# Wheat Genomics: Present Status and Future Prospects

Authors: P. K. Gupta; R. R. Mir; A. Mohan; J. Kumar
Journal: International Journal of Plant Genomics
 (2008)

---

## Abstract

Wheat  (Triticum aestivum L.), with a large genome (16000 Mb) and high proportion
(∼80%) of repetitive sequences, has been a difficult crop for genomics research. However, the availability of extensive cytogenetics stocks has been an asset, which facilitated
significant progress in wheat genomic research in recent years. For instance, fairly dense
molecular maps (both genetic and physical maps) and a large set of ESTs allowed
genome-wide identification of gene-rich and gene-poor regions as well as QTL including
eQTL. The availability of markers associated with major economic traits also allowed
development of major programs on marker-assisted selection (MAS) in some countries,
and facilitated map-based cloning of a number of genes/QTL. Resources for functional
genomics including TILLING and RNA interference (RNAi) along with some new
approaches like epigenetics and association mapping are also being successfully used for
wheat genomics research. BAC/BIBAC libraries for the subgenome D and some
individual chromosomes have also been prepared to facilitate sequencing of gene space.
In this brief review, we discuss all these advances in some detail, and also describe
briefly the available resources, which can be used for future genomics research in this
important crop.


---

## Body

## 1. Introduction

Wheat is one of the most important staple food crops of the world, occupying 17% (one sixth) of
crop acreage worldwide, feeding about 40% (nearly half) of the world population
and providing 20% (one fifth) of total food calories and protein in human
nutrition. Although wheat production during the last four decades had a steady
significant increase, a fatigue has been witnessed during the last few years,
leading to the lowest current global wheat stocks ever since 1948/49.
Consequently, wheat prices have also been soaring, reaching the highest level
of US $ 10 a bushel as against US $ 4.50 a year ago (http://www.planetark.com/dailynewsstory.cfm/newsid/44968/story.htm).
As against this, it is projected that, in order to meet growing human needs,
wheat grain production must increase at an annual rate of 2%, without any
additional land to become available for this crop [1]. In
order to meet this challenge, new level of understanding of the structure and
function of the wheat genome is required.Wheat is adapted to temperate regions of
the world and was one of the first crops to be domesticated some 10000 years
ago. At the cytogenetics level, common wheat is known to have three subgenomes
(each subgenome has 7 chromosomes, makingn=21)
that are organized in seven homoeologous groups, each homoeologous group has three closely related
chromosomes, one from each of the three related subgenomes. The diploid
progenitors of the A, B, and D subgenomes have been identified, although there
has always been a debate regarding the progenitor of B genome (reviewed in [1]). It has also been found that common wheat behaves much like a
diploid organism during meiosis, but its genome can tolerate aneuploidy because
of the presence of triplicate genes. These features along with the availability
of a large number of aneuploids [particularly including a complete set of
monosomics, a set of 42 compensating nullisomic-tetrasomics and a complete set
of 42 ditelocentrics developed by Sears [2]] and more than 400 segmental deletion lines [developed later by
Endo and Gill [3]] facilitated greatly the wheat genomics research.Molecular tools have recently been used in
a big way for cytogenetic studies in wheat, so that all recent cytogenetic
studies in wheat now have a molecular component, thus paving the path for wheat
genomics research. However, these studies in the area of molecular cytogenetics
have been relatively difficult in bread wheat due to its three closely related
subgenomes and a large genome (1C = >16 billion base pairs) with high
proportion (>80%) of repetitive DNA. Despite this, significant progress in
the area of molecular cytogenetics and cytogenomics of wheat has been made
during the last two decades, thus making it amenable to genomics research. For
instance, molecular maps in bread wheat, emmer wheat, and einkorn wheat
utilizing a variety of molecular markers are now available, where gene rich
regions (GRRs) and recombination hotspots have also been identified (for a review,
see [4, 5]).In recent years, a number of initiatives
have been taken to develop new tools for wheat genomics research. These include
construction of large insert libraries and development of massive EST
collections, genetic and physical molecular maps, and gene targeting systems.
For instance, the number of wheat ESTs has increased from a mere∼5 in 1999
[6] to a massive >1 240 000 in January 2008 (http://www.ncbi.nlm.nih.gov/),
thus forming the largest EST collection in any crop as a resource for genome
analysis. These ESTs are being used for a variety of activities including
development of functional molecular markers, preparation of transcript maps,
and construction of cDNA arrays. A variety of molecular markers that were
developed either from ESTs or from genomic DNA also helped to discover
relationships between genomes [7] and to compare marker-trait
associations in different crops. Comparative genomics, involving major crop
grasses including wheat, has also been used not only to study evolutionary
relationships, but also to design crop improvement programs [8].
Functional genomics research in wheat, which though lagged far behind relative
to that in other major food crops like maize and rice, has also recently
witnessed significant progress. For instance, RNA interference, TILLING, and
“expression genetics” leading to mapping of eQTLs have been used to identify
functions of individual genes [9]. This allowed development of
sets of candidate genes for individual traits, which can be used for
understanding the biology of these traits and for development of perfect
diagnostic marker(s) to be used not only for map-based cloning of genes, but
also for MAS [9, 10]. In order to sequence the
GRRs of wheat genome, a multinational collaborative program named International
Genome Research on Wheat (IGROW) was earlier launched, which later took the
shape of International Wheat Genome Sequencing Consortium (IWGSC) [11]. This will accelerate the progress on genome sequencing and will
allow analysis of structure and function of the wheat genome. Keeping the above
background in mind, Somers [12] identified the following five thrust areas of
research for wheat improvement: (i) genetic mapping, (ii) QTL analysis, (iii)
molecular breeding, (iv) association mapping, and (v) software
development. In this communication, we
briefly review the recent advances in all these areas of wheat genomics and
discuss their impact on wheat improvement programs.


## 2. Molecular Maps of Wheat Genome

### 2.1. Molecular Genetic Maps

Although some efforts toward mapping of molecular markers on wheat genome were initially made during late
1980s [13], a systematic construction of molecular maps in wheat
started only in 1990, with the organization of International Triticeae Mapping
Initiative (ITMI), which coordinated the construction of molecular maps of
wheat genome. Individual groups (headed by R Appels, PJ Sharp, ME Sorrells, J
Dvorak, BS Gill, GE Hart, and MD Gale) prepared the maps for chromosomes
belonging to each of the seven different homoeologous groups. A detailed account
on mapping of chromosomes of individual homoeologous groups and that of the
whole wheat genome is available elsewhere [14]; an updated
version is available at GrainGenes (http://wheat.pw.usda.gov/), and summarized
in Table 1. Integrated or composite maps involving more than one type of
molecular markers have also been prepared in wheat (particularly the SSR, AFLP,
SNP, and DArT markers (see Table 1)). Consensus maps, where map information from
multiple genomes or multiple maps was merged into a single comprehensive map,
were also prepared in wheat [15, 16]. On these maps,
classical and newly identified genes of economic importance are being placed to
facilitate marker-assisted selection (MAS). Many genes controlling a variety of
traits (both qualitative and quantitative) have already been tagged/mapped
using a variety of molecular markers (for references, see [14, 17]). The density of wheat genetic maps was improved with the
development of microsatellite (SSR) markers leading to construction of SSR maps
of wheat [18–20]. Later,
Somers et al. [16] added more SSR markers to these earlier maps and prepared
a high-density SSR consensus map. At present, >2500 mapped genomic SSR
(gSSR) markers are available in wheat, which will greatly facilitate the
preparation of high-density genetic maps, so that we will be able to identify
key recombination events in breeding populations and fine-map genes. In
addition to gSSRs, more then 300 EST-SSR could also be placed on the genetic map of wheat
genome [21–23]. However, more
markers are still needed, particularly for preparation of high-density physical
maps for gene cloning [24]. Availability of a number of
molecular markers associated each with individual traits will also facilitate
marker-assisted selection (MAS) during plant breeding.Table 1
A list of some important molecular maps developed in wheat.
Map type/class of wheatPopulation used for mappingNo. of loci mappedGenetic map length (cM)ReferenceRFLP mapsDiploid wheat (D-genome)F2 [T. tauschii (TA1691 var. meyeri × TA1704 var. typica)]1521554[25]Diploid wheat (D-genome)F2 [Aegilops tauschii var. meyeri(TA1691) ×Ae. tauschii var. typica(TA 1704)]546—[26]SSR mapsBread wheatITMI RILs (W7984× Opata85)279—[18]Bread wheatRILs (Synthetic× Opata)12352569[16]Bread wheatRILs (W7984× Opata85)14062654[27]Bread wheatDHs (Kitamoe× Munstertaler)4643441[28]Bread wheat*RILs (Chuan-Mai18× Vigour18)2443150[29]AFLP mapsBread wheat*RILs (Wangshuibai× Alondra’s)2502430[30]Composite mapsEinkorn wheatF2s/F3s (T.
monococcumssp . monococcum DV92 ×T. monococcum ssp. aegilopoides C3116)
(marker loci-mainly RFLPs)3335714[31]Einkorn wheatRILs (Triticum boeoticum×T. monococcum)
marker loci-RFLPs, SSR1771262[5]Durum wheatRILs
(T. durum var. Messapia ×T. turgidium var. MG4343)
(marker loci-RFLP, Glu3B,
others)2131352[32]Durum wheatRILs
(T. durum var. Messapia ×T. turgidium var. MG4343)
(marker loci-AFLPs, RFLPs)882063[33]Durum wheatRILs (Jennah Khetifa× Cham10
(marker loci-RFLPs, SSRs, AFLPs)2063598[34]Durum wheat*RILs (Omrabi 5×T dicoccoides 600545)
(marker loci-SSRs, AFLPs)3122289[35]Bread wheatRILs (T. aestivum L. var.
Forno ×T. spelta L. var. Oberkulmer)
(marker loci-RFLPs, SSRs)2302469[36]Bread wheat*DHs (CM-82036× Remus) (marker loci-RFLPs, AFLPs, SSRs,
etc.)3841860[37]Bread wheat*DHs (Savannah× Senat)
(marker loci-SSRs, AFLPs)345 (17)2300[38]Bread wheat*RILs (Renan× Récital)
(marker loci-SSRs, RFPLs, AFLPs)265 (17)2722[39, 40]Bread wheatF5s(Arina × Forno)
(marker loci-RFLPs, SSRs)3963086[41]Bread wheatDHs (Courtot× Chinese Spring)
(marker loci-RFLPs, SSRs, AFLPs)6593685[42]Bread wheat*DHs (Frontana× Remus)
(marker loci-SSRs, STSs, AFLPs,
etc.)5352840[43]Bread wheatRILs (Grandin× BR34) (marker loci-TRAPs, SSRs)3523045[44]Bread wheat*DHs (Spring× SQ1) (marker loci-AFLPs, SSRs)5673521[45]Bread wheat*RILs (Dream× Lynx) (marker loci-SSRs, STSs, AFLPs)283 (17)1734[46]Bread wheat*DHs
(AC Karma× 87E03-S2B1)
(marker loci-STSs, SSRs, etc.)167 (15)2403[47]Bread wheat*DHs (Trident× Molineux)
(marker loci-SSRs, STSs, RFLPs,
etc.)2513061[48]Bread wheat*DH (Arina× Riband) (marker loci-AFLPs, SSRs)2791199[49]Bread wheat*DHs (RL4452× AC Domain) (marker loic-SSRs, genes, etc.)3692793[50]Bread wheat*RILs (Chuan 35050× Shannong 483)
(marker loci-SSRs, EST-SSRs,
ISSRs, SRAPs,TRAPs, Glu loci)3813636[51]Bread wheat*DHs (Shamrock× Shango) (marker loci-SSRs, DArTs)2631337[52]Bread wheatDHs Cranbrook× Halberd
(Marker loci-SSRs, RFLPs, AFLPs, DArTs,
STSs)7492937[53]*These are framework linkage map
prepared for QTL analyses.In addition to random DNA markers (RDM),
gene targeted markers (GTMs) and functional markers (FMs) are also being used
in wheat to facilitate identification of genes responsible for individual
traits and to improve possibilities of using MAS in wheat breeding. As a
corollary, functional markers (FMs) are also being developed from the available
gene sequences [10]. These markers were also used to construct
transcript and molecular functional maps. Recently, microarray-based
high-throughput diversity array technology (DArT) markers were also developed
and used for preparing genetic maps in wheat [53, 54]. Large-scale genotyping for dozens to thousands of SNPs is also being undertaken using
several high-density platforms including Illumina’s GoldenGate and ABI’s
SNaPshot platforms (http://wheat.pw.usda.gov/SNP/new/index.shtml). The genotyping activity may be extended further through the use
of Solexa’s high throughput and low-cost resequencing technology.


### 2.2. Molecular Marker-Based Physical Maps

Molecular markers in bread wheat
have also been used for the preparation of physical maps, which were then
compared with the available genetic maps involving same markers. These maps
allowed comparisons between genetic and physical distances to give information
about variations in recombination frequencies and cryptic structural changes
(if any) in different regions of individual chromosomes. Several methods have
been employed for the construction of physical maps.


#### 2.2.1. Deletion Mapping

In wheat, physical mapping of genes to individual
chromosomes began with the development of aneuploids [55], which led to
mapping of genes to individual chromosomes. Later, deletion lines of wheat
chromosomes developed by Endo and Gill [3] were extensively used as a tool
for physical mapping of molecular markers. Using these deletion stocks, genes
for morphological characters were also mapped to physical segments of wheat
chromosomes directly in case of unique and genome specific markers or
indirectly in case of duplicate or triplicate loci through the use of
intergenomic polymorphism between the A, B, and D subgenomes (see Table 2 for
details of available physical maps). In addition to physical mapping of genomic
SSRs, ESTs and EST-SSRs were also subjected to physical mapping (see Table 2). As a
part of this effort, a major project (funded by National Science Foundation, USA)
on mapping of ESTs in wheat was successfully completed by a consortium of 13
laboratories in USA leading to physical mapping of ∼16000 EST loci (http://wheat.pw.usda.gov/NSF/progress_mapping.html; [56] (see Table 2)).Table 2
Deletion-based physical maps of common wheat.
Homoeologous group/Marker lociNo. of deletionReferencechromosome/armmappedstocks used119 RFLPs18[57]150 RFLPs56[58]230 RFLPs21[59]243 SSRs25[60]329 RFLPs25[61]440 RFLPs39[62]5155 RFLPs65[63]5245
RFLPs,
3 SSRs36[64]5S100 RFLPs17[65]5A22 RFLPs19[66]624 RFLPs26[67]6210 RFLPs45[68]6S82 RFLPs14[69]716 RFLPs41[70]791 RFLPs,
6 RAPDs54[71]6B, 2D,
and 7D16 SSRs13[72]1BS24 AFLPs8[73]4DL61 AFLPs,
2 SSRs,
2 RFLPs8[74]1BS22 ESTs2[75]Whole
genome725 SSRs118[76]Whole
genome260 BARC117[27]Whole
genome313 SSRs162[77]Whole
genome16000 ESTs101http://wheat.pw.usda.gov/NSF/progressmapping.htmlWhole
genome266 eSSRs105[78]Whole
genome672 EST-SSRs101[79]


#### 2.2.2. In Silico Physical Mapping

As many as 16000 wheat EST loci assigned to
deletion bins, as mentioned above, constitute a useful source for in silico
mapping, so that markers with known sequences can be mapped to wheat
chromosomes through sequence similarity with mapped EST loci available at
GrainGene database (http://wheat.pw.usda.gov/GG2/blast.shtml). Using the above approach, Parida et al. [80] were able to map 157 SSR
containing wheat unique sequences (out of 429 class I unigene-derived
microsatellites (UGMS) markers developed in wheat) to chromosome bins. These
bin-mapped UGMS markers provide valuable information for a targeted mapping of
genes for useful traits, for comparative genomics, and for sequencing of
gene-rich regions of the wheat genome. Another set of 672 loci belonging to 275
EST-SSRs of wheat and rye was assigned to individual bins through in silico and
wet-lab approaches by Mohan et al. [79]. A few cDNA clones associated with QTL
for FHB resistance in wheat were also successfully mapped using in silico approach [81].


#### 2.2.3. Radiation-Hybrid Mapping

Radiation hybrid (RH) mapping was first
described by Goss and Harris [82] and was initially used by Cox et al. [83]
for physical mapping in animals/humans. In wheat, the approach has been used at
North Dakota State University (NDSU) utilizaing addition and substituition of
individual D-genome chromosomes into tetraploid durum wheat. For RH mapping of
1D, durum wheat alien substitution line for chromosome 1D (DWRH-1D), harboring
nuclear-cytoplasmic compatibility gene scsae was used. These RH
lines initially allowed detection of 88 radiation-induced breaks involving 39
1D specific markers. Later, this 1D RH map was further expanded to a resolution
of one break every 199 kb of DNA, utilizing 378 markers [84]. Using the same approach, construction of radiation hybrid map for
chromosome 3B is currently in progress (S. Kianian personal communication).


### 2.3. BAC-Based Physical Maps

BAC-based physical map of wheat
D genome is being constructed using the diploid species,Aegilops tauschii, with
the aim to identify and map genes and later sequence the gene-rich regions
(GRRs). For this purpose, a large number of BACs were first fingerprinted and
assembled into contigs. Fingerprint contigs (FPCs) and the data related to
physical mapping of the D genome are available in the database (http://wheat.pw.usda.gov/PhysicalMapping/index.html). BACs
belonging to chromosome 3B are also being fingerprinted (with few BACs already anchored to wheat bins), and a
whole genome BAC-based physical map of hexaploid wheat is proposed to be
constructed under the aegis of IWGSC in its pilot studies (see later).


## 2.1. Molecular Genetic Maps

Although some efforts toward mapping of molecular markers on wheat genome were initially made during late
1980s [13], a systematic construction of molecular maps in wheat
started only in 1990, with the organization of International Triticeae Mapping
Initiative (ITMI), which coordinated the construction of molecular maps of
wheat genome. Individual groups (headed by R Appels, PJ Sharp, ME Sorrells, J
Dvorak, BS Gill, GE Hart, and MD Gale) prepared the maps for chromosomes
belonging to each of the seven different homoeologous groups. A detailed account
on mapping of chromosomes of individual homoeologous groups and that of the
whole wheat genome is available elsewhere [14]; an updated
version is available at GrainGenes (http://wheat.pw.usda.gov/), and summarized
in Table 1. Integrated or composite maps involving more than one type of
molecular markers have also been prepared in wheat (particularly the SSR, AFLP,
SNP, and DArT markers (see Table 1)). Consensus maps, where map information from
multiple genomes or multiple maps was merged into a single comprehensive map,
were also prepared in wheat [15, 16]. On these maps,
classical and newly identified genes of economic importance are being placed to
facilitate marker-assisted selection (MAS). Many genes controlling a variety of
traits (both qualitative and quantitative) have already been tagged/mapped
using a variety of molecular markers (for references, see [14, 17]). The density of wheat genetic maps was improved with the
development of microsatellite (SSR) markers leading to construction of SSR maps
of wheat [18–20]. Later,
Somers et al. [16] added more SSR markers to these earlier maps and prepared
a high-density SSR consensus map. At present, >2500 mapped genomic SSR
(gSSR) markers are available in wheat, which will greatly facilitate the
preparation of high-density genetic maps, so that we will be able to identify
key recombination events in breeding populations and fine-map genes. In
addition to gSSRs, more then 300 EST-SSR could also be placed on the genetic map of wheat
genome [21–23]. However, more
markers are still needed, particularly for preparation of high-density physical
maps for gene cloning [24]. Availability of a number of
molecular markers associated each with individual traits will also facilitate
marker-assisted selection (MAS) during plant breeding.Table 1
A list of some important molecular maps developed in wheat.
Map type/class of wheatPopulation used for mappingNo. of loci mappedGenetic map length (cM)ReferenceRFLP mapsDiploid wheat (D-genome)F2 [T. tauschii (TA1691 var. meyeri × TA1704 var. typica)]1521554[25]Diploid wheat (D-genome)F2 [Aegilops tauschii var. meyeri(TA1691) ×Ae. tauschii var. typica(TA 1704)]546—[26]SSR mapsBread wheatITMI RILs (W7984× Opata85)279—[18]Bread wheatRILs (Synthetic× Opata)12352569[16]Bread wheatRILs (W7984× Opata85)14062654[27]Bread wheatDHs (Kitamoe× Munstertaler)4643441[28]Bread wheat*RILs (Chuan-Mai18× Vigour18)2443150[29]AFLP mapsBread wheat*RILs (Wangshuibai× Alondra’s)2502430[30]Composite mapsEinkorn wheatF2s/F3s (T.
monococcumssp . monococcum DV92 ×T. monococcum ssp. aegilopoides C3116)
(marker loci-mainly RFLPs)3335714[31]Einkorn wheatRILs (Triticum boeoticum×T. monococcum)
marker loci-RFLPs, SSR1771262[5]Durum wheatRILs
(T. durum var. Messapia ×T. turgidium var. MG4343)
(marker loci-RFLP, Glu3B,
others)2131352[32]Durum wheatRILs
(T. durum var. Messapia ×T. turgidium var. MG4343)
(marker loci-AFLPs, RFLPs)882063[33]Durum wheatRILs (Jennah Khetifa× Cham10
(marker loci-RFLPs, SSRs, AFLPs)2063598[34]Durum wheat*RILs (Omrabi 5×T dicoccoides 600545)
(marker loci-SSRs, AFLPs)3122289[35]Bread wheatRILs (T. aestivum L. var.
Forno ×T. spelta L. var. Oberkulmer)
(marker loci-RFLPs, SSRs)2302469[36]Bread wheat*DHs (CM-82036× Remus) (marker loci-RFLPs, AFLPs, SSRs,
etc.)3841860[37]Bread wheat*DHs (Savannah× Senat)
(marker loci-SSRs, AFLPs)345 (17)2300[38]Bread wheat*RILs (Renan× Récital)
(marker loci-SSRs, RFPLs, AFLPs)265 (17)2722[39, 40]Bread wheatF5s(Arina × Forno)
(marker loci-RFLPs, SSRs)3963086[41]Bread wheatDHs (Courtot× Chinese Spring)
(marker loci-RFLPs, SSRs, AFLPs)6593685[42]Bread wheat*DHs (Frontana× Remus)
(marker loci-SSRs, STSs, AFLPs,
etc.)5352840[43]Bread wheatRILs (Grandin× BR34) (marker loci-TRAPs, SSRs)3523045[44]Bread wheat*DHs (Spring× SQ1) (marker loci-AFLPs, SSRs)5673521[45]Bread wheat*RILs (Dream× Lynx) (marker loci-SSRs, STSs, AFLPs)283 (17)1734[46]Bread wheat*DHs
(AC Karma× 87E03-S2B1)
(marker loci-STSs, SSRs, etc.)167 (15)2403[47]Bread wheat*DHs (Trident× Molineux)
(marker loci-SSRs, STSs, RFLPs,
etc.)2513061[48]Bread wheat*DH (Arina× Riband) (marker loci-AFLPs, SSRs)2791199[49]Bread wheat*DHs (RL4452× AC Domain) (marker loic-SSRs, genes, etc.)3692793[50]Bread wheat*RILs (Chuan 35050× Shannong 483)
(marker loci-SSRs, EST-SSRs,
ISSRs, SRAPs,TRAPs, Glu loci)3813636[51]Bread wheat*DHs (Shamrock× Shango) (marker loci-SSRs, DArTs)2631337[52]Bread wheatDHs Cranbrook× Halberd
(Marker loci-SSRs, RFLPs, AFLPs, DArTs,
STSs)7492937[53]*These are framework linkage map
prepared for QTL analyses.In addition to random DNA markers (RDM),
gene targeted markers (GTMs) and functional markers (FMs) are also being used
in wheat to facilitate identification of genes responsible for individual
traits and to improve possibilities of using MAS in wheat breeding. As a
corollary, functional markers (FMs) are also being developed from the available
gene sequences [10]. These markers were also used to construct
transcript and molecular functional maps. Recently, microarray-based
high-throughput diversity array technology (DArT) markers were also developed
and used for preparing genetic maps in wheat [53, 54]. Large-scale genotyping for dozens to thousands of SNPs is also being undertaken using
several high-density platforms including Illumina’s GoldenGate and ABI’s
SNaPshot platforms (http://wheat.pw.usda.gov/SNP/new/index.shtml). The genotyping activity may be extended further through the use
of Solexa’s high throughput and low-cost resequencing technology.


## 2.2. Molecular Marker-Based Physical Maps

Molecular markers in bread wheat
have also been used for the preparation of physical maps, which were then
compared with the available genetic maps involving same markers. These maps
allowed comparisons between genetic and physical distances to give information
about variations in recombination frequencies and cryptic structural changes
(if any) in different regions of individual chromosomes. Several methods have
been employed for the construction of physical maps.


### 2.2.1. Deletion Mapping

In wheat, physical mapping of genes to individual
chromosomes began with the development of aneuploids [55], which led to
mapping of genes to individual chromosomes. Later, deletion lines of wheat
chromosomes developed by Endo and Gill [3] were extensively used as a tool
for physical mapping of molecular markers. Using these deletion stocks, genes
for morphological characters were also mapped to physical segments of wheat
chromosomes directly in case of unique and genome specific markers or
indirectly in case of duplicate or triplicate loci through the use of
intergenomic polymorphism between the A, B, and D subgenomes (see Table 2 for
details of available physical maps). In addition to physical mapping of genomic
SSRs, ESTs and EST-SSRs were also subjected to physical mapping (see Table 2). As a
part of this effort, a major project (funded by National Science Foundation, USA)
on mapping of ESTs in wheat was successfully completed by a consortium of 13
laboratories in USA leading to physical mapping of ∼16000 EST loci (http://wheat.pw.usda.gov/NSF/progress_mapping.html; [56] (see Table 2)).Table 2
Deletion-based physical maps of common wheat.
Homoeologous group/Marker lociNo. of deletionReferencechromosome/armmappedstocks used119 RFLPs18[57]150 RFLPs56[58]230 RFLPs21[59]243 SSRs25[60]329 RFLPs25[61]440 RFLPs39[62]5155 RFLPs65[63]5245
RFLPs,
3 SSRs36[64]5S100 RFLPs17[65]5A22 RFLPs19[66]624 RFLPs26[67]6210 RFLPs45[68]6S82 RFLPs14[69]716 RFLPs41[70]791 RFLPs,
6 RAPDs54[71]6B, 2D,
and 7D16 SSRs13[72]1BS24 AFLPs8[73]4DL61 AFLPs,
2 SSRs,
2 RFLPs8[74]1BS22 ESTs2[75]Whole
genome725 SSRs118[76]Whole
genome260 BARC117[27]Whole
genome313 SSRs162[77]Whole
genome16000 ESTs101http://wheat.pw.usda.gov/NSF/progressmapping.htmlWhole
genome266 eSSRs105[78]Whole
genome672 EST-SSRs101[79]


### 2.2.2. In Silico Physical Mapping

As many as 16000 wheat EST loci assigned to
deletion bins, as mentioned above, constitute a useful source for in silico
mapping, so that markers with known sequences can be mapped to wheat
chromosomes through sequence similarity with mapped EST loci available at
GrainGene database (http://wheat.pw.usda.gov/GG2/blast.shtml). Using the above approach, Parida et al. [80] were able to map 157 SSR
containing wheat unique sequences (out of 429 class I unigene-derived
microsatellites (UGMS) markers developed in wheat) to chromosome bins. These
bin-mapped UGMS markers provide valuable information for a targeted mapping of
genes for useful traits, for comparative genomics, and for sequencing of
gene-rich regions of the wheat genome. Another set of 672 loci belonging to 275
EST-SSRs of wheat and rye was assigned to individual bins through in silico and
wet-lab approaches by Mohan et al. [79]. A few cDNA clones associated with QTL
for FHB resistance in wheat were also successfully mapped using in silico approach [81].


### 2.2.3. Radiation-Hybrid Mapping

Radiation hybrid (RH) mapping was first
described by Goss and Harris [82] and was initially used by Cox et al. [83]
for physical mapping in animals/humans. In wheat, the approach has been used at
North Dakota State University (NDSU) utilizaing addition and substituition of
individual D-genome chromosomes into tetraploid durum wheat. For RH mapping of
1D, durum wheat alien substitution line for chromosome 1D (DWRH-1D), harboring
nuclear-cytoplasmic compatibility gene scsae was used. These RH
lines initially allowed detection of 88 radiation-induced breaks involving 39
1D specific markers. Later, this 1D RH map was further expanded to a resolution
of one break every 199 kb of DNA, utilizing 378 markers [84]. Using the same approach, construction of radiation hybrid map for
chromosome 3B is currently in progress (S. Kianian personal communication).


## 2.2.1. Deletion Mapping

In wheat, physical mapping of genes to individual
chromosomes began with the development of aneuploids [55], which led to
mapping of genes to individual chromosomes. Later, deletion lines of wheat
chromosomes developed by Endo and Gill [3] were extensively used as a tool
for physical mapping of molecular markers. Using these deletion stocks, genes
for morphological characters were also mapped to physical segments of wheat
chromosomes directly in case of unique and genome specific markers or
indirectly in case of duplicate or triplicate loci through the use of
intergenomic polymorphism between the A, B, and D subgenomes (see Table 2 for
details of available physical maps). In addition to physical mapping of genomic
SSRs, ESTs and EST-SSRs were also subjected to physical mapping (see Table 2). As a
part of this effort, a major project (funded by National Science Foundation, USA)
on mapping of ESTs in wheat was successfully completed by a consortium of 13
laboratories in USA leading to physical mapping of ∼16000 EST loci (http://wheat.pw.usda.gov/NSF/progress_mapping.html; [56] (see Table 2)).Table 2
Deletion-based physical maps of common wheat.
Homoeologous group/Marker lociNo. of deletionReferencechromosome/armmappedstocks used119 RFLPs18[57]150 RFLPs56[58]230 RFLPs21[59]243 SSRs25[60]329 RFLPs25[61]440 RFLPs39[62]5155 RFLPs65[63]5245
RFLPs,
3 SSRs36[64]5S100 RFLPs17[65]5A22 RFLPs19[66]624 RFLPs26[67]6210 RFLPs45[68]6S82 RFLPs14[69]716 RFLPs41[70]791 RFLPs,
6 RAPDs54[71]6B, 2D,
and 7D16 SSRs13[72]1BS24 AFLPs8[73]4DL61 AFLPs,
2 SSRs,
2 RFLPs8[74]1BS22 ESTs2[75]Whole
genome725 SSRs118[76]Whole
genome260 BARC117[27]Whole
genome313 SSRs162[77]Whole
genome16000 ESTs101http://wheat.pw.usda.gov/NSF/progressmapping.htmlWhole
genome266 eSSRs105[78]Whole
genome672 EST-SSRs101[79]


## 2.2.2. In Silico Physical Mapping

As many as 16000 wheat EST loci assigned to
deletion bins, as mentioned above, constitute a useful source for in silico
mapping, so that markers with known sequences can be mapped to wheat
chromosomes through sequence similarity with mapped EST loci available at
GrainGene database (http://wheat.pw.usda.gov/GG2/blast.shtml). Using the above approach, Parida et al. [80] were able to map 157 SSR
containing wheat unique sequences (out of 429 class I unigene-derived
microsatellites (UGMS) markers developed in wheat) to chromosome bins. These
bin-mapped UGMS markers provide valuable information for a targeted mapping of
genes for useful traits, for comparative genomics, and for sequencing of
gene-rich regions of the wheat genome. Another set of 672 loci belonging to 275
EST-SSRs of wheat and rye was assigned to individual bins through in silico and
wet-lab approaches by Mohan et al. [79]. A few cDNA clones associated with QTL
for FHB resistance in wheat were also successfully mapped using in silico approach [81].


## 2.2.3. Radiation-Hybrid Mapping

Radiation hybrid (RH) mapping was first
described by Goss and Harris [82] and was initially used by Cox et al. [83]
for physical mapping in animals/humans. In wheat, the approach has been used at
North Dakota State University (NDSU) utilizaing addition and substituition of
individual D-genome chromosomes into tetraploid durum wheat. For RH mapping of
1D, durum wheat alien substitution line for chromosome 1D (DWRH-1D), harboring
nuclear-cytoplasmic compatibility gene scsae was used. These RH
lines initially allowed detection of 88 radiation-induced breaks involving 39
1D specific markers. Later, this 1D RH map was further expanded to a resolution
of one break every 199 kb of DNA, utilizing 378 markers [84]. Using the same approach, construction of radiation hybrid map for
chromosome 3B is currently in progress (S. Kianian personal communication).


## 2.3. BAC-Based Physical Maps

BAC-based physical map of wheat
D genome is being constructed using the diploid species,Aegilops tauschii, with
the aim to identify and map genes and later sequence the gene-rich regions
(GRRs). For this purpose, a large number of BACs were first fingerprinted and
assembled into contigs. Fingerprint contigs (FPCs) and the data related to
physical mapping of the D genome are available in the database (http://wheat.pw.usda.gov/PhysicalMapping/index.html). BACs
belonging to chromosome 3B are also being fingerprinted (with few BACs already anchored to wheat bins), and a
whole genome BAC-based physical map of hexaploid wheat is proposed to be
constructed under the aegis of IWGSC in its pilot studies (see later).


## 3. In Situ Hybridization Studies in Wheat

In bread wheat, in situ hybridization (ISH) involving radioactively labeled probes was initially
used to localize repetitive DNA sequences, rRNA and alien DNA segments [104–106]. Later,
fluorescence in situ hybridization (FISH), multicolor FISH (McFISH, simultaneous
detection of more than one probe), and genome in situ hybridization (GISH, total
genomic DNA as probe) were used in several studies. FISH with some repeated
sequences as probes was used for identification of individual chromosomes
[107–110]. FISH was also utilized to physically map rRNA multigene family [111, 112], RFLP markers [110, 113], and
unique sequences [114–116] and
also for detecting and locating alien chromatin introgressed into wheat [117–119].A novel high-resolution FISH strategy using super-stretched flow-sorted chromosomes was also used (extended DNA
fibre-FISH; [120–122]) to fine map DNA
sequences [123, 124] and to confirm integration of
transgenes into the wheat genome [125].Recently, BACs were also utilized as probes for the so called BAC-FISH which helped not only in the discrimination
between the three subgenomes, but also in the identification of intergenomic translocations,
molecular cytogenetic markers, and individual chromosomes [126].
BAC-FISH also helped in localization of genes (BACs carrying genes) and in
studying genome evolution and organization among wheat and its relatives [110, 127, 128].


## 4. Map-Based Cloning in Wheat

In wheat, a number of genes for some important traits including disease resistance,
vernalization response, grain protein content, free threshing habit, and
tolerance to abiotic stresses have been recently cloned/likely to be cloned via
map-based cloning (see Table3). The first genes to be isolated
from wheat by map-based cloning included three resistance genes, against fungal
diseases, including leaf rust (Lr21; [88, 129, 130] and Lr10;
[87]) and powdery mildew (Pm3b ; [94]). A
candidate gene for the Q locus conferring free threshing character to
domesticated wheat was also cloned [92]. This gene influences
many other domestication-related traits like glume shape and tenacity, rachis
fragility, plant height, spike length, and ear-emergence time. Another
important QTL, Gpc-B1, associated with increased grain protein, zinc,
and iron content has been cloned, which will contribute in breeding enhanced
nutritional value wheat in future [96]. Cloning of three genes
for vernalization response (VRN1, VRN2, VRN3) helped in postulating a
hypothetical model summarizing interactions among these three genes [89–91, 131].Table 3
Genes already cloned or likely to be cloned through map-based cloning in wheat.
Gene/QTLTraitReferenceLr1Leaf rust resistance[85, 86]Lr10Leaf rust resistance[87]Lr21Leaf rust resistance[88]VRN1Vernalization response[89]VRN2Vernalization response[90]VRN3Vernalization response[91]QFree threshing character[92, 93]Pm3bPowdery mildew resistance[94, 95]GPC-B1High grain protein content[96, 97]Qfhs.Ndsu-3bsFusarium head blight resistance[98]Yr5Resistance to stripe rust[99]BBoron tolerance[100]Fr2Frost resistancehttp://www.agronomy.ucdavis.edu/DubcovskyEPS-1Flowering timehttp://www.agronomy.ucdavis.edu/DubcovskyTsn1Host-selective toxinPtr ToxA[101]Ph1Chromosome pairing locus[102]Sr2Stem rust resistance[103]


## 5. EST Databases and their Uses

During the last 8–10 years, more
than 1240455 wheat ESTs have become available in the public domain as in
January 2008 (http://www.ncbi.nlm.nih.gov/). A
number of cDNA libraries have been used for this purpose. These ESTs proved to
be an enormous resource for a variety of studies including development of
functional molecular markers (particularly SSRs and SNPs), construction of a
DNA chip, gene expression, genome organization, and comparative genomics
research.


### 5.1. EST-Derived SSRs

Wheat ESTs have been extensively used for SSR mining (1SSR/10.6 kb; [80]), so that in our own laboratory and elsewhere detected by author, a large number of
SSRs have already been developed from EST sequences [22, 78, 80, 132–134]. These EST-SSRs served as a valuable source for a variety
of studies including gene mapping, marker-aided selection (MAS), and eventually
positional cloning of genes. The ESTs and EST-derived SSRs were also subjected
to genetic and physical mapping (see above).Since EST-SSRs are derived from the
expressed portion of the genome, which is relatively more conserved, these
markers show high level of transferability among species and genera [133, 135]. However, the transferability of wheat EST-SSRs to
closely related triticeae species (Triticum and Aegilops species)
is higher as compared to more distant relatives such as barley, maize, rice,
sorghum, oats, and rye. The EST-SSRs thus also prove useful in comparative mapping, transfer
of markers to orphanage wild species, and for genetic diversity estimates
[79, 132, 134, 136–139].


### 5.2. EST-Derived SNPs and the International SNP Consortium

In recent years, single nucleotide polymorphisms (SNPs) have become the
markers of choice. Therefore, with the aim to discover and map SNPs in
tetraploid and hexaploid wheats, an International Wheat SNP Consortium was
constituted, and comprehensive wheat SNP database was developed (http://wheat.pw.usda.gov/SNP/new/index.shtml).
Approximately 6000 EST unigenes from the database of mapped ESTs and other EST
databases were distributed to consortium members for locating SNPs, for
designing conserved primers for these SNPs and for validation of these SNP.
Considerable progress has been made in this direction in different
laboratories; the project data are accessible through http://wheat.pw.usda.gov/SNP/snpdb.html.
In May 2006, the database contained 17174 primers (forward and reverse), 1102
wheat polymorphic loci, and 2224 polymorphic sequence tagged sites in diploid
ancestors of polyploid wheat. Zhang et al. [140] also reported 246 gene loci
with SNPs and/or small insertions/deletions from wheat homoeologous group 5.
Another set of 101 SNPs (1SNP/212 bp) was discovered from genomic sequence
analysis in 26-bread wheat lines and one synthetic line (http://urgi.versailles.inra.fr/GnpSNP/,
[141]).


## 5.1. EST-Derived SSRs

Wheat ESTs have been extensively used for SSR mining (1SSR/10.6 kb; [80]), so that in our own laboratory and elsewhere detected by author, a large number of
SSRs have already been developed from EST sequences [22, 78, 80, 132–134]. These EST-SSRs served as a valuable source for a variety
of studies including gene mapping, marker-aided selection (MAS), and eventually
positional cloning of genes. The ESTs and EST-derived SSRs were also subjected
to genetic and physical mapping (see above).Since EST-SSRs are derived from the
expressed portion of the genome, which is relatively more conserved, these
markers show high level of transferability among species and genera [133, 135]. However, the transferability of wheat EST-SSRs to
closely related triticeae species (Triticum and Aegilops species)
is higher as compared to more distant relatives such as barley, maize, rice,
sorghum, oats, and rye. The EST-SSRs thus also prove useful in comparative mapping, transfer
of markers to orphanage wild species, and for genetic diversity estimates
[79, 132, 134, 136–139].


## 5.2. EST-Derived SNPs and the International SNP Consortium

In recent years, single nucleotide polymorphisms (SNPs) have become the
markers of choice. Therefore, with the aim to discover and map SNPs in
tetraploid and hexaploid wheats, an International Wheat SNP Consortium was
constituted, and comprehensive wheat SNP database was developed (http://wheat.pw.usda.gov/SNP/new/index.shtml).
Approximately 6000 EST unigenes from the database of mapped ESTs and other EST
databases were distributed to consortium members for locating SNPs, for
designing conserved primers for these SNPs and for validation of these SNP.
Considerable progress has been made in this direction in different
laboratories; the project data are accessible through http://wheat.pw.usda.gov/SNP/snpdb.html.
In May 2006, the database contained 17174 primers (forward and reverse), 1102
wheat polymorphic loci, and 2224 polymorphic sequence tagged sites in diploid
ancestors of polyploid wheat. Zhang et al. [140] also reported 246 gene loci
with SNPs and/or small insertions/deletions from wheat homoeologous group 5.
Another set of 101 SNPs (1SNP/212 bp) was discovered from genomic sequence
analysis in 26-bread wheat lines and one synthetic line (http://urgi.versailles.inra.fr/GnpSNP/,
[141]).


## 6. BAC/BIBAC Resources

BAC/BIBAC libraries have been produced in diploid, tetraploid, and hexaploid wheats
(see Table4). Chromosome-specific BAC libraries were also prepared in hexaploid
wheat [142–144]. These BAC
resources proved useful for a variety of studies including map-based cloning
(see Table 3), organization of wheat genome into gene-rich and gene-poor regions
that are loaded with retroelements [8, 145–147], and for physical mapping and sequencing
of wheat genome (http://wheatdb.ucdavis.edu:8080/wheatdb/, [11]).Table 4
BAC libraries available in wheat.
Species (accession)CoverageRestriction siteNo. of clones (clone size in kb)CuratorT. monococcum (DV92)5.6 XHind III276000 (115)J. DubcovskyT. dicoccoides (Langdon)5.0 XHind III516000 (130)J. DubcovskyT. urartu (G1812)4.9 XBamH I163200 (110)J. DvorakAe. tauschii (AL8/78)2.2 XEcoR I54000 (167)H.B. ZhangAe. tauschii (AL8/78)2.2 XHind III59000 (189)H.B. ZhangAe. tauschii (AL8/78)3.2 XHind III52000 (190)H.B. ZhangAe. tauschii (AL8/78)2.8 XBamH I59000 (149)H.B. ZhangAe. tauschii (AL8/78)2.4 XBamH I76000 (174)H.B. ZhangAe. tauschii (Aus 18913)4.2 XHind III144000 (120)E. LagudahAe. tauschii (AS75)4.1 XBamH I181248 (115)J. DvorakAe. speltoides (2-12-4-8-1-1-1)5.4 XBamH I237312 (115)J. DvorakT. aestivum (Glenlea)3.1 XBamH I & Hind III656640 (80)S. CloutierT. aestivum (Renan)3.2 XHind III478840 (150)B. ChalhoubT. aestivum (Renan)2.2 XEcoR I285312 (132)B. ChalhoubT. aestivum (Renan)1.5 XBamH I236160 (122)B. ChalhoubT. aestivum (Chinese Spring)Hind III950000 (54)Y. OgiharaT. aestivum (Chinese Spring)< 4%Mlu I>12000 (45)K. WillarsNot I>1000T. aestivum (Chinese Spring) 3B6.2 XHind III67968 (103)J. Dolezel & B. ChalhoubT. aestivum, (Chinese Spring) 1D, 4D & 6D3.4 XHind III87168 (85)J. Dolezel &
B. ChalhoubT. aestivum (Pavon) 1BS14.5 XHind III65280 (82)J. Dolezel &
B. ChalhoubT. aestivum (AVS-Yr5)3.6 XHind III422400 (140)X.M. ChenT. aestivum (Norstar)5.5 XHind III1200000 (75)R. Chibbar


## 7. Gene Distribution in Wheat: Gene-Rich and Gene-Poor Regions

Genetic and physical maps of the wheat genome, discussed above, have been utilized for
a study of gene distribution within the genome [58, 63, 148]. In order to identify and demarcate the gene-containing regions, 3025 loci
including 252 phenotypically characterized genes and 17 quantitative trait loci
(QTL) were physically mapped with the help of deletion stocks [149, 150]. It was shown that within the genome, genes are not
distributed randomly and that there are gene-rich regions (GRRs) and gene-poor
regions (GPRs), not only within the wheat genome, but perhaps in all eukaryotes
(for reviews, see [4, 151]).In wheat genome, 48 GRRs containing 94% of
gene markers were identified with an average of∼7 such GRRs (range 5–8) per
homoeologous group. It was also shown that different wheat chromosomes differed
for number and location of GRRs, with 21 GRRs on the short arms containing 35%
of the wheat genes, and the remaining 27 GRRs on the long arms containing about
59% of the genes. The GRRs also vary in their size and in gene-density with a
general trend of increased gene-density toward the distal parts of individual
chromosome arms. This is evident from the fact that more than 80% of the total
marker loci were mapped in the distal half of the chromosomes and ∼58% mapped
in the distal 20%.Among 48 GRRs, there were 18 GRRs (major
GRRs), which contained nearly 60% of the wheat genes, covering only 11% of the
genome, suggesting a very high density of genes in these GRRs, although the
number and density of genes in these 18 GRRs was also variable [149, 150]. It has also been shown that the size of GRRs decreases and
the number of GRRs increases, as the genome size increases from rice to wheat
[4]. For instance, the average size of gene clusters in rice is ∼300 kb
as compared to less than 50 kb in wheat and barley. However, no correlation
was observed between the chromosome size and the proportion of genes or the
size of the GRRs. For instance, group 3 has the longest chromosomes among the
wheat homoeologous groups but contained only 13% of the genes compared to group
5 chromosomes that contained 20% of genes [150].For the chromosomes of homoeologous group
1, the distribution of genes and recombination rates have been studied in a
relatively greater detail. Each chromosome of this group (1A, 1B, 1D) has eight
GRRs (ranging in size from 3 Mb to 35 Mb), occupying∼119 Mb of the 800-Mb-long chromosome. Using
this homoeologous group, it was confirmed that the GRRs differ in the number of
genes and gene-density even within a chromosome or its arms. For instance, the
“1S0.8 region” is the smallest of all GRRs, but has the highest gene-density,
which is ∼12 times that in the “1L1.0 region.”The distribution of GRRs has also been
compared with the distribution of chromosome breaks involved in the generation of
deletion stocks that are currently available and have been used for physical
mapping of wheat genome. It was found that the breakpoints are nonrandom, and
occur more frequently around the GRRs (one break every 7 Mb; [58, 67]); they seem to occur around GRRs twice as frequently as one would expect on
random basis (one break every 16 Mb; [149]). Consequently, GRRs
interspersed by <7-Mb-long GPRs will not be resolved and better resolution
would be needed to partition the currently known GRRs into mini-GRRs and GPRs.It has also been inferred that perhaps in eukaryotic genomes, the “gene-poor” regions preferentially enlarged during
evolution, as is obvious in wheat, where large, essentially, “gene-empty”
blocks of up to∼192 Mb are common. Taking polyploidy into account, 30%
gene-rich part of the genome is still ∼4 times larger than the entire rice
genome [149]. Therefore, gene distribution within the
currently defined GRRs of wheat would probably be similar to that in the rice
genome, except that the gene-clusters would be smaller and the interspersing
“gene-empty” regions would be larger, similar to barley as described above. It
has also been shown that the “gene-empty” regions of the higher eukaryotic
genomes are mainly comprised of retrotransposons and pseudogenes [152, 153]. The proportion of retrotransposons is significantly higher than pseudogenes,
especially in the larger genomes, like those of maize and bread wheat.


## 8. Variable Recombination Rates

The recombination rate has also been recently shown to vary in different regions of
the wheat genome. This was demonstrated through a comparison of consensus
physical and genetic maps involving 428 common markers [149, 150]. Recombination in the distal regions was generally found to be
much higher than that in the proximal half of individual chromosomes, and a
strong suppression of recombination was observed in the centromeric regions.
Recombination rate among GRRs present in the distal half of the chromosome was
highly variable with higher recombination in some proximal GRRs than in the
distal GRRs [149, 150]. The gene poor-regions
accounted for only ∼5% of recombination.It has also been reported that the
distribution of recombination rates along individual chromosomes is uneven in
all eukaryotes studied so far (for more references,
see [154, 155]). Among
cereals, the average frequency of recombination in rice (with the smallest
genome) is translated into a genetic distance of about 0.003 cM per kb with a
range of 0 to 0.06 cM per kb (http://rgp.dna.affrc.go.jp/Publicdata.html)
and that of wheat (the largest genome) is 0.0003 cM per kb with a range from 0 to
0.007 cM per kb. Non-recombinogenic regions were
observed in yeast as well as in rice, but the highest recombination rate for a
region appears to be ∼35-fold lower in rice and 140-fold lower in bread wheat
(relative to yeast). It may be due to differences in the resolution of
recombination rates, which is ∼400 kb in rice (in wheat the resolution is much lower
than in rice), whereas the resolution in recombination hotspots in yeast may be
as high as only <1 kb in length. Due to averaging over larger regions,
recombination in hotspots in rice and wheat may appear to be low relative to
that in yeast [4, 150, 151, 156].


## 9. Flow Cytogenetics and Microdissection of Chromosomes in Wheat

Flow cytogenetics and microdissection
facilitated physical dissection of the large wheat genome into smaller and
defined segments for the purpose of gene discovery and genome sequencing. Flow
karyotypes of wheat chromosomes were also prepared [157–159].
DNA obtained from the flow-sorted chromosomes has been used for the
construction of chromosome-specific large-insert DNA libraries, as has been
done for chromosome 4A [157, 159]. Later, all individual 42 chromosome arms involving
21 wheat chromosomes were also sorted out using flow cytometry [160]. In another study, it was also possible to microdissect 5BL
isochromosomes from meiotic cells and to use their DNA with degenerate oligonucleotide
primer PCR (DOP-PCR) to amplify chromosome arm-specific DNA sequences. These
amplified PCR sequences were then used as probes for exclusive painting of 5BL [161].Flow sorting in wheat has also been used for efficient construction of
bacterial artificial chromosome (BAC) libraries for individual chromosomes
[143, 162]. The use of these chromosome- and
chromosome arm-specific BAC libraries is expected to have major impact on wheat
genomics research [1]. For instance, the availability of
3B-specific BAC library facilitated map-based cloning of agronomically
important genes such as major QTL for Fusarium head blight resistance [98]. Flow cytometry can also be used to detect numerical and structural
changes in chromosomes and for the detection of alien chromosomes or segments
thereof (reviewed in detail by [163]).
For instance, a 1BL.1RS translocation could be detected by a characteristic
change in the flow karyotype [164]. In addition, DNA from
flow-sorted chromosomes can be used for hybridization on DNA arrays and chips,
with the aim of assigning DNA sequences to specific chromosome arms. This
technique will be extensively used now with the availability of Affymetrix
wheat GeneChip [165].


## 10. Wheat Gene Space Sequencing

International Triticeae Mapping Initiative (ITMI), at its meeting held at Winnipeg, Canada during
June 1–4, 2003, took the first initiative toward whole genome sequencing (WGS) in wheat and decided to
launch a project that was described as International Genome Research of Wheat (IGROW)
by B. S. Gill. A workshop on wheat genome sequencing was later organized in Washington, DC during November
11–13, 2003, which was followed by another meeting of IGROW during the National Wheat Workers
Workshop organized at Kansas, USA, during Feb 22–25, 2004 [166]. Consequently, IGROW developed into an International Wheat Genome
Sequencing Consortium (IWGSC). Chinese Spring (common wheat) was selected for
WGS, since it already had ample genetic and molecular resources [1].Three phases were proposed for sequencing
the wheat genome: pilot, assessment, and scale up. The first phase was
recommended for 5 years and is mainly focused on the short-term goal of IWGSC,
involving physical and genetic mapping along with sample sequencing of the
wheat genome aimed at better understanding of the wheat genome structure. The
assessment phase will involve determining which method(s) can be used in a
cost-effective manner to generate the sequence of the wheat genome. After a full
assessment, the scale-up phase will involve the deployment of optimal methods
on the whole genome, obtaining the genome sequence and annotation, which is the
long-term goal of IWGSC. With the availability of new sequencing technologies
provided by 454/Roche and those provided by Illumina/Solexa and ABI SOLiD
[167]; sequencing of gene space of the wheat genome, which was
once thought to be almost impossible, should become possible within the
foreseeable future.First pilot project for sequencing of gene space of wheat genome, led by
INRA in France, was initiated in 2004 using the largest wheat chromosome, 3B (1GB = 2x the rice genome) of
hexaploid wheat as a model. As many as 68000 BAC
clones from a 3B chromosome specific BAC library [143] were
fingerprinted and assembled into contigs, which were then anchored to wheat
bins, covering ∼80% of chromosome 3B. Currently, one or more of these contigs
are being sequenced [11], which will demonstrate the feasibility
of large-scale sequencing of complete gene space of wheat genome.


## 11. Functional Genomics

The determination of the functions of all the genes in a plant genome is the most challenging
task in the postgenomic era of plant biology. However, several techniques or
platforms, like serial analysis of gene expression (SAGE), massively parallel
signature sequencing (MPSS), and micro- and macroarrays, are now available in
several crops for the estimation of mRNA abundance for large number of genes
simultaneously. The microarrays have also been successfully used in wheat for
understanding alterations in the transcriptome of hexaploid wheat during grain
development, germination and plant development under abiotic stresses [168, 169]. Recently, a comparison was made between Affymetrix GeneChip
Wheat Genome Array (an in-house custom-spotted complementary DNA array) and
quantitative reverse transcription-polymerase chain reaction (RT-PCR) for the
study of gene expression in hexaploid wheat [170]. Also,
functional genomics approach in combination with “expression genetics” or
“genetical genomics” provides a set of candidate genes that can be used for
understanding the biology of a trait and for the development of perfect or
diagnostic marker(s) to be used in map-based cloning of genes and MAS [9]. A similar example was provided by Jordan et al. [9], when they
identified regions of wheat genome controlling seed development by mapping 542
eQTLs, using a DH mapping popultion that was earlier used for mapping of SSRs
and QTL analysis of agronomic and seed quality traits [171].
Expression analysis using mRNA from developing seeds from the same mapping
population was also conducted using Affymetrix GeneChip Wheat Genome Array
[172].


### 11.1. RNA Interference for Wheat Functional Genomics

RNA interference (RNAi), which was the subject of the 2006 Nobel Prize in
Physiology or Medicine, is also being extensively utilized for improvement of crop
plants [173]. This technique does not involve introduction of
foreign genes and thus provides an alternative to the most controversial
elements of genetic modification. Plans in Australia are underway, where the
knowledge gained from RNAi approach will be used for developing similar wheats
by conventional method of plant breeding, as suggested by CSIRO scientists for
developing high-fibre wheat [174]. In bread wheat, in particular, the
technology provides an additional advantage of silencing all genes of a
multigene family including homoeoloci for individual genes, which are often
simultaneously expressed, leading to a high degree of functional gene
redundancy [175]. It has been shown that delivery of specific
dsRNA into single epidermal cells in wheat transiently interfered with gene
function [176, 177]. Yan et al. [90]
and Loukoianov et al. [178] used RNAi for stable transformation and to
demonstrate that RNAi-mediated reduction of VRN2 and VRN1 transcript levels, respectively, accelerated and delayed flowering initiation
in winter wheat. Similarly, Regina et al. [179] used RNAi to generate
high-amylose wheat. However, none of the above studies reported long-term
phenotypic stability of RNAi-mediated gene silencing over several generations,
neither did they report any molecular details on silencing of homoeologous
genes. However, Travella et al. [180] showed RNAi results in stably inherited
phenotypes suggesting that RNAi can be used as an efficient tool for functional
genomic studies in polyploid wheat. They introduced dsRNA-expressing constructs
containing fragments of genes encoding Phytoene Desaturase (PDS)
or the signal transducer of ethylene, Ethylene Insensitive 2 (EIN2)
and showed stably inherited phenotypes of transformed wheat plants that were
similar to mutant phenotypes of the two genes in diploid model plants.
Synthetic microRNA constructs can also be used as an alternative to large RNA
fragments for gene silencing, as has been demonstrated for the first time in
wheat by Yao et al. [181] by discovering and predicting targets for 58 miRNAs,
belonging to 43 miRNA families (20 of these are conserved and 23 are novel to
wheat); more importantly four of these miRNAs are monocot specific. This study
will serve as a foundation for the future functional genomic studies. The
subject of the use of RNAi for functional genomics in wheat has recently been
reviewed [173].


### 11.2. TILLING in Wheat

Recently, Targeting Induced
Local Lesions IN Genomes (TILLING) was developed as a reverse genetic approach
to take advantage of DNA sequence information and to investigate functions of
specific genes [182]. TILLING was initially developed for
model plant Arabidopsis thaliana [183] having fully
sequenced diploid genome and now has also been successfully used in complex
allohexaploid genome of wheat, which was once considered most challenging
candidate for reverse genetics [184].To demonstrate the utility of TILLING for
complex genome of bread wheat, Slade et al. [185] created TILLING library in
both bread and durum wheat and targeted waxy locus, a well characterized
gene in wheat encoding granule bound starch synthase I (GBSSI). Loss
of all copies of this gene results in the production of waxy starch
(lacking amylose). Production of waxy wheat by traditional breeding was
difficult due to lack of genetic variation at one of the waxy loci.
However, targeting waxy loci by
TILLING [185], using locus specific PCR primers led to
identification of 246 alleles (196 alleles in hexaploid and 50 alleles in
tetraploid) using 1920 cultivars of wheat (1152 hexaploid and 768 tetraploid).
This made available novel genetic diversity at waxy loci and provided a
way for allele mining in important germplasm of wheat. The approach also
allowed evaluation of a triple homozygous mutant line containing mutations in
two waxy loci (in addition to a naturally occurring deletion of the third locus)
and exhibiting a near waxy phenotype.Another example of on-going research using
TILLING in wheat is the development of EMS mutagenised populations ofT.
aestivum (cv. Cadenza, 4200 lines, cv. Paragon, 6000 lines), T. durum (cv. Cham1, 4,200 lines), and T. monococcum (Accession DV92, 3000 lines)
under the Wheat Genetic Improvement Network (WGIN; funded by Defra and BBSRC in
the UK and by the EU Optiwheat programme). The aim of this program is to search
noval variant alleles for Rht-b1c,RAR-1, SGT-1, and NPR-1 genes
(personal communication: andy.phillips@bbsrc.ac.uk
and Simon.Orford@bbsrc.ac.uk).The above examples provide
proof-of-concept for TILLING other genes, whose mutations may be desired in
wheat or other crops. However, homoeolog-specific primers are required in order
to identify new alleles via TILLING in wheat. In case ofwaxy, the
sequences of the three homoeologous sequences were already known, which
facilitated primer designing, but TILLING of other genes may require cloning
and sequencing of these specific genes in order to develop homoeolog-specific
target primers.


## 11.1. RNA Interference for Wheat Functional Genomics

RNA interference (RNAi), which was the subject of the 2006 Nobel Prize in
Physiology or Medicine, is also being extensively utilized for improvement of crop
plants [173]. This technique does not involve introduction of
foreign genes and thus provides an alternative to the most controversial
elements of genetic modification. Plans in Australia are underway, where the
knowledge gained from RNAi approach will be used for developing similar wheats
by conventional method of plant breeding, as suggested by CSIRO scientists for
developing high-fibre wheat [174]. In bread wheat, in particular, the
technology provides an additional advantage of silencing all genes of a
multigene family including homoeoloci for individual genes, which are often
simultaneously expressed, leading to a high degree of functional gene
redundancy [175]. It has been shown that delivery of specific
dsRNA into single epidermal cells in wheat transiently interfered with gene
function [176, 177]. Yan et al. [90]
and Loukoianov et al. [178] used RNAi for stable transformation and to
demonstrate that RNAi-mediated reduction of VRN2 and VRN1 transcript levels, respectively, accelerated and delayed flowering initiation
in winter wheat. Similarly, Regina et al. [179] used RNAi to generate
high-amylose wheat. However, none of the above studies reported long-term
phenotypic stability of RNAi-mediated gene silencing over several generations,
neither did they report any molecular details on silencing of homoeologous
genes. However, Travella et al. [180] showed RNAi results in stably inherited
phenotypes suggesting that RNAi can be used as an efficient tool for functional
genomic studies in polyploid wheat. They introduced dsRNA-expressing constructs
containing fragments of genes encoding Phytoene Desaturase (PDS)
or the signal transducer of ethylene, Ethylene Insensitive 2 (EIN2)
and showed stably inherited phenotypes of transformed wheat plants that were
similar to mutant phenotypes of the two genes in diploid model plants.
Synthetic microRNA constructs can also be used as an alternative to large RNA
fragments for gene silencing, as has been demonstrated for the first time in
wheat by Yao et al. [181] by discovering and predicting targets for 58 miRNAs,
belonging to 43 miRNA families (20 of these are conserved and 23 are novel to
wheat); more importantly four of these miRNAs are monocot specific. This study
will serve as a foundation for the future functional genomic studies. The
subject of the use of RNAi for functional genomics in wheat has recently been
reviewed [173].


## 11.2. TILLING in Wheat

Recently, Targeting Induced
Local Lesions IN Genomes (TILLING) was developed as a reverse genetic approach
to take advantage of DNA sequence information and to investigate functions of
specific genes [182]. TILLING was initially developed for
model plant Arabidopsis thaliana [183] having fully
sequenced diploid genome and now has also been successfully used in complex
allohexaploid genome of wheat, which was once considered most challenging
candidate for reverse genetics [184].To demonstrate the utility of TILLING for
complex genome of bread wheat, Slade et al. [185] created TILLING library in
both bread and durum wheat and targeted waxy locus, a well characterized
gene in wheat encoding granule bound starch synthase I (GBSSI). Loss
of all copies of this gene results in the production of waxy starch
(lacking amylose). Production of waxy wheat by traditional breeding was
difficult due to lack of genetic variation at one of the waxy loci.
However, targeting waxy loci by
TILLING [185], using locus specific PCR primers led to
identification of 246 alleles (196 alleles in hexaploid and 50 alleles in
tetraploid) using 1920 cultivars of wheat (1152 hexaploid and 768 tetraploid).
This made available novel genetic diversity at waxy loci and provided a
way for allele mining in important germplasm of wheat. The approach also
allowed evaluation of a triple homozygous mutant line containing mutations in
two waxy loci (in addition to a naturally occurring deletion of the third locus)
and exhibiting a near waxy phenotype.Another example of on-going research using
TILLING in wheat is the development of EMS mutagenised populations ofT.
aestivum (cv. Cadenza, 4200 lines, cv. Paragon, 6000 lines), T. durum (cv. Cham1, 4,200 lines), and T. monococcum (Accession DV92, 3000 lines)
under the Wheat Genetic Improvement Network (WGIN; funded by Defra and BBSRC in
the UK and by the EU Optiwheat programme). The aim of this program is to search
noval variant alleles for Rht-b1c,RAR-1, SGT-1, and NPR-1 genes
(personal communication: andy.phillips@bbsrc.ac.uk
and Simon.Orford@bbsrc.ac.uk).The above examples provide
proof-of-concept for TILLING other genes, whose mutations may be desired in
wheat or other crops. However, homoeolog-specific primers are required in order
to identify new alleles via TILLING in wheat. In case ofwaxy, the
sequences of the three homoeologous sequences were already known, which
facilitated primer designing, but TILLING of other genes may require cloning
and sequencing of these specific genes in order to develop homoeolog-specific
target primers.


## 12. Comparative Genomics

In cereals, a consensus map of 12 grass genomes including wheat is now
available, representing chromosome segments of each genome relative to those in
rice on the basis of mapping of anchor DNA markers [186]. Some
of the immediate applications of comparative genomics in wheat include a study
of evolution [187] and isolation/characterization of genes using
the model genome of rice. The genes, which have been examined using comperative
genomics approach include the pairing gene, Ph1 [102, 188], gene(s) controlling preharvest sprouting (PHS; [189]), receptor-like kinase loci [190], gene for grain hardness [191], genes for glume
coloration and pubescence (Bg, Rg; [192]), and the Pm3 gene,
responsible for resistance against powdery mildew [187].Conservation of Colinearity and Synteny
Among cereals, using molecular
markers, colinearity was first reported among A, B, and D subgenomes of wheat
[13, 193], and later in the high-gene density regions
of wheat and barley. At the Lrk10 locus in wheat and its orthologous
region in barley, a gene density of one gene per 4-5 kb was
observed, which was similar to that found in A. thaliana [6]. Conservation of colinearity between homoeologous A genomes of
diploid einkorn wheat and the hexaploid was also exploited for chromosome
walking leading to cloning of candidate gene for the leaf rust resistance locus Lr10 in
bread wheat [194]. Lr10 locus along with
LMW/HMW loci of diploid wheat, when compared with their orthologs from
tetraploid and hexaploid wheats, was found to be largely conserved except some
changes that took place in intergenic regions [195–197]. On the basis of divergence of intergenic DNA (mostly
transposable elements), tetraploid and hexaploid wheats were shown to have
diverged about 800000 years ago [197]. Similarly, the divergence of diploid
from the tetraploid/hexaploid lineage was estimated to have occurred about 2.6–3 million years
ago [195, 196].
Notwithstanding the above initial demonstration of colinearity using molecular markers, later
studies based on genome sequences suggested disruption of microcolinearity in
many regions thus complicating the use of rice as a model for cross-species
transfer of information in these genomic regions. For instance, Guyot et al.
[198] conducted an in silico study and reported a mosaic conservation of genes
within a novel colinear region in wheat chromosome 1AS and rice chromosome 5S.
Similarly, Sorrells et al. [199] while comparing 4485 physically mapped wheat
ESTs to rice genome sequence data belonging to 2251 BAC/PAC clones, resolved numerous
chromosomal rearrangements. The above findings also received support from
sequence analysis of the long arm of rice chromosome 11 for rice-wheat synteny
[200].More recently, the grass genusBrachypodium is emerging as a better model system
for wheat belonging to the genus Triticum, because of a more recent
divergence of these two genera (35–40 million years) relative to wheat-rice
divergence [201–203]. Also,
sequence of Brachypodium, which is likely to become available in the
near future, may help further detailed analyses of colinearity and synteny
among grass genomes. This has already been demonstrated through a comparison of
371 kb sequence of B. sylvaticum with orthologous regions from rice and
wheat [204]. In this region, Brachypodium and wheat
showed perfect macrocolinearity, but rice was shown to contain ∼220 kb
inversion relative to Brachypodium sequence. Also, in Ph1 region,
more orthologous genes were identified between the related species B. sylvaticum and wheat than between wheat
and rice, thus once again demonstrating relative utility of Brachypodium genome as a better
model than rice genome for wheat comparative genomics [102, 188].


## 13. Epigenetics in Wheat

Epigenetics refers to a heritable change that is not a result of a change in DNA sequence,
but, instead, results due to a chemical modification of nucleotides in the DNA
or its associated histone proteins in the chromatin. Several studies have
recently been intiated to study the epigenetic modifications in the wheat
genome. For instance, methylation-sensitive amplified polymorphism (MSAP) has
been used to analyze the levels of DNA methylation at four different stages
(2d, 4d, 8d, and 30d after pollination) of seed development in bread wheat [205]. It was found that 36–38% of CCGG sites
were either fully methylated at the internal C’s and/or hemimethylated at the
external C’s at the four corresponding stages. Similarly, Shitsukawa et al.
[206] also studied genetic and epigenetic alterations among three homoeologs
in the two class E-type wheat genes for flower development, namely, wheat
SEPALLATA (WSEP) and wheat LEAFY HULL STERILE1 (WLHS1).
Analyses of gene structure, expression patterns, and protein functions showed
that no alterations were present in the WSEP homoeologs. By contrast,
the three WLHS1 homoeologs showed genetic and epigenetic alterations. It
was shown that WLHS1-B was predominantly silenced by cytosine
methylation, suggesting that the expression of three homoeologous genes is differentially
regulated by genetic or epigenetic mechanisms. Similar results were reported for several other
genes like TaHd1 involved in photoperiodic flowering pathway, Ha for
grain hardness, and TaBx for benzoxazinone biosynthesis [207–209].A prebreeding program in wheat (along with
barley and canola) based on epigenetically modified genes has also been
initiated in Australia at CSIRO, under the leadership of Dr. Liz Dennis and Dr.
Jim Peacock, with the support from Dr. Ben Trevaskis
(http://www.grdc.com.au/director/events/groundcover?item_id=A5B55D1DED8B9C20860C0CDE8C6EE077&article_id=A97C28B1F1614E34835D6BDB8CBDC75C).
This pioneering work will involve vernalization, the mechanism
that allows winter crops to avoid flowering until spring, when long days and
mild conditions favor seed setting and grain filling. They plan to breed
varieties with a wider range of heading dates and improved frost tolerance
during flowering. In wheat (as also in other cereals), the epigenetic component
is also built around VRN1 gene, which plays a role analogous to that of Flowering
Locus C (FLC) in Arabidopsis and canola. VRN1 is one of the most important
determinants of heading dates in winter cereals including wheat and also
accounts for difference between winter and spring wheat varieties. It has been
shown that during vegetative growth, VRN1 is repressed epigenetically;
this repression is lifted in spring, allowing the protein encoded by VRN1 to activate other genes involved in reproduction. As many as ∼3000 wheat
varieties are being looked at for variation in their VRN1 gene so as to
breed better combinations of heading date and frost tolerance
(http://www.grdc.com.au/director/events/groundcover?item_id=A5B55D1DED8B9C20860C0CDE8C6EE077&article_id=A97C28B1F1614E34835D6BDB8CBDC75C).Wheat Allopolyploidy and Epigenetics
Polyploidization induces genetic and epigenetic modifications in the genomes of higher plants including wheat
(reviewed in [210, 211]). Elimination of noncoding and
low-copy DNA sequences has been reported in synthetic allopolyploids of Triticum and Aegilops species [212–214]. In two
other studies, patterns of cytosine methylation were also examined throughout
the genome in two synthetic allotetraploids, using methylation-sensitive
amplification polymorphism (MSAP; [215, 216]). This analysis indicated that the parental
patterns of methylation were altered in the allotetraploid in 13% of the
genomic DNA analyzed. Gene silencing and activation were also observed when
3072 transcribed loci were analyzed, using cDNA-AFLP [217, 218]. This study demonstrated new, nonadditive patterns of
gene expression in allotetraploid, as indicated by the fact that 48 transcripts
disappeared and 12 transcripts that were absent in the diploid parents,
appeared in the allotetraploid. These results were found reproducible in two
independent synthetic allotetraploids. The disappearance of transcripts could
be related to gene silencing rather than gene loss and was partly associated
with cytosine methylation. In another similar study involving artificially
synthesized hexaploid wheats and their parents, down-regulation of some genes
and activation of some other genes, selected in a nonrandom manner, was
observed [219]. The genome-wide genetic and epigenetic alterations triggered by
allopolyploidy thus suggested plasticity of wheat genome. The reproducibility
of genetic and epigenetic events indicated a programmed rather than a chaotic
response and suggests that allopolyploidy is sensed in a specific way that
triggers specific response rather than a random mutator response [218].


## 14. Quantitative Trait Loci (QTL) and Protein Quantitative Loci (PQLs) in Wheat

A large number of QTL studies for various
traits have been conducted in bread wheat, leading to mapping of QTL for these
traits on different chromosomes. In most of these studies, either single marker
regression approach or QTL interval mapping has been utilized. Although most of
these studies involved mapping of QTL with main effects only, there are also
reports of QTL, which have no main effects but have significant digenic
epistatic interactions and QTL× environment interactions [220–222]. A detailed account of studies involving gene tagging
and QTL analyses for various traits conducted in wheat is available elsewhere
[14, 223]. More up-to-date accounts on QTL
studies (summarized in Table 5) are also available for disease resistance [224], for resistance against abiotic stresses [225], grain size, and grain number [226], and for several other
traits including yield and yield contributing characters, plant type, and
flowering time [222, 227]. Advanced backcross QTL
(AB-QTL) analysis, proposed by Tanksley and Nelson [228], has also been
utilized in wheat to identify QTL for a number of traits including yield and
yield components, plant height, and ear emergence [129, 229].
More recently AB-QTL analysis was practiced for the identification of QTL for
baking quality traits in two BC2F3 populations of winter
wheat [230].Table 5
A list of gene/QTL tagged/mapped
in wheat. RSL = recombinant
substitution line, CSL = chromosome substitution line, RIL = recombinant inbred
lines, DH = double haploid, RICL = recombinant inbred chromosome lines, SCRI = single-chromosome
recombinant lines, AL
= addition lines,
BIL = backcross inbred lines, NIL = near isogenic lines, TC = test cross.
TraitGene/QTL (chromosome)Mapping populationReferenceDisease(i) Leaf rust resistanceLr9 (6BL)NILs[248]Lr1 (5DL)F2[249]Lr24 (3DL)F2[250]Lr10 (1AS)F2[251]Lr28 (4AL)F2:3[252]Lr3 (6BL)F2[253]Lr35 (2B)F2[254]Lr47 (7A)BC1F2[255]LrTr (4BS)F2[256]Lr19 (7DL)Deletion lines[257]Lr39 (=Lr41)(2DS)F2[258]Lr37 (2AS)NILs[259]Lr20 (7AL)F2[260]Lr19 (7D)F2[261]Lr21/Lr40 (1DS)F2[88]Lr1 (5DL)F2:3 families[262]Lr28 (-)F2:3[263]Lr34 (7D)RILs[264]Lr52 (LrW) (5B)F2[265]Lr16 (2BS)DH[50]Lr19 (7DL)F2[266]Lr24 (3DL)F2[266]Lr34 (7DS)RILs[267]Lr22a (2DS)F2[268]Lr1 (5DL)RILs[269]Unknown (5B)F2:3 lines[270]QTL (7D, 1BS)RILs[271]QTL (2D, 2B)F2[272]QTL (7DS, linked toLr34)RILs[273](ii) Stripe rust resistanceYr15 (1B)F2[274]YrH52 (1B)F2[275]Yrns-B1 (3BS)F3 lines[276]Yr15 (1B)F2 lines[277]Yr28 (4DS)RILs[273]Yr9 (1B/1R)BC7F2:3[278]Yr17 (2A)NILs[259]Yr26 (1BS)F2 lines[279]Yr10 (1B)F2 lines[280]Yr5 (2B)BC7F3[131]Yr18 (7D)RILs[264]Yr36 (6B)RILs[281]YrCH42 (1B)F2[282]YrZH84 (7BL)F2, F3[283]Yr34 (5AL)DH[284]Yr26 (1B)F2:3 lines[285]QTL (2D, 5B, 2B, 2A)RILs[286]QTL (2AL, 2AS, 2BL, 6BL)DH[287](iii) Stem rust resistanceSr22 (7A)F2[288]Sr38 (2AS)NILs[259]Sr2 (3BS)F3 lines[289](iv) Fusarium head blight
resistanceFhb2 (6BS)RILs[290]QTL (5A, 3B, 1B)DH[37]QTL (3BS, 3A, 5B)RILs[291]QTL (3B)Advanced lines[292]QTL (3B, 6B, 2B)RILs[293, 294]QTL (6D, 4A, 5B)RILs[295]QTL (3A, 5A)DH[43]QTL (1B, 3B)RILs[30]QTL (2B)RILs[296]QTL (3B)DH[297]QTL (6AL, 1B, 2BL, 7BS)RILs[46]QTL (3A)RICLs[298]QTL (4D)DH[49]QTL (3BS, 5AS, 2DL)RILs[299]QTL (1BS, 1DS, 3B, 3DL, 5BL, 7BS,
7AL)RILs[300]QTL (7E)RILs[301](v) Scab resistanceQTL (2AS, 2BL, 3BS)RILs[302]QTL (3BS)F3:4 lines[303](vi) Powdery mildew resistancePm2 (5DS)F2[304]Pm18 (5DS)F2[304]Pm12 (6B)F2[305]Pm21 (6AL)BC lines[306]Pm3g (1A)DH[307]Pm24 (1DS)F2:3 lines[308]Pm26 (2BS)RSI[309]Pm6 (2BL)NILs[310]Pm27 (6B)F2[311]Pm8/Pm17 (1BL)F3 families[312]Pm3 (1AS)RILs[313]Pm1 (7AL)F2[260]Pm29 (7D)F2&F4 lines[314]Pm30 (5BS)BC2F2 lines[315]Pm13 (3S)AL[316]Pm5e (7BL)F2[130]Pm4a (2A)F2[317]PmU (7AL)F2[318]Pm34 (5D)F2:3 lines[319]PmY39 (2B)BC3F4:5[320]Pm35 (5DL)F2:3 lines[321]Pm5d (7BL)F3 lines[322]Pm12 (6B)BC3F2[323]MlRE, QTL
(6A, 5D)F3 lines[324]MlG (6AL)BC2F3[325]mlRD30 (7AL)F2[326]Mlm2033, Mlm80 (7A)F2[181]QTL (5A, 7B, 3D)RILs[327]QTL (1B, 2A, 2B)F2:3 lines[328]QTL (2B, 5D, 6A)DH[329]QTL (2B)F2[330]QTL (1BL, 2AL, 2BL)RILs[331](vii) Common bunt resistanceBt-10 (-)F2[332]QTL (1B, 7A)DH[333](viii) Tan spot andStagonospora
nodorum blotch resistanceQTL (1A, 4A, 1B, 3B)RILs[334]QTL (5B, 3B)Inbred, CS lines[335]tsn3a, tsn3b, tsn3c (3D)F2:3 lines[336](ix) Septoria tritici blotch
resistanceStb5 (7D)SCRI[337]QTL (3A)DH[38]QTL (1D, 2D, 6B)RILs[338](x) Barley yellow dwarf toleranceQTL (12 chromosomes)RILs[339](xi) Leaf and glume blotch
resistanceQTL (4B, 7B, 5A)RILs[340](xii) Wheat streak mosaic virus
resistanceWms1 (4D)F2[341]WSSMV (2DL)RILs[342](xiii) Yellow mosaic virus
resistanceYmYF (2D)F2[343](xiv) Eyespot (straw breaker foot rot) resistancePch2 (7AL)F2[344]Pch1 (7A)F3 lines[345]Pch1, Ep-D1 (7D)TC[346]Insect-pest(i) Green bug resistanceGb3 (7D)F2:3 lines[347]Gby (7A)F2:3 lines[348]Gb7 (7DL)RILs[349]Gb (7DL)F4:5 lines, F2[350](ii) Hessian fly resistance geneH23 (6D)F2[351]H24 (3D)F2[351]H3, H6, H9, H10, H12, H16, H17  (5A)NILs,F2[352]H5, H11, H13, H14 (1A)NILs,F2[353]H21 (2B)NILs,F2[354]H6 (-)F2[355]H13 (6DS)F2:3[356]H26, H13 (3D, 6D)F2:3 lines[357]H22 (1D)F2:3 lines[358]H16 and H17 (1AS)BC1F2, F2:3 lines[359](iii) Russian wheat aphid
resistanceDn8, Dn9 (7DS,
1DL)F2[360]Dn1, Dn2, Dn5, Dn8F2[360]Dnx (7DS)Dn2 (7DS)F2[361]Dn4 (1D)F2[362]Dn6 (7D)F2[362]Nematodes(i) Cereal cyst nematode
resistanceCre1 (2B)NILs,F2[363]Cre5 (2AS)NILs[364]Cre6 (5A)F2[365]QTL (1B)DH[48](ii) Root-knot nematode
resistanceRkn-mn1 (3BL)BC3F2, F3 lines[366](iii) Root-lesion nematode
resistanceRlnn1 (7AL)DH[367]Quality and quality related traits(i) Seed dormancy or preharvest
sproutingQTL (4A)DH[368]QTL (4A)RILs, DH[369]QTL (3A)BC1F2[370]QTL (3A)RILs[371]QTL (3A)RILs[372]QTL (4A)DH[373](ii) Grain protein contentQTL (6B)RILs[374]QTL (2A, 3A, 4D, 7D,
2B, 5B, 7A)RILs[39]QTL (2A, 2B,
2D, 3D, 4A, 6B, 7A, 7D)RILs[375]QTL (2AS, 6AS, 7BL)BILs[376](iii) OthersFlour colourQTL (3A, 7A)RILs[377]Milling yieldQTL (3A, 7D)RILs[378]Bread-making qualityQTL (5DS, 1B, 6A, 3B, 1A)DH[379]Milling traitsQTL (7A, 6B)RILs[35]Grain dry matter and N
accumulation, protein compositionQTL (1A, 2B, 3A, 6A, 5A, 7A, 7D)RILs[380]Mixograph-extensibilityQTL (5A)DH[381]Kernel hardness and dough
strengthQTL (1A, 5D, 1B, 1D, 5B)Inbred lines[382]Purple grain colourPp1, Pp3b, Pp3a (2A, 7BL)F2[383]Quality traitsQTL (5DS, 6DS, 2DS, 1AS, 1BS,
6DS)RILs[384]Low-molecular-weight gluteninLMW-GS (-)F5 lines[385]Bread-making qualityQTL (3A, 7A)RILs[40]Milling and baking qualityQTL (4B, 6D)BC2F3[230]Endosperm colourQTL,Psy1-1 (2A, 4B, 6B,
7B)DH[386]Agronomic traits(i) Plant heightRht-B1, Rht-D1 (4BS, 4DS)DH[387]Rht8 (2D)RILs[388]Rht8 (2DS)DH, Inbred lines[389](ii) Tiller inhibition genetin3 (3A)F2[390](iii) Spherical grain and compact
spikess16219, C17648B1 (3B, 5A)F2[391](iv) Ear-emergence time and plant
heightQTL (5A)RILs[392](v) Heading dateQTL (2BS)DH[393]QTL,Ppd-B1,Ppd-D1 (2B, 2D, 5A, 2B)RILs[394]QTL (2DS)RILs[395]QTL (2A, 2B, 2D, 5A, 5B, 5D, 4A,
4B)RILs[396](vi) Grain yield and related
traitsQTL (5A)RILs[397]QTL (2D, 3B, 3D, 5D, 7D)BC2F2:4 lines[398]QTL (1D, 2A, 6B, 7D)RILs[51]QTL (7AL, 7BL, 1D, 5A)DH[45]QTL (4AL)RILs[399]QTL (1B, 4D, 7D)DH[400](vii) Spike-related traitsQTL (7D)F2[401](viii) Grain weightQTL (1A, 2B, 7A)RILs[402](ix) OthersQTL (4A, 4B, 4D, 7D, 3B, 3D)DH[171]QTL (1D, 4D)DH[47]Growth related traits(i) Spike morphology, awn
development, vernalizationB, Q, VRN1 (5A)RILs[403](ii) Supernumerary spikeletbh (2D, 4A,
4B, 5A)F2:3 lines[404](iii) Sphaerococcum- like growth
habitS1, S2, S3 (3D, 3B, 3A)F2[405](iv) Thermosensitive earlinessEps-Am1 (1AL)F2[406](v) Coleoptiles pigmentationRc-A1, Rc-B1,
Rc-D1 (7A, 7B,
7D, 4BL)RILs[407](vi) Thermosensitive genic male-sterilewtms1 (2B)F2[408](vii) Hybrid necrosisNe1, Ne2 (5BL, 2BS)F2[409](viii) Leaf pubescence and hairy
leafHl1, Hl2, Aesp, QTL
(4BL, 7BS)F2[410](ix) Stem solidnesssst (3BL)DH[411](x) Lodging resistanceQTL (1BS, 2AS, 2D, 3AS, 4AS, 5AL,
5BL, 6BL, 7BL)RILs[412](xi) Stem strength and related
traitsQTL (3A, 3B, 1A, 2D)DH[413](xii) Brittle rachisQTL (3A, 3B)RICLs[414](xiii) Coleoptiles growthQTL (2B, 2D, 4A, 5D, 6B)DH[415](xiv) Kernel shatteringQTL (2B, 3B, 7A)RILs[416](xv) Seed developmentQTL (1D, 4B)DH[9](xvi) Longer coleoptilesQTL (6A)RILs[29](xvii) Viridescent phenotypeQTL (2B)DH[52]Biochemical(i) Casein kinaseCK2α (5A)F2[417](ii) NonglaucousnessIw3672 (2DS)F2[418](iii) Low lipoxygenaseLpx-B1.1, Lpx-A3 (4B, 4A)RILs[419](iv) Polyphenol oxidase (PPO)
genesPPO (2A, 2D)DH[420](v) ABA  signaling genesQTL (3A, 5A)RILs[421](vi) Polyphenol oxidaseQTL (2A)DH[422](vii) Water-soluble carbohydratesQTL (21 chromosome)DH[423]Abiotic stress(i) Photoperiod insensitivePpd-B1 (2BS)RILs, DH[424](ii) Aluminum toleranceALMT1 (4D)DH[425]QTL (4D, 3BL)RIL[426](iii) Boron toxicity toleranceQTL (Bo1) (7BL)DH[427](iv) Frost resistanceQTL (5B)RSI[428](v) Salt toleranceQTL (3A, 3B, 4DL, 6DL)RILs[429]Quantitative variation in
protein spots was also used for detection of protein quantitative loci (PQL) in
wheat. For instance, in a study, 170-amphiphilic protein spots that were
specific to either of the two parents of ITMIpop  were used for genotyping 101 inbred lines; 72 out of these 170
proteins spots were assigned to 15 different chromosomes, with highest number
of spots mapped to Group-1 chromosomes. QTL mapping approaches were also used
to map PQL; 96 spots out of the 170 specific ones showed at least one PQL.
These PQL were distributed throughout the genome. With the help of MALDI-TOF
spectrometry and database search, functions were also assigned to 93 specific
and 41 common protein spots. It was shown in the above study that majority of
these proteins are associated with membranes and/or play a role in plant
defense against external invasions [231].


### 15. Recent Insights into the Origin/Evolution of Wheat Genomes

In the genomics era, the subject
of origin and evolution of bread wheat has also been revisited. This gave new
insights into the identity of progenitors of the three subgenomes (A, B, D) of
bread wheat, and into the genome alterations, which presumably accompanied the
course of its evolution and domestication (see Figure1). These aspects of
evolution of bread wheat will be discussed briefly in this section.Figure 1
Schematic representation of the
evolutionary history of wheat species (Triticum and Aegilops).


### 15.1. Origin of A, B, and D Subgenomes

As mentioned earlier, bread wheat is a segmental
allohexaploid having three closely related subgenomes A, B, and D. Initial
analysis of the three subgenomes of bread wheat was mainly based on studies
involving chromosome pairing in interspecific hybrids, and karyotype analysis
in bread wheat as well as in the probable donors of the subgenomes (for reviews, see [232–236]).
However, more recently, molecular markers and DNA sequence data have
been used for the analysis of these subgenomes (see [237–239]). As a result, we have known with some
degree of certainty that T. urartu (2 n = 14) is the donor of subgenome A
and Ae. tauschii (synonyms, T.
tauschii, Ae. squarrosa) is the
donor of subgenome D; this has recently been confirmed through analysis of DNA
sequences of two genes, namely, Acc-1 (plastid acetyl-CoA carboxylase) and Pgk-1 (plastid 3-phosphoglycerate kinase) [240]. In contrast to this,
although Ae. speltoides was once considered as the probable donor of the B
subgenome ([241], for a review, see [237]),
studies carried out later showed that Ae. speltoides more closely resembles the
subgenome G of T. timopheevii rather
than to the subgenome B of bread wheat. DNA sequences of the above genes, Acc-1 and Pgk-1 also proved to be of no help in identification of the progenitor
of the subgenome B. There is, thus still no unanimity on the progenitor of the
subgenome B of bread wheat (for more details, see [242]), and there
are speculations that the donor of the subgenome B might have lost its identity
during evolution and may never be discovered.DNA sequences of genes other than the above two genes have also been used for the
study of origin and evolution of the component subgenomes of bread wheat. For
instance, in one such study, sequences from 14 loci (2 sequences from each of
the 7 chromosomes) belonging to the subgenome B of bread wheat, when compared
with those from five diploid species (from section Sitopsis) closely related to
the B subgenome of bread wheat, indicated that the B subgenome of bread wheat
and the genomes of the above five diploid species diverged greatly after the
origin of tetraploid wheat [243]. The above study also received support from the recent evidence of
independent origins of wheat B and G subgenomes [244]. In this
study, 70 AFLP loci were used to sample diversity among 480 wheat lines
collected from their natural habitats, which encompassed the entire range of
habitats for all S genome Aegilops species. Also, a comparison of 59 Aegilops representatives of S genome diversity with 2x, 4x chromosome number, and 11
nulli-tetrasomic wheat lines at 375 AFLP loci suggested that B genome
chromosomes of 6x wheat were derived
from chromosomes of Ae. speltoides, and no other species.
Further, an analysis of the haplotypes at nuclear and chloroplast loci ACC1, G6PDH,
GPT, PGK1, Q, VRN1, and ndhF for ∼70 Aegilops and Triticum lines (0.73 Mb sequenced) revealed
that both B and G genomes of polyploid wheats are unique samples of A.
speltoides haplotype diversity. However, it is likely that due to the
outbreeding nature of A. speltoides, no modern A. speltoides lines have preserved the B donor genotype in its ancestral state. The above
findings can be incorporated into a broader scheme of wheat genome evolution
(see Figure 1) with resolved positions of the B genome relative to S progenitors
and G sisters. Similar analysis of the D subgenome and its progenitor showed
that the D subgenome had more than one allele for a single locus derived from a
progenitor, suggesting that hexaploid wheat perhaps originated from tetraploid
wheat more than once utilizing different sources of Ae. tauschii [245]. Also, it was realized that
major part of the large genome (16000 Mb) of bread wheat is composed of
transposable elements (TEs). Therefore, the role of TEs in the evolution of
bread-wheat and allied genomes has also been examined [246, 247]. In these studies, some specific sequences from A and B
genomes of diploid species were located, respectively, in B- and A-subgenomes
of bread wheat, suggesting the role of TEs in transfer of sequences between A
and B subgenomes. A bioinformatics approach was also used on a large genomic
region (microgenomic approach) sequenced from T. monococcum (AA) and Ae.
tauschii (DD). This approach allowed a comparison of variation within
coding regions with that in the noncoding regions of the subgenomes.


### 15.2. Alterations that Accompanied Domestication

Domestication of
most crop plants including wheat involved transition from short day,
small-seeded plants with natural seed dispersal to photoperiod insensitive,
large-seeded nonshattering plants. A study of genetic loci underlying
domestication-related traits inT. dicoccoides was also conduced [430], where seven domestication
syndrome factors (DSFs) were proposed, each affecting 5–11 traits.
Following conclusions were made with respect to the domestication-related QTL.
(i) Some of these QTL had strong effect and were clustered. (ii) Strong QTL were
mainly associated with GRRs, where recombination rates are high. (iii) These QTL
predominantly occurred in the A genome, suggesting that A genome has played a
more important role than the B genome in evolution during domestication; this
is understandable, because einkorn diploid wheat (T. monococcum) carrying the
A genome was the first wheat to be domesticated, so that most of the
domestication related traits in different wheats must have been selected within
the A genome. Similar studies involving study of evolution during domestication
were also conducted in hexaploid wheats for seed size, free threshing habit,
rachis stiffness, photoperiod insensitivity, and so forth (for a review, see
[431]). In wheat, a primary component of domestication syndrome was
the loss of spike shattering, controlled by Br (brittle rachis) loci on
chromosome 3A and 3B [414]. Other traits of wheat domestication
syndrome shared by all domesticated wheats are the soft glumes, increased seed
size, reduced number of tillers, more erect growth, and reduced dormancy
[432]. A gene GPC-B1, which is an early regulator
of senescence with pleiotropic effects on grain nutrient content, has also been
found to affect seed size [96]. However, in some genotypes and
environments, the accelerated grain maturity conferred by functional GPC-B1 allele has been found associated with smaller seeds [433],
suggesting that indirect selection for large seeds may explain the fixation of
the nonfunctional GPC-B1 allele in both durum and bread wheats [96]. Among many genes relevant to wheat domestication syndrome, only Q and GPC-B1 have been successfully isolated so far, suggesting a need for
systematic effort to clone other genes, since it is possible that genetic
variation at these loci might have played an important role in the success of
wheat as a modern crop.


## 15. Recent Insights into the Origin/Evolution of Wheat Genomes

In the genomics era, the subject
of origin and evolution of bread wheat has also been revisited. This gave new
insights into the identity of progenitors of the three subgenomes (A, B, D) of
bread wheat, and into the genome alterations, which presumably accompanied the
course of its evolution and domestication (see Figure1). These aspects of
evolution of bread wheat will be discussed briefly in this section.Figure 1
Schematic representation of the
evolutionary history of wheat species (Triticum and Aegilops).


## 15.1. Origin of A, B, and D Subgenomes

As mentioned earlier, bread wheat is a segmental
allohexaploid having three closely related subgenomes A, B, and D. Initial
analysis of the three subgenomes of bread wheat was mainly based on studies
involving chromosome pairing in interspecific hybrids, and karyotype analysis
in bread wheat as well as in the probable donors of the subgenomes (for reviews, see [232–236]).
However, more recently, molecular markers and DNA sequence data have
been used for the analysis of these subgenomes (see [237–239]). As a result, we have known with some
degree of certainty that T. urartu (2 n = 14) is the donor of subgenome A
and Ae. tauschii (synonyms, T.
tauschii, Ae. squarrosa) is the
donor of subgenome D; this has recently been confirmed through analysis of DNA
sequences of two genes, namely, Acc-1 (plastid acetyl-CoA carboxylase) and Pgk-1 (plastid 3-phosphoglycerate kinase) [240]. In contrast to this,
although Ae. speltoides was once considered as the probable donor of the B
subgenome ([241], for a review, see [237]),
studies carried out later showed that Ae. speltoides more closely resembles the
subgenome G of T. timopheevii rather
than to the subgenome B of bread wheat. DNA sequences of the above genes, Acc-1 and Pgk-1 also proved to be of no help in identification of the progenitor
of the subgenome B. There is, thus still no unanimity on the progenitor of the
subgenome B of bread wheat (for more details, see [242]), and there
are speculations that the donor of the subgenome B might have lost its identity
during evolution and may never be discovered.DNA sequences of genes other than the above two genes have also been used for the
study of origin and evolution of the component subgenomes of bread wheat. For
instance, in one such study, sequences from 14 loci (2 sequences from each of
the 7 chromosomes) belonging to the subgenome B of bread wheat, when compared
with those from five diploid species (from section Sitopsis) closely related to
the B subgenome of bread wheat, indicated that the B subgenome of bread wheat
and the genomes of the above five diploid species diverged greatly after the
origin of tetraploid wheat [243]. The above study also received support from the recent evidence of
independent origins of wheat B and G subgenomes [244]. In this
study, 70 AFLP loci were used to sample diversity among 480 wheat lines
collected from their natural habitats, which encompassed the entire range of
habitats for all S genome Aegilops species. Also, a comparison of 59 Aegilops representatives of S genome diversity with 2x, 4x chromosome number, and 11
nulli-tetrasomic wheat lines at 375 AFLP loci suggested that B genome
chromosomes of 6x wheat were derived
from chromosomes of Ae. speltoides, and no other species.
Further, an analysis of the haplotypes at nuclear and chloroplast loci ACC1, G6PDH,
GPT, PGK1, Q, VRN1, and ndhF for ∼70 Aegilops and Triticum lines (0.73 Mb sequenced) revealed
that both B and G genomes of polyploid wheats are unique samples of A.
speltoides haplotype diversity. However, it is likely that due to the
outbreeding nature of A. speltoides, no modern A. speltoides lines have preserved the B donor genotype in its ancestral state. The above
findings can be incorporated into a broader scheme of wheat genome evolution
(see Figure 1) with resolved positions of the B genome relative to S progenitors
and G sisters. Similar analysis of the D subgenome and its progenitor showed
that the D subgenome had more than one allele for a single locus derived from a
progenitor, suggesting that hexaploid wheat perhaps originated from tetraploid
wheat more than once utilizing different sources of Ae. tauschii [245]. Also, it was realized that
major part of the large genome (16000 Mb) of bread wheat is composed of
transposable elements (TEs). Therefore, the role of TEs in the evolution of
bread-wheat and allied genomes has also been examined [246, 247]. In these studies, some specific sequences from A and B
genomes of diploid species were located, respectively, in B- and A-subgenomes
of bread wheat, suggesting the role of TEs in transfer of sequences between A
and B subgenomes. A bioinformatics approach was also used on a large genomic
region (microgenomic approach) sequenced from T. monococcum (AA) and Ae.
tauschii (DD). This approach allowed a comparison of variation within
coding regions with that in the noncoding regions of the subgenomes.


## 15.2. Alterations that Accompanied Domestication

Domestication of
most crop plants including wheat involved transition from short day,
small-seeded plants with natural seed dispersal to photoperiod insensitive,
large-seeded nonshattering plants. A study of genetic loci underlying
domestication-related traits inT. dicoccoides was also conduced [430], where seven domestication
syndrome factors (DSFs) were proposed, each affecting 5–11 traits.
Following conclusions were made with respect to the domestication-related QTL.
(i) Some of these QTL had strong effect and were clustered. (ii) Strong QTL were
mainly associated with GRRs, where recombination rates are high. (iii) These QTL
predominantly occurred in the A genome, suggesting that A genome has played a
more important role than the B genome in evolution during domestication; this
is understandable, because einkorn diploid wheat (T. monococcum) carrying the
A genome was the first wheat to be domesticated, so that most of the
domestication related traits in different wheats must have been selected within
the A genome. Similar studies involving study of evolution during domestication
were also conducted in hexaploid wheats for seed size, free threshing habit,
rachis stiffness, photoperiod insensitivity, and so forth (for a review, see
[431]). In wheat, a primary component of domestication syndrome was
the loss of spike shattering, controlled by Br (brittle rachis) loci on
chromosome 3A and 3B [414]. Other traits of wheat domestication
syndrome shared by all domesticated wheats are the soft glumes, increased seed
size, reduced number of tillers, more erect growth, and reduced dormancy
[432]. A gene GPC-B1, which is an early regulator
of senescence with pleiotropic effects on grain nutrient content, has also been
found to affect seed size [96]. However, in some genotypes and
environments, the accelerated grain maturity conferred by functional GPC-B1 allele has been found associated with smaller seeds [433],
suggesting that indirect selection for large seeds may explain the fixation of
the nonfunctional GPC-B1 allele in both durum and bread wheats [96]. Among many genes relevant to wheat domestication syndrome, only Q and GPC-B1 have been successfully isolated so far, suggesting a need for
systematic effort to clone other genes, since it is possible that genetic
variation at these loci might have played an important role in the success of
wheat as a modern crop.


## 16. Application of Genomics to Molecular Breeding of Wheat

### 16.1. Association Mapping in Wheat

Association mapping is a high-resolution method for mapping QTL based on linkage
disequilibrium (LD) and holds great promise for genetic dissection of complex
traits. It offers several advantages, which have been widely discussed
[434, 435]. In wheat, some parts of the
genome relative to other parts are more amenable to LD/association mapping for
QTL detection and fine mapping, since the level of LD is variable across the
length of a chromosome. As we know, LD decay over longer distances will
facilitate initial association of trait data with the haplotypes in a
chromosome region and LD decay over short distances will facilitate fine
mapping of QTL [12].Several studies involving association mapping in wheat have been conducted in the
recent past. For instance, association mapping has been conducted for kernel
morphology and milling quality [436] and for the
quantity of a high-molecular-weight glutenin [141, 437]. In another
study, 242 diversity array technology (DArT) markers were utilized for
association mapping of genes/QTL controlling resistance against stem rust (SR),
leaf rust (LR), yellow rust (YR), powdery mildew (PM), and those controlling
grain yield (GY). Phenotypic data from five historical CIMMYT elite spring
wheat yield trials (ESWYT) conducted in a large number of international
environments were utilized for this purpose and two linear mixed models were
applied to assess marker-trait associations after a study of population
structure and additive genetic covariance between relatives [438]. A total of 122, 213, 87, 63,
and 61 DArT markers were found to be significantly associated with YR, GY, LR,
SR, and PM, respectively. Association analysis was also conducted
between markers in the region of a major QTL responsible for resistance to Stagonospora
nodorum (causing glume blotch); it was concluded that association mapping
had a marker resolution, which was 390-fold more powerful than QTL analysis
conducted using an RIL mapping population [439]. Such
high-resolution mapping of traits and/or QTL to the level of individual genes,
using improved statistical methods, will provide new possibilities for studying
molecular and biochemical basis of quantitative trait variation and will help
to identify specific targets for crop improvement.


### 16.2. Marker-Assisted Selection in Wheat

A large number of marker-trait associations determined during the last decades facilitated the use of
molecular markers for marker-assisted selection (MAS) in bread wheat, which is
gaining momentum in several countries. In particular, major programs involving
MAS in wheat are currently underway in USA, Australia, and at CIMMYT in Mexico. In USA, a wheat
MAS consortium comprosing more than 20 wheat-breeding programs was constituted
at the end of 2001. The objective of this consortium was to apply and to
integrate MAS in public wheat breeding programs [440]. Under these
programs, MAS has been utilized for transfer of as many as 27 different insect
and pest resistance genes and 20 alleles with beneficial effects on bread
making and pasta quality into ∼180 lines adapted to the primary US production
regions. These programs led to release of germplasm consisting of 45 MAS-derived
lines [441]. Similarly, the program in Australia
involved improvement of 20 different traits (including resistance to some abiotic stresses) and has
already led to release of some improved cultivars ([442], Peter
Langridge personal communication). Among these traits, MAS has become a method
of choice for those agronomically important traits, where conventional
bioassays were expensive and unconvincing, as was the case in selection for
cereal cyst nematodes resistance carried out by Agriculture Victoria [443]. In addition to this, MAS has been incorporated in backcross
breeding in order to introgress QTL for improvement of transpiration efficiency
and for negative selection for undesirable traits such as yellow flour color [444]. Australian scientists also conducted a computer simulation in order
to design a genetically effective and economically efficient marker-assisted
wheat-breeding strategy for a specific outcome. This investigation involved an integration of both
restricted backcrossing and doubled haploid (DH) technology. Use of MAS at the
BC1F1 followed by MAS in haploids derived from pollen of
BC1F1 (prior to chromosome doubling) led to reduction of
cost of marker-assisted breeding up to 40% [445]. Later, this
MAS strategy was validated practically in a marker-assisted wheat-breeding
program in order to improve quality and resistance against rust disease (for
review, see [446]). At CIMMYT, markers associated with 25
different genes governing insect pest resistance, protein quality, homoeologous
pairing, and other agronomic characters are currently being utilized in wheat
breeding programs in order to develop improved wheat cultivars [447]. Some of the markers used in these programs are perfect markers that have been developed
from available nucleotide sequences of these genes. In future,
large-scale sequencing of GRRs (gene-rich regions), to be undertaken by IWGSC,
will also facilitate isolation of important genes for production of improved
transgenic crops, and for development of “perfect markers” for agronomically
important traits to be used in MAS [448, 449].


## 16.1. Association Mapping in Wheat

Association mapping is a high-resolution method for mapping QTL based on linkage
disequilibrium (LD) and holds great promise for genetic dissection of complex
traits. It offers several advantages, which have been widely discussed
[434, 435]. In wheat, some parts of the
genome relative to other parts are more amenable to LD/association mapping for
QTL detection and fine mapping, since the level of LD is variable across the
length of a chromosome. As we know, LD decay over longer distances will
facilitate initial association of trait data with the haplotypes in a
chromosome region and LD decay over short distances will facilitate fine
mapping of QTL [12].Several studies involving association mapping in wheat have been conducted in the
recent past. For instance, association mapping has been conducted for kernel
morphology and milling quality [436] and for the
quantity of a high-molecular-weight glutenin [141, 437]. In another
study, 242 diversity array technology (DArT) markers were utilized for
association mapping of genes/QTL controlling resistance against stem rust (SR),
leaf rust (LR), yellow rust (YR), powdery mildew (PM), and those controlling
grain yield (GY). Phenotypic data from five historical CIMMYT elite spring
wheat yield trials (ESWYT) conducted in a large number of international
environments were utilized for this purpose and two linear mixed models were
applied to assess marker-trait associations after a study of population
structure and additive genetic covariance between relatives [438]. A total of 122, 213, 87, 63,
and 61 DArT markers were found to be significantly associated with YR, GY, LR,
SR, and PM, respectively. Association analysis was also conducted
between markers in the region of a major QTL responsible for resistance to Stagonospora
nodorum (causing glume blotch); it was concluded that association mapping
had a marker resolution, which was 390-fold more powerful than QTL analysis
conducted using an RIL mapping population [439]. Such
high-resolution mapping of traits and/or QTL to the level of individual genes,
using improved statistical methods, will provide new possibilities for studying
molecular and biochemical basis of quantitative trait variation and will help
to identify specific targets for crop improvement.


## 16.2. Marker-Assisted Selection in Wheat

A large number of marker-trait associations determined during the last decades facilitated the use of
molecular markers for marker-assisted selection (MAS) in bread wheat, which is
gaining momentum in several countries. In particular, major programs involving
MAS in wheat are currently underway in USA, Australia, and at CIMMYT in Mexico. In USA, a wheat
MAS consortium comprosing more than 20 wheat-breeding programs was constituted
at the end of 2001. The objective of this consortium was to apply and to
integrate MAS in public wheat breeding programs [440]. Under these
programs, MAS has been utilized for transfer of as many as 27 different insect
and pest resistance genes and 20 alleles with beneficial effects on bread
making and pasta quality into ∼180 lines adapted to the primary US production
regions. These programs led to release of germplasm consisting of 45 MAS-derived
lines [441]. Similarly, the program in Australia
involved improvement of 20 different traits (including resistance to some abiotic stresses) and has
already led to release of some improved cultivars ([442], Peter
Langridge personal communication). Among these traits, MAS has become a method
of choice for those agronomically important traits, where conventional
bioassays were expensive and unconvincing, as was the case in selection for
cereal cyst nematodes resistance carried out by Agriculture Victoria [443]. In addition to this, MAS has been incorporated in backcross
breeding in order to introgress QTL for improvement of transpiration efficiency
and for negative selection for undesirable traits such as yellow flour color [444]. Australian scientists also conducted a computer simulation in order
to design a genetically effective and economically efficient marker-assisted
wheat-breeding strategy for a specific outcome. This investigation involved an integration of both
restricted backcrossing and doubled haploid (DH) technology. Use of MAS at the
BC1F1 followed by MAS in haploids derived from pollen of
BC1F1 (prior to chromosome doubling) led to reduction of
cost of marker-assisted breeding up to 40% [445]. Later, this
MAS strategy was validated practically in a marker-assisted wheat-breeding
program in order to improve quality and resistance against rust disease (for
review, see [446]). At CIMMYT, markers associated with 25
different genes governing insect pest resistance, protein quality, homoeologous
pairing, and other agronomic characters are currently being utilized in wheat
breeding programs in order to develop improved wheat cultivars [447]. Some of the markers used in these programs are perfect markers that have been developed
from available nucleotide sequences of these genes. In future,
large-scale sequencing of GRRs (gene-rich regions), to be undertaken by IWGSC,
will also facilitate isolation of important genes for production of improved
transgenic crops, and for development of “perfect markers” for agronomically
important traits to be used in MAS [448, 449].


## 17. Organellar Genomes and their Organization

The genomes of wheat chloroplast and mitochondrion have also been
subjected to a detailed study during the last decade. The results of these
studies will be briefly discussed in this section.


### 17.1. Chloroplast Genome

In bread wheat, 130–155 chloroplasts, each containing 125–170 circular DNA
molecules (135 kb), are present in each mesophyll cell, thus making 16000–26000 copies of
cpDNA within a cell. This makes 5–7% of the cellular DNA in the leaf and 10–14% of the DNA in
a mesophyll cell. In the related diploid species, there are 4900–6600 copies and
in tetraploid species, there are 9600–12400 copies of cpDNA per mesophyll cell.The wheat chloroplast genome, like all other plant chloroplast genomes,
has two inverted repeat regions, each copy (21-kb-long) separated from the
other by two single copy regions (12.8 kb, 80.2 kb). The gene content of wheat
chloroplast is the same as those of rice and maize plastomes, however some
structural divergence was reported in the gene coding regions, due to
illegitimate recombination between two short direct repeats and/or replication
slippage; this included the presence of some hotspot regions for length
mutations. The study of deletion patterns of open reading frames (ORFs) in the
inverted-repeat regions and in the borders between the inverted repeats and the
small single-copy regions supports the view that wheat and rice are related
more closely to each other than to maize (see [450, 451]). Deletions, insertions, and inversions have also been detected during
RFLP analysis of cpDNA, which gave eleven different cpDNA types, in the genus Triticum,
the bread wheat sharing entirely the cpDNA type with durum wheats, but not with
that of any of the diploid species. The cpDNA of Ae. speltoides showed
maximum similarity to those of T. aestivum, T. timopheevii, and T.
zhukovskyi, suggesting that Ae. speltoides should be the donor of
the B subgenome of common wheat [452].


### 17.2. Mitochondrial Genome

Wheat mtDNA is larger (430 kb) than cpDNA (135 kb) with a minimum of 10
repeats but encodes only 30–50% polypeptides
relative to cpDNA. Thus, large amount of mtDNA is noncoding, there being about
50 genes involved in RNA synthesis [453]. Mitochondrial genome of Chinese Spring has been sequenced using 25
cosmid clones of mitochondrial DNA, selected on the basis of their gene
content. This led to the identification of 55 (71) genes including the
following: 18 genes (20) for electron transport system, 4 genes for mitochondrial
biogenesis, 11 genes for ribosomal proteins, 2 genes for splicing and other
function, 3 genes (10) for rRNAs, and 17 genes (24) for tRNAs (the numerals in
parentheses represent number of genes, taking multiple copies of a gene as
separate genes). When mitochondrial gene maps were compared among wheat, rice,
and maize, no major synteny was found between them other than a block of two to
five genes. Therefore, mitochondrial genes seem to have thoroughly reshuffled
during speciation of cereals. In contrast, chloroplast genes show perfect
synteny among wheat, rice, and maize [451].


## 17.1. Chloroplast Genome

In bread wheat, 130–155 chloroplasts, each containing 125–170 circular DNA
molecules (135 kb), are present in each mesophyll cell, thus making 16000–26000 copies of
cpDNA within a cell. This makes 5–7% of the cellular DNA in the leaf and 10–14% of the DNA in
a mesophyll cell. In the related diploid species, there are 4900–6600 copies and
in tetraploid species, there are 9600–12400 copies of cpDNA per mesophyll cell.The wheat chloroplast genome, like all other plant chloroplast genomes,
has two inverted repeat regions, each copy (21-kb-long) separated from the
other by two single copy regions (12.8 kb, 80.2 kb). The gene content of wheat
chloroplast is the same as those of rice and maize plastomes, however some
structural divergence was reported in the gene coding regions, due to
illegitimate recombination between two short direct repeats and/or replication
slippage; this included the presence of some hotspot regions for length
mutations. The study of deletion patterns of open reading frames (ORFs) in the
inverted-repeat regions and in the borders between the inverted repeats and the
small single-copy regions supports the view that wheat and rice are related
more closely to each other than to maize (see [450, 451]). Deletions, insertions, and inversions have also been detected during
RFLP analysis of cpDNA, which gave eleven different cpDNA types, in the genus Triticum,
the bread wheat sharing entirely the cpDNA type with durum wheats, but not with
that of any of the diploid species. The cpDNA of Ae. speltoides showed
maximum similarity to those of T. aestivum, T. timopheevii, and T.
zhukovskyi, suggesting that Ae. speltoides should be the donor of
the B subgenome of common wheat [452].


## 17.2. Mitochondrial Genome

Wheat mtDNA is larger (430 kb) than cpDNA (135 kb) with a minimum of 10
repeats but encodes only 30–50% polypeptides
relative to cpDNA. Thus, large amount of mtDNA is noncoding, there being about
50 genes involved in RNA synthesis [453]. Mitochondrial genome of Chinese Spring has been sequenced using 25
cosmid clones of mitochondrial DNA, selected on the basis of their gene
content. This led to the identification of 55 (71) genes including the
following: 18 genes (20) for electron transport system, 4 genes for mitochondrial
biogenesis, 11 genes for ribosomal proteins, 2 genes for splicing and other
function, 3 genes (10) for rRNAs, and 17 genes (24) for tRNAs (the numerals in
parentheses represent number of genes, taking multiple copies of a gene as
separate genes). When mitochondrial gene maps were compared among wheat, rice,
and maize, no major synteny was found between them other than a block of two to
five genes. Therefore, mitochondrial genes seem to have thoroughly reshuffled
during speciation of cereals. In contrast, chloroplast genes show perfect
synteny among wheat, rice, and maize [451].


## 18. Conclusions

Significant progress during the last two decades has been made in
different areas of wheat genomics research. These include development of
thousands of molecular markers (including RFLPs, SSRs, AFLPs, SNPs, and DArT
markers), construction of molecular genetic and physical maps (including
radiation hybrid maps for some chromosomes) with reasonably high density of
markers, development of more than 1 million ESTs and their use for developing
functional markers, and the development of BAC/BIBAC resources for individual
chromosomes and entire subgenomes to facilitate genome sequencing. Functional
genomics approaches like TILLING, RNAi, and epigenetics have also been utilized
successfully, and a number of genes/QTL have been cloned to be used in future
wheat improvement programs. Organellar genomes including chloroplast and
mitochondrial genomes have been fully sequenced, and we are at the threshold of
initiating a major program of sequencing the gene space of the whole nuclear
genome in this major cereal. The available molecular tools also facilitated a
revisit of the wheat community to the problem of origin and evolution of the
wheat genome and helped QTL analysis (including studies involving LD and
association mapping) for identification of markers associated with all major
economic traits leading to the development of major marker-aided selection
(MAS) programs for wheat improvement in several countries.



---

*Source: 896451-2008-05-19.xml*