled using reads from 1-1, 1-2, 1-7, 1-8;
RRR was assembled using reads from 1-3, 1-4, 1-5, 1-6;
All was assembled using reads from all 8 samples.

142

Table 3.8 MegaBLAST results for single sample assembly against bulk (1-rrr, 1-RRR) and total
assembly (1-All)
Targets in
Targets
Had
No. of
Had Hit
Had hit
Targets in 1-RRR
*
^
Sample
1-All
in 1-rrr
hit in
In 1-rrr
(72,163)
Contigs In 1-All#
(98,059)
(60,745) 1-RRR
1-1
99.0
22.1
98.8
36.5
97.8
29.5
36109
1-2
99.4
17.4
99.2
28.2
98.7
23.4
26830
1-3
99.1
26.7
94.4
41.4
98.9
37.0
40316
1-4
99.0
26.8
93.7
41.5
98.9
37.2
39999
1-5
99.4
13.8
98.9
21.8
99.3
18.7
21323
1-6
99.1
24.9
94.8
39.0
99.0
34.4
37595
1-7
99.3
19.7
99.1
31.8
98.3
26.1
29919
1-8
99.2
22.1
99.0
36.5
97.7
29.3
33576
1-rrr
98.7
42.4
NA
NA
89.2
49.6
61036
1-RRR
98.4
52.5
92.7
49.4
NA
NA
72583
*
Sample 1-1 to 1-8 represented single sample data set; 1-rrr was assembled using reads from 1-1,
1-2, 1-7, 1-8; 1-RRR was assembled using reads from 1-3, 1-4, 1-5, 1-6;
#
The number shown below is the percentage of unique transcripts in single sample assembly had
a hit in 1-All, which was assembled from all 8 samples;
^
The number shown below is the percentage of unique targets in 1-All.

143

2

contigs within each single sample assembly (R =0.98). This indicated the effectiveness of total
assembly in capturing sample specific transcripts. The same correlation was shown when the
bulk assemblies (1-rrr, 1-RRR) were searched against the total assembly (1-all).
The low percentage of BLAST matches of a single sample assembly against total
assembly may indicate two scenarios: 1. each single sample had similar hits in the total assembly,
which can be a core set of highly expressed transcripts that expressed in all samples while the
rest transcripts are sample specific, or 2. each single sample hit different transcripts in the total
assembly, which might indicate a very diverse expression patterns across biological replicates.
Based on previous bowtie2 alignment result, the first scenario was more reasonable. To confirm
its validity, all the single samples were searched against each other by BLASTn to measure the
overlap of transcripts between single samples. The results showed a high percentage (60-95%)
of overlap between samples. Moreover, a frequency count of transcripts in total assembly hit by
single samples showed that around 13,500 transcripts were hit by six or more samples, which
represented 50-99% of transcripts shared by all eight samples. Therefore, the low BLAST match
by single sample in total assembly was mainly due to the diversity of samples in the total
assembly, which might be due to biological reason or insufficient sequencing depth.
In conclusion, when sequencing depth is sparse, a single sample assembly will only cover
the highly expressed genes. The merge-and-assemble strategy can be an effective strategy for
de novo assembly to include most transcripts expressed. However, if read depths are sufficient, a
bulk merge of all the biological replicates or individuals having similar phenotypes may gave a
more accurate representation of bulk-specific transcripts by reducing the risk of false-merging of
bulk-specific transcripts, especially in de novo assembly, and reducing the demand for
computation resources.
144

RNA-seq transcriptome profiles and DE calling
The “Tuxedo” pipeline, Tophat and Cufflinks, was used in this study with Aegilops
tauschii draft genome as reference (Jia et al., 2013; Trapnell et al., 2012). Samples were
sequenced on paired end module with 75bp and 100bp run lengths. Genes that had FPKM values
larger than the lower bound of the 95 percentile of FPKM distributions were considered as
expressed. Correlation between input read amounts and genes expressed was shown in both
2

2

datasets: R for 75bp PE reads set equals 0.68 and R for 100 bp PE reads set equals 0.81. To
identify DE transcripts, Cuffdiff2 was used based on the log-fold-change in transcripts
expression against the null hypothesis of no change accounting for read mapping and assignment
uncertainty (Trapnell et al. 2013). Furthermore, when sequencing depth is low, the lowly
expressed transcripts may only showed in one sample but not the other (based on the FPKM
cutoff). Thus the statistical test for DE calling can be biased. Thus, in order to reduce the false
positives, DE was called not only when it met software’s test statistic threshold, but also required
FPKM in both samples to be above the threshold expression level. Simulation results showed
that increasing biological replicates was more effective in terms of gaining statistical power for
calling DE transcripts when compared with increasing library/technical replicates or sequencing
depth. Sequencing depth could be reduced as low as 15% of the original depth without
substantial impacts on false positive or true positive rates (Robles et al., 2012). Hence, the use of
a more conservative DE calling criterion and including multiple biological replicates in
comparison can help reduce the false positive rate and the impact of a potential lack of depth.
Transcriptome profiling during PHS induction stage
Red wheat is known to be more resistant to PHS compared with white wheat (Gale and
Lenton, 1987). Previous QTL mapping results (Chapter 2) identified a QTL for α-amylase
145

activity co-located with the seed coat color loci on chromosome 3A. In current experiment,
individuals from the same QTL mapping population were selected at physiological maturity to
explore the relationship between seed coat color and PHS resistance at the transcriptome level.
PHS resistance was measured in terms of the expression of genes involved in germination
process. The correlation of gene expression pattern between biological replicates for both the
white wheat group (0 hr: 4 replicates; 48 hr: 3 replicates) and the red wheat group (0 hr: 4
replicates, 48 hr: 2 replicates) during the misting process were summarized in Table 3.9. The
highest correlation was found between biological replicates within the same seed color and same
misting treatment. The correlations between red wheat and white wheat at 0 hr were generally
higher than the correlation between them at 48 hr. This is intuitive since the base genetics
without treatment should be expected to have a higher correlation.
Four comparisons were conducted in this study to explore the common and differential
expression patterns of red wheat versus white wheat in response to a 48 hr misting treatment
(Table 3.1). Comparison A was aimed to draw the baseline of the genetic background differences
between red wheat and white wheat before misting. Comparison B was to identify the DE
transcripts between red wheat and white wheat after 48 hr misting treatment. This treatment was
shown to be effective in inducing PHS in greenhouse based on elevated α-amylase activity
(Chapter 2). In theory, there are two major categories of transcripts can be the potential targets in
response to misting treatment: 1. the transcripts showed as non-DE in comparison A, but showed
as DE in comparison B; 2. the DE transcripts shown in both comparisons but with opposite
regulation patterns. There were three unique transcripts in the category 1 while none in the
category 2 (Table B.2).

146

Table 3.9 Sample correlations between biological replicates used for differential expression
Sample
W-0
W-48hr- WW-0
W-0
W-0
WR-0
R-0
^
*
**
hr-2
hr-3
hr-4
48hr-2 48hr-3
hr-1
hr-2
correlation hr-1
1
W-0 hr-1
1.00
W-0 hr-2
0.99
1.00
W-0 hr-3
0.99
1.00
1.00
W-0 hr-4
0.99
1.00
1.00
1.00
W-48hr-1
0.99
0.97
0.97
0.97
1.00
W-48hr-2
0.95
0.92
0.90
0.92
0.97
1.00
W-48hr-3
0.99
0.99
0.98
0.99
0.98
0.96
1.00
R-0 hr-1
0.99
1.00
1.00
1.00
0.97
0.91
0.98
1.00
R-0 hr-2
1.00
0.99
0.99
1.00
0.98
0.93
0.99
1.00
1.00
R-0 hr-3
0.96
0.98
0.97
0.98
0.93
0.90
0.97
0.97
0.97
R-0 hr-4
0.99
1.00
0.99
1.00
0.97
0.92
0.98
1.00
1.00
R-48hr-1
0.99
0.97
0.96
0.97
0.99
0.98
0.99
0.96
0.98
R-48hr-2
0.91
0.86
0.83
0.86
0.94
0.98
0.92
0.84
0.88
*

W(R)-0 hr-1: White (Red) wheat, 0 hr misting, 1-4 is biological replicates;

**

W(R)-48hr-1: White (Red) wheat, 48 hr after misting, 1-3 is biological replicates.

147

R-0
hr-3

1.00
0.97
0.95
0.82

R-0
hr-4

1.00
0.97
0.86

RR48hr-1 48hr-2

1.00
0.95

1.00

On the other hand, the DE transcripts uniquely shown in A, and DE transcripts that were
common in comparisons A and B with same regulation pattern can be related to seed coat color
differences. Transcripts in this category included WRKY and MADS-box transcription factor.
For comparisons C and D, the genetic background effects were minimized since the comparisons
were done among red wheat samples (comparison C) and among white wheat samples
(comparison D). The only difference within each comparison was the misting treatment. There
are seventy-two DE transcripts that were common between comparisons C and D can be
attributed to how wheat respond to misting treatment while DE transcripts specific within each
group can be considered as their own response to misting (seven in red, five in white) (Table
B.2). The low amount of common DE transcripts between comparison C and D may indicate the
different gene networks involved in red and white wheat during their germination process.
Annotation of differentially expressed genes
The DE transcripts were assessed by testing the observed log-fold-change in its
expression against the null hypothesis of no significant change. To adjust for multiple-testing, an
FDR of 0.05 was used as the threshold. The DE transcripts can be further divided into two
categories depended on the gene expression was up or down regulated between the two groups
involved in comparisons. In this study, only about 1% of the transcripts were reported as
differentially expressed at 0 hr when compared between red and white wheat. However, the
comparison of the seed transcripts before and after misting treatment (Comparison C and D)
showed an approximately 10% of the genes differentially expressed (Table B.3), which was
comparable with a similar study conducted in pear, a 2-7% DE transcripts during dormancy
break process (Liu et al., 2012). Another study conducted using microarray to compare
Arabidopsis seeds with and without priming reported a 20% of DE genes (Ligternink et al. 2007).

148

This difference might be due to the stronger effects of priming treatment and the seed sensitivity.
The differentially expressed genes from comparison A and B were further traced back to their
biological functions based on the annotation files of Aegilops tauschii genome (Dr. Chi Zhang,
BGI, pers. comm., Table B.4). A total of 15,229, 73.2%, expressed loci, were assigned gene
ontology (GO) terms. The increased amount of DE transcripts when compared between 0 hr and
48 hr indicated the initiation of biological processes in response to either misting treatment or
germination. Many DE transcripts were involved in iron transport and DNA/protein binding
process, which indicated the activation of major biological events. The absence of candidate
genes involved in hormone balance pathway as differentially expressed genes may be due to the
sampling stage. In comparison A, a transcript (TCONS_00081538) coding for late
embryogenesis abundant (LEA) protein was found to have a differential expression for its
multiple isoforms. Its expression level in white wheat is about 6 times higher than red wheat
under non-mist condition. After misting for 48 hr, the expression level became similar between
red and white wheat. LEA protein is known to be involved in water-binding, to maintain protein
and membrane structure. It accumulates during late embryogenesis and then disappears during
germination (Gallardo et al., 2001). Therefore, the significant reduction of LEA expression in
comparison A may indicate that the germination process started in white wheat but not in red
wheat. In comparison B, genes related to a hydrolase family was shown as differentially
expressed. Hydrolase activity is important in seed germination by weakening the cell wall of
endosperm (Leubner-Metzger et al., 1995). White wheat showed a higher hydrolase activity,
which may indicate a more advanced stage into germination compared with red wheat.
There were also several transcription factors (TFs) found differentially expressed in
comparisons A and B (Table 3.1). The gene of one DE transcript was assigned to the CCCH-

149

Type Zinc Finger Family, which was previously identified to be involved in ABA and drought
stress in maize (Peng et al., 2012). It was up-regulated in red wheat more significantly than white
wheat after misting treatment. Other TFs identified with similar function are basic leucine zipper
(bZIP) and myeloblastosis (MYB). The earlier one was known to be involved in Arabidopsis
abscisic acid signal transduction pathway under drought stress (Uno et al. 2000); while the later
one was identified in rice panicle under drought stress (Gorantla et al. 2007). Another TFs found
were WRKY, which is known as the TF for multiple stress tolerance process and regulated in a
ABA-dependent manner (Zhu et al. 2013). These TFs can be the connecting dots to other
transcription networks involved in germination process.
GO enrichment of differentially expressed genes
GO enrichment of differentially expressed transcripts was conducted following the
method described in Miller et al. (2010). A total of 95 GO terms from four comparisons were
identified as significantly enriched (Fisher’s exact test, adjusted p-value < 0.01; Table 3.10 and
Table 3.11). In general, major categories of molecular function terms enriched in DE genes
including different types of binding proteins, transmembrane proteins and protein kinases were
identified. All of them are major players in signal transduction and processes like germination.
Proteins involved in oxidation reduction process, such as thioredoxin, were more germination
specific and had been reported as key regulators in germination. Several studies had been done to
explore its function in improving PHS resistance in cereals (Guo et al. 2013; Ren et al. 2007). In
this study multiple GO terms were shown to be both up and down regulated, however they are
usually related to different proteins.

150

Table 3.10 Gene Ontology (GO) categories significantly enriched in Up-regulated wheat genes in four comparisons
e
f
GO
Annotation
Comparison
DE loci
Non-DE loci
P-value
FDR
a

GO
GO:0003676
GO:0005515
GO:0005524
GO:0008270
GO:0006468

h

mf
mf
mf
mf

i

bp

GO:0055085

bp

GO:0055114

bp

GO:0016020
GO:0003677
GO:0004672

cc
mf
mf

GO:0004713

mf

GO:0005506
GO:0005515
GO:0005524
GO:0009055

mf
mf
mf
mf

GO:0016705

mf

GO:0020037
GO:0003676
GO:0003677

mf
mf
mf

j

nucleic acid binding
protein binding
ATP binding
zinc ion binding
protein
phosphorylation
transmembrane
transport
oxidation-reduction
process
membrane
DNA binding
protein kinase
activity
protein tyrosine
kinase activity
iron ion binding
protein binding
ATP binding
electron carrier
activity
oxidoreductase
activity
heme binding
nucleic acid binding
DNA binding

b

Non-GO

c

g

Trend

d

GO

Non-GO

A
A
A
A

2
2
2
2

27
27
27
27

1
1
1
1

6374
6374
6374
6374

5.92E-05
5.92E-05
5.92E-05
5.92E-05

1.89E-04
1.89E-04
1.89E-04
1.89E-04

Up
Up
Up
Up

B

4

315

1

12454

1.87E-06

9.10E-06

Up

B

3

316

1

12454

6.06E-05

1.90E-04

Up

B

7

312

1

12454

4.45E-11

5.15E-10

Up

B
B

5
4

314
315

1
1

12454
12454

5.54E-08
1.87E-06

4.46E-07
9.10E-06

Up
Up

B

4

315

1

12454

1.87E-06

9.10E-06

Up

B

4

315

1

12454

1.87E-06

9.10E-06

Up

B
B
B

4
6
5

315
313
314

1
2
1

12454
12453
12454

1.87E-06
6.22E-09
5.54E-08

9.10E-06
5.48E-08
4.46E-07

Up
Up
Up

B

2

317

1

12454

0.001834

3.69E-03

Up

B

2

317

1

12454

0.001834

3.69E-03

Up

B
C
C

4
11
18

315
2911
2904

1
1
1

12454
10053
10053

1.87E-06
7.09E-07
3.19E-11

9.10E-06
4.67E-06
4.03E-10

Up
Up
Up

151

Table 3.10 (cont’d)
GO:0003700 mf

GO:0003824
GO:0004553

mf
mf

GO:0004672

mf

GO:0004713

mf

GO:0005506
GO:0005515
GO:0006355

mf
mf
bp

GO:0006468

bp

GO:0006508
GO:0008152
GO:0055085

bp
bp
bp

GO:0055114

bp

GO:0005622
GO:0005634
GO:0016020
GO:0016021
GO:0003676
GO:0003677

cc
cc
cc
cc
mf
mf

sequence-specific
DNA binding
transcription factor
activity
catalytic activity
hydrolase activity,
hydrolyzing Oglycosyl compounds
protein kinase
activity
protein tyrosine
kinase activity
iron ion binding
protein binding
regulation of
transcription, DNAdependent
protein
phosphorylation
proteolysis
metabolic process
transmembrane
transport
oxidation-reduction
process
intracellular
nucleus
membrane
integral to membrane
nucleic acid binding
DNA binding

C

19

2903

1

10053

7.51E-12

1.16E-10

Up

C

10

2912

1

10053

2.90E-06

1.34E-05

Up

C

6

2916

1

10053

0.000734

1.86E-03

Up

C

7

2915

1

10053

0.000188

5.69E-04

Up

C

6

2916

1

10053

0.000734

1.86E-03

Up

C
C

18
25

2904
2897

1
2

10053
10052

3.19E-11
1.30E-14

4.03E-10
3.44E-13

Up
Up

D

12

1552

137

7122

0.001067

2.63E-03

Up

D

2

1562

177

7082

6.08E-13

1.25E-11

Up

D
D

2
2

1562
1562

64
115

7195
7144

0.000515
6.18E-08

1.38E-03
4.76E-07

Up
Up

D

4

1560

85

7174

0.000378

1.03E-03

Up

D

12

1552

184

7075

2.60E-06

1.23E-05

Up

D
D
D
D
D
D

4
5
5
1
4
6

1560
1559
1559
1563
1560
1558

99
123
171
92
104
176

7160
7136
7088
7167
7155
7083

3.95E-05
3.43E-06
1.43E-09
3.79E-07
1.87E-05
2.95E-09

1.43E-04
1.55E-05
1.47E-08
2.60E-06
7.21E-05
2.87E-08

Up
Up
Up
Up
Up
Up

152

Table 3.10 (cont’d)
GO:0003723 mf
GO:0003824 mf
GO:0004672 mf
GO:0004713

mf

GO:0005515
GO:0005524
GO:0008270
GO:0009055

mf
mf
mf
mf

GO:0016491

mf

GO:0020037

mf

RNA binding
catalytic activity
protein kinase
activity
protein tyrosine
kinase activity
protein binding
ATP binding
zinc ion binding
electron carrier
activity
oxidoreductase
activity
heme binding

D
D

2
6

1562
1558

51
94

7208
7165

0.003649
0.000885

7.26E-03
2.21E-03

Up
Up

D

2

1562

177

7082

6.08E-13

1.25E-11

Up

D

2

1562

146

7113

2.66E-10

2.89E-09

Up

D
D
D

14
6
9

1550
1558
1555

431
337
168

6828
6922
7091

3.46E-22
1.04E-21
7.32E-07

2.13E-20
4.81E-20
4.67E-06

Up
Up
Up

D

4

1560

82

7177

0.000534

1.41E-03

Up

D

5

1559

83

7176

0.00173

3.69E-03

Up

D

4

1560

75

7184

0.001594

3.60E-03

Up

a

GO, Number of significantly up-regulated genes with the GO annotation in question;

b

Non-GO, Number of significantly up-regulated genes without the GO annotation;

c

GO, Number of genes without significant expression change with the GO annotation;

d

Non-GO, Number of genes with no significant expression change that do not have the GO annotation;

e

f

Fisher’s exact test P value. The FDR value is calculated with R package with Benjamini & Hochberg (1995) method;

g

Trend: FPKM value comparison. For A (0 hr), B (48 hr): White < Red; for comparison C (Red), D (White): 0 hr <48 hr;

h

mf, Molecular function;

i

cc, Cellular component;

j

bp, Biological process.

153

Table 3.11 Gene Ontology (GO) categories significantly enriched in Down-regulated wheat genes in four comparisons
e
f
GO
Annotation
Comparison
DE loci
Non-DE loci
P-value
FDR
a

GO
GO:0005515

b

Non-GO

c

GO

Trend

g

d

Non-GO

h

protein binding

A

2

174

546

8050

0.001461

3.34E-03

Down

i

oxidation-reduction
process
iron ion binding
ATP binding
electron carrier
activity
oxidoreductase
activity
heme binding
membrane

B

3

210

1

10348

3.19E-05

1.20E-04

Down

B
B
B

2
2
2

211
211
211

1
1
1

10348
10348
10348

0.001198
0.001198
0.001198

2.77E-03
2.77E-03
2.77E-03

Down
Down
Down

B

2

211

1

10348

0.001198

2.77E-03

Down

B
C

2
3

211
1119

1
1

10348
9132

0.001198
0.004798

2.77E-03
9.34E-03

Down
Down

nucleic acid binding
DNA binding
RNA binding
structural constituent
of ribosome
catalytic activity
protein kinase activity
protein tyrosine kinase
activity
protein binding
ATP binding
zinc ion binding
sequence-specific
DNA binding

C
C
C
C

14
7
12
7

1108
1115
1110
1115

2
1
1
1

9131
9132
9132
9132

3.18E-12
1.34E-06
3.27E-11
1.34E-06

5.35E-11
7.75E-06
4.03E-10
7.75E-06

Down
Down
Down
Down

C
C
C

6
7
6

1116
1115
1116

1
1
2

9132
9132
9131

1.08E-05
1.34E-06
3.90E-05

4.25E-05
7.75E-06
1.43E-04

Down
Down
Down

C
C
C
C

35
5
4
3

1087
1117
1118
1119

4
1
1
1

9129
9132
9132
9132

7.67E-30
8.48E-05
0.000651
0.004798

7.09E-28
2.61E-04
1.70E-03
9.34E-03

Down
Down
Down
Down

mf

GO:0055114

bp

GO:0005506
GO:0005524
GO:0009055

mf
mf
mf

GO:0016705

mf

GO:0020037
GO:0016020

mf
j

GO:0003676
GO:0003677
GO:0003723
GO:0003735

cc
mf
mf
mf
mf

GO:0003824
GO:0004672
GO:0004713

mf
mf
mf

GO:0005515
GO:0005524
GO:0008270
GO:0043565

mf
mf
mf
mf

154

Table 3.11 (cont’d)
GO:0006355 bp

GO:0006468

bp

GO:0006508
GO:0006511

bp
bp

GO:0006886

bp

GO:0006952
GO:0008152
GO:0009058
GO:0016192

bp
bp
bp
bp

GO:0005622
GO:0005634
GO:0016020
GO:0016021
GO:0003676
GO:0003677
GO:0003700

cc
cc
cc
cc
mf
mf
mf

GO:0003723
GO:0003824
GO:0004672

mf
mf
mf

regulation of
transcription, DNAdependent
protein
phosphorylation
proteolysis
ubiquitin-dependent
protein catabolic
process
intracellular protein
transport
defense response
metabolic process
biosynthetic process
vesicle-mediated
transport
intracellular
nucleus
membrane
integral to membrane
nucleic acid binding
DNA binding
sequence-specific
DNA binding
transcription factor
activity
RNA binding
catalytic activity
protein kinase activity

D

7

1556

1

9402

8.28E-06

3.33E-05

Down

D

7

1556

1

9402

8.28E-06

3.33E-05

Down

D
D

5
4

1558
1559

1
1

9402
9402

0.000309
0.001823

8.54E-04
3.69E-03

Down
Down

D

7

1556

1

9402

8.28E-06

3.33E-05

Down

D
D
D
D

6
4
5
4

1557
1559
1558
1559

1
1
1
1

9402
9402
9402
9402

5.11E-05
0.001823
0.000309
0.001823

1.75E-04
3.69E-03
8.54E-04
3.69E-03

Down
Down
Down
Down

D
D
D
D
D
D
D

6
9
4
4
16
9
4

1557
1554
1559
1559
1547
1554
1559

1
1
1
1
2
1
1

9402
9402
9402
9402
9401
9402
9402

5.11E-05
2.08E-07
0.001823
0.001823
3.12E-12
2.08E-07
0.001823

1.75E-04
1.48E-06
3.69E-03
3.69E-03
5.35E-11
1.48E-06
3.69E-03

Down
Down
Down
Down
Down
Down
Down

D
D
D

5
11
7

1558
1552
1556

1
1
1

9402
9402
9402

0.000309
5.00E-09
8.28E-06

8.54E-04
4.63E-08
3.33E-05

Down
Down
Down

155

Table 3.11 (cont’d)
GO:0004713
mf
GO:0005515
GO:0005524
GO:0005525
GO:0008270
GO:0009055
GO:0016787
GO:0030170

mf
mf
mf
mf
mf
mf
mf

GO:0043531

mf

protein tyrosine kinase
activity
protein binding
ATP binding
GTP binding
zinc ion binding
electron carrier activity
hydrolase activity
pyridoxal phosphate
binding
ADP binding

D

7

1556

1

9402

8.28E-06

3.33E-05

Down

D
D
D
D
D
D
D

45
19
5
22
5
4
5

1518
1544
1558
1541
1558
1559
1558

4
1
1
1
1
1
1

9399
9402
9402
9402
9402
9402
9402

5.75E-34
1.32E-15
0.000309
4.26E-18
0.000309
0.001823
0.000309

1.06E-31
4.07E-14
8.54E-04
1.58E-16
8.54E-04
3.69E-03
8.54E-04

Down
Down
Down
Down
Down
Down
Down

D

6

1557

1

9402

5.11E-05

1.75E-04

Down

a

GO, Number of significantly down-regulated genes with the GO annotation in question;

b

Non-GO, Number of significantly down-regulated genes without the GO annotation;

c

GO, Number of genes without significant expression change with the GO annotation;

d

Non-GO, Number of genes with no significant expression change that do not have the GO annotation;

e

Fisher’s exact test P value;

f

The FDR value is calculated with R package with Benjamini & Hochberg (1995) method;

g

Trend: : FPKM value comparison. For A (0 hr), B (48 hr): White > Red; for comparison C (Red), D (White): 0 hr >48 hr;

h

mf, Molecular function;

i

cc, Cellular component;

j

bp, Biological process.

156

For example in comparison A, the two that were up-regulated, one was a Zinc-Finger
CCCH protein and the other was Lysine-specific demethylase 3B, which was responsible for
histone demethylation and epigenetic transcriptional regulation. The BLASTp search showed the
down-regulated protein belongs to thioredoxin like superfamily. Thioredoxin (Trx) was reported
as a catalyst for germination and α-amylase activation (Wong et al. 2002). Several transgenic
studies manipulated Trx gene expression level proved its direct impact to the germination speed
(Guo et al. 2013; Ren et al. 2007). The higher the Trx content, the easier the seed to germinate.
Therefore, the higher Trx in white wheat may partially explain why white wheat is more
susceptible to PHS. In general, comparison A had the smallest percentage (0.6%) of
differentially expressed genes (Table B.3). But the differentially expressed “binding” related loci
indicated the induction of biological processesmight be able to explain the potential
physiological difference between red and white wheat at the starting point.
In comparison B, “membrane” is the only GO term that belonged to “cellular components”
that is up-regulated, which indicate a more drastic change in white wheat than red wheat after 48
hr of misting. By reviewing the BLASTp hit of the loci matching this GO term, multiple hits
were shown to be closely related to germination process. Out of the five loci with the same GO,
three are transporter related proteins. The other two are pentatricopeptide repeat (PPR) and
transparent testa 12 (tt12) proteins. PPR was recently shown to be enriched in Arabidopsis
germination specific gene set and only transiently expressed during germination process (Narsai
et al. 2011), while tt12 is known to be related to seed dormancy (Debeaujon et al. 2000). The
lower the tt12, like here in white wheat, the lower dormancy it would have when compared with
red wheat.

157

When compared between comparison C (red wheat) and D (white wheat), white wheat
had a more comprehensive set of GO terms enriched, which indicated a more advanced stage in
germination due to the various biological pathways activated. Further pathway analysis will be
beneficial to understand the genetic network underlying the current induction process, while the
proteins underlying each enriched GO term will be valuable for mining of PHS resistance gene.
DE transcripts without annotations
About 50% of DE transcripts identified from current study had putative biological
function related to germination process (Figure 3.3). However, the rest of the DE transcripts
were mapped to the nearby regions of annotated genes. These genes were ignored in current
analysis but maybe useful due to several reasons. First, Cufflinks has the ability to assemble
novel transcripts and the novel transcripts might contribute to some part of these mapped DE
transcripts. Secondly, the reference genome used was a draft release that was likely to miss a
number of genes. Thus, our results can even be used to improve the annotation. Lastly, a diploid
progenitor was used as the reference genome. The genetic distance between common wheat
genome and Aegilops tauschii genome might also affect the final mapping results, which is
indicated by a relatively low Tophat mapping rates. This diploid genome can be considered as a
simplified version of wheat genome but the complexity will be much less than the hexaploid
wheat. Therefore, there is also a chance that the transcripts mapped to nearby exonic regions are
putative splicing forms in common wheat but not captured in the current genome annotation.
Similar situations have been reported in model species C. reinhardtii as “intergenic
transcriptional unit” by Miller et al. (2010). Further analysis is needed to understand impact of
using diploid genome as reference for polyploidy study and the origin of the “missing”
transcripts.

158

Conclusions
Wheat as a commodity crop has been bred for fast and uniform germination
unintentionally since its domestication. Seed with low dormancy is more susceptible to problems
such as pre-harvest sprouting. Previous studies focused on evaluating the sprout damage which
could not resolve the molecular events leading to the initiation of the PHS process. Thus, an
understanding of the initial stage through expression profiling may be critical to understand how
plants interact with this environmental change and ultimately help us understand the network
involved in the process. In this study, NGS technology was used to characterize the
transcriptome of red wheat and white wheat during a PHS induction treatment (misting). In order
to build a set of high confidence transcripts, Genome Guided and de novo assembly strategies
were evaluated with other public databases and compared between each other for the transcript
concordance, completeness, and contiguity. The results showed that both assemblies had more
than 90% of transcripts were authentic wheat transcripts while a good concordance was shown
between the two methods for around 50% of their transcripts assembled. When the transcripts
were compared with cDNA libraries with similar growth stage or treatments, more than 60% of
the cDNA were shown in both assembled transcripts. When the two assemblies were compared
with a comprehensive set of wheat ESTs, both assemblies showed a lower representation but
were still in line with previous studies done in seed germination field. In current study, the GG
assembly was done with the A. tauschii genome instead of hexploid wheat genome, which is not
available at current stage. This approximation may affect the quality of GG to some extent and
the non-specific read alignment rate may be one of the phenomenon. Further investigation is
required to understand the use of diploid genome for polyploidy genome assembly.

159

Due to the fast growth of NGS, genotyping by sequencing at population level has been
tested in multiple crops, such as maize (Elshire et al. 2011) and complex polyploid like
switchgrass, but wheat has not been tested. With current technology, it may still not be feasible
to produce sequencing depth that is high enough to identify most lowly expressed transcripts or
rare alleles. Therefore, current study compared the assemblies from three different assembly
strategies using different amount of reads for sequence redundancy and completeness. The
comparison had a direct application for researchers want to do multiplexing to reduce
experimental cost and increase throughput. Our results confirmed the fact that the more input
reads, the more transcripts you will get from a in a de novo assembly. The comparison between
single sample assembly and merged sample assembly suggested that when sequencing depth is
shallow, the assembly can only cover highly expressed transcripts, so it is important to merge
samples in order to get a better coverage of the transcripts expressed during this stage. When
sequencing depth is sufficient, merged sample assembly didn’t significantly increase the
redundancy when compared with a single sample assembly, while there is a potential to falsely
merge fragmented transcripts together. Moreover, with the more reads feeding into the assembler,
the computation power required to assemble it can quickly become the next challenge. The
assembly strategy that merged biological replicates or bulk individuals with similar phenotype
prior to assembly was favored here. This strategy can provide a good balance between keeping
sample-specific transcripts and eliminating unnecessary fusion transcripts while leveraging more
reads to generate a better assembly covering most transcripts expressed in the merged bulk.
In the end, differential expression and GO enrichment of DE transcripts were performed.
Comparison of common or unique transcripts between comparisons confirmed multiple
candidate genes involved in germination process, most of which were identified by previous

160

studies. However, further validation of DE transcripts is needed before making final conclusion.
The series of studies conducted here showed the efficiency of using RNA-seq to identify
potential gene network behind complex phenotypes such as PHS. The set of transcripts
assembled with Trinity can be incorporated to public wheat transcripts while DE transcripts can
be used for mining of candidate genes and gene networks for PHS resistance.

161

APPENDIX

162

Table B.1 BLASTn results of 28 Trinity assembled transcripts longer than 10,000bp
query
Origin subject
%id
q len mismatch
comp46213_c0_seq1
DV
gi514709750 85.67 9768 1322
comp46213_c0_seq2
DV
gi514709750 85.67 9768 1322
comp46213_c0_seq4
DV
gi514709750 85.67 9768 1322
comp46213_c0_seq5
DV
gi514709754 85.89 5585 752
comp46213_c0_seq6
DV
gi514709754 85.89 5585 752
comp46258_c0_seq2
DV
gi357166713 90.33 10847 1019
comp46260_c0_seq1
DV
gi357150792 90.65 10540 920
comp46260_c0_seq2
DV
gi357150792 91.14 8390 714
comp46260_c0_seq3
DV
gi357150792 91.14 8390 714
comp46260_c0_seq4
DV
gi357150792 90.65 10540 920
comp46261_c0_seq1
DV
gi357150722 89.96 11006 1044
comp46261_c0_seq2
DV
gi357150722 90.09 10988 1046
comp46277_c0_seq1
DV
gi357140567 87.13 7071 856
comp46277_c0_seq3
DV
gi357140567 87.13 7071 856
s16289-comp49_c0_seq1
GG
gi357150722 90.09 10988 1049
s49346-comp37_c0_seq2
GG
gi357150792 90.95 10423 899
s49472-comp63_c0_seq2
GG
gi357118204 86.02 5522 615
s50393-comp64_c0_seq1
GG
gi514817801 83.82 6217 948
s55518-comp12_c0_seq11
GG
gi514742213 85.22 9965 1409
s56197-comp29_c0_seq2
GG
gi357116227 92.14 9791 717
s56199-comp29_c0_seq4
GG
gi357116227 92.21 10496 761
s56201-comp29_c0_seq6
GG
gi357116227 92.21 10496 761
s56203-comp29_c0_seq8
GG
gi357116227 92.14 9791 717
s56204-comp29_c0_seq9
GG
gi357116227 92.14 9791 717
s56205-comp29_c0_seq10
GG
gi357116227 92.06 11707 870
s56206-comp29_c0_seq11
GG
gi357116227 92.14 9791 717
s56207-comp29_c0_seq12
GG
gi357116227 92.06 11707 870
s58116-comp170_c0_seq1
GG
gi357140567 87.22 7073 852
163

gap
54
54
54
25
25
22
44
18
18
44
21
14
26
26
13
29
76
37
46
24
28
28
24
24
30
24
30
28

qs
442
624
560
624
560
2
112
1
1
112
238
238
9136
9050
221
197
3286
493
4
883
265
265
1963
1963
135
883
135
9107

qe
10185
10367
10303
6199
6135
10830
10632
8376
8376
10632
11214
11196
16177
16091
11182
10604
8745
6668
9937
10632
10717
10717
11712
11712
11797
10632
11797
16154

ss
9914
9914
9914
8946
8946
739
13316
11197
11197
13316
1
1
8361
8361
1
13216
6934
11299
4476
1914
1211
1211
1914
1914
1
1914
1
8361

se
201
201
201
3389
3389
11573
2823
2823
2823
2823
10974
10974
15406
15406
10974
2823
1508
5100
14407
11692
11692
11692
11692
11692
11692
11692
11692
15406

E
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

bit
10211
10211
10211
5915
5915
14192
13947
11350
11350
13947
14146
14218
7967
7967
14222
13983
5775
5854
10183
13766
14798
14798
13766
13766
16417
13766
16417
8006

Table B.2 Differentially expressed transcripts with BLASTp and KEGG annotations
Transcripts involved in biological process in response to misting
(Criteria: Common DE in Comparison C and D (yes, |log2| >1), same regulation trend)
Gene loci
Protein
Regulation BLASTp, KEGG hit
XLOC_001947 AEGTA27601 Down
Os07g0530600; K09773 hypothetical protein
XLOC_002637 AEGTA29317 Down
Os03g0278200; K12450 UDP-glucose 4,6-dehydratase [EC:4.2.1.76]
XLOC_003835 AEGTA13659 Down
hypothetical protein LOC100261060; K13148 integrator complex subunit 11
XLOC_003945 AEGTA26638 Down
hypothetical protein; K03234 elongation factor EF-2 [EC:3.6.5.3]
XLOC_004384 AEGTA33193 Down
Disease resistance protein RPM1, putative (EC:3.1.3.16); K13457 h
XLOC_004857 AEGTA03473 Down
Os02g0709200; K00817 histidinol-phosphate aminotransferase [EC:2.6.1.9]
XLOC_007331 AEGTA32170 Down
hypothetical protein; K03240 translation initiation factor eIF-2B epsilon subunit
XLOC_008042 AEGTA21328 Down
#N/A
XLOC_009705 AEGTA06240 Down
similar to DNA-J; K09518 DnaJ homolog subfamily B member 12
XLOC_010154 AEGTA04881 Down
#N/A
XLOC_017331 AEGTA27691 Down
hypothetical protein ; K03671 thioredoxin 1
XLOC_023567 AEGTA20698 Down
Os05g0353400; K14293 importin subunit beta-1
XLOC_024149 AEGTA15798 Down
MRPR1; NBS-LRR type disease resistance protein; K13457
XLOC_026354 AEGTA09072 Down
60S ribosomal protein L44; K02929 large subunit ribosomal protein L44e
XLOC_031084 AEGTA06575 Down
#N/A
XLOC_031986 AEGTA15563 Down
hypothetical protein; K01952 phosphoribosylformylglycinamidine synthase
XLOC_034101 AEGTA10192 Down
hypothetical protein LOC100252764; K13457 disease resistance protein RPM1
XLOC_035455 AEGTA30415 Down
hypothetical protein; K14301 nuclear pore complex protein Nup107
XLOC_037451 AEGTA15768 Down
nucleolar GTP-binding protein, putative; K06943 nucleolar GTP-binding protein
XLOC_043418 AEGTA27224 Down
hypothetical protein; K12382 saposin
XLOC_043506 AEGTA22028 Down
Os03g0117200; K11752
XLOC_044135 AEGTA20589 Down
similar to predicted protein; K09667 polypeptide N-acetylglucosaminyltransferase
XLOC_044528 AEGTA08085 Down
protein binding; K12821 pre-mRNA-processing factor 40
XLOC_045838 AEGTA00465 Down
#N/A

164

Table B.2 (cont’d)
Gene loci
XLOC_001266
XLOC_002814
XLOC_003561
XLOC_006066
XLOC_006511
XLOC_006868
XLOC_007857
XLOC_008762
XLOC_009320
XLOC_011302
XLOC_012837
XLOC_013890
XLOC_014787
XLOC_016339
XLOC_017794

Protein
AEGTA21145
AEGTA30004
AEGTA27889
AEGTA16028
AEGTA13579
AEGTA13757
AEGTA11337
AEGTA32945
AEGTA22368
AEGTA24565
AEGTA06614
AEGTA06050
AEGTA29360
AEGTA01779
AEGTA31244

Regulation
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up

XLOC_019735
XLOC_019806
XLOC_021127
XLOC_021706
XLOC_021742
XLOC_022405
XLOC_023590

AEGTA26202
AEGTA24649
AEGTA12305
AEGTA02683
AEGTA26495
AEGTA28595
AEGTA07926

Up
Up
Up
Up
Up
Up
Up

XLOC_024570
XLOC_025915
XLOC_027523

AEGTA43357
AEGTA15810
AEGTA27951

Up
Up
Up

BLASTp, KEGG hit
Os11g0456300; K03094 S-phase kinase-associated protein 1
peroxidase, putative; K00430 peroxidase [EC:1.11.1.7]
hypothetical protein; K14497 protein phosphatase 2C [EC:3.1.3.16]
hypothetical protein LOC100383693; K09843 (+)-abscisic acid 8'-hydroxylase
Glycosyltransferase QUASIMODO1, putative; K13648
EPHX2; epoxide hydrolase 2, cytoplasmic; K08726 soluble epoxide hydrolase
Os11g0456300; K03094 S-phase kinase-associated protein 1
#N/A
Os07g0629000; K09422 myb proto-oncogene protein, plant
hypothetical protein; K01187 alpha-glucosidase [EC:3.2.1.20]
Os02g0672200; K11596 argonaute
#N/A
#N/A
DREB2A; DNA binding / transcription activator/ transcription factor; K09286
ATHB-1 (ARABIDOPSIS HOMEOBOX 1);
DNA binding / protein homodimerization; K09338 homeobox-leucine zipper
hypothetical protein; K00487 trans-cinnamate 4-monooxygenase
hypothetical protein LOC100253371; K03164 DNA topoisomerase II
#N/A
Os04g0494100; K01183 chitinase [EC:3.2.1.14]
#N/A
Os03g0700400; K00454 lipoxygenase [EC:1.13.11.12]
CYP78A8; electron carrier/ heme binding / iron ion binding
/ monooxygenase/ oxygen binding; K00517 [EC:1.14.-.-]
Os09g0400000; K00083 cinnamyl-alcohol dehydrogenase [EC:1.1.1.195]
#N/A
#N/A
165

Table B.2 (cont’d)
Gene loci
XLOC_028765
XLOC_029950
XLOC_030124
XLOC_030694
XLOC_031756
XLOC_031834
XLOC_032234
XLOC_034661
XLOC_034895
XLOC_035449
XLOC_036512
XLOC_037454
XLOC_039379
XLOC_039818

Protein
AEGTA20358
AEGTA03883
AEGTA06401
AEGTA09239
AEGTA04393
AEGTA14386
AEGTA28681
AEGTA14505
AEGTA10808
AEGTA27654
AEGTA12433
AEGTA27979
AEGTA29279
AEGTA24810

Regulation
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up

XLOC_039862
XLOC_040636
XLOC_040851
XLOC_044228
XLOC_044263
XLOC_044525

AEGTA30807
AEGTA14662
AEGTA08681
AEGTA45260
AEGTA17702
AEGTA13133

Up
Up
Up
Up
Up
Up

XLOC_045499
XLOC_045907
XLOC_046079

AEGTA18150
AEGTA31210
AEGTA17028

Up
Up
Up

BLASTp, KEGG hit
hypothetical protein; K09285 AP2-like factor, ANT lineage
hypothetical protein ; K01175 [EC:3.1.-.-]
AG; agamous; K09264 MADS-box transcription factor, plant
GE13493 gene product from transcript GE13493-RA; K14572 midasin
ISU3; ISU3 (ISCU-LIKE 3); structural molecule; K04488 nitrogen fixation protein
hypothetical protein; K11982 E3 ubiquitin-protein ligase RNF115/126
hypothetical protein; K09753 cinnamoyl-CoA reductase [EC:1.2.1.44]
hypothetical protein; K13091 RNA-binding protein 39
APK2A; APK2A (PROTEIN KINASE 2A); ATP binding / kinase; K00924
hypothetical protein LOC100267258; K08081 tropine dehydrogenase
#N/A
Os03g0700400; K00454 lipoxygenase [EC:1.13.11.12]
mybH; SWIRM domain-containing protein; K11865 protein MYSM1
INT3; INT3 (NOSITOL TRANSPORTER 3); carbohydrate transmembrane
transporter/ sugar:hydrogen symporter; K08150 MFS transporter, SP family
#N/A
#N/A
hypothetical protein; K01620 threonine aldolase [EC:4.1.2.5]
hypothetical protein; K02021 putative ABC transport system ATP-binding protein
#N/A
BT2; BT2 (BTB AND TAZ DOMAIN PROTEIN 2); protein binding /
transcription factor/ transcription regulator; K00517 [EC:1.14.-.-]
#N/A
hypothetical protein; K01090 protein phosphatase [EC:3.1.3.16]
#N/A

166

Table B.2 (cont’d)
Transcripts involved in seed color difference
(Criteria: 1-DE (yes, |log2| >1)/2-nonDE)
Gene loci
XLOC_005537
XLOC_024894
XLOC_037700

Protein
AEGTA03848
AEGTA19657
AEGTA27908

Regulation
Up
Down
Down

BLASTp, KEGG hit
CHD3; chromodomain helicase DNA binding protein 3; K11642
WRKY transcription factor, putative; K13424 WRKY transcription factor 33
hypothetical protein; K09264 MADS-box transcription factor, plant

Transcripts involved in red wheat's response to misting
Criteria: DE loci only shown in comparison C not D.
Gene loci
Protein
Regulation BLASTp, KEGG hit
XLOC_019174 AEGTA21896 Up
Ammonium transporter 2 member 1 [Aegilops tauschii]
XLOC_020144 AEGTA10711 Up
LOX3; LOX3; electron carrier/ iron ion binding / lipoxygenase/ metal ion binding /
oxidoreductase, incorporation of two atoms of oxygen; K00454 lipoxygenase
XLOC_022250 AEGTA11275 Up
Os01g0718300; K13415 protein brassinosteroid insensitive 1 [EC:2.7.10.1 2.7.11.1]
XLOC_032818 AEGTA03520 Up
ERF1 (ETHYLENE RESPONSE FACTOR 1); DNA binding / transcription
activator/ transcription factor; K14516 ethylene-responsive transcription factor 1
XLOC_038715 AEGTA19454 Up
hypothetical protein; K13027 tyrosine N-monooxygenase [EC:1.14.13.41]
XLOC_041562 AEGTA19458 Up
hypothetical protein LOC100260645; K01179 endoglucanase [EC:3.2.1.4]
XLOC_040917 AEGTA00880 Down
Serine/threonine-protein kinase PBS1 [Triticum urartu]
Transcripts involved in white wheat's response to misting
(Criteria: DE loci only shown in comparison D not C)
Gene loci
Protein
Regulation BLASTp, KEGG hit
XLOC_010277 AEGTA23816 Up
IDP65; LOC100193874; K02894 large subunit ribosomal protein L23e
XLOC_018723 AEGTA25569 Down
alpha gliadin [Triticum aestivum]
XLOC_019390 AEGTA13087 Down
atp1-1; ATPase subunit 1; K02132 F-type H+-transporting ATPase subunit alpha
XLOC_040814 AEGTA26248 Down
alpha-gliadin [Triticum aestivum]
XLOC_044316 AEGTA29379 Down
alpha-gliadin protein [Aegilops tauschii × Secale cereale]

167

Table B.3 Differential expressed transfrags based on Cuffdiff categorization
Comparisons

A

gene
isoform

*

B

C

D

89 (0.6)

#

375 (1.6)

2046 (9.5)

1690 (9.2)

73 (0.4)

230 (0.6)

1171 (3.6)

838 (3.3)

^

75 (0.5)

301 (1.0)

1633 (6.2)

1161 (5.2)

&

12 (0.3)

15 (0.2)

236 (3.5)

116 (2.1)

promoter

0

0

0

129 (2.5)

splicing

0

0

0

222 (5.8)

tss
cds

*

Comparison A: white vs red, 0h non-mist; B: white vs red, 48h mist; C. White, 0h vs
48h; D. Red, 0h vs 48h.
#
The number in the parenthesis represents the percentage of DE transcripts in the total
number of transcripts that are testable. DE were called with the threshold of FDR=0.05
^
tss: transcription start site
&

coding sequences

168

Table B.4 GO terms that match proteins at differentially expressed loci
GO:0000015; phosphopyruvate hydratase complex; Cellular Component
GO:0000036; acyl carrier activity; Molecular Function
GO:0000045; autophagic vacuole assembly; Biological Process
GO:0000049; tRNA binding; Molecular Function
GO:0000062; fatty-acyl-CoA binding; Molecular Function
GO:0000079; regulation of cyclin-dependent protein kinase activity; Biological Process
GO:0000086; G2/M transition of mitotic cell cycle; Biological Process
GO:0000105; histidine biosynthetic process; Biological Process
GO:0000139; Golgi membrane; Cellular Component
GO:0000145; exocyst; Cellular Component
GO:0000148; 1,3-beta-D-glucan synthase complex; Cellular Component
GO:0000151; ubiquitin ligase complex; Cellular Component
GO:0000154; rRNA modification; Biological Process
GO:0000155; two-component sensor activity; Molecular Function
GO:0000156; two-component response regulator activity; Molecular Function
GO:0000159; protein phosphatase type 2A complex; Cellular Component
GO:0000160; two-component signal transduction system (phosphorelay); Biological Process
GO:0000162; tryptophan biosynthetic process; Biological Process
GO:0000166; nucleotide binding; Molecular Function
GO:0000172; ribonuclease MRP complex; Cellular Component
GO:0000175; 3'-5'-exoribonuclease activity; Molecular Function
GO:0000179; rRNA (adenine-N6,N6-)-dimethyltransferase activity; Molecular Function
GO:0000184; nuclear-transcribed mRNA catabolic process, nonsense-mediated decay;
Biological Process
GO:0000213; tRNA-intron endonuclease activity; Molecular Function
GO:0000226; microtubule cytoskeleton organization; Biological Process
GO:0000228; nuclear chromosome; Cellular Component
GO:0000247; C-8 sterol isomerase activity; Molecular Function
GO:0000272; polysaccharide catabolic process; Biological Process
GO:0000275; mitochondrial proton-transporting ATP synthase complex, catalytic core F(1);
Cellular Component
GO:0000276; mitochondrial proton-transporting ATP synthase complex, coupling factor F(o);
Cellular Component
GO:0000287; magnesium ion binding; Molecular Function
GO:0000398; nuclear mRNA splicing, via spliceosome; Biological Process
GO:0000439; core TFIIH complex; Cellular Component
GO:0000502; proteasome complex; Cellular Component
GO:0000724; double-strand break repair via homologous recombination; Biological Process

169

Table B.4 (cont’d)
GO:0000774; adenyl-nucleotide exchange factor activity; Molecular Function
GO:0000775; chromosome, centromeric region; Cellular Component
GO:0000785; chromatin; Cellular Component
GO:0000786; nucleosome; Cellular Component
GO:0000808; origin recognition complex; Cellular Component
GO:0000922; spindle pole; Cellular Component
GO:0000976; transcription regulatory region sequence-specific DNA binding; Molecular
Function
GO:0001104; RNA polymerase II transcription cofactor activity; Molecular Function
GO:0001510; RNA methylation; Biological Process
GO:0001522; pseudouridine synthesis; Biological Process
GO:0001671; ATPase activator activity; Molecular Function
GO:0001682; tRNA 5'-leader removal; Biological Process
GO:0001882; nucleoside binding; Molecular Function
GO:0003333; amino acid transmembrane transport; Biological Process
GO:0003676; nucleic acid binding; Molecular Function
GO:0003677; DNA binding; Molecular Function
GO:0003678; DNA helicase activity; Molecular Function
GO:0003684; damaged DNA binding; Molecular Function
GO:0003689; DNA clamp loader activity; Molecular Function
GO:0003697; single-stranded DNA binding; Molecular Function
GO:0003700; sequence-specific DNA binding transcription factor activity; Molecular Function
GO:0003712; transcription cofactor activity; Molecular Function
GO:0003713; transcription coactivator activity; Molecular Function
GO:0003714; transcription corepressor activity; Molecular Function
GO:0003723; RNA binding; Molecular Function
GO:0003725; double-stranded RNA binding; Molecular Function
GO:0003735; structural constituent of ribosome; Molecular Function
GO:0003743; translation initiation factor activity; Molecular Function
GO:0003746; translation elongation factor activity; Molecular Function
GO:0003747; translation release factor activity; Molecular Function
GO:0003755; peptidyl-prolyl cis-trans isomerase activity; Molecular Function
GO:0003774; motor activity; Molecular Function
GO:0003777; microtubule motor activity; Molecular Function
GO:0003779; actin binding; Molecular Function
GO:0003824; catalytic activity; Molecular Function
GO:0003827; alpha-1,3-mannosylglycoprotein 2-beta-N-acetylglucosaminyltransferase
activity; Molecular Function
GO:0003830; beta-1,4-mannosylglycoprotein 4-beta-N-acetylglucosaminyltransferase activity;
Molecular Function

170

Table B.4 (cont’d)
GO:0003840; gamma-glutamyltransferase activity; Molecular Function
GO:0003843; 1,3-beta-D-glucan synthase activity; Molecular Function
GO:0003852; 2-isopropylmalate synthase activity; Molecular Function
GO:0003854; 3-beta-hydroxy-delta5-steroid dehydrogenase activity; Molecular Function
GO:0003857; 3-hydroxyacyl-CoA dehydrogenase activity; Molecular Function
GO:0003868; 4-hydroxyphenylpyruvate dioxygenase activity; Molecular Function
GO:0003871; 5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase
activity; Molecular Function
GO:0003872; 6-phosphofructokinase activity; Molecular Function
GO:0003879; ATP phosphoribosyltransferase activity; Molecular Function
GO:0003883; CTP synthase activity; Molecular Function
GO:0003885; D-arabinono-1,4-lactone oxidase activity; Molecular Function
GO:0003887; DNA-directed DNA polymerase activity; Molecular Function
GO:0003896; DNA primase activity; Molecular Function
GO:0003899; DNA-directed RNA polymerase activity; Molecular Function
GO:0003905; alkylbase DNA N-glycosylase activity; Molecular Function
GO:0003910; DNA ligase (ATP) activity; Molecular Function
GO:0003913; DNA photolyase activity; Molecular Function
GO:0003916; DNA topoisomerase activity; Molecular Function
GO:0003917; DNA topoisomerase type I activity; Molecular Function
GO:0003918; DNA topoisomerase (ATP-hydrolyzing) activity; Molecular Function
GO:0003919; FMN adenylyltransferase activity; Molecular Function
GO:0003924; GTPase activity; Molecular Function
GO:0003935; GTP cyclohydrolase II activity; Molecular Function
GO:0003950; NAD+ ADP-ribosyltransferase activity; Molecular Function
GO:0003951; NAD+ kinase activity; Molecular Function
GO:0003964; RNA-directed DNA polymerase activity; Molecular Function
GO:0003968; RNA-directed RNA polymerase activity; Molecular Function
GO:0003978; UDP-glucose 4-epimerase activity; Molecular Function
GO:0003980; UDP-glucose:glycoprotein glucosyltransferase activity; Molecular Function
GO:0003984; acetolactate synthase activity; Molecular Function
GO:0003987; acetate-CoA ligase activity; Molecular Function
GO:0003989; acetyl-CoA carboxylase activity; Molecular Function
GO:0003993; acid phosphatase activity; Molecular Function
GO:0003995; acyl-CoA dehydrogenase activity; Molecular Function
GO:0003997; acyl-CoA oxidase activity; Molecular Function
GO:0004003; ATP-dependent DNA helicase activity; Molecular Function
GO:0004013; adenosylhomocysteinase activity; Molecular Function
GO:0004014; adenosylmethionine decarboxylase activity; Molecular Function

171

Table B.4 (cont’d)
GO:0004017; adenylate kinase activity; Molecular Function
GO:0004019; adenylosuccinate synthase activity; Molecular Function
GO:0004030; aldehyde dehydrogenase [NAD(P)+] activity; Molecular Function
GO:0004044; amidophosphoribosyltransferase activity; Molecular Function
GO:0004045; aminoacyl-tRNA hydrolase activity; Molecular Function
GO:0004055; argininosuccinate synthase activity; Molecular Function
GO:0004066; asparagine synthase (glutamine-hydrolyzing) activity; Molecular Function
GO:0004070; aspartate carbamoyltransferase activity; Molecular Function
GO:0004075; biotin carboxylase activity; Molecular Function
GO:0004089; carbonate dehydratase activity; Molecular Function
GO:0004096; catalase activity; Molecular Function
GO:0004097; catechol oxidase activity; Molecular Function
GO:0004106; chorismate mutase activity; Molecular Function
GO:0004109; coproporphyrinogen oxidase activity; Molecular Function
GO:0004112; cyclic-nucleotide phosphodiesterase activity; Molecular Function
GO:0004121; cystathionine beta-lyase activity; Molecular Function
GO:0004129; cytochrome-c oxidase activity; Molecular Function
GO:0004134; 4-alpha-glucanotransferase activity; Molecular Function
GO:0004143; diacylglycerol kinase activity; Molecular Function
GO:0004144; diacylglycerol O-acyltransferase activity; Molecular Function
GO:0004148; dihydrolipoyl dehydrogenase activity; Molecular Function
GO:0004161; dimethylallyltranstransferase activity; Molecular Function
GO:0004163; diphosphomevalonate decarboxylase activity; Molecular Function
GO:0004170; dUTP diphosphatase activity; Molecular Function
GO:0004175; endopeptidase activity; Molecular Function
GO:0004176; ATP-dependent peptidase activity; Molecular Function
GO:0004177; aminopeptidase activity; Molecular Function
GO:0004181; metallocarboxypeptidase activity; Molecular Function
GO:0004185; serine-type carboxypeptidase activity; Molecular Function
GO:0004190; aspartic-type endopeptidase activity; Molecular Function
GO:0004197; cysteine-type endopeptidase activity; Molecular Function
GO:0004198; calcium-dependent cysteine-type endopeptidase activity; Molecular Function
GO:0004221; ubiquitin thiolesterase activity; Molecular Function
GO:0004222; metalloendopeptidase activity; Molecular Function
GO:0004252; serine-type endopeptidase activity; Molecular Function
GO:0004298; threonine-type endopeptidase activity; Molecular Function
GO:0004315; 3-oxoacyl-[acyl-carrier-protein] synthase activity; Molecular Function
GO:0004325; ferrochelatase activity; Molecular Function
GO:0004326; tetrahydrofolylpolyglutamate synthase activity; Molecular Function

172

Table B.4 (cont’d)
GO:0004329; formate-tetrahydrofolate ligase activity; Molecular Function
GO:0004332; fructose-bisphosphate aldolase activity; Molecular Function
GO:0004345; glucose-6-phosphate dehydrogenase activity; Molecular Function
GO:0004348; glucosylceramidase activity; Molecular Function
GO:0004351; glutamate decarboxylase activity; Molecular Function
GO:0004356; glutamate-ammonia ligase activity; Molecular Function
GO:0004357; glutamate-cysteine ligase activity; Molecular Function
GO:0004358; glutamate N-acetyltransferase activity; Molecular Function
GO:0004363; glutathione synthase activity; Molecular Function
GO:0004367; glycerol-3-phosphate dehydrogenase [NAD+] activity; Molecular Function
GO:0004368; glycerol-3-phosphate dehydrogenase activity; Molecular Function
GO:0004370; glycerol kinase activity; Molecular Function
GO:0004372; glycine hydroxymethyltransferase activity; Molecular Function
GO:0004386; helicase activity; Molecular Function
GO:0004392; heme oxygenase (decyclizing) activity; Molecular Function
GO:0004399; histidinol dehydrogenase activity; Molecular Function
GO:0004402; histone acetyltransferase activity; Molecular Function
GO:0004407; histone deacetylase activity; Molecular Function
GO:0004420; hydroxymethylglutaryl-CoA reductase (NADPH) activity; Molecular Function
GO:0004421; hydroxymethylglutaryl-CoA synthase activity; Molecular Function
GO:0004425; indole-3-glycerol-phosphate synthase activity; Molecular Function
GO:0004427; inorganic diphosphatase activity; Molecular Function
GO:0004435; phosphatidylinositol phospholipase C activity; Molecular Function
GO:0004449; isocitrate dehydrogenase (NAD+) activity; Molecular Function
GO:0004450; isocitrate dehydrogenase (NADP+) activity; Molecular Function
GO:0004452; isopentenyl-diphosphate delta-isomerase activity; Molecular Function
GO:0004459; L-lactate dehydrogenase activity; Molecular Function
GO:0004470; malic enzyme activity; Molecular Function
GO:0004476; mannose-6-phosphate isomerase activity; Molecular Function
GO:0004478; methionine adenosyltransferase activity; Molecular Function
GO:0004497; monooxygenase activity; Molecular Function
GO:0004499; N,N-dimethylaniline monooxygenase activity; Molecular Function
GO:0004506; squalene monooxygenase activity; Molecular Function
GO:0004512; inositol-3-phosphate synthase activity; Molecular Function
GO:0004514; nicotinate-nucleotide diphosphorylase (carboxylating) activity; Molecular
Function
GO:0004516; nicotinate phosphoribosyltransferase activity; Molecular Function
GO:0004518; nuclease activity; Molecular Function
GO:0004519; endonuclease activity; Molecular Function

173

Table B.4 (cont’d)
GO:0004523; ribonuclease H activity; Molecular Function
GO:0004525; ribonuclease III activity; Molecular Function
GO:0004526; ribonuclease P activity; Molecular Function
GO:0004527; exonuclease activity; Molecular Function
GO:0004540; ribonuclease activity; Molecular Function
GO:0004550; nucleoside diphosphate kinase activity; Molecular Function
GO:0004553; hydrolase activity, hydrolyzing O-glycosyl compounds; Molecular Function
GO:0004556; alpha-amylase activity; Molecular Function
GO:0004560; alpha-L-fucosidase activity; Molecular Function
GO:0004563; beta-N-acetylhexosaminidase activity; Molecular Function
GO:0004568; chitinase activity; Molecular Function
GO:0004571; mannosyl-oligosaccharide 1,2-alpha-mannosidase activity; Molecular Function
GO:0004573; mannosyl-oligosaccharide glucosidase activity; Molecular Function
GO:0004576; oligosaccharyl transferase activity; Molecular Function
GO:0004579; dolichyl-diphosphooligosaccharide-protein glycotransferase activity; Molecular
Function
GO:0004594; pantothenate kinase activity; Molecular Function
GO:0004601; peroxidase activity; Molecular Function
GO:0004609; phosphatidylserine decarboxylase activity; Molecular Function
GO:0004610; phosphoacetylglucosamine mutase activity; Molecular Function
GO:0004612; phosphoenolpyruvate carboxykinase (ATP) activity; Molecular Function
GO:0004615; phosphomannomutase activity; Molecular Function
GO:0004616; phosphogluconate dehydrogenase (decarboxylating) activity; Molecular
Function
GO:0004617; phosphoglycerate dehydrogenase activity; Molecular Function
GO:0004618; phosphoglycerate kinase activity; Molecular Function
GO:0004619; phosphoglycerate mutase activity; Molecular Function
GO:0004629; phospholipase C activity; Molecular Function
GO:0004634; phosphopyruvate hydratase activity; Molecular Function
GO:0004640; phosphoribosylanthranilate isomerase activity; Molecular Function
GO:0004645; phosphorylase activity; Molecular Function
GO:0004649; poly(ADP-ribose) glycohydrolase activity; Molecular Function
GO:0004650; polygalacturonase activity; Molecular Function
GO:0004652; polynucleotide adenylyltransferase activity; Molecular Function
GO:0004654; polyribonucleotide nucleotidyltransferase activity; Molecular Function
GO:0004655; porphobilinogen synthase activity; Molecular Function
GO:0004657; proline dehydrogenase activity; Molecular Function
GO:0004659; prenyltransferase activity; Molecular Function
GO:0004664; prephenate dehydratase activity; Molecular Function

174

Table B.4 (cont’d)
GO:0004665; prephenate dehydrogenase (NADP+) activity; Molecular Function
GO:0004672; protein kinase activity; Molecular Function
GO:0004673; protein histidine kinase activity; Molecular Function
GO:0004674; protein serine/threonine kinase activity; Molecular Function
GO:0004707; MAP kinase activity; Molecular Function
GO:0004713; protein tyrosine kinase activity; Molecular Function
GO:0004719; protein-L-isoaspartate (D-aspartate) O-methyltransferase activity; Molecular
Function
GO:0004721; phosphoprotein phosphatase activity; Molecular Function
GO:0004725; protein tyrosine phosphatase activity; Molecular Function
GO:0004735; pyrroline-5-carboxylate reductase activity; Molecular Function
GO:0004743; pyruvate kinase activity; Molecular Function
GO:0004746; riboflavin synthase activity; Molecular Function
GO:0004747; ribokinase activity; Molecular Function
GO:0004748; ribonucleoside-diphosphate reductase activity; Molecular Function
GO:0004750; ribulose-phosphate 3-epimerase activity; Molecular Function
GO:0004751; ribose-5-phosphate isomerase activity; Molecular Function
GO:0004765; shikimate kinase activity; Molecular Function
GO:0004781; sulfate adenylyltransferase (ATP) activity; Molecular Function
GO:0004784; superoxide dismutase activity; Molecular Function
GO:0004788; thiamine diphosphokinase activity; Molecular Function
GO:0004789; thiamine-phosphate diphosphorylase activity; Molecular Function
GO:0004794; L-threonine ammonia-lyase activity; Molecular Function
GO:0004797; thymidine kinase activity; Molecular Function
GO:0004806; triglyceride lipase activity; Molecular Function
GO:0004807; triose-phosphate isomerase activity; Molecular Function
GO:0004809; tRNA (guanine-N2-)-methyltransferase activity; Molecular Function
GO:0004812; aminoacyl-tRNA ligase activity; Molecular Function
GO:0004813; alanine-tRNA ligase activity; Molecular Function
GO:0004815; aspartate-tRNA ligase activity; Molecular Function
GO:0004816; asparagine-tRNA ligase activity; Molecular Function
GO:0004819; glutamine-tRNA ligase activity; Molecular Function
GO:0004820; glycine-tRNA ligase activity; Molecular Function
GO:0004821; histidine-tRNA ligase activity; Molecular Function
GO:0004822; isoleucine-tRNA ligase activity; Molecular Function
GO:0004824; lysine-tRNA ligase activity; Molecular Function
GO:0004825; methionine-tRNA ligase activity; Molecular Function
GO:0004827; proline-tRNA ligase activity; Molecular Function
GO:0004828; serine-tRNA ligase activity; Molecular Function
GO:0004829; threonine-tRNA ligase activity; Molecular Function
175

Table B.4 (cont’d)
GO:0004830; tryptophan-tRNA ligase activity; Molecular Function
GO:0004831; tyrosine-tRNA ligase activity; Molecular Function
GO:0004832; valine-tRNA ligase activity; Molecular Function
GO:0004834; tryptophan synthase activity; Molecular Function
GO:0004835; tubulin-tyrosine ligase activity; Molecular Function
GO:0004842; ubiquitin-protein ligase activity; Molecular Function
GO:0004852; uroporphyrinogen-III synthase activity; Molecular Function
GO:0004857; enzyme inhibitor activity; Molecular Function
GO:0004861; cyclin-dependent protein kinase inhibitor activity; Molecular Function
GO:0004864; protein phosphatase inhibitor activity; Molecular Function
GO:0004866; endopeptidase inhibitor activity; Molecular Function
GO:0004867; serine-type endopeptidase inhibitor activity; Molecular Function
GO:0004869; cysteine-type endopeptidase inhibitor activity; Molecular Function
GO:0004871; signal transducer activity; Molecular Function
GO:0004930; G-protein coupled receptor activity; Molecular Function
GO:0004965; G-protein coupled GABA receptor activity; Molecular Function
GO:0004970; ionotropic glutamate receptor activity; Molecular Function
GO:0005053; peroxisome matrix targeting signal-2 binding; Molecular Function
GO:0005083; small GTPase regulator activity; Molecular Function
GO:0005085; guanyl-nucleotide exchange factor activity; Molecular Function
GO:0005086; ARF guanyl-nucleotide exchange factor activity; Molecular Function
GO:0005089; Rho guanyl-nucleotide exchange factor activity; Molecular Function
GO:0005093; Rab GDP-dissociation inhibitor activity; Molecular Function
GO:0005094; Rho GDP-dissociation inhibitor activity; Molecular Function
GO:0005097; Rab GTPase activator activity; Molecular Function
GO:0005198; structural molecule activity; Molecular Function
GO:0005215; transporter activity; Molecular Function
GO:0005216; ion channel activity; Molecular Function
GO:0005234; extracellular-glutamate-gated ion channel activity; Molecular Function
GO:0005247; voltage-gated chloride channel activity; Molecular Function
GO:0005249; voltage-gated potassium channel activity; Molecular Function
GO:0005267; potassium channel activity; Molecular Function
GO:0005315; inorganic phosphate transmembrane transporter activity; Molecular Function
GO:0005337; nucleoside transmembrane transporter activity; Molecular Function
GO:0005351; sugar:hydrogen symporter activity; Molecular Function
GO:0005375; copper ion transmembrane transporter activity; Molecular Function
GO:0005381; iron ion transmembrane transporter activity; Molecular Function
GO:0005452; inorganic anion exchanger activity; Molecular Function
GO:0005471; ATP:ADP antiporter activity; Molecular Function

176

Table B.4 (cont’d)
GO:0005488; binding; Molecular Function
GO:0005506; iron ion binding; Molecular Function
GO:0005507; copper ion binding; Molecular Function
GO:0005509; calcium ion binding; Molecular Function
GO:0005515; protein binding; Molecular Function
GO:0005516; calmodulin binding; Molecular Function
GO:0005524; ATP binding; Molecular Function
GO:0005525; GTP binding; Molecular Function
GO:0005529; sugar binding; Molecular Function
GO:0005542; folic acid binding; Molecular Function
GO:0005543; phospholipid binding; Molecular Function
GO:0005544; calcium-dependent phospholipid binding; Molecular Function
GO:0005576; extracellular region; Cellular Component
GO:0005618; cell wall; Cellular Component
GO:0005622; intracellular; Cellular Component
GO:0005634; nucleus; Cellular Component
GO:0005643; nuclear pore; Cellular Component
GO:0005663; DNA replication factor C complex; Cellular Component
GO:0005664; nuclear origin of replication recognition complex; Cellular Component
GO:0005665; DNA-directed RNA polymerase II, core complex; Cellular Component
GO:0005666; DNA-directed RNA polymerase III complex; Cellular Component
GO:0005667; transcription factor complex; Cellular Component
GO:0005669; transcription factor TFIID complex; Cellular Component
GO:0005672; transcription factor TFIIA complex; Cellular Component
GO:0005674; transcription factor TFIIF complex; Cellular Component
GO:0005675; holo TFIIH complex; Cellular Component
GO:0005680; anaphase-promoting complex; Cellular Component
GO:0005681; spliceosomal complex; Cellular Component
GO:0005694; chromosome; Cellular Component
GO:0005730; nucleolus; Cellular Component
GO:0005737; cytoplasm; Cellular Component
GO:0005739; mitochondrion; Cellular Component
GO:0005740; mitochondrial envelope; Cellular Component
GO:0005741; mitochondrial outer membrane; Cellular Component
GO:0005742; mitochondrial outer membrane translocase complex; Cellular Component
GO:0005743; mitochondrial inner membrane; Cellular Component
GO:0005744; mitochondrial inner membrane presequence translocase complex; Cellular
Component
GO:0005746; mitochondrial respiratory chain; Cellular Component
GO:0005759; mitochondrial matrix; Cellular Component
177

Table B.4 (cont’d)
GO:0005777; peroxisome; Cellular Component
GO:0005778; peroxisomal membrane; Cellular Component
GO:0005779; integral to peroxisomal membrane; Cellular Component
GO:0005783; endoplasmic reticulum; Cellular Component
GO:0005787; signal peptidase complex; Cellular Component
GO:0005789; endoplasmic reticulum membrane; Cellular Component
GO:0005794; Golgi apparatus; Cellular Component
GO:0005795; Golgi stack; Cellular Component
GO:0005798; Golgi-associated vesicle; Cellular Component
GO:0005801; cis-Golgi network; Cellular Component
GO:0005815; microtubule organizing center; Cellular Component
GO:0005838; proteasome regulatory particle; Cellular Component
GO:0005839; proteasome core complex; Cellular Component
GO:0005840; ribosome; Cellular Component
GO:0005853; eukaryotic translation elongation factor 1 complex; Cellular Component
GO:0005856; cytoskeleton; Cellular Component
GO:0005874; microtubule; Cellular Component
GO:0005875; microtubule associated complex; Cellular Component
GO:0005938; cell cortex; Cellular Component
GO:0005942; phosphatidylinositol 3-kinase complex; Cellular Component
GO:0005945; 6-phosphofructokinase complex; Cellular Component
GO:0005956; protein kinase CK2 complex; Cellular Component
GO:0005971; ribonucleoside-diphosphate reductase complex; Cellular Component
GO:0005975; carbohydrate metabolic process; Biological Process
GO:0005985; sucrose metabolic process; Biological Process
GO:0005986; sucrose biosynthetic process; Biological Process
GO:0005992; trehalose biosynthetic process; Biological Process
GO:0006006; glucose metabolic process; Biological Process
GO:0006007; glucose catabolic process; Biological Process
GO:0006012; galactose metabolic process; Biological Process
GO:0006014; D-ribose metabolic process; Biological Process
GO:0006021; inositol biosynthetic process; Biological Process
GO:0006032; chitin catabolic process; Biological Process
GO:0006071; glycerol metabolic process; Biological Process
GO:0006072; glycerol-3-phosphate metabolic process; Biological Process
GO:0006073; cellular glucan metabolic process; Biological Process
GO:0006075; (1-&gt;3)-beta-D-glucan biosynthetic process; Biological Process
GO:0006081; cellular aldehyde metabolic process; Biological Process
GO:0006094; gluconeogenesis; Biological Process
GO:0006096; glycolysis; Biological Process
178

Table B.4 (cont’d)
GO:0006098; pentose-phosphate shunt; Biological Process
GO:0006099; tricarboxylic acid cycle; Biological Process
GO:0006102; isocitrate metabolic process; Biological Process
GO:0006108; malate metabolic process; Biological Process
GO:0006122; mitochondrial electron transport, ubiquinol to cytochrome c; Biological Process
GO:0006139; nucleobase-containing compound metabolic process; Biological Process
GO:0006164; purine nucleotide biosynthetic process; Biological Process
GO:0006165; nucleoside diphosphate phosphorylation; Biological Process
GO:0006183; GTP biosynthetic process; Biological Process
GO:0006184; GTP catabolic process; Biological Process
GO:0006200; ATP catabolic process; Biological Process
GO:0006207; 'de novo' pyrimidine base biosynthetic process; Biological Process
GO:0006221; pyrimidine nucleotide biosynthetic process; Biological Process
GO:0006228; UTP biosynthetic process; Biological Process
GO:0006241; CTP biosynthetic process; Biological Process
GO:0006259; DNA metabolic process; Biological Process
GO:0006260; DNA replication; Biological Process
GO:0006265; DNA topological change; Biological Process
GO:0006269; DNA replication, synthesis of RNA primer; Biological Process
GO:0006270; DNA-dependent DNA replication initiation; Biological Process
GO:0006278; RNA-dependent DNA replication; Biological Process
GO:0006281; DNA repair; Biological Process
GO:0006282; regulation of DNA repair; Biological Process
GO:0006284; base-excision repair; Biological Process
GO:0006289; nucleotide-excision repair; Biological Process
GO:0006298; mismatch repair; Biological Process
GO:0006302; double-strand break repair; Biological Process
GO:0006303; double-strand break repair via nonhomologous end joining; Biological Process
GO:0006306; DNA methylation; Biological Process
GO:0006308; DNA catabolic process; Biological Process
GO:0006310; DNA recombination; Biological Process
GO:0006333; chromatin assembly or disassembly; Biological Process
GO:0006334; nucleosome assembly; Biological Process
GO:0006338; chromatin remodeling; Biological Process
GO:0006351; transcription, DNA-dependent; Biological Process
GO:0006352; transcription initiation, DNA-dependent; Biological Process
GO:0006353; transcription termination, DNA-dependent; Biological Process
GO:0006355; regulation of transcription, DNA-dependent; Biological Process
GO:0006357; regulation of transcription from RNA polymerase II promoter; Biological
Process
179

Table B.4 (cont’d)
GO:0006364; rRNA processing; Biological Process
GO:0006366; transcription from RNA polymerase II promoter; Biological Process
GO:0006367; transcription initiation from RNA polymerase II promoter; Biological Process
GO:0006370; mRNA capping; Biological Process
GO:0006379; mRNA cleavage; Biological Process
GO:0006383; transcription from RNA polymerase III promoter; Biological Process
GO:0006388; tRNA splicing, via endonucleolytic cleavage and ligation; Biological Process
GO:0006396; RNA processing; Biological Process
GO:0006397; mRNA processing; Biological Process
GO:0006400; tRNA modification; Biological Process
GO:0006402; mRNA catabolic process; Biological Process
GO:0006412; translation; Biological Process
GO:0006413; translational initiation; Biological Process
GO:0006414; translational elongation; Biological Process
GO:0006415; translational termination; Biological Process
GO:0006418; tRNA aminoacylation for protein translation; Biological Process
GO:0006419; alanyl-tRNA aminoacylation; Biological Process
GO:0006421; asparaginyl-tRNA aminoacylation; Biological Process
GO:0006422; aspartyl-tRNA aminoacylation; Biological Process
GO:0006425; glutaminyl-tRNA aminoacylation; Biological Process
GO:0006426; glycyl-tRNA aminoacylation; Biological Process
GO:0006427; histidyl-tRNA aminoacylation; Biological Process
GO:0006428; isoleucyl-tRNA aminoacylation; Biological Process
GO:0006430; lysyl-tRNA aminoacylation; Biological Process
GO:0006431; methionyl-tRNA aminoacylation; Biological Process
GO:0006433; prolyl-tRNA aminoacylation; Biological Process
GO:0006434; seryl-tRNA aminoacylation; Biological Process
GO:0006435; threonyl-tRNA aminoacylation; Biological Process
GO:0006436; tryptophanyl-tRNA aminoacylation; Biological Process
GO:0006437; tyrosyl-tRNA aminoacylation; Biological Process
GO:0006438; valyl-tRNA aminoacylation; Biological Process
GO:0006452; translational frameshifting; Biological Process
GO:0006457; protein folding; Biological Process
GO:0006461; protein complex assembly; Biological Process
GO:0006464; protein modification process; Biological Process
GO:0006465; signal peptide processing; Biological Process
GO:0006468; protein phosphorylation; Biological Process
GO:0006470; protein dephosphorylation; Biological Process
GO:0006471; protein ADP-ribosylation; Biological Process

180

Table B.4 (cont’d)
GO:0006476; protein deacetylation; Biological Process
GO:0006479; protein methylation; Biological Process
GO:0006486; protein glycosylation; Biological Process
GO:0006487; protein N-linked glycosylation; Biological Process
GO:0006505; GPI anchor metabolic process; Biological Process
GO:0006506; GPI anchor biosynthetic process; Biological Process
GO:0006508; proteolysis; Biological Process
GO:0006511; ubiquitin-dependent protein catabolic process; Biological Process
GO:0006520; cellular amino acid metabolic process; Biological Process
GO:0006526; arginine biosynthetic process; Biological Process
GO:0006527; arginine catabolic process; Biological Process
GO:0006529; asparagine biosynthetic process; Biological Process
GO:0006536; glutamate metabolic process; Biological Process
GO:0006537; glutamate biosynthetic process; Biological Process
GO:0006542; glutamine biosynthetic process; Biological Process
GO:0006544; glycine metabolic process; Biological Process
GO:0006556; S-adenosylmethionine biosynthetic process; Biological Process
GO:0006561; proline biosynthetic process; Biological Process
GO:0006562; proline catabolic process; Biological Process
GO:0006563; L-serine metabolic process; Biological Process
GO:0006564; L-serine biosynthetic process; Biological Process
GO:0006568; tryptophan metabolic process; Biological Process
GO:0006571; tyrosine biosynthetic process; Biological Process
GO:0006597; spermine biosynthetic process; Biological Process
GO:0006605; protein targeting; Biological Process
GO:0006614; SRP-dependent cotranslational protein targeting to membrane; Biological
Process
GO:0006621; protein retention in ER lumen; Biological Process
GO:0006625; protein targeting to peroxisome; Biological Process
GO:0006626; protein targeting to mitochondrion; Biological Process
GO:0006629; lipid metabolic process; Biological Process
GO:0006631; fatty acid metabolic process; Biological Process
GO:0006633; fatty acid biosynthetic process; Biological Process
GO:0006635; fatty acid beta-oxidation; Biological Process
GO:0006637; acyl-CoA metabolic process; Biological Process
GO:0006644; phospholipid metabolic process; Biological Process
GO:0006659; phosphatidylserine biosynthetic process; Biological Process
GO:0006662; glycerol ether metabolic process; Biological Process
GO:0006665; sphingolipid metabolic process; Biological Process

181

Table B.4 (cont’d)
GO:0006694; steroid biosynthetic process; Biological Process
GO:0006696; ergosterol biosynthetic process; Biological Process
GO:0006725; cellular aromatic compound metabolic process; Biological Process
GO:0006730; one-carbon metabolic process; Biological Process
GO:0006750; glutathione biosynthetic process; Biological Process
GO:0006754; ATP biosynthetic process; Biological Process
GO:0006777; Mo-molybdopterin cofactor biosynthetic process; Biological Process
GO:0006779; porphyrin-containing compound biosynthetic process; Biological Process
GO:0006783; heme biosynthetic process; Biological Process
GO:0006788; heme oxidation; Biological Process
GO:0006796; phosphate-containing compound metabolic process; Biological Process
GO:0006801; superoxide metabolic process; Biological Process
GO:0006807; nitrogen compound metabolic process; Biological Process
GO:0006808; regulation of nitrogen utilization; Biological Process
GO:0006810; transport; Biological Process
GO:0006811; ion transport; Biological Process
GO:0006812; cation transport; Biological Process
GO:0006813; potassium ion transport; Biological Process
GO:0006814; sodium ion transport; Biological Process
GO:0006820; anion transport; Biological Process
GO:0006821; chloride transport; Biological Process
GO:0006826; iron ion transport; Biological Process
GO:0006839; mitochondrial transport; Biological Process
GO:0006855; drug transmembrane transport; Biological Process
GO:0006857; oligopeptide transport; Biological Process
GO:0006869; lipid transport; Biological Process
GO:0006879; cellular iron ion homeostasis; Biological Process
GO:0006885; regulation of pH; Biological Process
GO:0006886; intracellular protein transport; Biological Process
GO:0006887; exocytosis; Biological Process
GO:0006888; ER to Golgi vesicle-mediated transport; Biological Process
GO:0006891; intra-Golgi vesicle-mediated transport; Biological Process
GO:0006897; endocytosis; Biological Process
GO:0006904; vesicle docking involved in exocytosis; Biological Process
GO:0006909; phagocytosis; Biological Process
GO:0006913; nucleocytoplasmic transport; Biological Process
GO:0006915; apoptotic process; Biological Process
GO:0006950; response to stress; Biological Process
GO:0006952; defense response; Biological Process

182

Table B.4 (cont’d)
GO:0006974; response to DNA damage stimulus; Biological Process
GO:0006979; response to oxidative stress; Biological Process
GO:0007010; cytoskeleton organization; Biological Process
GO:0007017; microtubule-based process; Biological Process
GO:0007018; microtubule-based movement; Biological Process
GO:0007021; tubulin complex assembly; Biological Process
GO:0007030; Golgi organization; Biological Process
GO:0007031; peroxisome organization; Biological Process
GO:0007034; vacuolar transport; Biological Process
GO:0007047; cellular cell wall organization; Biological Process
GO:0007049; cell cycle; Biological Process
GO:0007050; cell cycle arrest; Biological Process
GO:0007067; mitosis; Biological Process
GO:0007090; regulation of S phase of mitotic cell cycle; Biological Process
GO:0007154; cell communication; Biological Process
GO:0007155; cell adhesion; Biological Process
GO:0007165; signal transduction; Biological Process
GO:0007186; G-protein coupled receptor signaling pathway; Biological Process
GO:0007205; activation of protein kinase C activity by G-protein coupled receptor protein
signaling pathway; Biological Process
GO:0007264; small GTPase mediated signal transduction; Biological Process
GO:0007275; multicellular organismal development; Biological Process
GO:0007585; respiratory gaseous exchange; Biological Process
GO:0008017; microtubule binding; Molecular Function
GO:0008020; G-protein coupled photoreceptor activity; Molecular Function
GO:0008026; ATP-dependent helicase activity; Molecular Function
GO:0008033; tRNA processing; Biological Process
GO:0008060; ARF GTPase activator activity; Molecular Function
GO:0008061; chitin binding; Molecular Function
GO:0008080; N-acetyltransferase activity; Molecular Function
GO:0008083; growth factor activity; Molecular Function
GO:0008094; DNA-dependent ATPase activity; Molecular Function
GO:0008097; 5S rRNA binding; Molecular Function
GO:0008104; protein localization; Biological Process
GO:0008107; galactoside 2-alpha-L-fucosyltransferase activity; Molecular Function
GO:0008108; UDP-glucose:hexose-1-phosphate uridylyltransferase activity; Molecular
Function
GO:0008121; ubiquinol-cytochrome-c reductase activity; Molecular Function
GO:0008131; primary amine oxidase activity; Molecular Function
GO:0008138; protein tyrosine/serine/threonine phosphatase activity; Molecular Function
183

Table B.4 (cont’d)
GO:0008146; sulfotransferase activity; Molecular Function
GO:0008152; metabolic process; Biological Process
GO:0008168; methyltransferase activity; Molecular Function
GO:0008170; N-methyltransferase activity; Molecular Function
GO:0008171; O-methyltransferase activity; Molecular Function
GO:0008173; RNA methyltransferase activity; Molecular Function
GO:0008198; ferrous iron binding; Molecular Function
GO:0008199; ferric iron binding; Molecular Function
GO:0008219; cell death; Biological Process
GO:0008233; peptidase activity; Molecular Function
GO:0008234; cysteine-type peptidase activity; Molecular Function
GO:0008235; metalloexopeptidase activity; Molecular Function
GO:0008236; serine-type peptidase activity; Molecular Function
GO:0008237; metallopeptidase activity; Molecular Function
GO:0008242; omega peptidase activity; Molecular Function
GO:0008270; zinc ion binding; Molecular Function
GO:0008276; protein methyltransferase activity; Molecular Function
GO:0008283; cell proliferation; Biological Process
GO:0008289; lipid binding; Molecular Function
GO:0008290; F-actin capping protein complex; Cellular Component
GO:0008295; spermidine biosynthetic process; Biological Process
GO:0008299; isoprenoid biosynthetic process; Biological Process
GO:0008308; voltage-gated anion channel activity; Molecular Function
GO:0008312; 7S RNA binding; Molecular Function
GO:0008318; protein prenyltransferase activity; Molecular Function
GO:0008324; cation transmembrane transporter activity; Molecular Function
GO:0008373; sialyltransferase activity; Molecular Function
GO:0008374; O-acyltransferase activity; Molecular Function
GO:0008375; acetylglucosaminyltransferase activity; Molecular Function
GO:0008378; galactosyltransferase activity; Molecular Function
GO:0008380; RNA splicing; Biological Process
GO:0008408; 3'-5' exonuclease activity; Molecular Function
GO:0008409; 5'-3' exonuclease activity; Molecular Function
GO:0008417; fucosyltransferase activity; Molecular Function
GO:0008430; selenium binding; Molecular Function
GO:0008440; inositol-1,4,5-trisphosphate 3-kinase activity; Molecular Function
GO:0008455; alpha-1,6-mannosylglycoprotein 2-beta-N-acetylglucosaminyltransferase
activity; Molecular Function
GO:0008466; glycogenin glucosyltransferase activity; Molecular Function
GO:0008474; palmitoyl-(protein) hydrolase activity; Molecular Function
184

Table B.4 (cont’d)
GO:0008478; pyridoxal kinase activity; Molecular Function
GO:0008479; queuine tRNA-ribosyltransferase activity; Molecular Function
GO:0008483; transaminase activity; Molecular Function
GO:0008508; bile acid:sodium symporter activity; Molecular Function
GO:0008519; ammonium transmembrane transporter activity; Molecular Function
GO:0008531; riboflavin kinase activity; Molecular Function
GO:0008535; respiratory chain complex IV assembly; Biological Process
GO:0008553; hydrogen-exporting ATPase activity, phosphorylative mechanism; Molecular
Function
GO:0008565; protein transporter activity; Molecular Function
GO:0008601; protein phosphatase type 2A regulator activity; Molecular Function
GO:0008610; lipid biosynthetic process; Biological Process
GO:0008612; peptidyl-lysine modification to hypusine; Biological Process
GO:0008616; queuosine biosynthetic process; Biological Process
GO:0008641; small protein activating enzyme activity; Molecular Function
GO:0008643; carbohydrate transport; Biological Process
GO:0008649; rRNA methyltransferase activity; Molecular Function
GO:0008652; cellular amino acid biosynthetic process; Biological Process
GO:0008654; phospholipid biosynthetic process; Biological Process
GO:0008676; 3-deoxy-8-phosphooctulonate synthase activity; Molecular Function
GO:0008686; 3,4-dihydroxy-2-butanone-4-phosphate synthase activity; Molecular Function
GO:0008716; D-alanine-D-alanine ligase activity; Molecular Function
GO:0008725; DNA-3-methyladenine glycosylase activity; Molecular Function
GO:0008762; UDP-N-acetylmuramate dehydrogenase activity; Molecular Function
GO:0008792; arginine decarboxylase activity; Molecular Function
GO:0008831; dTDP-4-dehydrorhamnose reductase activity; Molecular Function
GO:0008835; diaminohydroxyphosphoribosylaminopyrimidine deaminase activity; Molecular
Function
GO:0008836; diaminopimelate decarboxylase activity; Molecular Function
GO:0008839; dihydrodipicolinate reductase activity; Molecular Function
GO:0008853; exodeoxyribonuclease III activity; Molecular Function
GO:0008883; glutamyl-tRNA reductase activity; Molecular Function
GO:0008889; glycerophosphodiester phosphodiesterase activity; Molecular Function
GO:0008897; holo-[acyl-carrier-protein] synthase activity; Molecular Function
GO:0008898; homocysteine S-methyltransferase activity; Molecular Function
GO:0008915; lipid-A-disaccharide synthase activity; Molecular Function
GO:0008942; nitrite reductase [NAD(P)H] activity; Molecular Function
GO:0008963; phospho-N-acetylmuramoyl-pentapeptide-transferase activity; Molecular
Function
GO:0008964; phosphoenolpyruvate carboxylase activity; Molecular Function

185

Table B.4 (cont’d)
GO:0008977; prephenate dehydrogenase activity; Molecular Function
GO:0008987; quinolinate synthetase A activity; Molecular Function
GO:0009039; urease activity; Molecular Function
GO:0009041; uridylate kinase activity; Molecular Function
GO:0009052; pentose-phosphate shunt, non-oxidative branch; Biological Process
GO:0009055; electron carrier activity; Molecular Function
GO:0009058; biosynthetic process; Biological Process
GO:0009059; macromolecule biosynthetic process; Biological Process
GO:0009060; aerobic respiration; Biological Process
GO:0009072; aromatic amino acid family metabolic process; Biological Process
GO:0009073; aromatic amino acid family biosynthetic process; Biological Process
GO:0009082; branched chain family amino acid biosynthetic process; Biological Process
GO:0009086; methionine biosynthetic process; Biological Process
GO:0009089; lysine biosynthetic process via diaminopimelate; Biological Process
GO:0009094; L-phenylalanine biosynthetic process; Biological Process
GO:0009097; isoleucine biosynthetic process; Biological Process
GO:0009098; leucine biosynthetic process; Biological Process
GO:0009107; lipoate biosynthetic process; Biological Process
GO:0009113; purine base biosynthetic process; Biological Process
GO:0009116; nucleoside metabolic process; Biological Process
GO:0009168; purine ribonucleoside monophosphate biosynthetic process; Biological Process
GO:0009186; deoxyribonucleoside diphosphate metabolic process; Biological Process
GO:0009228; thiamine biosynthetic process; Biological Process
GO:0009229; thiamine diphosphate biosynthetic process; Biological Process
GO:0009231; riboflavin biosynthetic process; Biological Process
GO:0009236; cobalamin biosynthetic process; Biological Process
GO:0009245; lipid A biosynthetic process; Biological Process
GO:0009247; glycolipid biosynthetic process; Biological Process
GO:0009252; peptidoglycan biosynthetic process; Biological Process
GO:0009269; response to desiccation; Biological Process
GO:0009306; protein secretion; Biological Process
GO:0009308; amine metabolic process; Biological Process
GO:0009311; oligosaccharide metabolic process; Biological Process
GO:0009312; oligosaccharide biosynthetic process; Biological Process
GO:0009331; glycerol-3-phosphate dehydrogenase complex; Cellular Component
GO:0009396; folic acid-containing compound biosynthetic process; Biological Process
GO:0009405; pathogenesis; Biological Process
GO:0009408; response to heat; Biological Process
GO:0009415; response to water; Biological Process

186

Table B.4 (cont’d)
GO:0009416; response to light stimulus; Biological Process
GO:0009432; SOS response; Biological Process
GO:0009435; NAD biosynthetic process; Biological Process
GO:0009443; pyridoxal 5'-phosphate salvage; Biological Process
GO:0009451; RNA modification; Biological Process
GO:0009452; RNA capping; Biological Process
GO:0009507; chloroplast; Cellular Component
GO:0009512; cytochrome b6f complex; Cellular Component
GO:0009521; photosystem; Cellular Component
GO:0009522; photosystem I; Cellular Component
GO:0009523; photosystem II; Cellular Component
GO:0009536; plastid; Cellular Component
GO:0009538; photosystem I reaction center; Cellular Component
GO:0009584; detection of visible light; Biological Process
GO:0009607; response to biotic stimulus; Biological Process
GO:0009611; response to wounding; Biological Process
GO:0009654; oxygen evolving complex; Cellular Component
GO:0009664; plant-type cell wall organization; Biological Process
GO:0009678; hydrogen-translocating pyrophosphatase activity; Molecular Function
GO:0009690; cytokinin metabolic process; Biological Process
GO:0009725; response to hormone stimulus; Biological Process
GO:0009765; photosynthesis, light harvesting; Biological Process
GO:0009767; photosynthetic electron transport chain; Biological Process
GO:0009772; photosynthetic electron transport in photosystem II; Biological Process
GO:0009790; embryo development; Biological Process
GO:0009934; regulation of meristem structural organization; Biological Process
GO:0009966; regulation of signal transduction; Biological Process
GO:0009982; pseudouridine synthase activity; Molecular Function
GO:0009987; cellular process; Biological Process
GO:0010024; phytochromobilin biosynthetic process; Biological Process
GO:0010038; response to metal ion; Biological Process
GO:0010044; response to aluminum ion; Biological Process
GO:0010181; FMN binding; Molecular Function
GO:0010277; chlorophyllide a oxygenase [overall] activity; Molecular Function
GO:0010285; L,L-diaminopimelate aminotransferase activity; Molecular Function
GO:0010309; acireductone dioxygenase [iron(II)-requiring] activity; Molecular Function
GO:0010333; terpene synthase activity; Molecular Function
GO:0010380; regulation of chlorophyll biosynthetic process; Biological Process
GO:0015002; heme-copper terminal oxidase activity; Molecular Function

187

Table B.4 (cont’d)
GO:0015018; galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase activity;
Molecular Function
GO:0015031; protein transport; Biological Process
GO:0015035; protein disulfide oxidoreductase activity; Molecular Function
GO:0015074; DNA integration; Biological Process
GO:0015078; hydrogen ion transmembrane transporter activity; Molecular Function
GO:0015079; potassium ion transmembrane transporter activity; Molecular Function
GO:0015137; citrate transmembrane transporter activity; Molecular Function
GO:0015171; amino acid transmembrane transporter activity; Molecular Function
GO:0015205; nucleobase transmembrane transporter activity; Molecular Function
GO:0015232; heme transporter activity; Molecular Function
GO:0015238; drug transmembrane transporter activity; Molecular Function
GO:0015297; antiporter activity; Molecular Function
GO:0015299; solute:hydrogen antiporter activity; Molecular Function
GO:0015385; sodium:hydrogen antiporter activity; Molecular Function
GO:0015450; P-P-bond-hydrolysis-driven protein transmembrane transporter activity;
Molecular Function
GO:0015629; actin cytoskeleton; Cellular Component
GO:0015662; ATPase activity, coupled to transmembrane movement of ions, phosphorylative
mechanism; Molecular Function
GO:0015746; citrate transport; Biological Process
GO:0015851; nucleobase transport; Biological Process
GO:0015886; heme transport; Biological Process
GO:0015930; glutamate synthase activity; Molecular Function
GO:0015934; large ribosomal subunit; Cellular Component
GO:0015935; small ribosomal subunit; Cellular Component
GO:0015936; coenzyme A metabolic process; Biological Process
GO:0015937; coenzyme A biosynthetic process; Biological Process
GO:0015969; guanosine tetraphosphate metabolic process; Biological Process
GO:0015977; carbon fixation; Biological Process
GO:0015979; photosynthesis; Biological Process
GO:0015986; ATP synthesis coupled proton transport; Biological Process
GO:0015991; ATP hydrolysis coupled proton transport; Biological Process
GO:0015992; proton transport; Biological Process
GO:0016020; membrane; Cellular Component
GO:0016021; integral to membrane; Cellular Component
GO:0016043; cellular component organization; Biological Process
GO:0016068; type I hypersensitivity; Biological Process
GO:0016070; RNA metabolic process; Biological Process
GO:0016075; rRNA catabolic process; Biological Process
GO:0016125; sterol metabolic process; Biological Process
188

Table B.4 (cont’d)
GO:0016149; translation release factor activity, codon specific; Molecular Function
GO:0016151; nickel cation binding; Molecular Function
GO:0016157; sucrose synthase activity; Molecular Function
GO:0016161; beta-amylase activity; Molecular Function
GO:0016165; lipoxygenase activity; Molecular Function
GO:0016168; chlorophyll binding; Molecular Function
GO:0016192; vesicle-mediated transport; Biological Process
GO:0016208; AMP binding; Molecular Function
GO:0016209; antioxidant activity; Molecular Function
GO:0016226; iron-sulfur cluster assembly; Biological Process
GO:0016272; prefoldin complex; Cellular Component
GO:0016301; kinase activity; Molecular Function
GO:0016303; 1-phosphatidylinositol-3-kinase activity; Molecular Function
GO:0016307; phosphatidylinositol phosphate kinase activity; Molecular Function
GO:0016310; phosphorylation; Biological Process
GO:0016311; dephosphorylation; Biological Process
GO:0016428; tRNA (cytosine-5-)-methyltransferase activity; Molecular Function
GO:0016429; tRNA (adenine-N1-)-methyltransferase activity; Molecular Function
GO:0016459; myosin complex; Cellular Component
GO:0016469; proton-transporting two-sector ATPase complex; Cellular Component
GO:0016485; protein processing; Biological Process
GO:0016491; oxidoreductase activity; Molecular Function
GO:0016558; protein import into peroxisome matrix; Biological Process
GO:0016567; protein ubiquitination; Biological Process
GO:0016568; chromatin modification; Biological Process
GO:0016570; histone modification; Biological Process
GO:0016575; histone deacetylation; Biological Process
GO:0016592; mediator complex; Cellular Component
GO:0016597; amino acid binding; Molecular Function
GO:0016614; oxidoreductase activity, acting on CH-OH group of donors; Molecular Function
GO:0016615; malate dehydrogenase activity; Molecular Function
GO:0016616; oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP
as acceptor; Molecular Function
GO:0016619; malate dehydrogenase (oxaloacetate-decarboxylating) activity; Molecular
Function
GO:0016620; oxidoreductase activity, acting on the aldehyde or oxo group of donors, NAD or
NADP as acceptor; Molecular Function
GO:0016624; oxidoreductase activity, acting on the aldehyde or oxo group of donors, disulfide
as acceptor; Molecular Function
GO:0016627; oxidoreductase activity, acting on the CH-CH group of donors; Molecular
Function
189

Table B.4 (cont’d)
GO:0016630; protochlorophyllide reductase activity; Molecular Function
GO:0016636; oxidoreductase activity, acting on the CH-CH group of donors, iron-sulfur
protein as acceptor; Molecular Function
GO:0016638; oxidoreductase activity, acting on the CH-NH2 group of donors; Molecular
Function
GO:0016651; oxidoreductase activity, acting on NADH or NADPH; Molecular Function
GO:0016671; oxidoreductase activity, acting on a sulfur group of donors, disulfide as acceptor;
Molecular Function
GO:0016679; oxidoreductase activity, acting on diphenols and related substances as donors;
Molecular Function
GO:0016701; oxidoreductase activity, acting on single donors with incorporation of molecular
oxygen; Molecular Function
GO:0016702; oxidoreductase activity, acting on single donors with incorporation of molecular
oxygen, incorporation of two atoms of oxygen; Molecular Function
GO:0016705; oxidoreductase activity, acting on paired donors, with incorporation or reduction
of molecular oxygen; Molecular Function
GO:0016706; oxidoreductase activity, acting on paired donors, with incorporation or reduction
of molecular oxygen, 2-oxoglutarate as one donor, and incorporation of one atom each of
oxygen into both donors; Molecular Function
GO:0016708; oxidoreductase activity, acting on paired donors, with incorporation or reduction
of molecular oxygen, NADH or NADPH as one donor, and incorporation of two atoms of
oxygen into one donor; Molecular Function
GO:0016717; oxidoreductase activity, acting on paired donors, with oxidation of a pair of
donors resulting in the reduction of molecular oxygen to two molecules of water; Molecular
Function
GO:0016740; transferase activity; Molecular Function
GO:0016742; hydroxymethyl-, formyl- and related transferase activity; Molecular Function
GO:0016743; carboxyl- or carbamoyltransferase activity; Molecular Function
GO:0016746; transferase activity, transferring acyl groups; Molecular Function
GO:0016747; transferase activity, transferring acyl groups other than amino-acyl groups;
Molecular Function
GO:0016756; glutathione gamma-glutamylcysteinyltransferase activity; Molecular Function
GO:0016757; transferase activity, transferring glycosyl groups; Molecular Function
GO:0016758; transferase activity, transferring hexosyl groups; Molecular Function
GO:0016760; cellulose synthase (UDP-forming) activity; Molecular Function
GO:0016762; xyloglucan:xyloglucosyl transferase activity; Molecular Function
GO:0016763; transferase activity, transferring pentosyl groups; Molecular Function
GO:0016765; transferase activity, transferring alkyl or aryl (other than methyl) groups;
Molecular Function
GO:0016769; transferase activity, transferring nitrogenous groups; Molecular Function
GO:0016772; transferase activity, transferring phosphorus-containing groups; Molecular
Function

190

Table B.4 (cont’d)
GO:0016773; phosphotransferase activity, alcohol group as acceptor; Molecular Function
GO:0016779; nucleotidyltransferase activity; Molecular Function
GO:0016780; phosphotransferase activity, for other substituted phosphate groups; Molecular
Function
GO:0016787; hydrolase activity; Molecular Function
GO:0016788; hydrolase activity, acting on ester bonds; Molecular Function
GO:0016790; thiolester hydrolase activity; Molecular Function
GO:0016791; phosphatase activity; Molecular Function
GO:0016798; hydrolase activity, acting on glycosyl bonds; Molecular Function
GO:0016810; hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds; Molecular
Function
GO:0016811; hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear
amides; Molecular Function
GO:0016812; hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic
amides; Molecular Function
GO:0016813; hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear
amidines; Molecular Function
GO:0016817; hydrolase activity, acting on acid anhydrides; Molecular Function
GO:0016818; hydrolase activity, acting on acid anhydrides, in phosphorus-containing
anhydrides; Molecular Function
GO:0016820; hydrolase activity, acting on acid anhydrides, catalyzing transmembrane
movement of substances; Molecular Function
GO:0016829; lyase activity; Molecular Function
GO:0016831; carboxy-lyase activity; Molecular Function
GO:0016832; aldehyde-lyase activity; Molecular Function
GO:0016841; ammonia-lyase activity; Molecular Function
GO:0016844; strictosidine synthase activity; Molecular Function
GO:0016846; carbon-sulfur lyase activity; Molecular Function
GO:0016847; 1-aminocyclopropane-1-carboxylate synthase activity; Molecular Function
GO:0016853; isomerase activity; Molecular Function
GO:0016857; racemase and epimerase activity, acting on carbohydrates and derivatives;
Molecular Function
GO:0016868; intramolecular transferase activity, phosphotransferases; Molecular Function
GO:0016872; intramolecular lyase activity; Molecular Function
GO:0016874; ligase activity; Molecular Function
GO:0016876; ligase activity, forming aminoacyl-tRNA and related compounds; Molecular
Function
GO:0016881; acid-amino acid ligase activity; Molecular Function
GO:0016884; carbon-nitrogen ligase activity, with glutamine as amido-N-donor; Molecular
Function
GO:0016887; ATPase activity; Molecular Function

191

Table B.4 (cont’d)
GO:0016891; endoribonuclease activity, producing 5'-phosphomonoesters; Molecular
Function
GO:0016901; oxidoreductase activity, acting on the CH-OH group of donors, quinone or
similar compound as acceptor; Molecular Function
GO:0016972; thiol oxidase activity; Molecular Function
GO:0016984; ribulose-bisphosphate carboxylase activity; Molecular Function
GO:0016987; sigma factor activity; Molecular Function
GO:0016992; lipoate synthase activity; Molecular Function
GO:0016998; cell wall macromolecule catabolic process; Biological Process
GO:0017004; cytochrome complex assembly; Biological Process
GO:0017038; protein import; Biological Process
GO:0017056; structural constituent of nuclear pore; Molecular Function
GO:0017069; snRNA binding; Molecular Function
GO:0017089; glycolipid transporter activity; Molecular Function
GO:0017111; nucleoside-triphosphatase activity; Molecular Function
GO:0017148; negative regulation of translation; Biological Process
GO:0017150; tRNA dihydrouridine synthase activity; Molecular Function
GO:0017176; phosphatidylinositol N-acetylglucosaminyltransferase activity; Molecular
Function
GO:0018024; histone-lysine N-methyltransferase activity; Molecular Function
GO:0018106; peptidyl-histidine phosphorylation; Biological Process
GO:0018279; protein N-linked glycosylation via asparagine; Biological Process
GO:0018298; protein-chromophore linkage; Biological Process
GO:0018342; protein prenylation; Biological Process
GO:0019001; guanyl nucleotide binding; Molecular Function
GO:0019008; molybdopterin synthase complex; Cellular Component
GO:0019139; cytokinin dehydrogenase activity; Molecular Function
GO:0019205; nucleobase-containing compound kinase activity; Molecular Function
GO:0019211; phosphatase activator activity; Molecular Function
GO:0019239; deaminase activity; Molecular Function
GO:0019277; UDP-N-acetylgalactosamine biosynthetic process; Biological Process
GO:0019288; isopentenyl diphosphate biosynthetic process, mevalonate-independent pathway;
Biological Process
GO:0019295; coenzyme M biosynthetic process; Biological Process
GO:0019307; mannose biosynthetic process; Biological Process
GO:0019318; hexose metabolic process; Biological Process
GO:0019363; pyridine nucleotide biosynthetic process; Biological Process
GO:0019538; protein metabolic process; Biological Process
GO:0019439; aromatic compound catabolic process; Biological Process
GO:0019556; histidine catabolic process to glutamate and formamide; Biological Process

192

Table B.4 (cont’d)
GO:0019646; aerobic electron transport chain; Biological Process
GO:0019684; photosynthesis, light reaction; Biological Process
GO:0019752; carboxylic acid metabolic process; Biological Process
GO:0019773; proteasome core complex, alpha-subunit complex; Cellular Component
GO:0019843; rRNA binding; Molecular Function
GO:0019867; outer membrane; Cellular Component
GO:0019887; protein kinase regulator activity; Molecular Function
GO:0019898; extrinsic to membrane; Cellular Component
GO:0019901; protein kinase binding; Molecular Function
GO:0019904; protein domain specific binding; Molecular Function
GO:0019953; sexual reproduction; Biological Process
GO:0020037; heme binding; Molecular Function
GO:0022857; transmembrane transporter activity; Molecular Function
GO:0022891; substrate-specific transmembrane transporter activity; Molecular Function
GO:0022900; electron transport chain; Biological Process
GO:0030001; metal ion transport; Biological Process
GO:0030036; actin cytoskeleton organization; Biological Process
GO:0030054; cell junction; Cellular Component
GO:0030060; L-malate dehydrogenase activity; Molecular Function
GO:0030071; regulation of mitotic metaphase/anaphase transition; Biological Process
GO:0030117; membrane coat; Cellular Component
GO:0030126; COPI vesicle coat; Cellular Component
GO:0030127; COPII vesicle coat; Cellular Component
GO:0030131; clathrin adaptor complex; Cellular Component
GO:0030132; clathrin coat of coated pit; Cellular Component
GO:0030145; manganese ion binding; Molecular Function
GO:0030151; molybdenum ion binding; Molecular Function
GO:0030163; protein catabolic process; Biological Process
GO:0030170; pyridoxal phosphate binding; Molecular Function
GO:0030173; integral to Golgi membrane; Cellular Component
GO:0030234; enzyme regulator activity; Molecular Function
GO:0030244; cellulose biosynthetic process; Biological Process
GO:0030246; carbohydrate binding; Molecular Function
GO:0030247; polysaccharide binding; Molecular Function
GO:0030259; lipid glycosylation; Biological Process
GO:0030288; outer membrane-bounded periplasmic space; Cellular Component
GO:0030410; nicotianamine synthase activity; Molecular Function
GO:0030418; nicotianamine biosynthetic process; Biological Process
GO:0030488; tRNA methylation; Biological Process

193

Table B.4 (cont’d)
GO:0030515; snoRNA binding; Molecular Function
GO:0030529; ribonucleoprotein complex; Cellular Component
GO:0030598; rRNA N-glycosylase activity; Molecular Function
GO:0030599; pectinesterase activity; Molecular Function
GO:0030677; ribonuclease P complex; Cellular Component
GO:0030688; preribosome, small subunit precursor; Cellular Component
GO:0030833; regulation of actin filament polymerization; Biological Process
GO:0030904; retromer complex; Cellular Component
GO:0030955; potassium ion binding; Molecular Function
GO:0030976; thiamine pyrophosphate binding; Molecular Function
GO:0030983; mismatched DNA binding; Molecular Function
GO:0031011; Ino80 complex; Cellular Component
GO:0031072; heat shock protein binding; Molecular Function
GO:0031120; snRNA pseudouridine synthesis; Biological Process
GO:0031145; anaphase-promoting complex-dependent proteasomal ubiquitin-dependent
protein catabolic process; Biological Process
GO:0031167; rRNA methylation; Biological Process
GO:0031227; intrinsic to endoplasmic reticulum membrane; Cellular Component
GO:0031305; integral to mitochondrial inner membrane; Cellular Component
GO:0031361; integral to thylakoid membrane; Cellular Component
GO:0031418; L-ascorbic acid binding; Molecular Function
GO:0031461; cullin-RING ubiquitin ligase complex; Cellular Component
GO:0031625; ubiquitin protein ligase binding; Molecular Function
GO:0031966; mitochondrial membrane; Cellular Component
GO:0032012; regulation of ARF protein signal transduction; Biological Process
GO:0032040; small-subunit processome; Cellular Component
GO:0032065; cortical protein anchoring; Biological Process
GO:0032259; methylation; Biological Process
GO:0032312; regulation of ARF GTPase activity; Biological Process
GO:0032313; regulation of Rab GTPase activity; Biological Process
GO:0032324; molybdopterin cofactor biosynthetic process; Biological Process
GO:0032549; ribonucleoside binding; Molecular Function
GO:0032955; regulation of barrier septum assembly; Biological Process
GO:0032957; inositol trisphosphate metabolic process; Biological Process
GO:0032968; positive regulation of transcription elongation from RNA polymerase II
promoter; Biological Process
GO:0033014; tetrapyrrole biosynthetic process; Biological Process
GO:0033177; proton-transporting two-sector ATPase complex, proton-transporting domain;
Cellular Component
GO:0033178; proton-transporting two-sector ATPase complex, catalytic domain; Cellular
Component
194

Table B.4 (cont’d)
GO:0033897; ribonuclease T2 activity; Molecular Function
GO:0033903; endo-1,3(4)-beta-glucanase activity; Molecular Function
GO:0033925; mannosyl-glycoprotein endo-beta-N-acetylglucosaminidase activity; Molecular
Function
GO:0033926; glycopeptide alpha-N-acetylgalactosaminidase activity; Molecular Function
GO:0034755; iron ion transmembrane transport; Biological Process
GO:0034968; histone lysine methylation; Biological Process
GO:0035004; phosphatidylinositol 3-kinase activity; Molecular Function
GO:0035091; phosphatidylinositol binding; Molecular Function
GO:0035434; copper ion transmembrane transport; Biological Process
GO:0035435; phosphate ion transmembrane transport; Biological Process
GO:0035556; intracellular signal transduction; Biological Process
GO:0042026; protein refolding; Biological Process
GO:0042147; retrograde transport, endosome to Golgi; Biological Process
GO:0042176; regulation of protein catabolic process; Biological Process
GO:0042218; 1-aminocyclopropane-1-carboxylate biosynthetic process; Biological Process
GO:0042254; ribosome biogenesis; Biological Process
GO:0042256; mature ribosome assembly; Biological Process
GO:0042309; homoiothermy; Biological Process
GO:0042318; penicillin biosynthetic process; Biological Process
GO:0042393; histone binding; Molecular Function
GO:0042398; cellular modified amino acid biosynthetic process; Biological Process
GO:0042545; cell wall modification; Biological Process
GO:0042546; cell wall biogenesis; Biological Process
GO:0042549; photosystem II stabilization; Biological Process
GO:0042578; phosphoric ester hydrolase activity; Molecular Function
GO:0042586; peptide deformylase activity; Molecular Function
GO:0042623; ATPase activity, coupled; Molecular Function
GO:0042626; ATPase activity, coupled to transmembrane movement of substances; Molecular
Function
GO:0042651; thylakoid membrane; Cellular Component
GO:0042719; mitochondrial intermembrane space protein transporter complex; Cellular
Component
GO:0042742; defense response to bacterium; Biological Process
GO:0042765; GPI-anchor transamidase complex; Cellular Component
GO:0042802; identical protein binding; Molecular Function
GO:0042803; protein homodimerization activity; Molecular Function
GO:0043022; ribosome binding; Molecular Function
GO:0043039; tRNA aminoacylation; Biological Process
GO:0043043; peptide biosynthetic process; Biological Process

195

Table B.4 (cont’d)
GO:0043085; positive regulation of catalytic activity; Biological Process
GO:0043086; negative regulation of catalytic activity; Biological Process
GO:0043140; ATP-dependent 3'-5' DNA helicase activity; Molecular Function
GO:0043161; proteasomal ubiquitin-dependent protein catabolic process; Biological Process
GO:0043169; cation binding; Molecular Function
GO:0043234; protein complex; Cellular Component
GO:0043461; proton-transporting ATP synthase complex assembly; Biological Process
GO:0043531; ADP binding; Molecular Function
GO:0043565; sequence-specific DNA binding; Molecular Function
GO:0043631; RNA polyadenylation; Biological Process
GO:0043666; regulation of phosphoprotein phosphatase activity; Biological Process
GO:0043682; copper-transporting ATPase activity; Molecular Function
GO:0043754; dihydrolipoyllysine-residue (2-methylpropanoyl)transferase activity; Molecular
Function
GO:0044070; regulation of anion transport; Biological Process
GO:0044237; cellular metabolic process; Biological Process
GO:0044262; cellular carbohydrate metabolic process; Biological Process
GO:0044267; cellular protein metabolic process; Biological Process
GO:0044431; Golgi apparatus part; Cellular Component
GO:0045039; protein import into mitochondrial inner membrane; Biological Process
GO:0045040; protein import into mitochondrial outer membrane; Biological Process
GO:0045116; protein neddylation; Biological Process
GO:0045156; electron transporter, transferring electrons within the cyclic electron transport
pathway of photosynthesis activity; Molecular Function
GO:0045226; extracellular polysaccharide biosynthetic process; Biological Process
GO:0045261; proton-transporting ATP synthase complex, catalytic core F(1); Cellular
Component
GO:0045263; proton-transporting ATP synthase complex, coupling factor F(o); Cellular
Component
GO:0045300; acyl-[acyl-carrier-protein] desaturase activity; Molecular Function
GO:0045454; cell redox homeostasis; Biological Process
GO:0045735; nutrient reservoir activity; Molecular Function
GO:0045892; negative regulation of transcription, DNA-dependent; Biological Process
GO:0045893; positive regulation of transcription, DNA-dependent; Biological Process
GO:0045901; positive regulation of translational elongation; Biological Process
GO:0045905; positive regulation of translational termination; Biological Process
GO:0045980; negative regulation of nucleotide metabolic process; Biological Process
GO:0046034; ATP metabolic process; Biological Process
GO:0046080; dUTP metabolic process; Biological Process
GO:0046168; glycerol-3-phosphate catabolic process; Biological Process

196

Table B.4 (cont’d)
GO:0046274; lignin catabolic process; Biological Process
GO:0046373; L-arabinose metabolic process; Biological Process
GO:0046422; violaxanthin de-epoxidase activity; Molecular Function
GO:0046488; phosphatidylinositol metabolic process; Biological Process
GO:0046556; alpha-N-arabinofuranosidase activity; Molecular Function
GO:0046836; glycolipid transport; Biological Process
GO:0046854; phosphatidylinositol phosphorylation; Biological Process
GO:0046872; metal ion binding; Molecular Function
GO:0046873; metal ion transmembrane transporter activity; Molecular Function
GO:0046907; intracellular transport; Biological Process
GO:0046912; transferase activity, transferring acyl groups, acyl groups converted into alkyl on
transfer; Molecular Function
GO:0046923; ER retention sequence binding; Molecular Function
GO:0046933; hydrogen ion transporting ATP synthase activity, rotational mechanism;
Molecular Function
GO:0046938; phytochelatin biosynthetic process; Biological Process
GO:0046949; fatty-acyl-CoA biosynthetic process; Biological Process
GO:0046961; proton-transporting ATPase activity, rotational mechanism; Molecular Function
GO:0046983; protein dimerization activity; Molecular Function
GO:0047134; protein-disulfide reductase activity; Molecular Function
GO:0047325; inositol tetrakisphosphate 1-kinase activity; Molecular Function
GO:0047617; acyl-CoA hydrolase activity; Molecular Function
GO:0047750; cholestenol delta-isomerase activity; Molecular Function
GO:0047800; cysteamine dioxygenase activity; Molecular Function
GO:0048015; phosphatidylinositol-mediated signaling; Biological Process
GO:0048037; cofactor binding; Molecular Function
GO:0048038; quinone binding; Molecular Function
GO:0048046; apoplast; Cellular Component
GO:0048193; Golgi vesicle transport; Biological Process
GO:0048278; vesicle docking; Biological Process
GO:0048280; vesicle fusion with Golgi apparatus; Biological Process
GO:0048478; replication fork protection; Biological Process
GO:0048500; signal recognition particle; Cellular Component
GO:0048544; recognition of pollen; Biological Process
GO:0050080; malonyl-CoA decarboxylase activity; Molecular Function
GO:0050242; pyruvate, phosphate dikinase activity; Molecular Function
GO:0050307; sucrose-phosphate phosphatase activity; Molecular Function
GO:0050511; undecaprenyldiphospho-muramoylpentapeptide beta-Nacetylglucosaminyltransferase activity; Molecular Function
GO:0050660; flavin adenine dinucleotide binding; Molecular Function

197

Table B.4 (cont’d)
GO:0050661; NADP binding; Molecular Function
GO:0050662; coenzyme binding; Molecular Function
GO:0050790; regulation of catalytic activity; Biological Process
GO:0050825; ice binding; Molecular Function
GO:0050826; response to freezing; Biological Process
GO:0050832; defense response to fungus; Biological Process
GO:0050897; cobalt ion binding; Molecular Function
GO:0051020; GTPase binding; Molecular Function
GO:0051082; unfolded protein binding; Molecular Function
GO:0051087; chaperone binding; Molecular Function
GO:0051186; cofactor metabolic process; Biological Process
GO:0051205; protein insertion into membrane; Biological Process
GO:0051258; protein polymerization; Biological Process
GO:0051276; chromosome organization; Biological Process
GO:0051287; NAD binding; Molecular Function
GO:0051301; cell division; Biological Process
GO:0051536; iron-sulfur cluster binding; Molecular Function
GO:0051537; 2 iron, 2 sulfur cluster binding; Molecular Function
GO:0051539; 4 iron, 4 sulfur cluster binding; Molecular Function
GO:0051603; proteolysis involved in cellular protein catabolic process; Biological Process
GO:0051726; regulation of cell cycle; Biological Process
GO:0051861; glycolipid binding; Molecular Function
GO:0051920; peroxiredoxin activity; Molecular Function
GO:0052716; hydroquinone:oxygen oxidoreductase activity; Molecular Function
GO:0052725; inositol-1,3,4-trisphosphate 6-kinase activity; Molecular Function
GO:0052726; inositol-1,3,4-trisphosphate 5-kinase activity; Molecular Function
GO:0055085; transmembrane transport; Biological Process
GO:0055114; oxidation-reduction process; Biological Process
GO:0070402; NADPH binding; Molecular Function
GO:0070403; NAD+ binding; Molecular Function
GO:0070461; SAGA-type complex; Cellular Component
GO:0071266; 'de novo' L-methionine biosynthetic process; Biological Process
GO:0071805; potassium ion transmembrane transport; Biological Process
GO:0072488; ammonium transmembrane transport; Biological Process
GO:0097157; pre-mRNA intronic binding; Molecular Function
GO:2001070; starch binding; Molecular Function

198

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CHAPTER 4 Future directions

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The resources established from this study included wheat lines that captured multiple
QTL, a high-density genetic map, seed-specific transcript assembly. All together, they set up the
stage for leveraging next generation sequencing (NGS) technology for PHS study in wheat.
There are several potential areas can be explored based on the results from current research.
The development of heterogeneous inbred family (HIF) (Tuinstra et al., 1997) from lines
containing recombination around QTL regions in the ‘Vida’ × MTHW0471 population would be
a good way to validate QTL identified from current study. HIF focused on the residual
heterozygosity around identified QTL regions. Recombinants selected for HIF have the potential
to segregate within QTL regions. Multiple HIF can be developed to cover different combinations
of multiple QTLs. With the high density SNP genotyping results, several QTL identified in
current study were located between markers separated in less than 2 cM. With the help of HIF,
fine mapping of these QTL will be relatively faster when compared with fine-mapping using
neo-isogenic lines. The QTL that is of most interest is the major QTL on Chromosome 2B which
explained nearly 40% of phenotypic variation of α-amylase activity. Other ones that had QTL ×
QTL interactions can also be further assessed by comparing between HIF families containing
different combinations of these QTL. Moreover, Liu et al. (2013) cloned a gene on the short arm
of Chromosome 3A from a white wheat population, which was claimed to be able to improve
PHS resistance significantly. The SNP markers reported from that paper can be used to screen
our population for recombinants around that region. If yes, a potential fine-mapping study would
be able to explore the allelic variation between red and white wheat lines within that region.
SNP identification from NGS data was proven feasible in autopolyploid species recently
(Krasileva et al., 2013; Trick et al. 2012). Thus, an attempt to identify novel SNPs from the
sequenced individuals could be valuable for map saturation or future fine-mapping. On the other

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hand, a fine-mapping using bulk segregant analysis (BSA) with RNA-seq reads had been done in
durum wheat, which showed the potential of using next-generation sequencing data for markertrait associations. Reads by comparing red wheat and white wheat bulks can help to identify
SNPs within genic regions. When combining NGS with HIF, the SNP identification can be
narrowed down to genic regions with a potential of collocating with differentially expressed
genes.
During the design of wheat 9K SNP array, the non-genome specific SNPs, and especially
D-genome markers, were the major limiting factors for the generation of a high-density maps.
The genotyping results need to be re-clustered based on population segregation and all the
heterozygotes information were lost during the re-cluster process (Cavanagh et al. 2013).
Therefore, the development of genome-specific SNP is critical when considering for future map
enrichment.
A recent paper utilizing progenitor genome SNPs to categorize genomic origin in cotton
can also be adapted to wheat system with some modifications (Page et al., 2013). The recently
published wheat A and D progenitor genome sequence can be used to help categorize genomespecific reads and call SNP within each sub-genome (Jia et al., 2013; Ling et al., 2013). Two
other alternative strategies currently adopted by other groups. These resources can also be used
for the validation of our categorization method. First is to use flow-sorted genome-specific
chromosomes and identify chromosome specific SNP from there. This strategy is currently
adopted by the International Wheat Genome Sequencing Consortium (IWGSC) and survey
sequence of chromosome specific sequences will be made available for public use soon (Kellye
Eversole, IWGSC, pers. comm.). The second strategy used in durum is similar to the SNPcategorization method mentioned above but using phasing information instead. (Krasileva et al.,

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2013). Except for making the SNP calling easier and more accurate, collection of genome
specific reads is also critical to build sub-genome specific transcriptome, which should reduce
the chance of mis-assembly due to homeolog issue. As seen in a couple studies in polyploidy, the
expression bias between sub-genome can be huge. Thus, the third potential application of
categorized sub-genome specific reads is to capture the sub-genome level differential expression
and even when DE at whole genome level is not significant. On the other hand, the extracted
sub-genome can be more amenable to using diploid wheat progenitors as reference genome than
using hexaploid wheat genome, which is not available at this stage. As shown in current study,
the use of A. tauschii genome as reference can cause potential loss of sub-genome specific loci,
which might bias the downstream analysis.
To the best of author’s knowledge, this study is one of the first studies systematically
evaluate the 9k-SNP array and NGS technology in hexaploid wheat genetic study. The analysis
revealed previous identified and novel QTL and potential candidate genes involved in PHS
process. The pipeline developed here can also be used in other trait discovery pipelines. With
these power tools, the dissection of complex trait in polyploidy species like wheat will become
more accurate and efficient.

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