Plant Biotechnology Journal (2011), pp. 1–14
doi: 10.1111/j.1467-7652.2011.00628.x
Research article
Transcript-specific, single-nucleotide polymorphism
discovery and linkage analysis in hexaploid bread wheat
(Triticum aestivum L.)
Alexandra M. Allen1, Gary L.A. Barker1, Simon T. Berry2, Jane A. Coghill1, Rhian Gwilliam3, Susan Kirby3, Phil
Robinson3, Rachel C. Brenchley4, Rosalinda D’Amore4, Neil McKenzie5, Darren Waite5, Anthony Hall4,
Michael Bevan5, Neil Hall4 and Keith J. Edwards1,*
1
School of Biological Sciences, University of Bristol, Bristol, UK
2
Limagrain UK Limited, Woolpit Business Park, Suffolk, UK
3
KBioscience, Unit 7, Maple Park, Hertfordshire, UK
4
School of Biological Sciences, University of Liverpool, Liverpool, UK
5
John Innes Centre, Norwich Research Park, Norwich, UK
Received 5 April 2011;
accepted 24 April 2011.
*Correspondence (fax 44 117 925 7374;
email [email protected])
Keywords: wheat, next-generation
sequencing, KASPar genotyping,
single-nucleotide polymorphism.
Summary
Food security is a global concern and substantial yield increases in cereal crops are required to
feed the growing world population. Wheat is one of the three most important crops for
human and livestock feed. However, the complexity of the genome coupled with a decline in
genetic diversity within modern elite cultivars has hindered the application of marker-assisted
selection (MAS) in breeding programmes. A crucial step in the successful application of MAS
in breeding programmes is the development of cheap and easy to use molecular markers,
such as single-nucleotide polymorphisms. To mine selected elite wheat germplasm for intervarietal single-nucleotide polymorphisms, we have used expressed sequence tags derived from
public sequencing programmes and next-generation sequencing of normalized wheat complementary DNA libraries, in combination with a novel sequence alignment and assembly
approach. Here, we describe the development and validation of a panel of 1114 single-nucleotide polymorphisms in hexaploid bread wheat using competitive allele-specific polymerase
chain reaction genotyping technology. We report the genotyping results of these markers on
23 wheat varieties, selected to represent a broad cross-section of wheat germplasm including
a number of elite UK varieties. Finally, we show that, using relatively simple technology, it is
possible to rapidly generate a linkage map containing several hundred single-nucleotide polymorphism markers in the doubled haploid mapping population of Avalon · Cadenza.
Introduction
With the world’s population forecasted to reach nine billion by
2050, food security has become a critical global challenge for
the 21st century. It has been estimated that cereal production
needs to increase by 50% by 2030 (Foresight: The Future of
Food and Farming, 2011). Wheat is the dominant cereal crop
grown in temperate countries, and globally, one of the three
most important crops for human and livestock feed (Shewry,
2009). Consequently, increasing wheat yields is now one of the
top priorities for agricultural research (Reaping the benefits: Science and the sustainable intensification of global agriculture,
2009). When compared with other crops, the increase in wheat
yields has slowed since the ‘green revolution’ of the 20th century (Alston et al., 2010). While diploid crops such as rice and
barley have benefited from extensive genetic analysis and
molecular breeding programmes, the complexity of the wheat
genome has hindered these types of studies.
The allohexaploid genome of modern bread wheat (AABBDD)
is derived from the hybridization of the diploid DD genome of
Aegilops tauschii with the AABB tetraploid genome of Triticum
turgidum (Dubcovsky and Dvorak, 2007). The genetic bottleneck caused by this polyploid speciation event approximately
10 000 years ago has been intensified by the domestication of
wheat, resulting in a decline of genetic diversity within modern
inbred, elite cultivars (Haudry et al., 2007). To increase the
genetic diversity of bread wheat, it has been suggested that
greater use could be made of the various wild relatives of
wheat and that by introducing genes from these relatives it
might be possible to tap novel sources of stress, pest and disease resistance (Reynolds et al., 2011). However, such strategies, sometimes referred to as pre-breeding, can be resource
intensive (Valkoun, 2001). A crucial step in making wheat prebreeding more efficient is the development of molecular markers capable of tracking the introduced genomic regions in large
numbers of lines. There are several challenges to developing
molecular markers in a polyploid species such as wheat, for
instance, the molecular markers must be capable of distinguishing between the relatively large numbers of polymorphisms
seen in homoeologous and paralogous genes compared with
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd
1
2 Alexandra M. Allen et al.
the relatively infrequent varietal polymorphisms (Barker and
Edwards, 2009). Homoeologous ⁄ paralogous and varietal singlenucleotide polymorphisms (SNPs) have previously been studied
and used in polyploid crops (Akhunov et al., 2009; Bernardo
et al., 2009; Bundock et al., 2009; Edwards et al., 2009; Imelfort et al., 2009); however, these studies have also shown that
distinguishing intervarietal markers from intergenomic polymorphisms is complicated and prone to error. For example, a previous in silico study by our group showed that of the 71 000
putative SNPs identified in the wheat EST database, only 3500
appear to be intervarietal (5%) with the majority being SNPs
in homoeologous and paralogous genes (Barker and Edwards,
2009). These results, together with the large size of the wheat
genome (17 300 Mb), mean that despite the global importance of wheat, there are still relatively few validated varietal
SNP markers in regular use. While this situation remains, it is
likely that, when compared with other crops such as maize and
rice, wheat will continue to lag behind in terms of markerassisted selection in breeding programmes (Mochida and
Shinozaki, 2010).
The development of SNP-based genotyping platforms has
lead to an increase in the number of protocols available for
analysing the genetic variation in numerous species (Perkel,
2008). However, as with the development of SNPs, large-scale
genotyping in polyploid species is still a significant challenge
because of the presence of the homoeologous and paralogous
genes. Despite these challenges, a number of platforms have
recently been developed to perform high-density genotyping
(large numbers of SNPs, with small numbers of individual
plants), and these have been successfully employed to genotype
wheat (Akbari et al., 2006; Akhunov et al., 2010; Bérard et al.,
2009; Chao et al., 2010). However, these technologies can be
difficult to optimize and as such they have yet to be generally
adopted by the wheat community leaving few options for
wheat breeders and geneticists who wish to carry out mediumto low-density genotyping on large or very large numbers of
individual plants.
In the present study, we undertook to mine the UK wheat
germplasm for varietal SNPs. To do this, we used both the available wheat expressed sequence tags (ESTs) present in the public
database and next-generation sequencing (NGS), together with
a novel sequence alignment and assembly approach to identify
varietal SNPs in lines of interest to European wheat breeders.
We then investigated whether these SNPs could be validated by
a relatively new, high-throughput genotyping procedure, the
KBioscience Competitive Allele-Specific polymerase chain reaction (KASPar) assay (Orrù et al., 2009). Finally, we made use of
the existing Avalon · Cadenza mapping population to examine
whether the validated SNPs could be efficiently placed onto the
existing linkage groups identified within this doubled haploid
population.
whole-seedling cDNA from five wheat varieties (Avalon,
Cadenza, Rialto, Savannah, Recital) on the Illumina GAIIx platform (Table 1). For each line, we generated between 24 and
45 million, 75-base pair (bp) paired-end reads. SNP discovery,
carried out as described in experimental procedures, resulted in
the identification of 14 078 putative SNPs in 6255 distinct reference sequences covering 2.7 megabases in total. On average,
this equates to five varietal SNPs per kilobase in the reference
sequences containing one or more SNP (Table S2).
SNP validation and characterization
A subset of 1659 putative SNPs were selected for validation
with genomic DNA. Of these, 213 (from 199 contigs covering
25 773 bases) were derived from sequences obtained from the
NCBI database and 1446 (1237 contigs covering 5 59 059
bases) from the NGS carried out as part of this study. For the
SNPs mined from the NGS data, these were selected on the
basis of their predicted polymorphism level in the sequenced
varieties. In most cases, loci were limited to one SNP per cDNA
contig, to maximize the genome coverage. SNPs were validated
using the KASPar genotyping platform on 21 hexaploid wheat
varieties, a diploid and a tetraploid wheat (Table 2). The wheat
varieties were selected following a survey of UK academics and
wheat breeders to ensure that material of use to the whole
community was represented, e.g. the inclusion of parents of
other published mapping populations. Of the 1659 putative
varietal SNPs, 1114 (67%) were polymorphic between the different varieties, 70 (4%) were monomorphic in the hexaploid
varieties and 475 (29%) failed to generate a useful amplification signal (Table S3). Where a SNP probe failed to generate a
useful signal, no attempt was made to redesign the primer. To
compare the different SNP generation techniques, these statistics were assessed separately. Of the 213 SNPs generated from
the EST database, 174 (82%) were polymorphic between the
23 varieties, four were monomorphic (2%) and 35 failed to
generate a useful signal (16%; Table S1), while of the 1446
SNPs from NGS data, 940 (65%) were polymorphic, 66 were
monomorphic (4.5%) and 440 failed to generate a useful signal
(30%; Table S2). Based upon the SNP genotyping data of the
23 varieties screened, the polymorphism information content
(PIC) values of the validated SNPs varied from 0.080 to 0.375
with an average value of 0.300 (Figure 1). The PIC values of the
SNPs generated from the EST database (average 0.306) were
not significantly different from those generated from NGS data
(average 0.298).
To test whether the non-polymorphic SNPs were homoeologous rather than homologous SNPs, we screened 28 on the
Chinese Spring nullisomic lines (Sears, 1954; Devos et al.,
Table 1 Summary of wheat lines used and next-generation
sequences obtained
Results
No. sequences
SNP discovery
Two complimentary approaches were taken to identify putative
varietal SNPs. First, screening of the publicly available wheat
EST database (generated from numerous cDNA libraries, from a
random collection of varieties relating to different stages of
grain development and various stress treatments) yielded
3500 putative varietal SNPs in 8668 sequences (Table S1).
These data were supplemented by sequencing normalized
uniquely assigned
No. of SNPs
Variety
No. of sequences
to reference
(compared to Avalon)
Avalon
33 199 506
17 419 590
N⁄A
Cadenza
34 296 300
16 938 920
3251
Rialto
25 522 334
13 241 931
2944
Savannah
24 349 192
12 271 061
3492
Recital
43 961 514
22 151 006
3792
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 3
Table 2 Details of wheat varieties used in this study
Wheat Line
Breeder ⁄ Source
Ploidy
Planting season
Pedigree
Avalon
PBIC
Hexaploid
Winter
Maris-Ploughman · Bilbo
Alchemy
Limagrain UK Ltd
Hexaploid
Winter
Claire · (Consort · Woodstock)
Alcedo
NIAB
Hexaploid
Winter
(Record, AUT · Poros) · Carstens-VIII
Bacanora
CIMMYT
Hexaploid
Spring
(Jupateco-73 · BlueJay) · Ures-81
Brompton
Elsoms Seeds Ltd
Hexaploid
Winter
CWW 92.1 · Caxton
Cadenza
PBIC
Hexaploid
Spring
Axona · Tonic
Chinese Spring (L 42)
JIC
Hexaploid
Spring
Chinese Land Race
Claire
Limagrain UK Ltd
Hexaploid
Winter
Wasp · Flame
Hereward
RAGT seeds Ltd
Hexaploid
Winter
Norman ‘sib’ · Disponent
Opata
CIMMYT
Hexaploid
Spring
Bluejay · Jupateco-73
Recital
C.C. Benoist
Hexaploid
Winter?
Mexique-267 (R-267) · 9369
Renan
INRA
Hexaploid
Winter
(Mironovskaya-808 · Maris-Huntsman) · Courtot
Rialto
PBIC
Hexaploid
Winter
Haven · Fresco
Robigus
KWS UK Ltd
Hexaploid
Winter
Z836 · 1366 (PUTCH)
Riband · Brigadier
Savannah
Zeneca Seeds UK Ltd.
Hexaploid
Winter
Shamrock
Verneuil Recherche
Hexaploid
Winter
COMPL TIG 323-1-3m · CWW 4899 ⁄ 25
Soissons
Maison Florimond Desprez, Franc
Hexaploid
Winter
Iena (Jena) · HN35
Synthetic
JIC
Hexaploid
Spring
Unknown
Weebil
CIMMYT
Hexaploid
Spring
Unknown
Xi19
Limagrain UK Ltd
Hexaploid
Winter
(Cadenza · Rialto) · Cadenza
TA1618
WGGRC
Diploid
Spring
Aegilops Tauschii (D genome)
Croc
WGGRC
Tetraploid
Spring
Triticum turgidum subsp. Dicoccoides (AB genome)
Synthetic 39
WGGRC
Hexaploid
Spring
TA1618 · Croc
PBIC, Plant Breeding International Cambridge, Ltd. (UK); NIAB, National Institute of Agricultural Botany; CIMMYT, International Maize and Wheat Improvement
Centre; JIC, John Innes Centre; INRA, French National Institute for Agricultural Research; WGGRC, Wheat Genetic and Genomic Resources Center.
250
Hereward and Shamrock. The synthetic varieties formed a clear
outlying group, with the other varieties falling into two main
groupings. The first included subgroups of Renan, Robigus and
Avalon and Hereward, Shamrock, Brompton, Rialto and
Alchemy. The second group contained more diverse material
including the French lines Recital and Soissons, and the German
variety Alcedo.
200
Genetic map construction
150
An Avalon · Cadenza doubled haploid population comprising
190 individuals was scored for 548 loci (53 from the EST data
set and 495 from the NGS data set) using KASPar genotyping.
Of these, 38 loci showed significant segregation distortion and
were removed from the data set before constructing the linkage groups. Using the information generated, in conjunction
with the 574 non-SNP markers previously available for this population, we were able to map 480 SNP markers (50 from the
EST data set and 430 from the NGS data set) to 21 linkage
groups representing chromosomes. A further 20 loci mapped to
unassigned linkage groups and 10 loci were unlinked (Table 3,
Figure 3, Table S4). The linkage groups ranged from 53.0 to
240.5 centiMorgans (cM) in size, with 9–108 markers. The total
map length was 2999 cM, with an average spacing of 2.8 cM
between loci. The total map lengths of each of the three genomes were approximately similar: 1131 cM for the A genome,
1075 cM for the B genome and 792 cM for the D genome
(Table 3). However, there was a significant difference in the distribution of SNP marker loci between the three genomes with
the A genome having 354 markers (163 SNPs), the B genome
having 526 markers (267 SNPs) and the D genome having 174
450
400
Number of markers
350
300
100
50
0
0.0–0.10 0.10–0.15 0.15–0.20 0.20–0.25 0.25–0.30 0.31–0.35 0.35–0.40
Polymorphism information content (PIC)
Figure 1 Distribution of polymorphism information content (PIC) of
SNPs among the 23 wheat varieties. The PIC was calculated for
each marker according to Botstein et al. (1980), using Excel
Microsatellite Toolkit add-in software (Park, 2001).
1999); of these, we were able to confirm that 26 (93%) were
in fact homoeologous SNPs and were therefore specific for
either the A, B or D genome, but not a specific variety.
To investigate the genetic relationship between the 23 lines,
we carried out a hierarchical cluster analysis using all 1114 validated SNPs between the varieties used in this study (Figure 2).
Several varieties clustered tightly together including the pairs of
Cadenza and Xi19; Claire and Alchemy; Opata and Weebil and
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
4 Alexandra M. Allen et al.
Figure 2 Indicator of genetic relationships between the of 23 wheat lines screened with 1114 KASPar probes. Hierarchical cluster analysis was performed
in PASW (SPSS Inc.) using the Squared Euclidean Distance method.
markers (50 SNPs). The homoeologous chromosome groups also
showed variation in size and marker density; the combined
group 5 chromosome linkage maps measured the largest at
613 cM, while the group 4 linkage groups measured a total of
267 cM. Marker density was highest in the group 1 linkage
maps (average 1.7 cM between loci) and lowest in the group 7
linkage maps (average 3.8 cM between loci).
Our mapped SNP data set contained 48 loci that we had provisionally mapped to specific chromosome regions using the
Kansas deletion lines (Endo and Gill, 1996; Qi et al., 2003).
Comparison of the map locations determined using both the
Kansas deletion lines and the genetic mapping confirmed the
map location for 29 of the 48 SNP markers (60%). A further 10
SNP loci mapped to the same homoeologous group, which gives
a total of 81% of the loci in common mapping to the same homoeologous group. To extend this analysis, we also BLASTN
searched the 500 mapped SNP marker reference sequences
against the deletion-mapped ESTs produced by Qi et al. (2004).
Of the 155 SNP markers contained within these two data sets,
107 (69%) mapped to the same homoeologous group.
The same 500 mapped sequences were also BLASTN
searched against the Brachypodium distachyon genome
sequence (International Brachypodium Initiative., 2010) resulting
in 266 matches. A syntenic match was assumed where two or
more consecutive wheat mapped SNPs matched the same
B. distachyon chromosome. These criteria resulted in 205 of the
266 mapped wheat SNPs being placed on the B. distachyon
genome. SNP coverage was generally too low to explore synteny in detail, but where sufficient markers existed, as for wheat
linkage group 2 versus B. distachyon chromosome 5, the syntenic relationship was consistent with known chromosome relationships (Figure 5).
The putative functions of 500 sequences containing validated
and mapped SNP markers were determined, via BLASTX
searches, against the National Centre for Biotechnology Information (NCBI) non-redundant protein sequence database using
an e-value cut-off of 10)5 (Table S5). This search indicated that
213 sequences (43%) had detectable similarity to sequences in
the database, while 287 sequences (57%) failed to detect any
sequences with a similarity at the level at or above the cut of
value. Of the 213 sequences with detectable sequence similarities, we were able to identify 184 unique hits, as determined by
the SNP having either a unique hit in the database or having a
similar hit, but with a different map location. The remaining 29
SNPs were distributed between 13 contigs; one contig having
four SNPs, one contig having three SNPs and eleven contigs
having two SNPs each.
Optimization of primer design for
homoeologous-specific KASPar markers
Although no attempt was made to design homoeologousspecific probes, 111 of the 1114 validated KASPar probes
(10%) appeared to be specific for a single homoeologous
gene. In practical terms, these probes can be used to routinely
screen for heterozygotes in segregating populations, as well as
establishing the purity of inbred lines. Because such probes
might be of considerable interest to wheat breeders, we examined whether it was possible to convert generic KASPar probes
to homoeologous-specific assays. To do this, we used the publicly available 5 · sequence coverage of the Chinese Spring
wheat genome (http://www.cerealsdb.uk.net/search_reads.htm)
to design homoeologous-specific KASPar reverse primers near
the existing varietal SNP ‘BS00000329’. In its original form,
KASPar probe BS00000329 detects either an ‘A’ or an ‘AG’
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 5
Table 3 Summary of linkage groups and mapped loci
Linkage
Group
Chromosome
Total
Number
Average spacing
Size
Number
of Bristol
between loci
(cM)
of Loci
SNP loci
(cM)
SNPs, a technique that yielded 13239 putative varietal SNPs in
6255 sequences, with an average of five varietal SNPs per kilobase across the five varieties sequenced. Both SNP discovery
techniques identified a similar number of varietal SNPs per kilobase and are in agreement with previous estimates for varietal
SNPs in wheat (Ravel et al., 2007; Barker and Edwards, 2009).
1
1A
104.9
52
28
2.0
2
1B
148.9
108
58
1.4
SNP validation and characterization
3
1D
108.9
50
20
2.2
4
2A
180.5
53
22
3.4
5
2B
181.1
85
43
2.1
6
2D
96.9
35
11
2.8
7
3A
155.5
37
18
4.2
8
3B
224.6
74
33
3.0
9
3D
53.0
15
1
3.5
10
4A
143.1
40
18
3.6
11
4B
53.7
26
16
2.1
12
4D
70.2
9
1
7.8
13
5A
179.5
51
23
3.5
14
5B
240.5
104
53
2.3
15
5D
193.1
20
8
9.7
16
6A
155.7
67
35
2.3
17
6B
132.0
82
37
1.6
18
6D
57.9
10
2
5.8
19
7A
212.3
54
19
3.9
20
7B
94.2
47
27
2.0
21
7D
212.3
35
7
6.1
2.8
A subset of putative varietal SNPs (1659) were selected for validation using the KASPar genotyping platform. The KASPar system gave a conversion rate of 67% (1114 SNPs) using a
standard set of PCR conditions, which is extremely important
for routine MAS applications. However, this figure of 67%
could probably be increased with primer design and PCR optimization if absolutely necessary. This conversion rate is lower
than previous studies on diploid species such as mouse (77%;
Petkov et al., 2004), but relatively high for a complex polyploid
such as wheat (Edwards et al., 2009). Of the putative varietal
SNPs, 4% were monomorphic in the hexaploid varieties, but
polymorphic between the hexaploid varieties and the diploid
and ⁄ or the tetraploid. This pattern suggests that these markers
represent intravarietal homoeologous SNPs. To test this theory,
we screened 28 markers from this category against the Chinese
Spring nullisomic lines and confirmed that 26 (93%) were in
fact homoeologous SNPs. These markers are likely to have been
misidentified as varietal SNPs in the discovery pipeline. Nevertheless, 4% is low when considering that intravarietal homoeologous SNPs account for 74% of all SNPs in wheat (Barker and
Edwards, 2009) and clearly demonstrates that our data sets
have been significantly enriched for varietal SNPs. The failure of
the remaining SNP markers to validate (29% failed to produce
a defined or useful cluster) is likely to be because of the presence of paralogous sequences, primer design issues (such as
primers spanning intron ⁄ exon boundaries or incorporating different homoeologous SNPs) and ⁄ or the need to optimize PCR
conditions. We made no attempts to optimize any failed assays,
and therefore, it is possible that these SNPs could be validated
with alternative primers.
The PIC values of the validated markers ranged from a minimum of 0.08–0.375, with an average value of 0.300 (Figure 1),
a figure comparable to other estimates in wheat (Chao et al.,
2009; Edwards et al., 2009). No significant difference was
observed between the PIC values of markers from different
genomes or different homoeologous groups. A hierarchical cluster analysis performed on the genotyping data at the SNP loci
used in this study indicates possible genetic relationships
between the 23 lines (Figure 2). Although the SNP markers
were selected based upon their polymorphism between just a
few lines (Avalon, Cadenza, Rialto, Savannah and Recital), the
expected clustering of varieties was observed, suggesting that
the SNP markers were representative of the genome. In particular, the close relationship between the varieties Cadenza and
Xi19 (derived from a Cadenza cross) and Claire and Alchemy
(derived from a Claire cross) is illustrated in the dendrogram.
The different varieties are split into two main clades with
the synthetics as an outlying group. The first clade includes
subgroups of Renan, Robigus and Avalon and Hereward, Shamrock, Brompton, Rialto and Alchemy; all winter wheat varieties.
The second clade contains more divergent material including
the French lines Recital and Soissons, and the German variety
Alcedo. A close relationship in this grouping is indicated
between Opata, Weebil and Bacanora, all spring wheat lines
Total
2998.8
1054
480
A genome
1131.4
354
163
3.2
B genome
1075.0
526
267
2.0
D genome
792.3
174
50
4.6
Group 1
362.8
210
106
1.7
Group 2
458.4
173
76
2.6
Group 3
433.1
126
52
3.4
Group 4
267.0
75
35
3.6
Group 5
613.1
175
84
3.5
Group 6
345.6
159
74
2.2
Group 7
518.7
136
53
3.8
genotype. Using the available 5 · Chinese Spring genomic
sequences, we were able to design a new KASPar reverse primer that converted the original generic probe into a homoeologous-specific KASPar probe detecting either an ‘A’ or a ‘G’
genotype (Figure 5b,c).
Discussion
SNP discovery
This study represents the first large scale assembly of genotyping and genetic map information for elite UK wheat varieties
based on individual SNP markers. The SNPs described in this
study were mined from two sources; the NCBI wheat EST database and next-generation cDNA sequencing data. Both sources
of data specifically targeted the identification of varietal SNPs in
coding regions, to generate molecular markers potentially linked
to genes of interest and include both synonymous and nonsynonymous point mutations. The EST data yielded 3500 putative varietal SNPs from 8668 sequences, with an average of 4.3
varietal SNPs per kilobase (Barker and Edwards, 2009). The existing EST data were supplemented by mining NGS data for varietal
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
6 Alexandra M. Allen et al.
1A
1B
Xgdm033
Xpsp2999
Xgwm136
Xcfd015 XBS00004382
XBS00001635 XBS00010039
XBS00012226 Xwmc336
XBS00010054
XBS00003761 XBS00003572
Xbarc119
XBS00009926 XBS00009885
XBS00003851
XBS00009866 XBS00010004
XBS00010452 XBS00009495
XBS00009444 XBS00010416
XBS00010595 XBS00010481
XBS00003758 XBS00009603
Xpsp3027
Xgwm164 Xgwm498
XBS00000853
XBS00009559
XBS00003829 XBS00003656
Glu-A1
Xwmc093
XBS00009459
XBS00003743
XBS00009700
XBS00003863
Xgwm099
0.0
12.1
15.0
23.0
23.6
28.4
29.0
34.8
50.0
50.6
56.5
57.5
58.1
59.2
63.2
64.3
64.9
65.4
66.6
70.2
70.7
75.7
78.0
84.7
19.3
24.9
39.8
47.8
53.6
56.8
57.3
59.7
61.4
63.7
65.0
67.1
67.7
69.3
69.8
75.7
78.4
80.0
81.1
81.6
86.5
88.8
93.1
106.6
116.4
117.0
143.3
145.6
2A
0.0
Xgwm614
Xbarc124
XBS00003735 XBS00010624
XBS00004385
48.8
56.1
62.0
Xgwm296
XBS00014736 XBS00003845
XBS00003867 XBS00016676
Xgwm359
XBS00009933
74.7
XBS00009730
88.2
90.5
91.1
91.6
92.2
92.7
93.3
98.9
123.5
135.7
138.9
141.0
146.9
160.0
Xgwm445
Xbarc011 XBS00003779
XBS00003574
XBS00003898 XBS00004004
XBS00009256
XBS00003878
XBS00002994
XBS00010672 Xgwm122
Xwmc453
Ppd-A1
Xwmc181
XBS00001260
XBS00003663
XBS00004090
XBS00009959
XBS00009295
180.5
Xgwm526
93.8
0.0
0.5
1.8
6.7
16.9
17.4
25.1
27.3
28.4
43.5
46.4
62.8
73.4
73.9
77.7
78.7
79.3
79.8
80.9
81.4
83.6
90.2
94.1
95.3
125.2
126.3
139.8
140.3
153.2
162.7
169.4
170.0
XBS00003806
0.0
16.9
Xcfd015
24.5
26.9
38.0
43.1
56.0
57.1
57.6
58.8
64.2
64.7
65.3
69.1
Xwmc336
XBS00003751
Xcfd021
XBS00010661
XBS00010486
Xgwm458
Xbarc240
Xcfd019
Glu-D1 XBS00004282
XBS00003558
XBS00003559
XBS00003816
Xgdm111
XBS00009412
XBS00010402 XBS00010498
XBS00003725 XBS00003573
XBS00010669 XBS00009375
XBS00003687 XBS00009625
XBS00010713 XBS00000424
XBS00009389
107.2
108.4
108.9
2B
24.7
25.2
30.2
51.2
1D
Xgwm033
Xpsp3000 Xgwm264
XBS00003930
XBS00010592
XBS00003831
XBS00001634 XBS00010076
XBS00003880 XBS00003917
XBS00010508 XBS00010032
XBS00003684
XBS00021052
XBS00003619 XBS00009381
XBS00014413 XBS00003839
XBS00003822
XBS00000496
Xgwm011 Xgwm018
Xbarc240
XBS00009675 XBS00009977
XBS00003575 XBS00003844
XBS00009921 XBS00009502
XBS00009952 XBS00010549
XBS00009547 XBS00009270
XBS00003567 XBS00003634
XBS00003677 XBS00003600
XBS00003627
XBS00009945
XBS00001224
XBS00012056
Xwmc419
XBS00010779 XBS00010475
XBS00012456 XBS00010888
XBS00001414
XBS00010625
XBS00010536
Glu-B1
XBS00003934
XBS00009706 XBS00010009
XBS00004328
XBS00012502
XBS00000651 XBS00010436
Xcfd002 XBS00003633
XBS00002250
XBS00004129
XBS00009909 XBS00010610
XBS00009709 Xwmc044
XBS00003702
Xbarc080
XBS00001128
0.0
13.2
14.4
15.8
18.6
2D
Xbarc035 Xgwm210
XBS00010734
Xwmc489
XBS00009972
XBS00010318
XBS00002660
XBS00003833 XBS00010637
XBS00012209 XBS00010700
Xwmc243 XBS00009801
XBS00003719
XBS00009848
Xbarc010
Xgwm257
XBS00010055
XBS00004433
XBS00009305
XBS00003714
XBS00003810
XBS00009722
XBS00010012 XBS00003788
XBS00009989
Xwmc175
Xgwm501
XBS00003585
XBS00009247 XBS00009461
Xcfd073
Xgwm526
XBS00004405 XBS00003597
XBS00010361 XBS00010640
XBS00009791 XBS00009864
XBS00003589 XBS00003673
XBS00009437 XBS00009519
XBS00010081
XBS00009460
Xbarc159
XBS00010685
XBS00003584
XBS00010323 XBS00010442
XBS00009901 XBS00009533
XBS00009637 XBS00010393
0.0
5.2
15.3
17.6
22.6
25.1
27.3
Xbarc124 Xcfd056
XBS00009771
XBS00003791 XBS00009976
Xcfd036
Xcfd044 Xgwm261
Xgwm503 Xgwm296
Xgwm132
65.9
69.6
72.3
76.3
76.9
78.1
81.7
83.9
87.2
87.7
92.0
96.9
Xcfd161
Xgwm539
Xbarc011
Xwmc245
XBS00000905 XBS00021483
Xwmc018
XBS00009932
XBS00000002 XBS00003804
XBS00010514 XBS00010681
XBS00003660
Xgwm102
Xwmc453
0.0
Xgwm349
10.1
Xgwm320
Figure 3 Genetic linkage maps of wheat derived from 190 Avalon · Cadenza doubled haploid lines. Each linkage group was assigned to a chromosome indicated above the linkage group and chromosomes are arranged with the short arm above the long arm. Simple sequence repeat (SSR) markers are shown in red, known genes are shown in green and SNP loci mapped in this study are shown in black (designated XBS). Map distances,
calculated using the Kosambi mapping function (Kosambi, 1944), are shown in centiMorgans (cM).
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 7
3A
XBS00003774
XBS00009793 XBS00010059
XBS00003733
0.0
1.1
23.6
XBS00000628
37.9
48.0
60.8
61.4
63.6
66.5
68.7
69.3
71.0
75.4
77.1
77.6
102.8
Xgwm369
Xcfd079
Xwmc505
XBS00003801
Xbarc019
Xwmc489 XBS00003837
XBS00003964
XBS00009465
XBS00011012
Xwmc264
XBS00010022
XBS00003778
XBS00003768 XBS00003924
XBS00010414 XBS00000646
Xgwm155
128.1
XBS00009649
155.5
XBS00003849
78.7
0.0
12.0
14.8
18.5
26.1
26.6
27.7
32.3
46.8
47.3
49.0
49.6
50.2
51.3
73.4
83.3
89.3
90.9
92.0
92.6
93.2
94.8
95.4
3B
95.9
97.5
98.6
99.7
100.3
103.0
114.1
138.8
156.7
157.8
178.0
181.4
183.0
4A
Xcfd079
XBS00004108
XBS00009839
Xgwm493
XBS00003596
XBS00001335
XBS00012316
Xgwm389
Xbarc075
XBS00009992
XBS00012531
XBS00003814
Xbarc133
XBS00009393 XBS00004074
XBS00003902
XBS00003871
XBS00003864
XBS00003708
Xgwm285
XBS00010715
XBS00009289
Xbarc164 XBS00009475
XBS00009651
XBS00009579 XBS00000793
XBS00012452 XBS00003781
XBS00003727
XBS00012388
XBS00009892
XBS00001242 XBS00020861
XBS00000978
Xgwm131
XBS00010572
Xwmc326
XBS00003836
XBS00003931
XBS00003717
XBS00003884
Xgwm547
5.3
12.0
20.2
Xgwm052
Xcfd004 Xgwm456
Xcfd062 Xgdm072
Xwmc505
Xgwm645
Xgwm314
50.3
57.3
Xcfd009
XBS00003688
0.0
4.6
4B
4D
XBS00003914
Xcfd002
0.0
XBS00009480 XBS00010115
13.2
14.3
XBS00003776
XBS00001207
10.8
11.4
12.5
13.7
16.5
17.6
18.2
20.3
26.3
27.9
XBS00010339
XBS00009974 XBS00001241
23.0
24.7
XBS00009253
XBS00003646
Xbarc020
Xwmc089
XBS00009530
XBS00009915
XBS00003858 XBS00009672
Xgwm513
XBS00010409 XBS00003759
XBS00010067
XBS00003765
XBS00004407
33.4
36.1
38.4
XBS00010455
Xdupw004 XBS00003623
Xbarc170
34.5
Xwmc349
40.6
Xgwm538
43.9
45.0
XBS00003674
XBS00010504
51.2
Xwmc258
53.7
56.1
Xwmc161
XBS00003704 XBS00003927
XBS00009333
59.9
XBS00010582
0.0
1.2
3D
0.0
Xgwm350
5.0
5.6
6.7
7.2
XBS00003692
XBS00003587
XBS00009703
XBS00009680
13.7
Xbarc078
17.5
Xgwm160
31.9
XBS00000346 XBS00009882
XBS00009590 Xbarc070
22.5
0.0
Rht-D1
11.2
12.5
15.7
Xcfd023
Xwmc089
XBS00003854
56.3
Xgwm194
70.3
Xgwm624
Figure 3 Continued
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
8 Alexandra M. Allen et al.
5A
5B
0.0
6.8
Xbarc010
XBS00004373
30.8
XBS00003696
55.6
58.5
69.5
76.0
77.1
81.8
82.9
84.0
89.8
92.5
95.3
Xgwm595a
Xgwm129
Xwmc489
Xwmc150
Xgwm293
XBS00005311
XBS00000645
Xgwm156
XBS00015653
XBS00000373
Xgwm595b Xgwm186
XBS00009658 XBS00003746
XBS00009531
XBS00000435
XBS00000648
Xgwm617
104.0
107.2
113.2
118.7
Vrn-A1
XBS00011360 XBS00010702
XBS00010419
XBS00001735
0.0
0.6
10.3
Xgwm126
Xgwm595c
Xgwm291
XBS00010048
XBS00001953 XBS00003693
XBS00000037 XBS00000028
XBS00009274 XBS00003501
XBS00000006
0.0
5.9
18.1
23.2
23.7
97.3
97.9
108.9
109.4
110.2
117.0
118.2
121.8
129.7
130.8
131.9
134.6
136.2
140.5
149.9
152.7
153.2
154.3
6A
6B
0.0
1.7
0.0
6.2
6.7
Xgwm334
XBS00012414
XBS00012150 XBS00003632
XBS00004278 XBS00009759
2.8
3.4
4.6
36.5
38.6
49.9
51.6
62.9
67.1
67.6
68.2
68.7
70.3
81.0
5D
XBS00009264
Xgwm234 XBS00003739
Xdupw115
XBS00010491
XBS00003900
XBS00003876 Xbarc074
XBS00009719
XBS00003705
XBS00003681
Xgwm213
XBS00003726
XBS00003715
XBS00003789
XBS00001817
XBS00009607
XBS00009311 XBS00010636
XBS00010675 XBS00010033
XBS00000865
XBS00003736 XBS00009851
XBS00011782
XBS00010341 XBS00003879
XBS00010542 XBS00009931
XBS00010581
XBS00009373 XBS00009581
XBS00009382 XBS00003724
XBS00000701
XBS00010590
XBS00009414
XBS00004427
XBS00003565
Xgwm408
XBS00003904
XBS00011453
XBS00003770 XBS00003637
XBS00003615
XBS00003740
XBS00000592 XBS00010573
XBS00003592
XBS00009621
XBS00010802
XBS00011183 XBS00003655
Xbarc142 XBS00009388
0.0
12.9
16.6
28.8
38.5
56.1
58.3
64.7
66.4
70.0
70.5
71.1
72.2
72.7
84.7
86.5
92.4
93.8
95.7
96.8
XBS00009584 XBS00003635
XBS00003861 Xbarc023
XBS00009746 XBS00009664
XBS00001037
XBS00009376
Xbarc107
Xbarc171
XBS00003581 XBS00001132
XBS00009871
XBS00004377 XBS00003881
XBS00009988 XBS00009783
XBS00003616 XBS00010586
XBS00003916 XBS00003877
XBS00009261 XBS00010730
XBS00001405 XBS00003676
Xgwm570 XBS00003835
104.2
Xgwm169
115.6
Xgwm617
129.0
129.5
130.1
130.6
131.2
136.8
XBS00003339
XBS00010526
XBS00010408
XBS00003818 XBS00003958
XBS00003905 XBS00003723
Xdupw167
40.3
41.0
58.3
59.4
61.9
63.1
63.6
64.1
67.8
70.0
70.5
71.6
83.1
0.0
Xgwm190
43.1
48.6
51.3
Xgwm293
Xgwm358
Xgwm159
68.9
Xgwm174
125.4
Xgdm063
137.8
Xbarc110
162.3
169.3
169.9
170.9
172.5
174.2
XBS00003711 XBS00000622
XBS00003826
XBS00009294
XBS00003783
XBS00004065
XBS00011769
193.1
XBS00003563
XBS00010693
XBS00003772 XBS00009825
XBS00003812 XBS00003659
XBS00003593
XBS00012481
XBS00003785 XBS00009795
XBS00010580 XBS00009967
XBS00003792 XBS00003622
XBS00003748 XBS00010551
XBS00003786
Xcfd013 XBS00003852
XBS00010420 XBS00003893
XBS00010415
XBS00012491
Xwmc105
Xgwm680
Xgwm608
XBS00010537
XBS00003641
XBS00010328 XBS00010602
XBS00004016 XBS00009844
XBS00003620 XBS00009695
XBS00003897 XBS00019726
XBS00014363
XBS00003671
XBS00003562
Xgwm219
102.1
Xgwm285 Xwmc089
Xbarc134
117.7
XBS00010618
131.5
132.0
XBS00010627
XBS00009548
Pinb-D1
11.9
6D
0.0
1.2
2.3
Xbarc183
XBS00009806
XBS00010742
14.9
Xbarc173
43.6
45.9
47.2
Xgwm325
Xbarc054
Xcfd019
56.7
Xbarc175
Figure 3 Continued
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 9
7A
0.0
2.9
7B
XBS00009543
XBS00010006
11.2
Xgwm635
21.0
Xwmc388
27.5
XBS00003665 XBS00009404
0.0
1.8
2.4
Xgwm130
Xbarc127
XBS00002556
16.7
17.2
17.7
XBS00009978
XBS00010307
XBS00010605
26.9
29.6
32.1
Xbarc121
Xbarc108 Xgwm631
Xbarc029 Xbarc049
0.0
2.3
2.9
17.8
18.9
20.0
22.1
XBS00003350
XBS00009290
XBS00009556
Xwmc517
31.5
XBS00009398
36.4
XBS00009861 Xdupw398
51.0
51.6
60.4
62.6
XBS00009464
XBS00010660
Xgwm577
XBS00010327
XBS00009879
XBS00003672
XBS00005062
Xbarc032
XBS00009245
XBS00009379
XBS00004376
XBS00003756
XBS00003649
XBS00004171
XBS00003703
XBS00010142
64.5
66.2
67.4
69.9
70.4
74.7
48.8
50.4
52.0
XBS00009550
XBS00009975
XBS00003894
87.4
88.5
96.3
97.9
99.5
101.8
XBS00003621 XBS00001450
XBS00014199
XBS00004348 XBS00009886
XBS00003865
Xgwm344
XBS00000663 XBS00003720
119.8
Xcfa2040
Xbarc172 Xbarc176
XBS00003830
XBS00003494 XBS00003760
78.5
79.1
80.2
94.2
22.7
24.0
24.5
27.8
7D
Xpsp3113 Xbarc172
XBS00009457
Xgwm437
Xcfd021
39.1
Xwmc150
0.0
Xbarc070
XBS00010454
XBS00003664
XBS00010560
XBS00009619
XBS00004403
15.4
15.9
0.0
1.2
2.4
2.9
7.8
17.7
XBS00009565
XBS00010064
Xgwm428
XBS00003945
XBS00010071
XBS00009623 XBS00003749
Xbarc097 Xcfa2040
Xbarc076
Figure 3 Continued
developed by CIMMYT. Savannah does not appear to cluster
with the other hexaploid varieties, reflecting the exotic genetic
background to this line. The hierarchical cluster analysis illustrates the genetic relationship between the varieties used in this
study, but also indicates the reduction in germplasm variation
within elite lines and the need for introducing genetic diversity
into breeding programmes (Haudry et al., 2007).
Genetic map
A linkage map is often the first step towards understanding
genome assembly and evolution and provides an essential
framework for mapping agronomic traits of interest (Xia et al.,
2010). We present here the first SNP-based linkage map for
wheat, constructed using 500 transcript-linked varietal SNPs
generated in this study in combination with 574 previously
developed markers (Figure 3). The total map length was
2999 cM, similar in length to previous framework linkage maps
produced in wheat using microsatellite markers (2569 cM; Somers et al., 2004), DArT markers (2937 cM; Akbari et al., 2006)
and a combination of markers (3522 cM; Quarrie et al., 2005).
While each of the three genomes had similar map lengths
(1131 cM for the A genome; 1075 cM for the B genome and
792 cM for the D genome), there was a significant difference
between the distribution of markers (i.e. SSR, SNP, AFLP and
DArT loci) between the three genomes (A genome 354 mark-
ers; B genome 526 markers; D genome 174 markers), a ratio of
33 : 50 : 17, and this difference was more apparent when calculated for the 480 SNP markers alone (34 : 56 : 10). The ratio
of total markers on the three genomes is similar to that
observed by Quarrie et al. (2005) who found the distribution of
markers between the three genomes at a ratio of 40 : 40 : 20
for the A, B and D genomes, respectively. Other comparable
ratios from wheat linkage maps for the A ⁄ B ⁄ D genomes were
33 : 41 : 25 (Röder et al., 1998), 30 : 39 : 31 (Somers et al.,
2004) and 28 : 60 : 12 (Bernardo et al., 2009).
As might be expected, the distributions of markers on each
of the several homoeologous chromosome groups were not
uniform with large gaps of more than 30 cM on D genome
chromosomes and concentrations of markers around the centromeres that were particularly noticeable on A and B chromosomes. Our SNP marker set contained a considerably lower
number of D genome markers than previously developed marker sets; this is a likely consequence of SNP markers targeting
genic regions, while other marker types are distributed more
evenly throughout the genome. The lack of D genome markers
compared with the A and B genomes has been attributed to
the loss of polymorphism in coding regions during the genetic
bottleneck that accompanied the development of modern elite
cultivars and indicates that future SNP discovery efforts will be
required to specifically target the D genome to offset the
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
effects of lower levels of genetic diversity in this genome (Caldwell et al., 2004; Chao et al., 2009). Marker distribution
between the homoeologous linkage groups ranged from 7 to
20% of the total markers, with group 4 containing the lowest
number of markers, a pattern also seen in the linkage map of
Somers et al. (2004). In a genome-wide study of nucleotide
diversity by Akhunov et al. (2010) the group 4 chromosomes
were found to have low levels of diversity, with chromosomes
4A and 4B having similar diversity to the D genome. It has been
hypothesized that homoeologous group 4 chromosomes have a
lower number of genes than the remaining six homoeologous
groups (Qi et al., 2004) and hence a lower level of recombination and resulting genetic diversity (Akhunov et al., 2010).
The genetic map locations of the 500 SNP loci generated in
this study were compared with their predicted map location
from physical mapping experiments. Of 48 genetically mapped
loci, 81% mapped to the same homoeologous group predicted
by the Kansas deletion lines. A similar comparison of 155 SNP
loci with deletion-mapped ESTs revealed that 69% mapped to
the same homoeologous group (Qi et al., 2004). While this is a
relatively high level of correlation, discrepancies in the data are
not unexpected because of the numerous secondary and tertiary deletions seen in the deletion lines (Qi et al., 2004).
Genetic map locations of SNP loci that can also be placed on
physical maps are a potentially useful resource for identifying
and characterizing these additional deletions.
The same 500 mapped sequences were also BLASTN
searched against the B. distachyon genome sequence resulting
in 205 wheat SNPs being placed on the B. distachyon genome.
Although SNP coverage was generally low when mapped markers were divided between wheat chromosomes, where sufficient markers existed, as for wheat linkage group 2 versus
B. distachyon chromosome 5, the relationship was consistent
with known chromosomal relationships (International Brachypodium Initiative., 2010; Figure 4). We found that the synteny
between wheat group 2 and B. distachyon chromosome 5 was
generally in line with expectations; however, we did find that
two SNPs (BS0010442 and BS00009533) that mapped to the
same point in linkage group 2 were placed at opposite ends of
B. distachyon chromosome 5. To investigate this further, we
BLAST searched both the rice and Sorghum bicolor genomes
with the sequences flanking BS0010442 and BS00009533. We
found no match against the rice genome, but both SNPs
mapped to within 135 kilobases of each other on S. bicolor
chromosome 6. From these results, we tentatively conclude that
the ancestral state of these markers is to lie in close proximity
and that a chromosomal rearrangement, such as a reciprocal
telomeric translocation, has altered their relative positions in the
lineage leading to B. distachyon.
Screening the 500 sequences against the NCBI non-redundant protein sequence database generated 213 hits, of which
184 were unique. Owing to the nature of the normalized cDNA
libraries used, the unique sequences identified did not show a
significant bias to sequences associated with either leaf or root
gene expression (the tissues used to generate the cDNA
libraries); instead, they reveal an interesting mix of sequences,
such as various disease resistance-like genes, sequences involved
in DNA repair and various genes involved in numerous metabolic pathways. While this analysis indicated that a number of
sequences (13) had multiple SNPs, it also revealed that 17 SNPs
showed similarity to the same database entries, but mapped to
different regions of the genome. For instance, three SNPs
Position in brachypodium chr 5 (fl)
10 Alexandra M. Allen et al.
1.2
1
0.8
0.6
0.4
0.2
0
0
50
100
150
Position in wheat group 2 (cM)
200
Figure 4 Scatterplot showing the syntenic relationship between the
wheat SNP map positions generated in this study for linkage group 2
and the corresponding location in Brachypodium distachyon, shown as
fraction of length (fl) along chromosome 5. The wheat SNP flanking
sequences were matched to the B. distachyon genome using BLASTN
with an e-value cut-off of 1e)10. Wheat linkage group 2 is shown here
as it has the highest number of markers mapped onto the B. distachyon
genome. The point marked in red is SNP BS0010442, and that in black
is SNP BS00009533, both of which map to 181 cM in wheat linkage
group 2.
showed sequence similarity to rice sequence H0322F07.7; one
of these SNPs, BS00009929, mapped to an unassigned linkage
group, while the second SNP, BS00010640, mapped to 2B and
the third SNP, BS00014363, mapped to 6B. The presence of
homoeologous and paralogous sequences throughout the
wheat genome has been documented (Gupta et al., 2008).
However, the specific example shown here and the remaining
16 examples, in which similar sequences could be mapped to
individual locations, suggest that KASPar-based genotyping is
sufficiently sensitive and robust to map individual paralogous
genes in the wheat genome.
Homoeologous versus non-homoeologous-specific
KASPar probes
In wheat, the majority (90%) of the KASPar probes detect
both the polymorphic and non-polymorphic homoeologous loci.
While such probes are suitable for screening inbred wheat varieties, screening heterozygous material is more problematical. To
overcome this, we investigated the possibility of converting a
standard KASPar probe (BS0000329; Figure 5b) to a homoeologous-specific KASPar probe. Using the 5 · Chinese Spring
wheat genome sequence, we were able to design three homoeologous-specific reverse primers, which when used in conjunction with the original varietal SNP-specific KASPar primers
generated one of two possible results; a non-polymorphic probe
(when the homoeologous copy being amplified did not contain
a varietal SNP; Figure 5c) or a polymorphic probe (when the
homoeologous copy being amplified did contain a varietal SNP;
Figure 5d). Use of the redesigned homoeologous-specific primers to screen an F2 population confirmed that they were capable of discriminating between homozygotes and heterozygotes
(Figure 5e). Examination of the genomic sequence around several other KASPar loci suggested that it should be possible to
design homoeologous-specific primers for the majority of the
SNPs generated in this investigation. However, in the absence
of both suitable primer design software and a more complete
genome sequence, this process is time-consuming taking
approximately 30 min per probe and requires further experi-
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 11
(b)
(c)
(d)
(e)
(a)
Homoeolog 1v1
Homoeolog 2v1
Homoeolog 3v1
Homoeolog 1v2
Homoeolog 2v2
Homoeolog 3v2
K ASP ar pri mer A
K ASP ar Pri mer G
#1
#1
#1
#1
#1
#1
AAAATTCATG
AAAATTCATG
AAAATTCATG
AAAATTCATG
AAAATTCATG
AAAATTCATG
AATGCCCGCA
AATGCCCGCA
AATGCCCGCG
AATGCCCGCA
AATGCCCGCA
AATGCCCGCG
CGC G
GC G
GATACCTGTA
GATACCTGTA
GATACCTGTA
GATACCTGTA
GATACCTGTA
GATACCTGTA
G ATA CCT GTA
G ATA CCT GTA
GGCATGTACA
GGCATGTACA
GGCATGTGCA
GGCATGTACA
GGCATGTACA
GGCATGTACA
GG CAT GTA
GG CAT GTG
Homoeolog 1v1
#41
Homoeolog 2v1
#41
Homoeolog 3v1
#41
Homoeolog 1v2
#41
Homoeolog 2v2
#41
Homoeolog 3v2
#41
Reverse primer 1
Homoeolog 1 SRP
Homoeolog 2 SRP
Homoeolog 3 SRP
ATCGTTATCA
ATCGTTATCA
ATCGTTATCA
ATCGTTATCA
ATCGTTATCA
ATCGTTATCA
TAGCAATAGT
GTAAATTCTG
GTAAATTCTG
GTAAATTCTG
GTAAATTCTG
GTAAATTCTG
GTAAATTCTG
CATTTAAGAC
CCGTCCTAGA
CCGTCCTAGA
CCGTCCTAGA
CCGTCCTAGA
CCGTCCTAGA
CCGTCCTAGA
CTCCATCGCA
CTCCATCACA
CTCCATGACA
CTCCATCGCA
CTCCATCACA
CTCCATGACA
Homoeolog
Homoeolog
Homoeolog
Homoeolog
Homoeolog
Homoeolog
Homoeolog
Homoeolog
Homoeolog
TGTCTTGTTT
TGTCTTGTTT
TGTCTTGTTT
TGTCTTGTTT
TGTCTTGTTT
TGTCTTGTTT
ACAGAACAAA
ACAGAACAAA
ACAGAACAAA
1v1
2v1
3v1
1v2
2v2
3v2
1 SRP
2 SRP
3 SRP
#81
#81
#81
#81
#81
#81
GCGT
GTGT
CTGT
CCTAGCTTCC
CCTAGCTTCC
CCTAGCTTCC
CCTAGCTTCC
CCTAGCTTCC
CCTAGCTTCC
GGATC
GGATC
GGATC
TGGA
TGGA
TGGA
TGGA
TGGA
TGGA
Figure 5 Effect of primer design of specificity of the KASPar reaction. (a) Sequence of region around SNP BS00000329 in the three wheat homoeologous genes and across two varieties (v1 and v2) together with the six probes designed for this region. The varietal SNP (A ⁄ G) and the homoeologous
SNPs are highlighted. The reverse primer 1 and the three homoeologous-specific primers (SRP) are shown as designed in the reverse compliment to the
BS00000329 sequence. (b-d) KASPar plots following amplification with the various primer combinations. For b–c, the standard 20 hexaploid varieties
were used together with a negative control (E3, E6 and E9) and a diploid, tetraploid and resulting synthetic line. In all cases, the KASPar primers A and
G were used. In (b), the generic reverse primer was used; in (c), Homoeolog 1 SPR was used while in (d) Homoeolog 3 SPR was used. No results are
presented for Homoeolog 2 SPR as this gave the same result as Homoeolog 1 SPR. (e) KASPar plot of F2-derived material following amplification with
the KASPar primers A and G together with the Homoeolog 3 SRP primer. In (e), both homozygotes (Blue and red spots) and heterozygotes (green
spots) are shown as discrete clusters.
mental work to validate the appropriate primer combination.
Therefore, it is our belief that currently it is more productive to
design nonspecific KASPar probes and convert these to homoeologous-specific probes only when they are found to be useful
for a specific purpose, for instance, when they are found to be
tightly linked to a locus of interest.
Conclusions
To the best of our knowledge, this is the first report of a public
linkage map for hexaploid wheat containing several hundred
individual SNP markers. Bernardo et al. (2009) have reported a
linkage map of wheat consisting of 923 single feature polymorphisms (SFPs) obtained by using Affymetrix arrays on 71 recombination inbred lines from the cross Ning 7840 · Clark.
However, as reported, attempts to convert the array-based SFP
markers to useful SNP markers that can be used in single locus
assays were time-consuming and only partially successful with
33 working SNaPshot markers being generated from 58 SFPs
tested. In addition, we believe this report is also the first demonstration of KASPar-based technology to both genotype wheat
varieties and generate a linkage map. To generate the SNPbased linkage map described, we carried out 102 220 individual
KASPar reactions (538 probes · 190 plants). These reactions
were carried out within a 24-h period, using simple microplate
technology and a relatively inexpensive and widely available fluorescence resonance energy transfer plate reader. Using this
and similar technology, we believe that it will be possible for
wheat breeders to achieve one of their most important goals,
to rapidly and cheaply genotype thousands of plants with a
large and flexible number of markers.
Although we believe that the work described here meets the
requirements of both wheat breeders and academics, it is likely
that other technologies will be developed, which will increase
the rate of SNP-based genotyping ⁄ mapping and decrease the
cost of the individual data points. However, our studies have
also shown that there is need for further SNPs, especially for
the D genome and the homoeologous group 4 chromosomes,
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
12 Alexandra M. Allen et al.
but with continued SNP development genome-wide association
studies will soon become possible in wheat. The extensive
genomics resources developed by whole-genome shotgun
sequencing of Chinese Spring, coupled to re-sequencing multiple breeding lines, promises to dramatically increase the number
of informative SNPs, permitting unprecedented levels of precision genetic analysis in wheat breeding.
Our work has also highlighted the need to capture all of the
information associated with the SNP markers, for instance, the
sequence surrounding the SNP being assayed. This information
will allow the SNP to be adapted and used in a variety of genotyping platforms as and when they are developed. It is for this
reason that via this publication, we have made all the data
associated with the SNP, including the available flanking
sequences (usually 120 base pairs or greater) available via the
associated supplementary data (Tables S1 and S2). We hope
that by making this information available, it will encourage
other wheat geneticists to ensure that such markers are made
public and free to use. Only by following this principle will we
help to ensure that wheat breeders across the world have the
tools required to breed new varieties.
Experimental procedure
Plant material
The wheat lines included in this study were selected to give the
best representation of the UK wheat germplasm. Twenty-three
different wheat varieties were grown for nucleic acid extraction
(for details see Table 2). The Avalon · Cadenza population (supplied by the John Innes Centre) of doubled haploid (DH) individuals, derived from F1 progeny of a cross between cvs Avalon
and Cadenza, was developed by Clare Ellerbrook, Liz Sayers and
the late Tony Worland (John Innes Centre), as part of a Defra
funded project led by ADAS. The parents were originally chosen
(to contrast for canopy architecture traits) by Steve Parker (CSL),
Tony Worland and Darren Lovell (Rothamsted Research, West
Common, Harpenden, Hertfordshire, AL5 2JQ, UK).
All plants were grown in pots in a peat-based soil and maintained in a glasshouse at 15–25 C under a light regime of
16 h light, 8 h dark. Root and leaf tissues were harvested from
6-week-old plants. All harvested tissues were immediately frozen on liquid nitrogen and stored at )80 C until nucleic acid
extraction.
Preparation of normalized cDNA libraries
Total RNA was extracted from root and leaf tissue of five wheat
varieties (Avalon, Cadenza, Rialto, Savannah, Recital) using the
TRIzol RNA extraction protocol (Invitrogen Ltd., Paisley, UK)
according to the manufacturer’s instructions and purified using
the RNeasy MinElute Kit (QIAGEN Ltd., Crawley, UK). Complementary DNA (cDNA) was synthesized from total RNA using the
MINT kit (Evrogen, Moscow, Russia) according to the manufacturer’s instructions. Double-stranded cDNA was purified using
the QiaQuick PCR purification kit (QIAGEN Ltd.). The purified
cDNA samples were then normalized using the TRIMMER kit
(Evrogen) according to the manufacturer’s instructions and purified using the QiaQuick PCR purification kit (QIAGEN Ltd.).
Next-generation sequencing
For each variety, 5 lg of normalized cDNA was processed for
sequencing by the University of Bristol Transcriptomics Facility.
Independent sequence libraries were generated by following
the manufacturer’s protocol for sequencing of genomic DNA
(Illumina Inc., San Diego, CA), with a modification to allow multiplexing of samples. Five sets of custom adapters were
designed based upon the Illumina PCR primers plus an extra
4-base barcoding tag. Sequencing was carried out on a pairedend flowcell, using Illumina’s v.4 Cluster Generation and multiple
v.4 36-Cycle Sequencing Kits. The Illumina Genome Analyzer IIx,
was run for 2 · 76 bases of data acquisition. Image analysis
and base calling was performed with SCS 2.3 software (Illumina
Inc, San Diego, CA).
SNP discovery
Putative varietal SNPs were mined from two sources. First, public
wheat Expressed Sequence Tag (EST) data held at NCBI was used
as described by Barker and Edwards (2009). In this data set,
3500 SNPs were deemed to have a high probability of being varietal (as opposed to homoeologous), and from these, we selected
213 for validation. In the second approach, SNPs were mined
from NGS data as follows. A reference sequence data set comprising 91 368 sequences was produced by combining the data
from NCBI wheat unigene build 38 with the NGS data used in
this study. The NCBI sequences were sampled as pseudoreads of
75 bases, combined with the NGS data and de-novo assembled
using ABYSS (Simpson et al., 2009) on the Bristol Bluecrystal
HPC cluster. NGS from the five UK varieties were mapped to our
custom reference using ELAND (Illumina Inc.) with a seed length
of 32 bases and the resulting sorted export files used for downstream analysis. Uniquely mapped reads were analysed using a
series of custom PERL scripts designed to identify only differences
between varieties as opposed to those between each variety,
and the reference sequence. In this way, homoeologous SNPs
(which are not useful markers) were excluded from our SNP discovery pipeline. SNPs were called where at least two alternative
bases were found at a reference position, each represented by 2
or more independent reads or 5% of all reads examined (whichever was the greater). Only bases at the centre of a three base
window of PHRED quality ‡20 were included in the analysis.
Sequences were discarded if they showed more than 5%
sequence variation from the reference over their length or if they
mapped equally well to more than one locus as in either case
they were deemed to have uncertain mapping. Finally, where
multiple reads started at the same position in the reference, all
but one were ignored to guard against clonal reads being
sampled more than once. The NGS data will be made available
at: http://www.cerealsdb.uk.net/NGSdata/AllenSupplement.
SNP validation
Genomic DNA was prepared from leaf tissue using a phenol–
chloroform extraction method (Sambrook et al., 1989). Genomic DNA samples were treated with RNase-A (New England
Biolabs UK Ltd. Hitchin, UK) according to the manufacturer’s
instructions and purified using the QiaQuick PCR purification kit
(QIAGEN Ltd.).
For each putative varietal SNP, two allele-specific forward
primers and one common reverse primer (Table S6) were
designed (KBioscience, Hoddesdon, UK). Genotyping reactions
were performed in a Gene Pro Thermal cycler (Bioer Technology, Hangzhou, China) in a final volume of 5 lL containing 1X
KASP Reaction Mix (KBioscience), 0.07 lL Assay mix (containing
12 lM each allele-specific forward primer and 30 lM reverse pri-
ª 2011 The Authors
Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14
Wheat SNP markers 13
mer) and 10–20 ng genomic DNA. The following cycling conditions were used: 15 min at 94 C; 10 touchdown cycles of 20 s
at 94 C, 60s at 65–57 C (dropping 0.8 C per cycle); and
26–35 cycles of 20 s at 94 C, 60 s at 57 C. Fluorescence
detection of the reactions was performed using an Omega
Fluorostar scanner (BMG LABTECH GmbH, Offenburg, Germany),
and the data were analysed using the KlusterCaller 1.1 software
(KBioscience). The polymorphic information content (PIC) was
calculated for each marker according to Botstein et al. (1980),
using Excel Microsatellite Toolkit add-in software (Park, 2001).
Hierarchical cluster analysis was performed in PASW (SPSS Inc.
IBM Corporation, Somers, NY).
Genetic map construction
The software program MapDisto v. 1.7 (Lorieux, 2007) was
used to place the SNP markers in the previously established
genetic map for Avalon · Cadenza (http://www.wgin.org.uk/
resources/MappingPopulation/TAmapping.php). A chi-square
test was performed on all loci to test for segregation distortion
from the expected 1 : 1 ratio of each allele in a DH population,
and any loci showing significant distortion were removed from
the data set before constructing the linkage groups. Loci were
assembled into linkage groups using likelihood odds (LOD)
ratios with a LOD threshold of 6.0 and a maximum recombination frequency threshold of 0.40. The linkage groups were
ordered using the likelihoods of different locus-order possibilities and the iterative error removal function in MapDisto and
drawn in MapChart (Voorrips, 2002). The Kosambi mapping
function (Kosambi, 1944) was used to calculate map distances
(cM) from recombination frequency.
Comparative mapping of SNPs to ESTs and the
Brachypodium distachyon and Sorghum bicolor
genomes
The consensus sequence surrounding mapped SNPs were
matched to published, mapped wheat EST sequences (http://
wheat.pw.usda.gov/cgi-bin/westsql/map_locus.cgi) and to the
B. distachyon genome sequence (http://www.brachypodium.
org/) using NCBI BLASTN (Altschul et al., 1990) with an e-value
cut-off of 1e)10. Only the top BLASTN hit was used for comparative mapping.
Selected wheat SNP flanking sequences were also BLASTN
searched to the S. bicolor genome at http://www.phytozome.
net using Phytozome version 6.
Acknowledgements
We are grateful to the Biotechnology and Biological Sciences
Research Council, UK, for providing the funding for this work
(awards BB ⁄ F007523 ⁄ 1, BB ⁄ F010370 ⁄ 1, BB ⁄ G012865 ⁄ 1,
BB ⁄ I003207 ⁄ 1). We thank Dr Peter Jack from RAGT, Dr Ian MacKay from NIAB and Dr Simon Griffiths from the JIC for numerous
valuable discussions throughout this project. The population of
doubled haploid individuals, derived from F1 progeny of a cross
between cvs Avalon and Cadenza, was developed by Clare Ellerbrook, Liz Sayers and the late Tony Worland (John Innes Centre),
as part of a Defra funded project led by ADAS. The parents were
originally chosen (to contrast for canopy architecture traits) by
Steve Parker (CSL), Tony Worland and Darren Lovell (Rothamsted
Research). We are grateful to the Wheat Genetic Improvement
Network for making the mapping data relating to the cvs Avalon
x Cadenza population public. For further details of the Avalon x
Cadenza mapping population, please refer to the Wheat Genetic
Improvement Network web site at: http://www.wgin.org.uk/
resources/MappingPopulation/TAmapping.php.
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Supporting information
Additional Supporting information may be found in the online
version of this article:
Table
Table
Table
Table
Table
Table
S1
S2
S3
S4
S5
S6
EST-derived SNPs.
NGS-derived SNPs.
Genotyping calls.
Mapping data.
BLAST results.
KASPar assays and primer sequences.
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Plant Biotechnology Journal ª 2011 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–14