A consensus framework map of durum wheat Triticum durum

Transcription

A consensus framework map of durum wheat
(Triticum durum Desf.) suitable for linkage
disequilibrium analysis and genome-wide association
mapping
Marco Maccaferri1,6,*
*
Corresponding author
Email: [email protected]
Maria Angela Cane’1
Maria C Sanguineti1
Silvio Salvi1
Maria C Colalongo1
Andrea Massi2
Fran Clarke3
Ron Knox3
Curtis J Pozniak4
John M Clarke4
Tzion Fahima5
Jorge Dubcovsky6,7
Steven Xu8
Karim Ammar9
Ildikó Karsai10
Gyula Vida10
Roberto Tuberosa1
1
Department of Agricultural Science (DipSA), Viale Fanin 44, University of
Bologna, 40127 Bologna, Italy
2
Società Produttori Sementi Bologna (PSB), 40050 Argelato (BO), Italy
3
Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food
Canada, P.O. Box 1030, Swift Current, SK S9H 3X2, Canada
4
Crop Development Centre and Department of Plant Sciences, University of
Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
5
Department of Evolutionary and Environmental Biology, Institute of Evolution,
Faculty of Science and Science Education, University of Haifa, Mt. Carmel,
Haifa 31905, Israel
6
Department of Plant Sciences, University of California, Davis, CA 95616, USA
7
Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
8
USDA/ARS Cereal Crops Research Unit, NCSL 1605 Albrecht Blvd. N., Fargo,
ND 58102, USA
9
International Maize and Wheat Improvement Center (CIMMYT), Apartado
Postal 6-641, 06600, Mexico D.F., Mexico. Carretera Mexico-Veracruz KM. 45,
56130 Texcoco, Mexico
10
Center for Agricultural Research, Hungarian Academy of Sciences (ARIHAS), Brunszvik U. 2, H-2462, Martonvasar, Hungary
Abstract
Background
Durum wheat (Triticum durum Desf.) is a tetraploid grown in the medium to lowprecipitation areas of the Mediterranean Basin, North America and South-West Asia.
Genomics applications in durum wheat have the potential to boost exploitation of genetic
resources and to advance understanding of the genetics of important complex traits (e.g.
resilience to environmental and biotic stresses). A dense and accurate consensus map specific
for T. durum will greatly facilitate genetic mapping, functional genomics and marker-assisted
improvement.
Results
High quality genotypic data from six core recombinant inbred line populations were used to
obtain a consensus framework map of 598 simple sequence repeats (SSR) and Diversity
Array Technology® (DArT) anchor markers (common across populations). Interpolation of
unique markers from 14 maps allowed us to position a total of 2,575 markers in a consensus
map of 2,463 cM. The T. durum A and B genomes were covered in their near totality based
on the reference SSR hexaploid wheat map. The consensus locus order compared to those of
the single component maps showed good correspondence, (average Spearman' rank
correlation ρ value of 0.96). Differences in marker order and local recombination rate were
observed between the durum and hexaploid wheat consensus maps. The consensus map was
used to carry out a whole-genome search for genetic differentiation signatures and association
to heading date in a panel of 183 accessions adapted to the Mediterranean areas. Linkage
disequilibrium was found to decay below the r2 threshold = 0.3 within 2.20 cM, on average.
Strong molecular differentiations among sub-populations were mapped to 87 chromosome
regions. A genome-wide association scan for heading date from 27 field trials in the
Mediterranean Basin and in Mexico yielded 50 chromosome regions with evidences of
association in multiple environments.
Conclusions
The consensus map presented here was used as a reference for genetic diversity and mapping
analyses in T. durum, providing nearly complete genome coverage and even marker density.
Markers previously mapped in hexaploid wheat constitute a strong link between the two
species. The consensus map provides the basis for high-density single nucleotide
polymorphic (SNP) marker implementation in durum wheat.
Keywords
Triticum durum Desf, Consensus map, Linkage disequilibrium, Genome-wide association
mapping, Heading date, Quantitative trait loci
Background
Cultivated tetraploid wheat (durum wheat, Triticum durum Desf.) is genetically differentiated
from hexaploid wheat (Triticum aestivum L.) by as little as ca. 10,000 years of evolution.
Both species derived from domesticated emmer (Triticum dicoccum Schrank) through a
rather long human-driven selection process, including distinct and sequential domestication
bottlenecks and continuous gene flow from wild emmer (Triticum dicoccoides (Körn. ex
Asch. Graebner) Schweinf) [1]. In the past two decades, molecular genetics and genomics of
durum wheat, including breeding applications such as marker-assisted selection (MAS), have
largely relied on the molecular tools specifically developed for hexaploid wheat [2-5]. The
restriction fragment length polymorphisms (RFLPs) and the PCR-based molecular markers
such as the simple sequence repeats (SSRs), amplified fragment length polymorphisms
(AFLPs), and sequence tagged sites (STSs) developed for hexaploid wheat performed rather
efficiently in tetraploid wheat based on the commonalities between the two shared A- and Bgenomes [6-8]. However, marker’s polymorphism information content had to be re-assessed
directly in the durum wheat germplasm [9,10].
In the 2000’s, intra- and inter-specific durum wheat and durum wheat × wild emmer genetic
linkage maps were developed mainly using SSR loci [11,12] as well as the microarray-based
Diversity Array Technology (DArT®) markers [13-15].
Integration of the SSR-based linkage map data into consensus maps was first pursued in
hexaploid wheat by Somers et al. [16] and Crossa et al. [17], and then in durum wheat by
Trebbi et al. [18], Marone et al. [19] and Letta et al. [20]. In durum wheat, the limited number
of maps available to these studies has not yet allowed for full coverage of the genome, an
important prerequisite for genome-wide association mapping, meta-analysis and positional
cloning. The most recently published consensus map specific to tetraploid wheat [19] is
composed of 1,898 loci arranged in 27 linkage groups, a number that exceeds the 14 nuclear
chromosomes of this species.
The hexaploid wheat SSR-based consensus map (Ta-SSR-2004) by Somers et al. [16] has
been widely used as a reference for wheat genomics studies for more than a decade. In
hexaploid wheat, several mapping studies were published using SSR and DArT technologies,
revealing major genes and QTLs for grain yield, yield components, adaptive traits, response
to abiotic and biotic stresses and quality (reviewed in [3,21-23]). Collectively, these studies
laid the foundation for MAS applications in wheat (http://maswheat.ucdavis.edu). Within this
dynamic scenario, the availability of a high-quality consensus map more exhaustively
covering the entire genome is a valuable asset for genetic studies and breeding applications.
Furthermore, powerful association mapping (AM) and meta-QTL analysis require reference
genetic maps of a quality standard higher than that commonly adopted for coarse mapping in
bi-parental populations. AM and meta-QTL analysis are being increasingly adopted in wheat
genomics (162 records for association mapping studies and 7 records for meta- QTL analysis
in NCBI-PubMed), including durum wheat [20,24-27].
The development of integrated maps were approached based on: (i) ‘consensus’ mapping,
where de novo maps are produced by merging all the single segregation data-sets available to
develop a novel unified map, (ii) ‘interpolation’ mapping (or ‘projection-based’ mapping),
where integrated maps are built by regressing the single component map marker positions on
an initial reference map that provides the framework for sequential interpolation. The latter
method is computation and time efficient but relies on the availability of an accurate
reference with strong prerequisites such as comprehensive genome coverage and proven
representativeness.
Consensus mapping has the potential to overcome the major limitations typical of mapping
information based on single maps, such as local aberrations in crossover rates [28,29] and
presence of chromosome regions with low marker density / lack of polymorphism due to the
occurrence of identity by descent [30]. Moreover, consensus mapping provides more accurate
mapping information thanks to the higher number of progenies considered. Several software
packages were developed for consensus mapping, from the early ones that pool single
component segregation data and calculate consensus maps based on the assumption of
homogeneity of marker order and recombination rate across populations, such as Joinmap
(Van [31]) and Carthagene [32], to others that implement graph-theoretic models with marker
removal in case of conflicting orders (e.g. MergeMap by [33]) or attempt to define the
optimal consensus order of shared markers by a synchronous optimization of the local orders
in single component maps (e.g. Multipoint Consensus, [34]).
In this study, segregation data from six core mapping populations of durum wheat were
analysed based on a common procedure with stringent quality criteria. Following
identification and removal from the global computation of chromosome regions showing
local deviations from the homogeneity assumptions, mainly identified in single component
maps only, the segregation data from markers shared among populations were pooled in
Carthagene to obtain a consensus framework map that proved to be highly robust compared
to the single component maps. The value of this consensus map, enriched in density by the
interpolation of the unique markers, was assessed through the identification of signatures of
genetic differentiations among sub-populations of the durum wheat germplasm and the
identification of QTLs controlling heading date in the Mediterranean cultivation areas at the
genome-wide scale.
Results
Linkage maps from the individual populations (component maps)
The genotypic data of 14 mapping populations of tetraploid wheat (2n = AABB), either
recombinant inbred lines (RILs) or double haploids (DHs), were made available by
collaborating Institutions or were downloaded from the GrainGenes database. The genotypic
data were used to re-construct the maps of the original populations following a common
mapping procedure. The reconstructed maps are thereafter reported as component maps.
Details of the parental accessions, cumulative genetic distances and marker composition for
each of the component maps are reported in Table 1.
Table 1 Molecular markers mapped on the component maps used for consensus mapping and map integration
Cross
Acronym
Detailed
Population
type
DH/RIL/F2
Population
size
no.
Molecular Markers
SSR DArT/AFLP SNP/STS Biochemical/
morphological loci
Total
Linkage
marker no. group no.
Total
Inter-marker distance
length cM cM / marker
Mapping populations used for consensus framework mapping
Kf × Sv
Kofa × Svevo
RIL
249
303 0
8
0
311
25
1258.7
4.04
Co × Ll
Colosseo × Lloyd RIL
176
197 393
118
1
709
21
1810.3
2.55
Mr × Cl
Meridiano ×
RIL
181
202 653
86
0
941
25
1940.4
2.06
Claudio
Sm × Lv
Simeto × Levante RIL
180
170 403
2
0
575
27
1514.7
2.63
Ln × G18-16
Langdon × G18- RIL
93
127 171
2
1
301
21
1536.7
5.10
16
Kf × UC
Kofa × UC1113 RIL
152
225 0
20
2
247
27
787.4
3.18
Average
514
24
1474.7
3.26
Mapping population used for marker interpolation only
Gl × Dm
Gallareta ×
DH
127
19 128
0
0
147
15
1017.3
6.92
Demetra
DT707 × DT696
DT707 × DT696 DH
127
68 68
0
0
136
18
861.1
6.33
DT712 × Bl
DT712 ×
DH
89
392 0
0
0
392
23
1848.2
4.71
Blackbird
Lb × P749
Lebsock ×
DH
146
239 0
0
1
240
16
1463.4
6.09
PI94749
SvxCc
Svevo × Ciccio RIL
120
165 0
0
4
169
24
1214.8
7.19
PDW1216xMvTD10- PDW1216 x
RIL
182
0 440
0
0
440
18
984.3
2.23
98
MvTD10-98
Average
254
19
1231.5
5.57
Mapping population used for marker interpolation only, no segregation data available
PDW × Bh
PDW233 ×
RIL
140
128 0
18
12
158
18
1839.1
11.64
Bhalegaon 4
Lt × Pr
Latino × Primadur F2:3
121
45 66
9
0
120
6
422.3
3.51
each map, the cross, population type (recombinant inbred line, RIL or double haploid, DH ), number of mapped markers by marker type (simple sequence repeat, SSR; Diversity Array
Technology, DArT; amplified length polymorphisms, AFLP; single nucleotide polymorphisms, SNPs; sequence tagged sites, STSs; Mendelian loci), number of linkage groups, total map length
(cM) and average inter-marker distance (cM/marker) are reported.
A core-set of six RIL-based component maps was selected to obtain a robust framework map
subsequently used for interpolation of unique markers. All but one of these six RIL
populations were obtained from intraspecific T. durum crosses, with parental lines chosen
among diverse groups of the elite durum germplasm (Italian, CIMMYT, North and South
Western USA breeding groups, as reported in Table 1). One of the six populations included
in the core set was obtained from the interspecific cross of Langdon × T. dicoccoides (G1816), the wild progenitor of T. durum. In total, the core set of RIL populations included 1,031
lines. The single maps were constituted by 21 to 27 linkage groups, with total length ranging
from 788 (Ln × G18-16) to 1,940 cM (Mr × Cl), with an average of 1,475 cM, and intermarker distance varying from 2.6 (Mr × Cl) to 5.10 (Ln × G18-16) cM, with an average of
3.26 cM/marker.
Eight additional populations were used for marker interpolation. Four of the populations
included in this second set were DHs obtained from Agriculture and Agrifood Canada,
University of Saskatchewan and USDA. Three additional populations were recombinant
inbred and one was a population of F2:3 progenies (Latino × Primadur, [35]). For six out of
these eight populations the original segregation data were available and thus the linkage maps
were reconstructed according to the common procedure used for the common maps. The two
PDW233 × Bhalegaon 4 [36] and Latino × Primadur maps [35], for which the original
segregation data were not available, were inspected for their homogeneity in marker
distribution and inter-marker distances as compared to the component maps before to
integrate their map information data into the consensus map. The marker density of all of
these eight maps was inferior to that reached in the six core set RIL populations (Table 1),
while retaining a good level of genome coverage (total map length comprised between 861
and 1,848 cM).
Consensus map features
The genotypes from the core set of six RIL populations were used to build a reliable
consensus framework map based on common markers only, including 295 and 281 anchor
SSR and DArT markers, respectively, 21 anchor SNP/STSs and one morphological locus.
These markers were grouped into 17 linkage groups (Table 2). Representative estimates of
recombination rate were obtained across mapping populations, yielding a framework map of
common markers with total length of 2,239 cM (Table 2), with an average inter-marker
distance of 3.74 cM per common marker. The consensus framework map was thus only
15.4% longer than the longest among the six component maps (Mr × Cl), with the number of
independent linkage groups decreased from 21 to 17.
Table 2 Consensus framework and interpolated map marker summary
Pop acronym Molecular Markers
SSR DArT/AFLP SNP/STS Biochemical/
morphological loci
Total
Linkage
marker no. group no.
Total
Inter-marker
length cM distance cM /
marker
2239.4
3.74
Consensus
295 281
21
1
598
17
framework
Interpolated
960 1385
225
5
2575
17
2463.1
0.971
Number of mapped loci (simple sequence repeat, SSR; Diversity Array Technology, DArT; amplified length
polymorphisms, AFLP; single nucleotide polymorphisms, SNPs; sequence tagged sites, STSs; Mendelian loci) included in
the consensus framework map (obtained from the common markers from the six core-component maps) and in the final
interpolated map (with all markers), number of linkage groups, total map length (cM) and average inter-marker distance
(cM/marker) for both maps.
The six core RIL maps and the consensus framework map are reported in Additional file 1:
Figure S1 and Additional file 2: Table S1, with anchor markers reported in red font. In the
computation of the consensus map, portions of linkage groups from single maps were not
included in the merged data-set due to either excessive heterogeneity of recombination rate
(P < 0.001) or low density of anchor markers (highlighted by dashed boxes in the figure).
Frequency of common framework markers (subdivided by marker class) and map length of
each of the framework linkage groups are reported in Table 3. The 17 framework linkage
groups covered most of the14 durum wheat chromosomes. Two separate linkage groups were
obtained for chromosomes 1A, 2A and 3A. A relatively low number of anchor markers was
observed for chromosomes 1A, 4B and 5A (23, 28 and 28 markers, respectively) while up to
78 common markers were mapped on chromosome 7B, followed by 59 common markers on
chromosome 6B. The inter-marker distance between common markers ranged from 1.30
cM/marker for linkage group 3A1and 6.95 cM/marker for 5B. The two-point mapping LOD
score of framework markers averaged 47.8 across all chromosomes and ranged from 25.5 for
1A to 62.1 for 4A(Table 3).
Table 3 Consensus framework and interpolated map features summarized by linkage
group
LG
Interpolated
All LG Inter-marker
loci length distance cM /
no. cM
marker
5
5
0
10 25.5 ± 6.1
47.1
5.85
61 65.4
1.09
12
1
0
13 41.9 ± 9.4
54.7
4.95
32 57.0
1.84
24
17
1
40 47.7 ± 5.1
154.8
3.97
219 164.3
0.76
17
9
2
28 52.1 ± 6.7
77.1
2.86
87 104.9
1.22
6
10
1
17 57.1 ± 7.8
24.9
1.56
57 51.4
0.94
20
33
5
58 47.1 ± 3.7
225.7
3.96
232 233.9
1.01
2
4
0
6 42.2 ± 12.8
6.5
1.30
33 11.6
0.36
19
16
2
37 43.2 ± 5.9
150.9
4.19
103 156.2
1.53
26
18
1
45 28.9 ± 4.3
217.2
4.94
281 218.8
0.78
13
35
0
48 62.1 ± 5.6
140.0
2.98
186 143.0
0.77
73.7
2.73
120 94.9
0.78
19
5
4
28 43.2 ± 7.4
24
3
1
28 38.7 ± 4.0
161.5
5.98
106 168.9
1.61
18
11
0
29 35.1 ± 4.9
194.7
6.95
122 197.2
1.63
21
8
1
30 49.4 ± 6.6
137.1
3.52
178 145.0
0.82
22
37
0
59 53.5 ± 3.7
158.0
2.70
268 170.4
0.64
16
25
1
42 60.1 ± 7.0
215.5
5.26
178 223.0
1.26
31
44
3
78 50.8 ± 4.1
200.0
2.60
242 207.1
0.86
Number of mapped loci(simple sequence repeat, SSR, Diversity Array Technology, DArT, amplified length polymorphisms,
and other markers) included in the consensus framework and in the final interpolated map, detailed by linkage group (LG).
Common
SSR
1A1
1A2
1B1
2A1
2A2
2B
3A1
3A2
3B
4A
4B
5A
5B
6A
6B
7A
7B
Common
DArT
Common
Others
Consensus framework map
Total LOD
LG Length
cM
Inter-marker distance cM /
marker
The subsequent interpolation of the unique loci allowed us to map 2,575 loci in total, for a
total map length of 2,463 cM. This final consensus interpolated map was 27.0% longer (also
in terms of genome coverage) than the longest component maps Mr × Cl.
The category and number of markers mapped in the interpolated map are summarized in
Table 2 and detailedin Table 3. Depending on the chromosome/linkage group, the
interpolated map showed average inter-marker distances ranging from less than 1 cM/marker,
e.g. 0.36-0.94 cM/marker for linkage groups 1B, 2A2, 3A1, 3B, 4A, 4B, 6A, 6B and 7B, to
1.61-1.84 cM/marker for linkage groups 1A2, 5A, 5B (Table 3). The number of markers
mapped to the A genome (1,021) was considerably less than those mapped to the B genome
(1,484) while the cumulative genetic distances of the linkage groups of the two genomes
were balanced (1,126 cM and 1,287 for the A and B genomes, respectively).
The degree of monotony of consensus marker order as compared to the original component
maps (colinearity between the interpolated map and the single core component maps) was
checked by projection plots reported in Additional file 3: Figure S2. Few inconsistencies
were observed for closely linked markers mapped on genetic intervals of less than 2 cM.
For most of the detected order conflicts, the original data of the component populations were
re-inspected and, in most cases, it was found that the consensus marker order showed an
overall LOD score and probability close to the one present in the single discordant maps;
therefore, the consensus marker order was retained. Pair-wise Spearman rank correlations rho
(ρ) of marker order between the consensus and the single component maps were generally
very high across chromosomes. Out of 84 pair-wise comparisons, 76 showed ρ values higher
than 0.99, with 50 comprised between 0.999 and 1. When considering the regression r2
values, which highlight not only departure from colinearity but also local divergences in
recombination frequency, 51 out of 84 pair-wise comparisons showed values ≥ 0.99, 25 were
comprised between 0.95 and 0.99 and 8 were < 0.95. Overall the r2 values were thus slightly
lower as compared to rank correlation values.Local deviations from colinearity were
observed only for a few chromosomes and populations as follows: in chromosomes 1A (Kf ×
Sv and Sm × Lv), 2A (Sm × Lv), 3A (Lg × G18-16) and 4A (Kf × Sv).
The projection plots clearly showed that (i) marker density of the single durum wheat maps
differed markedly along the linkage groups and (ii) the interpolated map effectively
integrated the marker information from the component maps with improved marker density
and genome coverage along the consensus chromosomes, with only a slight increase in
linkage group total length. The single component maps showed a high frequency of locally
low marker density or even no marker coverage at all (gaps in the maps), particularly those
derived from the elite × elite T. durum crosses. These regions were irregularly scattered
across maps on most of the chromosomes (mosaic pattern). Examples can be observed in the
projection plots of chromosomes 1B (Kf × Sv, Mr × Cl, Sm × Lv and Ln × G18-16 maps), 2B
(Kf × Sv, Co × Ll, Mr × Cl and Sm × Lv maps), 3B (Kf × Sv, Co × Ll, Mr × Cl and Sm × Lv
maps), 5B (Kf × Sv, Co × Ll, Mr × Cl, and Sm × Lv maps), 6A (Mr × Cl and Sm × Lv maps),
7A (Kf × Sv and Cl × Ll maps) and 7B (Kf × Sv map). The linkage map obtained from T.
durum Langdon × T. dicoccoides G18-16 was much less affected by uneven marker density
distribution as compared to the intra-specific T. durum maps.
In some cases, chromosome regions showing low marker density were observed consistently
across the majority of the component maps and were thus considered as a constitutive feature.
Examples were the centromeric portion of chromosome 1A, the proximal region of 2A, and
the distal regions of 3A and 7A. As a result, these four chromosome regions also showed a
lower-than-average marker density in the consensus map.
The Durum interpolated map was compared with the reference SSR consensus map of
hexaploid wheat (Ta-SSR-2004; [16]; A and B wheat genomes only) (Table 4 and Additional
file 4: Figure S3A). The comparison was based on the SSR markers shared between the two
maps, which varied from 19 to 43 depending on the linkage group. Spearman rank
correlations rho (ρ) values were lower than those observed within the intra-specific maps of
durum wheat; the average (ρ) value was 0.955, ranging from 0.93-0.96 (chromosomes 1A,
1B, 6A and 6B) to 0.98 (chromosomes 2A, 2B, 5B and 7A). Coefficient of determination r2
values averaged 0.934 and ranged from less than 0.90 (chromosomes 1A, 3Aand 6A) up to
0.98-0.99 (chromosomes 1B, 2A, 3B, 5B and 7B). As compared to the Ta-SSR-2004 map,
the durum-specific map herein reported provided genome coverage of 100% for the majority
of the chromosomes, with the exception of chromosomes 1B and 5B were genome coverage
of the durum consensus was 12.8 and 18% less than Ta-SSR-2004 due to low coverage of
common markers on the distal ends of short arms. In chromosome 3A and 6B the durum
consensus extended the genome coverage in the distal chromosome regions as compared to
the Ta-SSR-2004.
Table 4 Comparison between the durum consensus interpolated map and the hexaploid
Ta-SSR-2004 consensus map
Durum consensus interpolated map
Common
Collinearity
markers
Ta-SSR-2004
Framework
Non
Total Spearman rank Regression
framework
correlation
chromosomes
no.
no.
no.
ρ
R2
1A
11
11
22
0.956
0.896
1B
16
14
30
0.930
0.950
2A
13
27
28
0.979
0.950
2B
15
27
42
0.983
0.935
3A
11
15
26
0.921
0.889
3B
15
26
41
0.967
0.975
4A
6
16
22
0.939
0.950
4B
12
13
25
0.963
0.900
5A
17
26
43
0.956
0.938
5B
12
17
29
0.982
0.977
6A
12
7
19
0.931
0.880
6B
12
20
32
0.940
0.921
7A
10
20
30
0.979
0.934
7B
21
14
35
0.957
0.981
Mean
21
18
30
0.956
0.934
Chr. 3B consensus
map
3B
21
45
66
0.981
0.961
Noticeable differences in local recombination rate were observed between the hexaploid and
the durum maps. For instance, the strong decline in marker density found in the durum pericentromeric regions of chromosomes 1A and 2A was not observed in the corresponding
hexaploid regions.
As for chromosome 1A, the high recombination rate (close to independence) observed in
durum wheat between the peri-centromeric regions of the two distal linkage groups (tagged
by anchor markers gwm164 and cfa2129) was not observed in Ta-SSR-2004, where the map
distance between gwm164 and cfa2129 was only 6.3 cM and the marker density in the local
centromeric region was not affected. Similarly, in chromosome 3A, the low-marker density,
highly recombinogenic region detected between anchor markers gwm71 (upper-side of LG)
and gwm294 (lower-side of LG) spanned only 22.8 cM in the hexaploid wheat consensus
map. The updated consensus map for chromosome 3B published by Paux et al. [37] and
derived from the analysis of 13 mapping populations was also considered. Sixty-six markers
(mainly SSRs) were common to both the hexaploid and tetraploid 3B maps. The correlation
coefficient between our map and the Paux et al [37] map was high (r = 0.980).
The Durum interpolated map was then compared with the most recent tetraploid consensus
map [19] obtained with different software (JoinMap). In this case, while the number of
mapped markers was only slightly lower than in our case (1,898 and 2,575 markers,
respectively), the results were different in terms of number of LG obtained (27 vs. 17,
respectively), and total map length (3,058 vs. 2,575 cM, respectively). Overall conservation
of colinearity of the two durum consensus maps and Ta-SSR-2004 was better for our map
(Additional file 4: Figure S3A) than for the Marone et al. [19] map (Additional file 5: Figure
S3B).
LD decay rate and molecular diversity in elite durum wheat germplasm
A panel of 183 elite durum accessions representative of the major gene pool cultivated in the
Mediterranean region for which LD and population structure were previously investigated
based on low-density genotyping with SSR markers (DurumPanel, [38]) was re-investigated
in depth based on the profilesof 1,200 markers included in the consensus map. Of these 1,200
markers, 957 showed minor allele frequency (MAF) equal or higher than 0.10 and were thus
considered informative for LD and AM analysis. This set of markers, including 334
SSR/STSs and 623 DArT, generated 35,145 pairwise combinations of intra-chromosomal loci
for which LD significance and r2 / D’ estimates were calculated. LD decay was investigated
through fitted regression and box plot distribution, (Figures 1 and 2, respectively), with a
focus on the short inter-marker distances (Figure 2 and Table 5). Non-linear modeling of LD
decay reported in Figure 1 showed an overall R2 fit value of 42%, an α value of 1.69 and an
effective population size (Ne) of 19.36. Based on the fitted model, at 0 cM genetic distance
the expected r2 value was equal to 0.6 while the r2 half-value decline (r2 = 0.3) was reached at
2.20 cM. The upper 95th percentile of the r2value distribution for unlinked marker pairs (intermarker distance ≥ 50 cM), widely considered as a threshold useful to separate the true linkage
LD from the background LD due to population structure, was equal to 0.096 (close to 0.10).
This trend was confirmed by inspecting the LD statistics for discrete marker pairwise classes
of incremental map distances (Table 5 and Figure 2). The class of completely linked markers
(with inter-marker distance = 0 cM, including 504 pairs) showed mean r2 = 0.67, median LD
r2 = 0.92, inter-quartile range from 0.25 to 1.0 and D’ = 0.86. The mean and median LD r2
decreased by half in the 0.5-1 cM inter-marker distance class (mean r2 = 0.36 and median LD
r2 = 0.27) and more than half in the 1-5 cM class, respectively; the median LD r2 values
dropped below 0.10 in the 5-10 cM class (mean r2 was 0.15 and median r2 = 0.07,
respectively). The LD estimates calculated for independent markers (pairs of markers more
than 50 cM apart; 18,139 pairs in total) showed mean r2 = 0.025 and D’ = 0.23.
Figure 1 Intra-chromosomal linkage disequilibrium (LD) decay plot as a function of
genetic distance (cM). LD decay assessed in a durum wheat panel of 183 elite (cultivars and
advanced lines) accessions adapted to the Mediterranean environments. LD estimates are
reported as squared correlations of allele frequencies (r2). Inter-marker genetic distances are
from the durum wheat consensus map. Non-linear modeling of LD was performed using the
Sved [39] equation.
Figure 2 Boxplot charts of linkage disequilibrium (LD) estimates for categorized intermarker genetic map distances (cM). Pairwise LD estimates are reported as squared
correlations of allele frequencies.Inter-marker genetic distances are from the durum wheat
consensus map.
Table 5 Linkage disequilibrium estimates for the elite durum wheat panel based on
mapped SSR and DArT markers
Pairwise Intermarker-distance
Linkage disequilibrium estimate
cM
no.
Average r2
Average D’
0
504
0.674
0.858
0 - 0.5
392
0.473
0.767
0.5 - 1.0
306
0.357
0.658
1.0 - 5.0
2,677 0.275
0.663
5.1 - 10.0
2,437 0.150
0.525
10.1 - 20.0
3,256 0.087
0.398
20.1 - 30.0
2,667 0.066
0.323
30.1 - 40.0
2,386 0.036
0.283
40.1-50.0
2,381 0.029
0.254
> 50
18,139 0.025
0.232
LD decay in a collection of 183 elite durum wheat accessions from the Mediterranean,
CIMMYT and ICARDA germplasm included in the association mapping Durum Panel.
Detailed pair-wise LD r2 values for all the consensus linkage groups are projected as heatplot triangular matrices in Additional file 6: Figure S4, together with the polymorphism index
content plots along the chromosomes (reported as standardized average PIC values of a threemarker sliding window) and the distributions of markers with highly significant (P ≤ 0.01)
genetic differentiation (FST indexes) among the main sub-populations represented in the
panel. When detectable, LD blocks with sizeable r2 values (≥0.40) were in most cases
observed within a 5 cM window size; relatively small LD blocks were detectable across the
full length of durum wheat chromosomes, from distal to proximal regions. However, a large
number of closely linked adjacent markers showed LD r2 values in the range of 0.3 or less.
A few long-range LD blocks extending more than 10 cM were also observed, particularly in
the proximal regions of chromosomes 1B, 6A and 7B. The polymorphism information
content (PIC) plots showed a trend for higher values in the distal regions of the chromosomes
and lower values in the peri-centromeric regions (e.g. chromosomes 1B, 3B, 6A and 7B).
Chromosome 1A exhibited the most extended peri-centromeric region with both low marker
density and diversity in the elite germplasm, causing a gap in the continuity of the chr. 1A
consensus map in this region. A similar phenomenon was observed for peri-centromeric
regions of chromosomes 2A, 2B and in 3AS. None of the six core RIL maps showed a
marker density sufficiently high to guarantee the continuity of the consensus linkage groups
in these four chromosomes. Additionally, numerous locally defined sharp decreases of PIC
values extending over stretches of 5 to 10 adjacent markers were commonly observed in both
centromeric and distal regions of several chromosomes (e.g. in chromosomes 1B, 2B, 3B,
4A, 5B, 6B, 7A and 7B, see Additional file 6: Figure S4), even in chromosome regions where
the marker density was not locally affected.
The population structure of the elite panel of accessions was previously investigated based on
a non-redundant set of 96 highly informative SSR markers (Maccaferri et al. [20,38]).
Hierarchical and non-hierarchical model-based cluster analyses showed that the population
structure could be described based on five main sub-populations corresponding to landracesand breeding-derived germplasm founders that originated from the Italian, CIMMYT and
ICARDA breeding programs distinct by time periods (e.g. early-, mid- and late-CIMMYT
derived sub-populations) or target environment (e.g. the ICARDA germplasm for dryland vs.
temperate areas). The founders were widely used in the national breeding programs of
Mediterranean countries and a high degree of admixture was detected among sub-populations
[38].
The increased density of genetically mapped markers based on the consensus map allowed
for a more in-depth investigation of the patterns of genetic differentiation present within and
among the five main sub-populations. AMOVA showed that the genetic variation within was
higher than the variation among sub-groups (82.8 vs. 17.2, Table 6), with the latter
(summarized by the pair-wise FSTs, Table 6) reflecting known temporal, geographic and
pedigree differences. A medium level of genetic differentiation was observed between the
two ICARDA sub-populations for dryland and temperate areas (pair-wise FST = 0.172, a
value equal to the overall FST value). More differentiation was found between the ICARDA
sub-population for dryland areas and the Italian and early-period CIMMYT germplasm (pairwise FST = 0.261). Lower-than-average differentiation was observed between the ICARDAtemperate and the Italian-early CIMMYT as well as the mid-CIMMYT. The late-CIMMYT
germplasm (FST = 0.361 and 0.306) was the most differentiated from the ICARDA-dryland
and Italian-early CIMMYT sub-populations.
Table 6 Analysis of molecular variance (AMOVA) for the elite durum wheat panel
based on mapped SSR and DArT markers
Variance components
Within
Among subpopulations
subpopulations
82.8%
17.2%
FST = 0.172
Sub-populations
No.
5
Pairwise FSTs
Sub-population
ICARDA-dryland (1)
ICARDA-temperate (2)
Italian, early CIMMYT (3)
Mid CIMMYT, ICARDA
(4)
Late CIMMYT (5)
Pairwise differences (no.)
Founder
(1)
Omrabi 5
Cham 1
0.172
Valnova, Mexicali 75 0.261
Yavaros 79
0.249
(2)
(3)
(4)
0.109
0.067
0.239
-
(5)
Altar 84
0.361
0.163
0.306
0.156 325.6
351.7
323.1 337.0
216.3
64.0
336.7
303.4 314.3
306.9
97.5
37.2
359.6 362.2
292.1
85.5
20.5
84.0
259.0 281.6
121.5
53.5
108.8
44.8 214.7
Analysis of molecular variance for the elite durum wheat panel of 183 accessions adapted to Mediterranean areas based on
the sub-population subdivision as from a previous population genetic structure analysis [20,38]. Average population pairwise
differences are reported as follows: (i) above diagonal: average number of pairwise differences between populations (Pi XY),
(ii) diagonal elements (bold): Average number of pairwise differences within population (Pi X), (iii) below diagonal:
Corrected average pairwise difference (Pi XY-(Pi X + Pi Y) / 2).
The improved marker density of the consensus map allowed us to search genome-wide for
chromosome regions showing evidence of high genetic differentiation among subpopulations (locus by locus AMOVA). Single markers or chromosome regions (stretches of
two or more adjacent markers, i.e. blocks) with sub-population pair-wises FST values greater
than the FST thresholds set at P 0.01 were observed in most of the chromosome arms and
projected on the consensus map (Additional file 6: Figure S4). Three main genetic
differentiation patterns were observed as follows: (i) FST differentiation “pattern 1”:
significant differentiation across the majority of sub-populations indicating differentiation by
time and by target environments (highly significant FSTs in 7 or more of the 10 possible subpopulation pairwise comparisons, (ii) FST differentiation “pattern 2”: significant
differentiation between ICARDA-dryland vs. all the other subpopulations (highly significant
FSTs in 3 or more of 4 pair-wise comparisons), (iii) FST differentiation “pattern 3”: significant
differentiation of either or both ICARDA-temperate and Italian, early-CIMMYT
subpopulations vs. the mid- and late-CIMMYT subpopulations (highly significant FSTs in 3 or
more out of 4 sub-population pair-wises). There were 11, 22 and 54 single
markers/chromosome regions in the durum wheat genome that showed genetic differentiation
pattern 1, 2 and 3, respectively (reported in Additional file 6: Figure S4). FST differentiation
pattern 1was mainly found on chromosomes 1B, 2A, 2B, 4B, 5A, 5B and 6B. High frequency
of both pattern 2 and 3 were observed in chromosomes 3A and 7B. Pattern 3 was also
frequently found in chromosomes 1B, 3B and 7A.
In chromosome 2A, PPD-A1 and four closely associated flanking markers showed highly
significant FST differences for all sub-population pair-wises (FST differentiation pattern 1). In
chromosome 2B, coincident withPPD-B1, (wPt-7320 at 47.7 cM position and associated
markers) a strong differentiation was observed between the Italian and ICARDA populations
(bred in temperate zones) vs. the CIMMYT sub-populations bred at sub-tropical latitudes (FST
differentiation pattern 3). As expected, Rht-B1 on chromosome 4B showed a type 2 FST
differentiation pattern with peaks of FST values for the pair-wise comparisons including the
ICARDA dryland population (with high frequency of tall accessions) vs. all the other
populations (nearly fixed for the semi-dwarfing Rht-B1b allele).
GWAS for heading date
The consensus map was used for a genome-wide association scan (GWAS) to detect
association of the 957 mapped SSR and DArT markers with MAF > 0.10 with time to
heading in the durum wheat association panel. Time to heading data were available for 27
field trials carried out in the Mediterranean region and in Mexico. The AMMI biplot analysis
showed that the heading date phenotypes from the 27 environments could be grouped
according to five macro-environments: Southern Europe (North to Southern Italy and Spain),
North Africa-Tunisia, North Africa-Morocco, West-Asia (Syria and Lebanon) and Mexico
(data not shown). The association scan was performed for each environment as well as for the
adjusted means of each of the five macro-environments.
Broad-sense heritability (h2) of heading data calculated across environments were high for
Southern European (0.85), Asian (0.89) and Mexican macro-areas (0.91), including 10, 8 and
5 environments, respectively, but lower for the North African Moroccan (0.69) and Tunisian
(0.68) macro-environments, each with two environments. The phenotypic data of six
genotypes known to carry the vernalization-sensitive vrn-1 allele at VRN-A1 were excluded
from the analysis.
The initial GWAS analysis showed strong experiment-wise significant associations at the two
chromosome regions corresponding to the location of Ppd-A1 and Ppd-B1 loci and numerous
chromosome regions with highly significant (P ≤ 0.01) marker-wise associations across
environments. Detailed results are reported in Table 7.
Table 7 Association results for heading date in the elite Durum Panel at the two major photoperiod-response homeologous Ppd-1 loci
Macro-environmental area
Causative locus: Ppd-A1
Single envs.
Adj. means across environments
P 1E-4 P 0.001 P 0.01 P 0.05 P-value
R2
no.
no.
no.
no.
%
Causative locus: Ppd-B1
Single envs.
Adj. means across environments
P 1E-4 P 0.001 P 0.01 P 0.05 P-value
R2
no.
no.
no.
no.
%
Southern Europe (10 envs.)
Across envs.
3.76E-08
18.76
8.68E-04
6.46
Single env.
4
2
1
3
6.02E-12
3.74 - 31.05
1
2
1
5
1.56E-06
4.64 - 12.71
North Africa – Tunisia (2 envs.)
Across envs.
2.65E-04
8.58
0.016
3.60
Single env
0
2
0
0
1.95E-05
6.78 - 8.91
0
0
1
1
0.010
1.80 - 4.75
West Asia (8 envs.)
(8 envs.)
Across envs.
4.76E-08
17.90
0.003
4.98
Single env.
5
3
0
0
2.40E-06
7.99 - 14.42
0
0
6
1
0.001
4.10 - 6.61
North Africa – Morocco (2 envs.)
Across envs.
1.16E-10
31.17
0.008
4.74
Single env.
2
0
0
0
6.30E-10
26.53 - 28.30
0
0
1
1
0.009
4.55
Mexico (5 envs.)
Across envs.
4.60E-13
34.31
0.007
4.19
Single env.
5
0
0
0
1.69E-14
24.66 - 39.98
0
0
2
0
0.001
4.61 - 5.97
Association analysis based on 183 accessions from the durum panel, evaluated for heading date across 27 environments and five macro-environmental areas in the Mediterranean Basin and
Mexico. The Ppd-A1 specific assay [40] and wpt-7320 DArT marker tightly associated to Ppd-B1 have been used. Significance of P ≤1.00E-4 refers to the experiment-wise significance
threshold of P ≤ 0.05. Results are reported for single environments and for the adjusted means across macro-environmental areas.
At Ppd-A1, association peaks were detected in coincidence of the diagnostic marker obtained
from the causal locus. Considering the adjusted means of macro-environmental areas, Pvalues of association ranged from a minimum of P = 3.76E-08 (R2 value = 18.8%) for the
Southern European area (that grouped the environments at higher latitudes among those
tested), to a maximum significance of P = 4.60E-14 (R2 value = 34.3%) in Mexico (with
environments located at lower latitudes). Among the four markers wmc177, cfa2201-2A,
gwm1198-2A and wPt-7026 located within 4.5 cM from the Ppd-A1 locus and with LD r2
values between 0.2 and0.4, significant associations with heading date were observed for
cfa2201-2A only (P ≤ 0.05, at 2.6 cM from Ppd-A1). For the region harboring Ppd-B1, highly
significant marker- and experiment-wise associations with heading date were detected for 10
DArT and one SSR markers mapped within an interval of 8.3 cM (from wPt-5513 at position
45.2 to wPt-6199 at position 53.5 on chromosome 2B). The majority of these markers formed
a unique linkage block with strong inter-marker LD r2 values (from 0.5 to 1.0, except for
gwm1198-2B). Peaks of association to heading date across environments were observed for
wPt-7320, the QTL-representative marker, at position 47.7 cM. This marker showed a range
of P-values from a minimum of P = 0.007 (R2 value = 4.19%) in Mexico to a maximum
association of P = 0.8.68E-04 (R2 value = 6.46%) in Southern Europe. According to Hanocq
et al. [41-43] and Bennett et al. [44], the small linkage block includes markers associated with
the predicted position of Ppd-B1.
As expected, the level of significance and magnitude of effects associated with the Ppd loci
were strongly influenced by latitude. The genotypic to phenotypic proportion of variance of
Ppd-QTLs, as estimated by R2 values on a single-environment basis, was significantly
correlated with latitude (correlation coefficient r = -0.60 for Ppd-A1 and r = 0.35 for Ppd-B1)
as presented in Figure 3. Ppd-A1 showed a strong trend towards increased R2 values at
latitudes lower than 40° (Southern Italy, Syria and Lebanon, Tunisia, Morocco and Mexico)
while the markers tagging Ppd-B1 showed an opposite trend.
Figure 3 Boxplot charts of linkage disequilibrium (LD) estimates for categorized intermarker genetic map distances (cM).
Boxplot charts of linkage disequilibrium (LD) estimates for categorized inter-marker
genetic map distances (cM). Pairwise LD estimates are reported as squared correlations of
allele frequencies.Inter-marker genetic distances are from the durum wheat consensus map.
The GWAS was repeated after including Ppd-A1 and Ppd-B1 representative markers as
covariates in the MLM association model. Based on this analysis, 50 unique chromosome
regions showed highly significant (P ≤ 0.01) association to the heading date adjusted means
of at least one or more of the five macro-environmental areas. Marker-features of these
regions are summarized in Additional file 7: Table S2 and Figure 4. None of these QTLharboring regions reached the experiment-wise adjusted Bonferroni significance threshold
based on the number of independent LD blocks (P ≤ 0.0001). However, the reported
associations were all supported by marker-wise significances (P ≤ 0.05) over multiple
environments (9.2 ± 4.1 on average, out of 27 tested environments), as detailed in Additional
file 7: Table S2. Out of the 50 AM-QTLs, 29 consisted of single-marker associations
(singletons) while 21 consisted of small blocks of multiple adjacent markers all associated to
the phenotype as well as to each other with LD r2 values ≥ 0.3. From the consensus map,
these QTLs included markers that were either coincident (i.e. at 0 cM distance) or closely
linked within a 3-4 cM interval. There were only three QTLs with a significance interval of 5
cM or more on chromosomes 2B (QTL-10, tagged by gwm1027, interval = 5.3 cM) and 3B
(QTL-19, tagged by wPt-9510, interval = 10.8 cM and QTL-21, tagged by wPt-9989, interval
= 8.5 cM). Based on the predicted LD decay in the collection, confidence interval boundaries
of 2.2 cM, corresponding to the genetic distance over which the LD decays to r2 = 0.3, were
attributed to both sides of the marker’ blocks significantly associated with the phenotype.
Thus, a confidence interval of 4.4 cM was attributed to the single markers associated with the
phenotype. The R2 values of the associations (on the macro-environment basis) were mostly
between 1.5 and 4.5% (Additional file 7: Table S2). The proportion of phenotypic variance
explained by simultaneously considering all the reported QTL-representative markers
(including the two Ppd-1 homeologs) ranged from a minimum of 59.6% for the Tunisian
macro-area to a maximum of 79.6% for Mexico, with R2 values of 64.6, 67.3 and 68.9% for
the Southern-European, Asian and Moroccan macro-areas, respectively.
Figure 4 Association mapping QTLs (AM-QTLs) for time to heading in the
Mediterranean regions reported on the tetraploid SSR and DArT® marker consensus
map. AM-QTLs are reported as red bars (significance intervals) on the left side of
chromosome bars. red thin-line wiskers extending the red bars corresponds to the commonly
adopted confidence intervals on both sides of AM-QTLs. Projected QTL confidence intervals
(CIs) from previous studies are reported as vertical bars of various colors other than red.
AM-QTLs were distributed in both distal and pericentromeric regions of the chromosomes.
Out of 50 AM-QTLs, 16 were identified in regions within 20 cM from the centromeres while
21 were positioned in the very distal telomeric chromosome regions. Thirty-two AM-QTLs
were positioned within the CIs of the hexaploid wheat meta-QTLs or the Kofa × Svevo
QTLs, although in many cases the CIs of hexaploid wheat meta-QTLs were excessively wide
(intervals ≥ 20 cM) to support QTL coincidence. The map location of the Ppd-1 homeologs
and 50 AM-QTLs (representative marker position and CIs) on the consensus map is reported
on Figure 4, along with the projections of the CI of the meta-QTLs and AM-QTLs found in
hexaploid wheat [41,42,45] and the QTLs from the durum wheat RIL population Kofa ×
Svevo [24]. Co-locations between AM-QTLs and hexaploid wheat meta-QTLs with CIs
shorter than 20 cM or K × S QTLs were observed for 16 AM-QTLs (QTL7, 9, 10, 15, 16, 23,
24, 26, 30, 31, 32, 42, 43, 46, 49 and 50; see Figure 4). Apart from Ppd-A1 and Ppd-B1,
homoelogous effects between A- and B-genome QTLs could be hypothesized for QTL11 and
QTL16 as shown by significant effects at both of the homeologous copies of the SSR
markercfd79. QTL43 (tagged by cfa2028 and wPt-7785) has been positioned very close
(within 3 cM) to the location of TaFT in chromosome 7A of hexaploid wheat reported by
Bonnin et al. [46]. The homeolog of TaFT in the B-genome, mapped by Yan et al [47] at 5
cM away from gwm569 in the distal portion of 7BS, was not identified with this survey,
although in this region a major QTL for heading date was reported in the Kofa × Svevo
mapping population (QTL projections reported in Figure 4).
Discussion
Integration of SSR and DArT in a common framework
The consensus framework and the final interpolated map reported here integrates a large
portion of the mapping efforts carried out in durum wheat. Our integrated data of 14 maps
based on a combination of segregation- and interpolation (‘projection’)-based methods.
Efficient projection-based integrations requires a pre-existing reference (fixed) framework
map with a large number of high-quality markers and nearly complete genome coverage that
serves as a backbone for marker projection from the component maps. For durum wheat, this
resource was not available. The reference maps initially assembled with RFLP and SSR
markers [6,11] were limited either in the number of progenies or markers used. Here, a core
of six maps with adequate number of progenies, medium-to-high number of SSR and DArT
mapped loci, homogeneous genetic constitution (recombinant inbred lines) and complete
genotype datasets available were identified as the best resource for assembling a robust
framework. All but one of these maps had parents derived from the cultivated durum wheat
germplasm. Based on these commonalities, this segregation data-set was considered as the
most suitable to produce a consensus framework based on actual genetic map distances
calculated from a data-set of lines with relatively homogeneous constitutions that well
represents the durum germplasm.
As compared to the projection-based method, building de novo consensus maps from a
pooled set of segregation data was recently criticized [48,49].However, in our case it proved
to be the best solution for obtaining a reference map with nearly complete genome coverage
because none of the elite × elite durum wheat maps showed complete genome coverage,
likely due to the scattered occurrence of identity by descent (IBD) in the genomes of parental
genotypes. The detection of shared long-range haplotypes in components of cultivated durum
wheat germplasm was previously reported based on multi-allelic mapped SSR markers
[30,50]. On the other hand, the presence of locally strong differences in recombination rate
among populations, which can affect the precision of marker ordering in de novo consensus
maps from pooled segregation data, was monitored and was not considered as a main
perturbing factor as could be the case when using populations derived from crosses between
more distantly-related materials [51]. The quality of the consensus framework map was
checked based on the overall high two-point linkage LOD values for common markers
throughout all chromosomes. Interpolation was then effectively used to project all the
markers that were uniquely mapped in each of the 14 maps available.
Despite the integration of six core maps, gaps in the consensus framework map were
observed for four regions on chromosomes 1A, 2A, 3A and 7A. At least for chromosomes 1A
and 2A, the presence of gaps of marker coverage was reported in the consensus map of
Marone et al. [19] and was found to correspond to the centromeric deletion-bins of wheat.
The biological reasons at the basis of this molecular evidence deserves further work based on
comparative genomics diversity studies among T. aestivum, T. durum, T. dicoccum and
particularly the wild emmer T. dicoccoides. This would allow one to test the hypothesis of
occurrence of local severe speciation and domestication-related bottlenecks/selective sweeps
[52], or, alternatively, the presence of strong local differences in genomic constitution and/or
alterations of the genetic to physical distance ratio among the Triticum species (in particular,
hexaploid vs. tetraploid wheat).
The robustness of the marker order obtained in our final interpolated map is demonstrated by
the overall high rank correlations (ρ) between the marker order of linkage groups from single
component maps and the marker order of the consensus linkage groups. Correlation
coefficients this high have rarely been observed in similar studies [48]. Comparison with the
Ta-SSR-2004 map of Somers et al. [16] in hexaploid wheat revealed good but less conserved
overall collinearity and low conservation of relative genetic distances. This could in part be
due to the different algorithms used to produce the consensus maps. Joinmap software
calculates consensus map by a regression algorithm only, while Carthagene allows one to
work with the maximum likelihood even for integrated datasets, coupled with effective
marker order refining procedures. This could also account for the lower-than-expected
performance of the durum consensus map reported here compared with that of Marone et al.
[19], as well as more obvious differences such as the raw data quality assessment and
mapping procedures.
Application of the consensus map for linkage disequilibrium and germplasm
diversity analysis
The final interpolated map was used to investigate at relatively high level of genetic
resolution important genomics and breeding applications in T. durum such as: (i) describing
the LD pattern and LD decay rate in elite germplasm, (ii) exploring the pattern of genetic
diversity among sub-populations of germplasm at the chromosome level, and (iii) performing
GWAS. To these ends, the durum wheat elite accession panel previously assembled for AM
studies was used as reference. The pattern of LD present in the panel was re-estimated after
Maccaferri et al. [38], where only low-density SSR markers were used. The higher SSR and
DArT marker density in the present map provided a more detailed and accurate picture of LD
decay in the crucial 0 - 5 cM interval. In particular, LD in the elite Mediterranean panel
decays below the r2 reference threshold of 0.3 within 2.20 cM. This r2 threshold is considered
as a reference as regards to the power of GWAS to detect association to QTLs with medium
to high effect [53]. Interestingly, the inferred average LD r2 value is 0.6 at 0 cM inter-marker
distance, i.e. at the maximum resolution provided by the linkage studies, is far below the
reference threshold of r2 = 0.8 currently accepted as the r2 value to select representative SNPs
for GWAS (tagSNPs, [54]) in those species where this resource is available. These data
indicate that actually the genetic resolution of AM in this elite germplasm might be higher
than expected based on previous assumptions and that, based on these results and on the
length of the durum consensus map, exhaustive GWAS in elite T. durum germplasm will
require a marker density in the order of tens of thousands markers. The reported results
indicate that AM had a mapping resolution of approximately 10 times higher in magnitude as
compared to that obtained with conventional linkage mapping, even in the elite germplasm of
an autogamous species such as durum wheat. The LD decay pattern of this elite
Mediterranean germplasm panel compares favorably with that of populations with a more
restricted genetic basis of elite durums from other areas. Somers et al. [55] for a global panel
with North American, European, CIMMYT and Argentinian germplasm reported similar
genetic distances of 2-3 cM for the LD decay in such germplasm [27,55]. As a comparison,
LD decay to 50% in US elite hexaploid winter and spring wheat was observed at larger
genetic intervals (5 to 7 cM, respectively, [56]). Another interesting feature of the LD decay
pattern observed along the chromosome is that, with only a few exceptions, the genetic
resolution did not markedly decline in the peri-centromeric regions.
The interpolated map proved useful also for a first in-depth investigation of the distribution of
the chromosome regions characterized by peaks of differentiation (high FST values) among
the durum sub-populations represented in the AM panel. Evidence of differentiation was
found for chromosome regions identified by three or more adjacent markers (LD blocks), and
not only at the single-marker level. The approach performed effectively, because high
diversity was detected near the major RhtB-1 and Ppd-A1 loci, as expected. In hexaploid
wheat, this approach was used based on SNPs at medium mapping density [56,57]. With the
durum panel, the three patterns of genetic differentiation among sub-populations that were
most frequently found along the chromosomes might correspond to regions harboring genes
governing the morpho-physiological, adaptive and qualitative traits that were differentiated
among sub-populations, as demonstrated for the PPD homeologs. Interestingly, a relatively
high number of chromosome regions were found to differentiate the more recently developed
CIMMYT elite durum breeding lineages from the others (chromosome regions with FST
differentiation pattern 3), suggesting that the breeding innovation process of adding new
valuable genetic diversity to the existing ones operated successfully.
Application of the consensus interpolated map for mapping QTLs controlling
heading date
The evidence for an average LD decay in the cultivated durum wheat germplasm within less
than 5 cM prompted us to assess the potential of GWAS analysis for an important adaptive
trait such as heading date and to use the consensus map as a framework for positioning AMQTLs.
The mapping resolution of AM-QTLs reported here (QTLs mapped with an average
confidence interval of 4.4 cM) appeared to be similar or higher to that observed for the same
trait in the cultivated germplasm of hexaploid wheat (10 cM confidence interval) investigated
by Le Gouis et al. [45]. Mapping resolution estimated at the whole-genome level using the
average LD decay rate was confirmed by the association results obtained for the markers
closely mapped to the major effector loci Ppd-A1 and Ppd-B1. The marker density used in the
present study (861 markers with MAF ≥ 0.10) appeared adequate for medium-density
genome-wide scans, as shown by the medium to high overall R2 values obtained by fitting the
multiple-QTL model with all the highly significant QTL effects [58].
The potential of GWAS as an effective tool for QTL discovery, when coupled with sufficient
marker density and reliable genetic map information, was evident from the high number of
QTLs (up to 50) that were mapped consistently across environments. Heading date in the
spring elite durum wheat germplasm was confirmed to be controlled by a few major loci
(namely, the Ppd-1 homeologs) as well as by a wide range of small-effect QTLs with R2
values below 5%, according to the ‘L-shaped’ distribution of QTL effects previously
observed in most crop germplasm QTL analyses [58-62].
Although the number of mapped markers used here for GWAS (957) is among the highest
reported for similar studies in wheat, it is also evident that reaching higher mapping precision
and complete genome coverage (inter-marker LD r2 values = 0.8, [63]) will require a marker
density at least ten times higher, even in cultivated germplasm. Currently the marker
technology in wheat is changing to single nucleotide polymorphisms (SNPs), which hold
great promise for high-throughput multiplex assays [18,57,64].
Until now, the SSR markers generally deployed in wheat genomics were mainly obtained
from genomic libraries, and only a limited number of DArT markers (up to 2,000) have been
sequenced in wheat (http://www.diversityarrays.com/DArT-map-sequences). Therefore,
comparative mapping across species with these two marker classes has limited resolution in
wheat. This notwithstanding, attempts to use new marker technologies such as SNPs have
already been carried out [26]. The use of transcript-derived SNPs will greatly facilitate the
investigation of the putative gene content in the QTL-harboring regions, eventually leading to
a more rapid identification of candidate genes at least for those cases where direct links
between the putative gene function and the phenotype can be established.
As shown from the comparison between the CIs of AM-QTLs and the projections of the CIs
from previous meta-QTL analysis carried out in hexaploid wheat, it was evident that the
meta-QTL CIs from bi-parental RIL studies were substantially wider than those of the AM-
QTLs, in the magnitude of 10 (minimum) to 30 cM (maximum) thus making it difficult to
accurately infer QTL co-locations. It is expected that in the near future the increasing number
of QTL mapping results will allow for higher mapping resolution of meta-QTL analysis [65].
Additionally, the adoption of common high-density SNP genotyping assays will also greatly
facilitate the comparison of QTL regions across studies, germplasm and laboratories.
From the comparison between the mapping locations of AM-QTLs and the QTLs for heading
date found in the intra-specific Kofa × Svevo durum mapping population, it was also evident
that the bi-parental population permitted mapping of several (up to 9) QTLs that were not
identified in the AM study. This result underlines the concept that the compilation of the
QTLs present in the cultivated germplasm is far from being complete by relying on a single
AM study. In particular, linkage analysis has the advantage over AM of being more efficient
in detecting alleles that are present in rare frequencies in the target germplasm, as could be
the case for the QTLs detected in the Kofa × Svevo durum mapping population, and alleles
whose distribution correlates to the genetic population structure. On the contrary, AM studies
carried out with the necessary marker density and population size show a favorable trade-off
between the phenotyping cost and the output in terms of QTL identification.
Conclusions
Overall, results from this study provide an exhaustive consensus framework map of SSR and
DArT markers for the durum wheat genome that was used to assess the genetic structure of
cultivated durum wheat germplasm at the level of single chromosome regions and to assess
the potential of GWAS to dissect the genetic basis of heading date. The reported results
showed that the marker density provided by the consensus map was sufficient to identify
chromosome regions harboring numerous small effect QTLs for heading date, at a resolution
level not possible with traditional bi-parental mapping studies. More importantly, the results
showed that there is scope for further refinement and expansion of these results as highdensity and high-throughput genotyping SNP platforms become available. In view of this
impending change in the commonly-applied genotyping technology, framework mapping
data-sets that fit commonly accepted quality thresholds are valuable to provide a link between
the QTL mapping results obtained in the last decade by means of SSR and DArT technology
and the up incoming SNP-based technology.
Methods
Core maps used for assembling the framework consensus map
Six maps from core recombinant inbred line (RIL) mapping populations were chosen for
building a reference consensus maps using common (anchor) loci. These maps were chosen
for marker density, presence of both SSR and DArT® markers and genome coverage (based
on the map projections on the Ta-SSR-2004 consensus map used as a reference). Five
populations were derived from T. durum intra-specific elite crosses and one population was
derived from the inter-specific cross T. durum × T. dicoccoides. Segregation data were
available for all the recombinant inbred lines.
Four mapping populations were developed and genotyped jointly with Produttori Sementi
Bologna (PSB), University of Bologna, and University of Udine, Italy. The Kofa × Svevo
population (Kf × Sv) included 249 RILs and was profiled with SSR markers [24,66]. The
Colosseo × Lloyd (Co × Ll), Meridiano × Claudio (Mr × Cl) and Simeto × Levante (Sm ×
Lv) populations included 176, 181 and 180 RILs, respectively, and were profiled with SSR
and DArT markers from the Durum-specific PstI/TaqI representation v. 2.0 Array (Triticarte,
Yarralumla, AU). The Mr × Cl linkage map was subsequently enriched with DArT markers
from the wheat high-density array v. 3.0. Further details are reported in Mantovani et al. [13]
and Maccaferri et al. [12,24,38,67]. The fifth population was developed at UC Davis from the
cross Kofa × UC1113 (Kf × UC), it included 93 lines and was profiled with SSR and SNP
markers [68]. The sixth mapping population was developed at University of Haifa, Israel,
from T. durum Langdon (Ln) × G18-16 (T. dicoccoides = T. turgidum (L.) Thell ssp.
dicoccoides Koern) and included 152 lines genotyped with SSR and DArT markers as
reported in Peleg et al. [15]. Additional details on the parental lines and the molecular
markers mapped in the populations are reported in Table 1.
Additional component maps used for marker interpolation
An additional set of eight mapping populations (14 maps in total) was used for interpolating
additional markers based on the markers common to the consensus framework map. These
maps included three double haploid (DH) populations from AAFC Semiarid Prairie
Agricultural Research Centre (SPARC; Swift Current, Saskatchewan, Canada), one DH
population from USDA-ARS Northern Crop Science Laboratory (Fargo, North Dakota, USA)
and two RIL populations from University of Bari (Italy) and ARI, Martonvasar, Hungary.
The three DH populations from SPARC were obtained from the crosses Gallareta × Demetra
(Gl × Dm, 127 DHs), DT707 × DT696 (127 DHs) and Strongfield × Blackbird (St × Bl 89
DHs). Blackbird is a Triticum carthlicum Nevski in Kom.), as described by Somers et al.
[69]. The fourth DH population, obtained from the cross Lebsock × T. carthlicum PI94749
(Lb × P749), included 146 lines and was described in Chu et al. [70]. The RIL population
from the University of Bari, obtained from the cross Svevo × Ciccio (Sv × Ci), included 120
F7 RILs that were profiled with SSR and expressed sequence tag (EST)-SSRs [71]. The RIL
population from ARI, Martonvasar, Hungary, included 182 RILs from the cross PWD1216 ×
MvTD10-98 that were profiled with DArT and AFLP markers (Karsai et al., unpublished).
Details of these additional DH and RIL mapping populations are reported in Table 1.
Additionally, selected linkage groups for the populations PDW233 × Bhalegaon 4 (PDW ×
Bh, [36]) and Latino × Primadur (Lt × Pr, [35]), respectively, and for which no segregation
mapping data were available, were also used for marker projection.
Single map linkage analysis
The segregation mapping data of the mapping populations were used to recalculate the
linkage maps using a combination of JoinMap v. 4.0 (Van [31]) for marker grouping and
Carthagene v. 2.0 [32] for marker ordering and mapping. Low-quality data filtering was
carried out as follows: (i) missing data were allowed to a maximum frequency of 0.15, (ii)
marker segregation distortion (departure from the expected 1:1 segregation ratio) was
allowed up to a probability threshold of P =1E-04, corresponding to a segregation distortion
not exceeding the 0.7 : 0.3 ratio.
Haldane mapping function was used for all mapping calculations. Marker grouping was
performed in JoinMap using the independence LOD method. Robust initial linkage groups
(LG) were obtained at LOD = 6.0. The assignment and orientation of linkage groups to wheat
chromosomes were carried out by checking for SSR loci common to the hexaploid wheat
consensus map (Ta-SSR-2004, http://wheat.pw.usda.gov/GG2/index.shtml) and/or in the
Triticarte website (http://www.triticarte.com.au). High quality LG maps were then obtained
in Carthagene using stringent mapping parameters. Mapping was carried out through two
iterative cycles of: (i) mapping, (ii) investigation of graphical genotypes for the presence of
suspicious data points (e.g. single markers that appeared to have recombination events to both
flanking marker sides within inter-marker distances of 5 cM or less, mostly derived from
unlikely double-crossover events, (iii) checking of raw data and, in case, replacement with
missing data. The inter-marker distance threshold for singleton data correction was raised to
10 cM for the DH linkage maps. After stringent mapping and correction of suspicious data,
the segregation data belonging to each wheat chromosome were pooled and re-analysed in
Carthagene using less stringent grouping thresholds (rec freq = 0.3 LOD = 3) . This produced
final maps for each mapping population.
Construction of the consensus framework map and marker interpolation
The consensus framework map was constructed based on the multipoint maximum likelihood
algorithm for consensus mapping, using the integrative function of Carthagene implemented
with the dsmergen command. This command merges the information from several
independent data-sets into a single consensus set by estimating a single recombination rate
for each marker pair based on all the available meiosis. The dsmergen command only accepts
data-sets of homogeneous mapping populations and it was applied to the data set of a marker
common to two (or more) individual maps (anchor markers) of the six core component RIL
populations (Kf × Sv, Co × Ll, Mr × Cl, Sm × Lv, Kf × UC and Ln × G18-16). Prior to
executing the framework mapping, heterogeneity of recombination rate between common
marker pairs was tested among all the six core populations by means of the χ2 test available in
Joinmap v. 4. In cases where strong heterogeneity of recombination rates was detected among
populations, a pre-selection of LG from populations with homogeneous recombination rates,
suitable for consensus framework mapping, was carried out. Mapping of the anchor markers
was carried out in Carthagene with the same procedure used for the single component maps.
This procedure produced a robust consensus framework with inter-marker genetic map
distances that were estimated across populations.
The loci that were uniquely mapped in all the six component maps and in the other additional
eight maps were projected by interpolation following the ‘neighbours’ mapping approach
described by Cone et al. [72]. Briefly, the position of a unique locus was projected to the
consensus reference map by interpolation of its relative position from the positions of the two
closest flanking common markers.
Linkage disequilibrium and genetic diversity analysis in a panel of elite
durum wheat
A panel of 183 durum wheat accessions (mainly cvs. and advanced lines) bred for
Mediterranean (Italy, Morocco, Spain, Syria, and Tunisia) countries, Southwestern USA and
Mexico were used to demonstrate the utility of the consensus map for characterizing linkage
disequilibrium (LD) and to carry out a genome-wide association analysis of loci for time to
heading in Mediterranean environments. The collection, hereafter reported as “durum panel”,
was assembled and maintained at the University of Bologna (Italy).
The durum panel was characterized with a core set of SSR and DArT markers, mostly
included in the consensus map. The accessions were profiled with 350 SSRs and the
Triticarte Durum PstI/TaqI representation v. 2.0 Array that yielded 900 high-quality
polymorphic DArT markers. In total, 1,211 markers were suitable for association mapping
(minor allele frequency > 0.10) analysis. Among those, 334 SSR/STS and 623 DArT markers
could be projected onto the consensus linkage map and were used for LD and markerphenotype association tests (957 markers).
The extent of LD between markers and the average LD decay rate in the durum panel were
estimated in TASSEL, v. 3.0 (www.maizegenetics.net, [73]), based on the consensus marker
order and genetic distances. Pairwise LD squared correlation coefficient (r2) values were
estimated for intra-chromosomal (syntenic) loci and were regressed on the pairwise genetic
distances (cM). Significance was computed with 10,000 permutations. The decline of LD
with distance (recombination rate in Morgans) was estimated by fitting the following
equation [74]:
LDi = r 2i − 1/ n = 1/ (α + β ⋅ di ) + ei ,
derived from the relationship between LD r2 values and effective population size (Ne), with β
= kNe:
E ( r 2 ) =1 / (α + kN e c ) + 1 / n
where LDi = (r2 – 1/n) is LD estimate adjusted for chromosome sample size (n). The di is the
distance in Morgans for the syntenic marker pair i as from the consensus map. The c value is
the recombination rate. The k value = 4 for autosomes. For curve fitting the parameter α was
set to 1 (no mutations). Non-linear regression modeling was performed in Lab Fit Curve
fitting software v. 7. The ei residuals were estimated by non-linear fitting of the model; the α
and β parameters were estimated iteratively by least squares method. The 95th percentile of
the LD r2 values distribution between non-syntenic markers was used as a threshold to
distinguish the LD mostly attributable to linkage from the background LD caused by
population structure [75].
Prior knowledge of the population structure of the durum panel was available from a previous
analysis of 96 highly informative and evenly spread SSRs with two complementary cluster
analysis methods: i) a distance-based analysis (NTSys software) and ii) a model-based
quantitative cluster analyses (STRUCTURE software). Results are detailed in Maccaferri et
al. [38] and in Letta et al. [20]. Briefly, the 183 durum accessions were grouped into five
main sub-populations corresponding to breeding lineages as follows: (1) the ICARDA
germplasm bred for the dryland areas, related to the West-Asian local landraces, (2) the
ICARDA germplasm bred for the temperate areas; 3) the Italian and early-period (1970s)
CIMMYT germplasm, (4) the mid-period CIMMYT germplasm (1980s), (5) the late-period
CIMMYT germplasm (from late 1980s onwards). In total, 11, 55, 26, 56 and 35 accessions
were associated to sub-populations 1 to 5, respectively (STRUCTURE analysis).
The genetic variation within and among the five sub-populations was assessed by analysis of
molecular variance (AMOVA) and FST statistics in ARLEQUIN v 3.5 [76], using all the
informative and mapped SSR and DArT marker genotype data available at one level of
population hierarchy. One thousand permutations of accessions within sub-populations were
used to estimate the significance of the differences. To further investigate the differentiation
of sub-populations at the chromosome region level, single locus-AMOVA analysis and FST
statistic calculation was performed for all sub-population pairwise comparisons (ten in total).
Highly significant (P ≤ 0.01) FST values were plotted on the durum consensus map. The
overall polymorphism information content (PIC) index of each locus, after standardization for
the number of alleles [30], was also plotted on the durum consensus map.
Association mapping for heading date across European and Mediterranean
environments
Heading date records of the durum panel were available for 27 field trials carried out in
multiple environments from 2003 to 2007 from (listed by decreasing latitude) Northern to
Southern Italy and Spain for Europe, Tunisia and Morocco for North Africa, Syria and
Lebanon for West Asia and Mexico. Single field trials (hereafter considered as single
environments) were carried out based on a modified augmented design layout with three
replicated checks (by rows and columns) and unreplicated accession plots of 4 m2. All trials
were fall-sown (October to December) and managed according to locally adopted agronomic
practices. Further details are reported in Maccaferri et al. [38]. Phenotypic data were analysed
in SAS statistical package, PROCMIXED procedure, by restricted maximum likelihood
(REML) to fit a mixed model with checks as a fixed effect and unreplicated entries as random
effects, as in Maccaferri et al. [12,38]. The best linear unbiased predictors (BLUPs) from the
random model (REML variance component estimation) were used in AM analyses. The
degree of relationships among environments was studied through an additive main effects and
multiplicative interaction (AMMI) analysis coupled with biplot for visual representation in
Genstat 16.0. Based on the results of the biplot analysis, groups of environments with
uniform patterns of heading date were identified, corresponding to five macro-environmental
areas. A combined analysis was carried out for each of the five main macro-environmental
areas. Adjusted phenotypic means and heritability values (h2) were calculated across
environments for each macro-environmental area. Broad-sense h2 values were calculated
according to the formula:
H 2 = σ 2 g / (σ 2 g + σ 2 gxe / n ) ,
where n is the number of environments.
Genome-wide association scan (GWAS) for loci governing heading date was conducted
using single-environment BLUPs and adjusted means for each of the five macroenvironmental groups in TASSEL program v 4.0 (http://www2.maizegenetics.net/), based on
a compressed mixed linear model (MLM) that included the 5 × 183 Q main population
structure estimate matrix from STRUCTURE analysis of the 96 non-redundant highly
informative SSR markers and a 183 × 183 K kinship matrix (Q + K MLM; [73]). The kinship
matrix was obtained using a simple-matching genetic similarity algorithm to estimate the
identity by state (IBS) portion of the genome between all pairs of accessions [77]. For the
association test, SSR alleles were converted to bi-allelic data and the association was
performed by testing each allele (with MAF > 0.10) vs. the pool of all the other alleles. For
the DArT markers, the test was carried out by contrasting the ‘allele presence’ status vs. the
‘allele absence’ status of two accession pools. Besides the marker-wise significance
calculated based on the F test, the experiment-wise significance was set by counting the
genome-wise number of unique marker linkage blocks; this was accomplished by using the
consensus map order and the LD r2 threshold of 0.3 to define the LD blocks between adjacent
markers (this threshold is widely recognized for partitioning the LD “useful” for mapping in
experimental designs for continuous traits, [53,78,79]). A Bonferroni-corrected, experiment-
wise significance threshold of P ≤ 0.05 was set by taking into account the genome-wide
number of unique linkage blocks.
Because the results of the first run of GWAS analysis showed that control of heading date in
the durum panel was dominated by the two PPD-1 homeologs (Ppd-A1 and Ppd-B1), GWAS
targeted to small-effect loci was repeated with the Ppd-A1 and Ppd-B1 representative markers
included as additional covariates in the mixed linear model. Due to the overall high
stringency of the experiment-wise significance threshold (the Pexp ≤ 0.05 corresponded to a
Pmarker ≤ 0.0001 marker-wise), small-effect QTL regions were also reported for those
markers/chromosome regions with highly significant (P ≤ 0.01) marker-wise associations to
the adjusted phenotypic means of one or more macro-environmental area.
Availability of supporting data
All the supporting data are included as additional files.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MM, SS, MCS, AM, FC, CP, TF, JD, IK, JMC, SX, KA, RT conceived the study,
participated in the design of the experiment and results interpretation, provided the genetic
segregation data. MM, MAC, MCS, MCC performed the consensus mapping analysis. MM,
MAC performed the genetic differentiation and association mapping analysis. GV, RK
provided the genetic segregation data. KA provided phenotypic data. MM drafted the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors thank Dr. Enrico Muzzi (University of Bologna) for the assistance in statistical
analysis. Research supported by the AGER agroalimentare e ricerca – Project “From seed to
Pasta – Multidisciplinary approaches for a more sustainable and high quality durum wheat
production”.
CJP, RK, FC acknowledge the financial support of Agriculture and Agri-Food Canada,
Genome Canada, Genome Prairie, The Saskatchewan Ministry of Agriculture, and the
Western Grains Research Foundation.
References
1. Dubcovsky J, Dvorak J: Genome plasticity a key factor in the success of polyploid
wheat under domestication. Science 2007, 316:1862–1866.
2. Feuillet C, Stein N, Rossini L, Praud S, Mayer K, Schulman A, Eversole K, Appels R:
Integrating cereal genomics to support innovation in the Triticeae. Funct Integr
Genomics 2012, 12:573–583.
3. Gupta PK, Mir RR, Mohan A, Kumar J: Wheat genomics: present status and future
prospects. Int J Plant Genomics 2008, 2008:896451.
4. Röder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW: A
microsatellite map of wheat. Genetics 1998, 149:2007–2023.
5. Varshney RK, Langridge P, Graner A: Application of genomics to molecular breeding
of wheat and barley. Adv Genet 2007, 58:121–155. Review.
6. Blanco A, Bellomo MP, Cenci A, De Giovanni C, D’Ovidio R, Iacono E, Laddomada B,
Pagnotta MA, Porceddu E, Sciancalepore A, Simeone R, Tanzarella OA: A genetic linkage
map of durum wheat. Theor Appl Genet 1998, 97:721–728.
7. Korzun V, Röder MP, Wendehake K, Pasqualone A, Lotti C, Ganal MW, Blanco A:
Integration of dinucleotide microsatellites from hexaploid bread wheat into a genetic
linkage map of durum wheat. Theor Appl Genet 1999, 98:1202–1207.
8. Lotti C, Salvi S, Pasqualone A, Tuberosa R, Blanco A: Integration of AFLP markers
into an RFLP-based map of durum wheat. Plant Breed 2000, 119:393–401.
9. Eujail ME, Sorrells M, Baum P, Wolters W, Powell W: Isolation of EST-derived
microsatellite markers for genotyping the A and B genomes of wheat. Theor Appl Genet
2002, 10:399–407.
10. Maccaferri M, Sanguineti MC, Donini P, Tuberosa R: Microsatellite analysis reveals a
progressive widening of the genetic basis in the elite durum wheat germplasm. Theor
Appl Genet 2003, 107:783–797.
11. Nachit MM, Elouafi I, Pagnotta MA, El Saleh A, Iacono E, Labhilili M, Asbati A, Azrak
M, Hazzam H, Benscher D, Khairallah M, Ribaut JM, Tanzarella OA, Porceddu E, Sorrells
ME: Molecular linkage map for an intraspecific recombinant inbred population of
durum wheat (Triticum turgidum L. var. durum). Theor Appl Genet 2001, 102:177–186.
12. Maccaferri M, Mantovani P, Tuberosa R, Deambrogio E, Giuliani S, Demontis A, Massi
A, Sanguineti MC: A major QTL for durable leaf rust resistance widely exploited in
durum wheat breeding programs maps on the distal region of chromosome arm 7BL.
Theor Appl Genet 2008, 117:1225–1240.
13. Mantovani P, Maccaferri M, Sanguineti MC, Tuberosa R, Catizone I, Wenzl P,
Thompson B, Carling J, Huttner E, DeAmbrogio E, Kilian A: An integrated DArT-SSR
linkage map of durum wheat. Mol Breeding 2008, 22:629–648.
14. Marone D, Del Olmo AI, Laido G, Sillero JC, Emeran AA, Russo MA, Ferragonio P,
Giovanniello V, Mazzucotelli E, De Leonardis AM: Genetic analysis of durable resistance
against leaf rust in durum wheat. Mol Breed 2009, 24:25–39.
15. Peleg Z, Saranga Y, Suprunova T, Ronin Y, Röder MS, Kilian A, Korol AB, Fahima T:
High-density genetic map of durum wheat × wild emmer wheat based on SSR and
DArT markers. Theor Appl Genet 2008, 117:103–115.
16. Somers DJ, Isaac P, Edwards K: A high-density microsatellite consensus map for
bread wheat (Triticum aestivum L.). Theor Appl Genet 2004, 109:1105–1114.
17. Crossa J, Burgueño J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh
RP, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch JH, Ortiz R: Association
analysis of historical bread wheat germplasm using additive genetic covariance of
relatives and population structure. Genetics 2007, 177:1889–1913.
18. Trebbi D, Maccaferri M, de Heer P, Sørensen A, Giuliani S, Salvi S, Sanguineti MC,
Massi A, van der Vossen EA, Tuberosa R: High-throughput SNP discovery and
genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet 2011, 123:555–
569.
19. Marone D, Laidò G, Gadaleta A, Colasuonno P, Ficco DB, Giancaspro A, Giove S, Panio
G, Russo MA, De Vita P, Cattivelli L, Papa R, Blanco A, Mastrangelo AM: A high-density
consensus map of A and B wheat genomes. Theor Appl Genet 2012, 125:1619–1638.
20. Letta T, Maccaferri M, Badebo A, Ammar K, Ricci A, Crossa J, Tuberosa R: Searching
for novel sources of field resistance to Ug99 and Ethiopian stem rust races in durum
wheat via association mapping. Theor Appl Genet 2013, 126:1237–1256.
21. Buerstmayr H, Ban T, Anderson JA: QTL mapping and marker-assisted selection for
Fusarium head blight resistance in wheat: a review. Plant Breed 2009, 128:1–26.
22. Miedaner T, Korzun V: Marker-assisted selection for disease resistance in wheat and
barley breeding. Phytopathology 2012, 102:560–566.
23. Paux E, Sourdille P, Mackay I, Feuillet C: Sequence-based marker development in
wheat: advances and applications to breeding. Biotechnol Adv 2012, 30:1071–1088.
24. Maccaferri M, Sanguineti MC, Corneti S, Ortega JL, Salem MB, Bort J, DeAmbrogio E,
del Moral LF, Demontis A, El-Ahmed A, Maalouf F, Machlab H, Martos V, Moragues M,
Motawaj J, Nachit M, Nserallah N, Ouabbou H, Royo C, Slama A, Tuberosa R: Quantitative
trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across
a wide range of water availability. Genetics 2008, 178:489–511.
25. Maccaferri M, Sanguineti MC, Mantovani P, Demontis A, Massi A, Ammar K, Kolmer J,
Czembor J, Breiman A, Tuberosa R: Association mapping of leaf rust response in durum
wheat. Mol Breed 2010, 26:189–228.
26. Marone D, Russo MA, Laidò G, De Vita P, Papa R, Blanco A, Gadaleta A, Rubiales D,
Mastrangelo AM: Genetic basis of qualitative and quantitative resistance to powdery
mildew in wheat: from consensus regions to candidate genes. BMC Genomics 2013,
14:562.
27. Reimer S, Pozniak CJ, Clarke FR, Clarke JM, Somers DJ, Knox RE, Singh AK:
Association mapping of yellow pigment in an elite collection of durum wheat cultivars
and breeding lines. Genome 2008, 51:1016–1025.
28. Bauer E, Falque M, Walter H, Bauland C, Camisan C, Campo L, Meyer N, Ranc N,
Rincent R, Schipprack W, Altmann T, Flament P, Melchinger AE, Menz M, MorenoGonzález J, Ouzunova M, Revilla P, Charcosset A, Martin OC, Schön CC: Intraspecific
variation of recombination rate in maize. Genome Biol 2013, 14:R103.
29. Saintenac C, Faure S, Remay A, Choulet F, Ravel C, Paux E, Balfourier F, Feuillet C,
Sourdille P: Variation in crossover rates across a 3-Mb contig of bread wheat (Triticum
aestivum) reveals the presence of a meiotic recombination hotspot. Chromosoma 2011,
120:185–198.
30. Maccaferri M, Stefanelli S, Rotondo F, Tuberosa R, Sanguineti MC: Relationships
among durum wheat accessions. I. Comparative analysis of SSR, AFLP, and phenotypic
data. Genome 2007, 50:373–384.
31. Ooijen V: JoinMap ® 4, Software for the calculation of genetic linkage maps in
experimental populations. Wageningen: Kyazma B.V; 2006.
32. de Givry S, Bouchez M, Chabrier P, Milan D, Schiex T: CARHTA GENE:
multipopulation integrated genetic and radiation hybrid mapping. Bioinformatics 2005,
21:1703–1704.
33. Wu Y, Close TJ, Lonardi S: Accurate construction of consensus genetic maps via
integer linear programming. IEEE/ACM Trans Comput Biol Bioinform 2011, 8:381–394.
34. Ronin Y, Mester D, Minkov D, Belotserkovski R, Jackson BN, Schnable PS, Aluru S,
Korol A: Two-phase analysis in consensus genetic mapping. G3 Genes, Genomes,
Genetics 2012, 2:537–549.
35. Blanco A, Colasuonno P, Gadaleta A, Mangini G, Schiavulli A, Simeone R, Digesù AM,
De Vita P, Mastrangelo AM, Cattivelli L: Quantitative trait loci for yellow pigment
concentration and individual carotenoid compounds in durum wheat. J Cereal Sci 2011,
54:255–264.
36. Patil RM, Oak MD, Tamhankar SA, Sourdille P, Rao VS: Mapping and validation of a
major QTL for yellow pigment content on 7AL in durum wheat (Triticumturgidum L.
ssp. durum). Mol Breeding 2008, 21:485–496.
37. Paux E, Sourdille P, Salse J, Saintenac C, Choulet F, Leroy P, Korol A, Michalak M,
Kianian S, Spielmeyer W, Lagudah E, Somers D, Kilian A, Alaux M, Vautrin S, Bergès H,
Eversole K, Appels R, Safar J, Simkova H, Dolezel J, Bernard M, Feuillet C: A physical
map of the 1-gigabase bread wheat chromosome 3B. Science 2008, 322:101–104.
38. Maccaferri M, Sanguineti MC, Demontis A, El-Ahmed A, del Moral Garcia L, Maalouf
F, Nachit M, Nserallah N, Ouabbou H, Rhouma S, Royo C, Villegas D, Tuberosa R:
Association mapping in durum wheat grown across a broad range of water regimes. J
Exp Bot 2011, 62:409–438.
39. Sved JA: Linkage disequilibrium and homozygosity of chromosome segments in
finite populations. Theor Popul Biol 1971, 2:125–141.
40. Wilhelm EP, Turner AS, Laurie DA: Photoperiod insensitive Ppd-A1a mutations in
tetraploid wheat (Triticum durum Desf.). Theor Appl Genet 2009, 118:285–294.
41. Griffiths S, Simmonds J, Leverington M, Wang Y, Fish L, Sayers L, Alibert L, Orford S,
Wingen L, Herry L, Faure S, Laurie D, Bilham L, Snape J: Meta-QTL analysis of the
genetic control of ear emergence in elite European winter wheat germplasm. Theor Appl
Genet 2009, 119:383–395.
42. Hanocq E, Laperche A, Jaminon O, Lainé AL, Le Gouis J: Most significant genome
regions involved in the control of earliness traits in bread wheat, as revealed by QTL
meta-analysis. Theor Appl Genet 2007, 114:569–584.
43. Hanocq E, Niarquin M, Heumez E, Rousset M, Le Gouis J: Detection and mapping of
QTL for earliness components in a bread wheat recombinant inbred lines population.
Theor Appl Genet 2004, 110:106–115.
44. Bennett D, Izanloo A, Edwards J, Kuchel H, Chalmers K, Tester M, Reynolds M,
Schnurbusch T, Langridge P: Identification of novel quantitative trait loci for days to ear
emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.)
population adapted to southern Australian conditions. Theor Appl Genet 2012, 124:697–
711.
45. Le Gouis J, Bordes J, Ravel C, Heumez E, Faure S, Praud S, Galic N, Remoué C,
Balfourier F, Allard V, Rousset M: Genome-wide association analysis to identify
chromosomal regions determining components of earliness in wheat. Theor Appl Genet
2012, 124:597–611.
46. Bonnin I, Rousset M, Madur D, Sourdille P, Dupuits C, Brunel D, Goldringer I: FT
genome A and D polymorphisms are associated with the variation of earliness
components in hexaploid wheat. Theor Appl Genet 2008, 116:383–394.
47. Yan L, Fu D, Li C, Blechl A, Tranquilli G, Bonafede M, Sanchez A, Valarik M, Yasuda
S, Dubcovsky J: The wheat and barley vernalization gene VRN3 is an orthologue of FT.
Proc Natl Acad Sci U S A 2006, 103:19581–19586.
48. Hudson CJ, Freeman JS, Kullan AR, Petroli CD, Sansaloni CP, Kilian A, Detering F,
Grattapaglia D, Potts BM, Myburg AA, Vaillancourt RE: A reference linkage map for
eucalyptus. BMC Genomics 2012, 13:240.
49. Khan MA, Han Y, Zhao YF, Troggio M, Korban SS: A multi-population consensus
genetic map reveals inconsistent marker order among maps likely attributed to
structural variations in the apple genome. PLoS One 2012, 7:e47864.
50. Maccaferri M, Sanguineti MC, Noli E, Tuberosa R: Population structure and longrange linkage disequilibrium in a durum wheat elite collection. Mol Breed 2005, 15:271–
290.
51. Peng J, Korol AB, Fahima T, Röder MS, Ronin YI, Li YC, Nevo E: Molecular genetic
maps in wild emmer wheat, Triticum dicoccoides: genome-wide coverage, massive
negative interference, and putative quasi-linkage. Genome Res 2000, 10:1509–1531.
52. Haudry A, Cenci A, Ravel C, Bataillon T, Brunel D, Poncet C, Hochu I, Poirier S,
Santoni S, Glémin S, David J: Grinding up wheat: a massive loss of nucleotide diversity
since domestication. Mol Biol Evol 2007, 24:1506–1517.
53. Ardlie KG, Kruglyak L, Seielstad M: Patterns of linkage disequilibrium in the human
genome. Nat Rev Genet 2002, 3:299–309.
54. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA: Selecting a
maximally informative set of single-nucleotide polymorphisms for association analyses
using linkage disequilibrium. Am J Hum Genet 2004, 74:106–120.
55. Somers DJ, Banks T, DePauw R, Fox S, Clarke J, Pozniak C, McCartney C: Genomewide linkage disequilibrium analysis in bread wheat and durum wheat. Genome 2007,
50:557–567.
56. Chao S, Dubcovsky J, Dvorak J, Ming-Cheng L, Baenziger SP, Matnyazov R, Clark DR,
Talbert LE, Anderson JA, Dreisigacker S, Glover K, Chen J, Campbell K, Bruckner PL,
Rudd JC, Haley S, Carver BF, Perry S, Sorrells ME, Akhunov ED: Population-and genomespecific patterns of linkage disequilibrium and SNP variation in spring and winter
wheat (Triticum aestivum L.). BMC Genomics 2010, 11:727.
57. Cavanagh CR, Chao S, Wang S, Huang BE, Stephen S, Kiani S, Forrest K, Saintenac C,
Brown-Guedira GL, Akhunova A, See D, Bai G, Pumphrey M, Tomar L, Wong D, Kong S,
Reynolds M, da Silva Lopez M, Bockelman H, Talbert L, Anderson JA, Dreisigacker S,
Baenziger S, Carter A, Korzun V, Morrell PL, Dubcovsky J, Morell MK, Sorrells ME,
Hayden MJ, et al: Genome-wide comparative diversity uncovers multiple targets of
selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad
Sci U S A 2013, 110:8057–8062.
58. Pasam RK, Sharma R, Malosetti M, van Eeuwijk FA, Haseneyer G, Kilian B, Graner A:
Genome-wide association studies for agronomical traits in a world wide spring barley
collection. BMC Plant Biol 2012, 12:16.
59. Bost B, de Vienne D, Moreau L, Dillmann C: Genetic and nongenetic bases for the Lshaped distribution of quantitative trait loci effects. Genetics 2001, 157:1773–1787.
60. Brachi B, Faure N, Horton M, Flahauw E, Vazquez A, Nordborg M, Bergelson J, Cuguen
J, Roux F: Linkage and association mapping of Arabidopsis thaliana flowering time in
nature. PLoS Genet 2010, 6:e1000940.
61. Reif JC, Gowda M, Maurer HP, Longin CFH, Korzun V, Ebmeyer E, Bothe R, Pietsch C,
Würschum T: Association mapping for quality traits in soft winter wheat. Theor Appl
Genet 2011, 122:961–970.
62. Salvi S, Castelletti S, Tuberosa R: An updated consensus map for flowering time
QTLs in maize. Maydica 2009, 54:501–512.
63. Wang WY, Barratt BJ, Clayton DG, Todd JA: Genome-wide association studies:
theoretical and practical concerns. Nat Rev Genet 2005, 6:109–118.
64. van Poecke RM, Maccaferri M, Tang J, Truong HT, Janssen A, van Orsouw NJ, Salvi S,
Sanguineti MC, Tuberosa R, van der Vossen EA: Sequence‐‐based SNP genotyping in
durum wheat. Plant Biotechnol J 2013, 11:809–817.
65. Salvi S, Corneti S, Bellotti M, Carraro N, Sanguineti MC, Castelletti S, Tuberosa R:
Genetic dissection of maize phenology using an intraspecific introgression library. BMC
Plant Biol 2011, 11:4.
66. Graziani M, Maccaferri M, Royo C, Salvatorelli F, Tuberosa R: QTL dissection of yield
components and morpho-physiological traits in a durum wheat elite population tested in
contrasting thermo-pluviometric conditions. Crop and Pasture Science 2014, 65:80–95.
67. Maccaferri M, Francia R, Ratti C, Rubies-Autonell C, Colalongo C, Ferrazzano G,
Tuberosa R, Sanguineti MC: Genetic analysis of Soil-borne cereal mosaic virus response
in durum wheat: evidence for the role of the major quantitative trait locus QSbm.ubo2BS and of minor quantitative trait loci. Mol Breed 2012, 29:973–988.
68. Zhang W, Chao S, Manthey F, Chicaiza O, Brevis JC, Echenique V, Dubcovsky J: QTL
analysis of pasta quality using a composite microsatellite and SNP map of durum wheat.
Theor Appl Genet 2008, 117:1361–1377.
69. Somers DJ, Fedak G, Clarke J, Cao W: Mapping of FHB resistance QTLs in tetraploid
wheat. Genome 2006, 49:1586–1593.
70. Chu C-G, Friesen TL, Chao S, Faris JD, Zhong SB, Xu SS: Novel tan spot resistance
QTLs detected using an SSR-based linkage map of tetraploid wheat. Mol Breed 2010,
25:327–338.
71. Gadaleta A, Giancaspro A, Giove SL, Zacheo S, Mangini G, Simeone R, Signorile A,
Blanco A: Genetic and physical mapping of new EST-derived SSRs on the A and B
genome chromosomes of wheat. Theor Appl Genet 2009, 118:1015–1025.
72. Cone KC, McMullen MD, Bi IV, Davis GL, Yim Y-S, Gardiner JM, Pol-acco ML,
Sanchez-Villeda H, Fang Z, Schroeder SG, Havermann SA, Bowers JE, Paterson AH,
Soderlund CA, Engler FW, Wing RA, Coe EH: Genetic, physical and informatics
resources for maize: on the road to an integrated map. Plant Physiol 2002, 130:1598–
1605.
73. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut
BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES: A unified mixed-model method for
association mapping that accounts for multiple levels of relatedness. Nat Genet 2006,
38:203–208.
74. Rexroad CE III, Vallejo RL: Estimates of linkage disequilibrium and effective
population size in rainbow trout. BMC Genet 2009, 10:83.
75. Breseghello F, Sorrells ME: Association mapping of kernel size and milling quality in
wheat (Triticum aestivum L.) cultivars. Genetics 2006, 172:1165–1177.
76. Excoffier L, Laval G, Schneider S: Arlequin (version 3.0): an integrated software
package for population genetics data analysis. Evol Bioinform Online 2007, 1:47–50.
77. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E: Efficient
control of population structure in model organism association mapping. Genetics 2008,
178:1709–1723.
78. Pritchard JK, Przeworski M: Linkage disequilibrium in humans: Models and data. Am
J Hum Genet 2001, 69:1–14.
79. Lawrence R, Day-Williams AG, Mott R, Broxholme J, Cardon LR, Zeggini E:
GLIDERS–a web-based search engine for genome-wide linkage disequilibrium between
HapMap SNPs. BMC Bioinformatics 2009, 10:367.
Additional files
Additional_file_1 as PPTX
Additional file 1: Figure S1. Representations of the consensus linkage map and of the six
core component maps. Graphical representation of the consensus linkage map and of the six
core linkage maps used to produce the consensus framework. The consensus linkage map is
on the right side of the linkage graphics. Anchor (common) markers are in red font. Markers
common to three or more maps are interconnected by dashed lines.
Additional_file_2 as XLSX
Additional file 2: Table S1. List of the markers included in the consensus linkage map. The
markers included in the consensus interpolated map are reported together with their
chromosome and map distances. Anchor (common) markers are highlighted in bold. Unique
(singleton) markers projected onto the consensus from single specific maps are reported
together with the indication of the specific maps (cell background colours).
Additional file 3: Figure S2. Projection plots of the six core component maps on the
consensus. The core component linkage group maps are reported in the X axes of the
projection plots while the consensus map is reported in the Y axes. Spearman rank
correlations are reported as ro (ρ) coefficients.
Additional file 4: Figure S3A. Projection plots of the tetraploid wheat consensus map on the
hexaploid SSR reference map (Ta-SSR-2004)
Additional file 5: Figure S3B. Projection plots of the tetraploid wheat consensus map on the
tetraploid mappreviously reported by Marone et al. [19]
Additional file 6: Figure S4. Detailed pairwise linkage disequilibrium plots, distribution of
loci with highly significant Fst differentiation patterns and of the polymorphism index
content values of SSR and DArT® markers along the tetraploid wheat consensus map, as
estimated from the elite durum wheat germplasm accessions from world-wide. Pairwise
linkage disequilibrium (LD) patterns are shown for the squared coefficients of determination
(r2) values between loci used to profile a panel of 183 elite durum accessions and mapped on
the consensus map (left side of chromosome bars). Loci with highly significant FST
differentiation values between durum sub-population groups are reported on the right side of
the chromosome bars, as long with the locus polymorphism index content (PIC) plots,
normalized for allele number.
Additional_file_7 as DOCX
Additional file 7: Table S2. GWAS results for heading date in the elite Durum Panel across
27 Mediterranean and Mexican environments using Ppd-A1 and Ppd-B1 representative
markers as covariates in the mixed linear model. Marker-trait associations, chromosome
locations and significance intervals based on the consensus map, P-values and (R2)
coefficients of determination for the chromosome regions harboring QTLs significant for
heading date adjusted means from field experiments conducted on five different macroenvironment areas (Southern Europe with 10 field experiments [environments], North Africa
(Tunisia) with 2 envs., West Asia with 8 envs., North Africa (Morocco) with 2 envs. and
Mexico with 5 envs.). A QTL was declared when a marker-wise highly significant effect (P ≤
0.01) was detected on the adj. means for at least one macro-environmental area. R2 values are
reported in brackets next to the P-values. The number of single environments where the
QTLs showed significant effects is also reported for each macro-area. Macro-area are sorted
from left to right based on decreasing latitude.
Linkage disequilibrium (LD) esƟmate (r2 value)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Figure 1
50
100
150
200
250
LD esƟmate (r2 value)
0
Figure 2
0.1-0.5 0.5-1
5-10 10-20 20-30
1-5
Inter-marker distance (cM)
30-40
40-50
> 50
Genotypic to phenotypic variance
determination coefficient (R2 %)
15.0
40.0
Ppd-A1
12.5
Ppd-B1
10.0
30.0
7.5
20.0
5.0
10.0
2.5
0.0
Figure 3
0.0
Latitude (°)
Latitude (°)
Chr. 1B
Chr. 1A
-10
0
10
QTL1
20
30
40
50
gdm33-1A
cfa2153-1A
gwm33-1A
wPt-731846-1A wPt-665375-1A wPt-730187-1A
gwm136-1A#
wPt-2527-1A# wPt-7296-1A wPt-0196-1A
kbo-0228-1A wPt-0595-1A
wPt-665864-1A
wPt-6564-1A
wPt-6280-1A# wPt-665724-1A wPt-1924-1A
cfd15-1A
wPt-6709-1A wPt-5876-1A 377889-1A
wPt-1167-1A kbo-0287-1A
wPt-8770-1A
wPt-9752-1A
wPt-3870-1A#
wPt-4177-1A# wPt-729972-1A wPt-667180-1A
wPt-731843-1A wPt-671698-1A
wPt-6882-1A wPt-4735-1A
kbo-0301-1A
tPt-5413-1A
wmc818-1A
wmc336-1A
wPt-734027-1A wPt-731476-1A
wmc95-1A#
wmc329-1A cfa2158-1A
wmc24-1A# barc148-1A barc1054-1A
wPt-3698-1A
gwm772-1A
barc119-1A
wmc469-1A# wmc333-1A cfd59-1A
barc145-1A cfd30-1A
gwm469-1A
wPt-4886-1A# wPt-5411-1A
gwm164-1A# wmc183-1A wmc826-1A
wmc120-1A
wPt-731018-1A
Chr. 2A
wPt-0983-1B
gwm550-1B wPt-0308-1B
0
10
20
QTL4
QTL5
barc128b-1B
gwm33-1B gwm264-1B
wmc49-1B
wPt-2654-1B
gwm374-1B#
wmc85a-1B#
wPt-8627-1B wPt-3007-1B wPt-0655-1B#
wPt-5006-1B
wPt-6592-1B# wPt-3753-1B# wPt-5347-1B# 372481-1B 372596-1B
wPt-741980-1B
tPt-5413-1B
wPt-1139-1B
barc128-1B
wPt-2949-1B wPt-7129-1B
barc312-1B
kbo-0016-1B
wPt-0655-1B
wPt-8744-1B
wPt-1981-1B
cfa2158b-1B
wPt-4726-1B# wPt-8971-1B#
wPt-3889-1B
wPt-741749-1B tPt-7183-1B
wPt-3582-1B wPt-740789-1B wPt-4605-1B wPt-668069-1B wPt-741676-1B
wPt-731196-1B wPt-2999-1B tPt-8831-1B
wmc406-1B
wPt-2597-1B wPt-3819-1B
wPt-9767-1B
gwm1100-1B
ksuE18-1B wmc193-1B
wPt-8716-1B
kbo-0014-1B kbo-0103-1B
wPt-733882-1B
wPt-8682-1B
wPt-1684-1B
wmc500a-1B#
kbo-0312-1B
wmc230-1B# barc8-1B#
gwm752a-1B
wPt-5899-1B wPt-4729-1B wPt-1876-1B
barc119-1B
wPt-0974-1B
wPt-1911-1B wPt-9864-1B kbo-0386-1B wmc619-1B
gwm413-1B#
wPt-1374-1B
wPt-4133-1B wPt-6325-1B
gwm11-1B
wPt-2694-1B
gwm273-1B
wmc626-1B cfd59-1B barc137-1B wmc546-1B barc120-1B
wPt-11532-1B
wPt-9283-1B
wmc85b-1B#
cfd65-1B
wPt-4728-1B
gwm903-1B
gwm18-1B wmc269-1B barc23-1B
gwm926-1B
BE500714_237-1B
barc240-1B# BE443797_436-1B
gwm1120-1B
barc187-1B
gwm759-1B
gwm762-1B
gwm947-1B# wmc500b-1B#
wmc31-1B
ksum28-1B
ubc812.1-1B
wPt-8168-1B
gwm456-1B wmc156-1B
kbo-0303-1B
kbo-0082-1B
wPt-6832-1B
wPt-2389-1B
gwm131-1B#
barc181-1B# gwm24-1B
wPt-3451-1B#
barc302-1B
wmc694-1B
wmc216-1B
wmc419-1B wPt-5485-1B wPt-3566-1B
wmc664-1B
wPt-9937-1B
wPt-11513-1B
gwm582b-1B
gdm101-1B
psr162-1B
wPt-5235-1B
wPt-6042-1B
kbo-0425-1B
cfa2129-1B# GluB1-1B
barc162-1B
gpw93013-1B
barc61-1B#
wPt-6690-1B# wPt-8280-1B
wPt-0506-1B# wPt-0202-1B wPt-3227-1B
cfd48-1B#
wPt-3579-1B wPt-0705-1B
CA651264-1B
ksum219-1B
wPt-5011-1B
gwm806-1B# wmc134-1B
rPt-7906-1B#
kbo-0203-1B
wPt-4465-1B
wPt-1247-1B# wPt-8251-1B wPt-2675-1B
wPt-9409-1B# wPt-1818-1B#
ksum76a-1B
tPt-7980-1B
gwm153-1B#
barc81-1B# kbo-0175-1B gwm274-1B
kbo-0199-1B wPt-2257-1B kbo-0200-1B
BE419757-1B
CA679329B-1B
wPt-1403-1B
gwm268-1B#
gwm124-1B# barc188-1B# wPt-0459-1B
gwm544-1B
gwm735-1B#
wPt5061-1B
wPt5907-1B
TC81688-1B
wPt4688-1B
rPt5412-1B
30
QTL6
0
20
30
cfa2129-1A#
ubc835a-1A
wPt-6505-1A wPt-0011-1A wPt-8882-1A
kbo-0434-1A
wPt-11558-1A
wPt-4658-1A#
dupw38-1A#
BE443588_204-1A
gwm633-1A
cfa2147-1A# barc213-1A# wmc716-1A#
306333-1A
CA594434-1A
TC91645-1A
cfa2219-1A# wmc254-1A#
gwm497-1A# gwm99-1A#
wPt-4399-1A
wmc59-1A
50
20
gwm71a-2A#
30
60
wPt-11572-1A
kbo-0128-1A
barc158b-1A
barc158a-1A#
gwm750-1A#
gwm1139-1A# barc211-1A
barc287-1A
barc17-1A
70
80
90
100
110
120
QTL8
130
QTL11
QTL12
0
10
20
QTL10
wmc177-2A#
Ppd-A1-2A
wmc602-2A
cfa2201-2A#
gwm1198-2A#
wPt-7026-2A#
30
40
40
50
PPD-B1
wmc453-2A
wmc522-2A#
50
gwm1115-2A
60
70
80
gwm122-2A# wmc296-2A#
gwm4-2A
gwm339-2A#
barc309-2A
gwm275-2A#
gwm515-2A
gwm95-2A# gwm249-2A
gwm425-2A wmc51cd-2A
wPt-8621-2A
wPt-6274-2A
gwm372-2A# wPt-1203-2A cfa2164-2A barc353-2A gwm630-2A
kbo-0375-2A kbo-0067-2A kbo-0062-2A kbo-0045-2A kbo-0381-2A
gwm1045-2A# barc10-2A
cfa2263-2A# gdm101-2A
gwm558-2A
wPt-8826-2A
gwm71b-2A# barc220-2A
gwm473-2A
wmc401-2A
gwm304-2A
wPt-8216-2A
gwm328-2A
TC81096-2A
60
70
80
90
90
100
100
wmc382-2B
wPt-3517-2B
wPt-8737-2B
wPt-0100-2B
kbo-0190-2B
wmc764-2B
wmc661-2B#
gwm210-2B# wmc489-2B#
wPt-7970-2B# 117413-2B barc124-2B
kbo-0089-2B# wPt-5738-2B
gwm614-2B
barc35-2B# kbo-0090-2B
wPt-1842-2B
wPt-3592-2B
wPt-2106-2B#
wPt-671778-2B
wPt-3547-2B
wPt-1601-2B
kbo-0171-2B# kbo-0196-2B
wPt-9958-2B
wPt-11501-2B
wmc25a-2B
wmc257-2B
wmc154-2B
barc10-2B
wmc213-2B
wPt-7666-2B
wmc243-2B# wPt-9169-2B
tPt-4627-2B 758366-2B
wPt-8404-2B# wmc25-2B
wmc112-2B
wPt-4664-2B
wPt-11518-2B
wPt-7932-2B
wPt-8097-2B
wPt-3561-2B#
wPt-6932-2B
wPt-7158-2B# wPt-9668-2B
barc318-2B
wPt-743545-2B wPt-5513-2B
wPt-5788-2B#
wPt-4195-2B
gwm1198-2B
wPt-2600-2B# wPt-7320-2B
kbo-0304-2B kbo-0305-2B
wPt-5672-2B#
wPt-5556-2B# wPt-7757-2B# wPt-4125-2B# wPt-6192-2B wmc770-2B
wPt-6199-2B# gwm429-2B# cfa2201-2B
wmc597-2B BQ168780_769-2B
BM140538_39-2B
wPt-671945-2B
wPt-8583-2B# wPt-8492-2B kbo-0013-2B wmc453-2B
wPt-0615-2B wPt-1064-2B#
wPt-2120-2B
wmc35-2B cnl158-2B barc7-2B
gwm410-2B# kbo-0380-2B# kbo-0119-2B
wPt-666389-2B
wPt-2314-2B# gwm148-2B#
gwm128-2B
wPt-0408-2B# wPt-7779-2B
wPt-6477-2B#
barc183-2B#
wPt-9316-2B#
wPt-0724-2B# wPt-666987-2B
wmc434-2B
barc13-2B
barc55-2B# barc40-2B
kbo-0350-2B
cfd11-2B
wmc27-2B
wmc474-2B
0
10
wmc746-2A cfa2043-2A
0
QTL15
10
20
0
QTL16
gwm779-3A#
30
barc321-3A
10
20
barc45-3A#
wmc50-3A cfa2163-3A#
wmc505-3A#
gwm5-3A#
gwm1620-3A#
wmc388-3A
wmc625-3A barc356-3A barc359-3A gwm4-3A
wmc651-3A gwm32-3A#
gwm666-3A gwm674-3A cfa2234-3A#
gwm133-3A#
wmc664-3A#
barc67-3A
gwm720-3A
40
QTL17
QTL18
50
wmc489-3A
barc324-3A
cfa2134-3A
30
40
50
60
gwm1159-3A#
gwm10-3A
gwm1121-3A
wPt-8104-3A
kbo-0159-3A
wPt-2202-3A wPt-5786-3A
cfd6-3A
60
kbo-0061-3A
ksm28-3A
wPt-5886-3A
wmc428-3A#
70
wmc264-3A
cfa2262-3A#
wPt-6218-3A wPt-4105-3A
wPt-5084-3A
barc197-3A
wPt-4939-3A
kbo-0235-3A#
wPt-11619-3A#
wPt-2756-3A#
wPt-1119-3A# wPt-4859-3A wPt-8362-3A
80
QTL19
90
70
tPt-7209-3A wPt-5943-3A wPt-6956-3A
wPt-3133-3A
gwm374-2B
barc230-2B
wmc272-2B barc1027-2B
wPt-2664-2B
wmc477-2B
gwm55a-2B
wmc265-2B#
gwm55b-2B
barc91-2B
gwm1177-2B
barc167-2B
barc169-2B
100
80
wPt-1092-3A
wPt-3816-3A# wPt-2659-3A# wPt-1562-3A#
gwm497-3A
cfa2193-3A# barc69-3A#
barc206-3A wmc559-3A
110
90
wPt-11559-3A
kbo-0001-2A kbo-0173-2A
110
wmc153-3A
wmc102-2B gwm55-2B
kbo-0033-2B
gwm294-2A
-20
100
barc128-2B
gwm155-3A
cfa2170-3A wmc516-3A
gwm1217-3A
120
130
110
wPt-0015-2A wPt-3865-2A
0
10
20
130
gwm1070-2A
wPt-1320-2A# kbo-0161-2A#
gwm1256-2A
wPt-1108-2A
gdm93-2A# gwm526-2A#
cfd251-2A#
wPt-9793-2A# wPt-9277-2A#
kbo-0222-2A
cfa2086-2A#
gwm311-2A# gwm1136-2A
ksum193-2A
wPt-1615-2A wPt-9797-2A# wPt-2858-2A wPt-9586-2A
wmc658-2A
wPt-3286-2A wmc445-2A wPt-7649a-2A
wPt-7739-2A# wPt-665889-2A wPt-667895-2A wPt-744006-2A wPt-799683-2A
wPt-664140-2A
gwm265-2A# wPt-5887-2A# wPt-0825-2A# wPt-4861-2A wPt-5850-2A
tPt-2163-2A wPt-669355-2A wPt-741201-2A
barc122-2A# wPt-741584-2A wPt-800788-2A
wPt-3281-2A
wPt-1499-2A# wPt-2696-2A
wPt-8403-2A
wPt-7649b-2A
cfd50-2A
tPt-6105-2A#
140
150
160
wmc441-2B gwm120-2B cfa2043-2B
wPt-1140-2B#
gwm388-2B
barc101-2B#
kbo-0145-2B#
gwm1249-2B#
wPt-3042-2B wmc16-2B
wPt-2430-2B# wPt-8697-2B
wmc175-2B# wPt-0794-2B wPt-669681-2B wPt-730172-2B gwm16-2B
wPt-0215-2B
wPt-8569-2B wPt-0094-2B
wPt-0906-2B wPt-7317-2B
kbo-0431-2B
wPt-11586-2B
gwm47-2B
wPt-11561-2B
wPt-8460-2B# wPt-3929-2B wPt-800425-2B wPt-800707-2B
gwm877-2B
wPt-1294-2B wPt-9654-2B wPt-0189-2B wPt-8340-2B
wPt-9350-2B#
wPt-0489-2B#
wPt-3651-2B
wPt-1646-2B
wPt-7305-2B
wPt-0694-2B
gwm4947-2B
wPt-5242-2B
kbo-0333-2B#
gwm1300-2B#
wmc332-2B#
wPt-3755-2B
gpw4043-2B
wPt-9257-2B wPt-0697-2B
gwm1070-2B# wPt-4223-2B
wPt-5989-2B#
wPt-9190-2B
wPt-7004-2B# wPt-2415-2B wPt-0510-2B
QTL14
170
130
140
150
150
160
QTL20
wPt-9160-3A# barc1177-3A# wPt-0398-3A# wPt-9626-3A
wPt-3612-3A
gwm1229-3A barc86-3A
170
180
190
200
200
220
Chr. 5B
20
30
40
Chr. 6A
0
wPt-9006-5B
gwm443-5B#
0
wPt-6577-5B
QTL30
10
117474-5B
wPt-1261-5B
gwm234-5B#
rPt-6127-5B
10
TC91851-5A
barc186-5A
barc303-5A#
gpw3003-5A gpw3049-5A
barc117-5A# gwm304-5A# gwm293-5A# wmc150-5A barc358-5A
barc56-5A wmc705-5A wmc183-5A gwm415-5A
wmc752-5A
gwm129-5A
wmc96-5A# barc1-5A
barc180-5A#
BJ262177-5A
gwm425-5A
gpw358-5A
gpw7218-5A
wPt-5751-5A
kbo-0293-5A
barc165-5A#
cfa2121-5A
barc40-5A
gwm156-5A#
barc360-5A
barc141-5A
20
30
wmc376-5B
wPt-5514-5B
gwm112-5B
wPt-7114-5B wPt-6692-5B wPt-1753-5B
gwm1180-5B
20
wPt-1951-5B
40
wPt-6825-5A wPt-4248-5A wPt-2543-5A
gwm186-5A
50
50
gwm1236-5A#
barc197-5A#
wPt-4677-5B
barc128-5B#
gwm843-5B
wPt-3663-5B
barc74-5B# wPt-6902-5B
gwm213-5B#
gwm335-5B#
gwm1108-5B
30
40
wmc759-5B
dupw205-5B#
wmc405-5B
gwm371-5B# barc331-5B
gwm639-5B
wmc537-5B#
60
50
gwm831-5B
wmc415-5B
60
70
gwm499-5B#
BE495277_339-5B
60
gwm554-5B
70
80
gwm639-5A
80
cfd2-5A
barc330-5A
gwm617-5A
wmc415-5A
wPt-7201-5A# tPt-0353-5A
wPt-9094-5A
gwm6a-5A
wmc415a-5A
gwm1043-5B
QTL32
80
100
wPt-3457-5B
gwm777-5B
100
110
barc151-5A#
ksum024-5A#
wmc388-5A gwm666-5A
kbo-0192-5A
gwm1570-5A# gpw2243-5A
wPt-730410-5A
kbo-0227-5A
VRN1-5A
kbo-0348-5A
120
130
barc142-5A
barc319-5A
cfa2185-5A barc355-5A BG607308_101-5A
cfa2141-5A# barc232-5A wmc160-5A
cfa2163-5A
kbo-0426-5A# kbo-0069-5A
kbo-0298-5A wPt-744643-5A
wmc110-5A#
gpw2328-5A
wPt-0498-5B#
110
QTL31
120
130
140
150
140
cfd39-5A
wPt-8262-5A#
wPt-0152-5A wPt-5135-5A wPt-5231-5A
150
QTL27
QTL28
QTL29
tPt-3719-5B
wPt-6022-5B
wPt-3661-5B
wPt-11526-5A
QTL26
wmc415b-5B
kbo-0077-5B kbo-0435-5B
70
90
90
QTL25
160
160
kbo-0184-5A
wPt-4043-5A# wmc577-5A
gwm6b-5A
gwm126-5A# gwm179-5A
gwm595-5A#
tPt-6495-5A
tPt-4184-5A
wmc727-5A# wmc524-5A# cfa2149-5A
wPt-733910-5A
gwm291-5A wPt-732802-5A cfa2110-5A
dupw043-5A
QTL21
210
30
gwm397-4A
Lp-A3-4A
20
QTL22
wPt-7145-3B
ksum157-3B#
wmc291-3B
gwm108-3B
gwm853-3B
wPt-0384-3B# wPt-0036-3B#
wPt-9577-3B# wPt-6785-3B
rPt-8252-3B
wPt-8480-3B
wPt-0766-3B
wPt-664981-3B wPt-672088-3B wPt-5358-3B
wPt-7968-3B
wPt-3678-3B
wPt-3725-3B
wPt-2698-3B#
wPt-9049-3B# cfa2170-3B# wPt-733571-3B
gwm705-3B
barc84-3B# wPt-11691-3B
wPt-8363-3B
wmc326-3B#
wPt-8752-3B
wPt-11049-3B wPt-6436-3B
wPt-2491-3B#
wPt-0345-3B
tPt-3621-3B
wPt-9635-3B wPt-1472-3B barc77-3B
wPt-0990-3B
wPt-4401-3B
wPt-6956-3B wPt-741748-3B tPt-7209-3B
cfd9-3B
wPt-4194-3B#
wPt-5947-3B# wPt-8559-3B# wPt-666802-3B tPt-1093-3B
wPt-5943-3B
gwm1266-3B
gwm299-3B
wPt-3342-3B
wPt-3480-3B wPt-8959-3B 304574-3B wPt-7705-3B wPt-0531-3B
wPt-9289-3B wPt-9786-3B
wPt-4991-3B
wPt-9488-3B# wPt-9989-3B wPt-665392-3B
wPt-0668-3B
wPt-0665-3B
wPt-0751-3B
wPt-3085-3B
kbo-0226-3B
wPt-1020-3B# kbo-0392-3B wPt-4412-3B
wPt-1151-3B#
wPt-5072-3B
wPt-0244-3B
wPt-3165-3B wPt-2172-3B
wPt-7340-3B
wPt-0343-3B
wPt-2416-3B
gwm340-3B# wmc274-3B# gwm247-3B# tPt-4541-3B psr454-3B
gwm114-3B gwm547-3B
wPt-0280-3B# wPt-8352-3B wPt-5825-3B wPt-8983-3B wPt-8396-3B
wPt-1822-3B wPt-8764-3B wPt-6961-3B
gwm181-3B# wmc632-3B cnl62-3B
wmc617-4A cfd257-4A
wmc650-4A
170
180
wPt-0566-5B wPt-0142-5B wPt-6971-5B
tPt-3714-5B
190
barc144-5B
wPt-0295-5B wPt-0054-5B# wPt-7279-5B wPt-5429-5B
wPt-2208-5B
wPt-4723-5B
0
QTL34
10
20
30
40
QTL35
50
60
QTL36
70
QTL37
QTL38
100
80
90
wPt-2014-6A
wPt-2823-6A
tPt-4209-6A#
wmc553-6A#
gwm570-6A
wmc179-6A#
barc353-6A#
100
barc351-6A
gwm1017-6A
110
QTL39
110
wPt-7572-6A
QTL33
gwm169-6A#
120
130
140
BE483091_472-6A
cfa2114-6A#
gwm617-6A#
kbo-0129-6A# kbo-0336-6A
wmc580-6A#
gwm427-6A#
gwm1089-6A# wPt-0502-6A
wPt-2632-6A kbo-0137-6A
kbo-0242-6A
wPt-732328-6A wPt-9474-6A wPt-731854-6A
wPt-3650-6A#
wPt-4229-6A
kbo-0091-6A kbo-0317-6A
wPt-732741-6A
dupw167-6A# wPt-1661-6A
wPt-5654-6A
wPt-0358-6A wPt-1642-6A
wPt-731203-6A
wPt-3247-6A# wPt-9000-6A
wPt-4663-6A
wPt-5696-6A
wPt-8124-6A
tPt-3786-6A
wPt-8373-6A wPt-11612-6A
tPt-7761-6A
wPt-7655-6A wPt-6678-6A tPt-6661-6A wPt-11668-6A wPt-2877-6A
wPt-7204-6A
QTL40
120
130
140
150
QTL41
gwm613-6B# ksm45-6B
wPt-0351a-6B# wPt-2133-6B
wPt-3116-6B# wPt-2689-6B# wPt-6994-6B# wPt-3304-6B# wPt-0245-6B#
wPt-8239-6B# wPt-4678-6B# wPt-7662-6B wPt-6497-6B wPt-0882-6B
wPt-0151-6B wPt-3800-6B
wPt-8641-6B
wPt-7203-6B kbo-0250-6B wPt-6282-6B
wPt-4031-6B
wPt-9990-6B# wPt-3130-6B# wPt-4720-6B# wPt-5383-6B# wPt-4386-6B#
wPt-2714-6B# wPt-7343-6B
wPt-5673-6B
wPt-3424-6B wPt-0073-6B wPt-1725-6B
wPt-0939-6B
kbo-0204-6B
wmc486-6B#
wmc297-6B
wPt-733609-6B wPt-4564-6B wPt-6594-6B
wPt-4900-6B#
wPt-6916-6B
wPt-743200-6B
wPt-7954-6B
wPt-2095-6B wPt-3376-6B wPt-3526-6B
wPt-1437-6B#
wPt-1241-6B
wPt-5256-6B# wPt-8814-6B
rPt-1040-6B wPt-1185-6B
wPt-7935a-6B
wPt-7748-6B
wPt-664250-6B wPt-730563-6B
wPt-6017-6B# wPt-8153-6B# wPt-7795-6B wPt-5838-6B
wPt-6207-6B
wPt-6292-6B
tPt-3506-6B tPt-4887-6B wPt-7576-6B wPt-4858-6B
wPt-1830-6B
wPt-0470-6B wPt-2297-6B
wPt-8264-6B
wPt-11542-6B
wPt-9209-6B kbo-0421-6B wPt-0383-6B
wPt-0554-6B#
cfd13-6B
wPt-11554-6B
TC85035-6B
wPt-5408-6B wPt-8412-6B
Gli-B2-6B
wPt-3309-6B#
wPt-5333a-6B# tPt-2451-6B wPt-3606-6B
tPt-C214-6B
gwm132-6B#
gwm390-6B
BJ236800-6B
gwm705-6B
wPt-6435-6B
wPt-4742-6B# wPt-666224-6B CA741546-6B
gwm508-6B# wPt-9667-6B wPt-744396-6B kbo-0347-6B wPt-669130-6B
wPt-3409-6B gwm768-6B
wPt-5333b-6B#
wmc597-6B
wPt-7540-6B
TC85303-6B
wPt-11560-6B
cfd0001-6B
wPt-8194-6B# wPt-7846-6B# wPt-2800-6B kbo-0337-6B wPt-0052-6B
TC85037-6B
wmc494-6B#
TC65966-6B
wPt-8783-6B
barc14-6B# gdm113-6B
TC84481-6B
wPt-3666-6B
gwm518-6B#
wPt-6247-6B# wPt-4893-6B barc68-6B
TC80528-6B
wPt-8721-6B#
barc1169-6B
wPt-7489-6B
barc101-6B#
BJ213673-6B
wmc398-6B#
barc136-6B
CA594434-6B
gwm191-6B
gwm58-6B
TC101037-6B
wPt-2000-6B wPt-8560-6B
wPt-2424-6B
barc198-6B# gwm88-6B wmc744-6B wmc737-6B wmc397-6B
barc146-6B gwm608-6B gwm133-6B
gwm816-6B# kbo-0291-6B
wmc105-6B BE604119_469-6B
gwm193-6B# gwm361-6B
cnl64-6B wmc265-6B
wPt-11589-6B
gwm771-6B
gwm107-6B
wPt-7935b-6B
wPt-8976-6B wPt-1364-6B wPt-4062-6B
wmc748-6B
wPt-11556-6B
wmc39-6B
barc125b-6B
tPt-162-6B
wmc182-6B
wmc608-6B
wPt-8086-6B
wPt-7129-6B
rPt-5537-6B wPt-6889-6B wPt-733958-6B
barc354-6B# gpw3241-6B R8/R12-190-6B
wmc539-6B
barc79-6B#
gwm907-6B# Ptokin1/R23-300-6B
RlkF/R3-230-6B
R9/R18-370-6B
wPt-1730-6B
gpw5076-6B
gwm626-6B
gwm1016-6B
gwm1682-6B#
barc223-6B
tPt-3689-6B wPt-2498-6B
wPt-8554-6B# wPt-8508-6B
barc24-6B#
COS34-6B
mag779-6B mag934-6B
R15/R12-1300-6B R15/R36-850-6B
wPt-5451-6B
R1/R19-280-6B
gwm1076-6B#
gwm889-6B# barc178-6B
gwm219-6B# kbo-0399-6B
wPt-3581-6B
kbo-0169-6B
dupw124-6B
barc125a-6B
wPt-5270-6B
wPt-0501-6B
R35/R13-160-6B
wPt-1761-6B wPt-9241-6B
wPt-1890-6B
barc134-6B# Ptokin1/R2-400-6B
gwm1486-6B
wmc621-6B#
wPt-9256-6B
COS13-6B wPt-4662-6B
R15/RR22-1100-6B
R15/R36-600-6B R15/R18-750-6B
wPt-3191-6B
wPt-6116-6B
wPt-9589-6B
BG274882-6B
wPt-7464-6B wPt-1325-6B
wPt-2162-6B# wPt-0696-6B# tPt-9048-6B# wPt-7443-6B wPt-5480-6B
wPt-4560-6B
0
QTL42
10
20
30
barc155a-4A wmc517-4A
DuPw004-4A
60
QTL43 FT=VRN3
70
wPt-4660-4A gwm565-4A gwm269-4A
dupw4-4A ksm29-4A
barc155-4A#
wPt-3435-4A
gwm265-4A# wmc258-4A#
gwm637-4A
60
90
gwm161-4A
50
70
80
90
100
110
140
Projected QTL confidence intervals (CIs) from
previous studies:
Meta-QTLs, hexaploid wheat,
Hanocq et al. (2006)
Griffiths et al. (2009
AM-QTL, hexaploid wheat,
Le Gouis et al. (2012)
QTL, durum wheat,
Maccaferri et al. (2008)
Chr. 7B
gwm569-7B
gwm471-7A
wPt-742051-7A
wPt-4625-7A# tPt-6794-7A# wPt-5257-7A# wPt-9496
wPt-6184-7A# wPt-744373-7A wPt-744631-7A wPt-73
wPt-666456-7A wPt-0002-7A wPt-4617-7A wPt-4993-7
wPt-7830-7A
wPt-8418-7A wPt-0744-7A kbo-0218-7A
312293-7A
wPt-5092-7A#
wPt-6273-7A#
wPt-3836-7A# wPt-7767-7A# wPt-1076-7A# wPt-417
wPt-1931-7A
wPt-6959-7A
cfd62-7A
wPt-1441-7A
wPt-2056-7A#
wPt-3619-7A
wPt-6668-7A# wPt-9207-7A# wPt-742244-7A wPt-965
wPt-740561-7A
wPt-9404-7A wPt-2799-7A
wPt-730913-7A
wPt-3135-7A# kbo-0139-7A# wPt-0393-7A wPt-5964wPt-3648-7A
barc70-7A#
wPt-6824-7A
gwm735-7A
wmc479-7A# gwm1187-7A
0
10
20
30
QTL46
wPt-9170-7A wPt-732044-7A wPt-664393-7A wPt-701
wPt-5105-7A
barc246-7A
40
50
cfa2049-7A
wPt-7188-7A# gwm60-7A# wPt-4299-7A
wPt-9314-7A
wPt-744074-7A wPt-1163-7A#
wPt-11672-7A
gwm130-7A
wPt-6846-7A
wPt-7214-7A#
wPt-9796-7A# wPt-3883-7A# wPt-7734-7A#
barc154-7A#
kbo-0166-7A
wmc283-7A# 377900-7A
wmc179-7A#
wPt-7785-7A
cfa2028-7A#
wPt-9926-7A# wPt-2321-7A
kbo-0294-7A
kbo-0364-7A
60
70
80
wmc83-7A
wmc405-7A#
wmc826-7A
cfa2147-7A
barc174-7A# kbo-0420-7A
cfa2174-7A
kbo-0066-7A
gwm1065-7A
barc282-7A
90
100
gwm573-7A
110
110
120
130
140
wPt-0321-7A
wmc745-7A
barc1034-7A
wPt-11547-7A tPt-1755-7A
barc165-7A
BE471272_393-7A barc180-7A BQ169669_378-7A
wPt-0321-7A
barc231-7A wmc9-7A
wmc596-7A wmc603-7A wmc422-7A
rPt-0950-7A
kbo-0158-7A wPt-7299-7A
gwm871a-7A
barc231a-7A
gwm1083-7A
wmc65-7A#
barc29-7A
wPt-8399-7A wPt-6842-7A
barc121-7A
gwm4-7A
wmc488-7A#
130
140
150
QTL47
wmc607-7A
wmc107-7A
dupw254-7A
150
160
wPt-9555-7A
gwm276-7A# wPt-6217-7A
wPt-4877-7A#
wPt-11507-7A
cfa2123-7A#
barc292-7A
170
160
wPt-5949-7A
Projected QTL confidence intervals (CIs) from
previous studies:
Hanocq et al. (2006)
Griffiths et al. (2009
AM-QTL, hexaploid wheat,
Le Gouis et al. (2012)
QTL, durum wheat,
Maccaferri et al. (2008)
Figure 4
QTL48
170
QTL45
180
wPt-7053-7A#
QTL49
QTL50
180
190
200
210
220
wPt-0961-7A wPt-4831-7A
wmc633-7A
gwm332-7A
wPt-3425-7A
wPt-6013-7A wPt-5558-7A
cfa2019-7A
gwm282-7A
wPt-7281-7A gwm550-7A
wPt-667038-7A wPt-733079-7A
wPt-6768-7A
gpw4100-7A
gpw2338-7A
gwm942-7A
wPt-4665-7A
wPt-11553-7A
wPt-8534-7A
wPt-3403-7A wPt-1976-7A
wPt-3782-7A wPt-7122-7A
cfa2040-7A
gwm344-7A# wmc273-7A# kbo-0172-7A
cfa2257-7A
wPt-744897-7A
BJ262177B-7A
ubw1-7A
gwm1061-7A# wPt-8365-7A# gwm698-7A
cfa2240-7A
wPt-1429-7A
wPt-7577-7A
wPt-6872-7A
cfd20-7A wPt-6219-7A
ubw32-7A
TC92445-7A
mag3433-7A ubw19-7A
wPt-0745-7A wPt-7763-7A
barc5-4B
kbo-0331-4B# kbo-0059-4B# kbo-0237-4B wPt-664166-4B kbo-0170-4B
wPt-6209-4B
wmc692-4B
pds1-4B
kbo-0379-4B wmc471-4B
cfd22a-4B
wPt-2214-4B
wPt-8092-4B
gwm1084-4B#
kbo-0346-4B
rPt-6847-4B#
kbo-0236-4B#
cfd39-4B#
wPt-7500-4B wPt-0230-4B
wPt-8291-4B#
wPt-3255-4B# wPt-11493-4B wPt-9223-4B wmc652-4B
barc60-4B# tPt-9147-4B wPt-7858-4B wPt-730435-4B wPt-729837-4B
wPt-733082-4B
wPt-8543-4B
wPt-3190-4B
wPt-9625-4B
wmc679-4B
gwm6-4B#
gwm930-4B
wmc349-4B
wPt-5990-4B
wPt-664164-4B wPt-664459-4B
wPt-7412-4B
kbo-0099-4B gwm155-4B
wmc47-4B# cfd54-4B#
dupw43-4B
gwm538-4B
kbo-0142-4A kbo-0198-4A kbo-0214-4A
wmc336-4A
60
wmc283-4A
wPt-2247-4A#
wmc718-4A
wPt-1550-4A wPt-2946-4A wPt-4240-4A
wPt-1262-4A wPt-3401-4A wPt-7558-4A#
wPt-1584-4A#
wPt-11573-4A
wPt-1091-4A
311417-4A wPt-7939-4A#
wPt-6728-4A
rPt-7285-4A wPt-3810-4A#
wPt-9414-4A
70
wPt-2542-4A#
wPt-3239-4A wPt-5249-4A# wPt-7289-4A
wPt-11529-4A
barc343-4A
kbo-0233-4A
wPt-1701-4A
wmc262-4A# barc84-4A
cfd31-4A#
wPt-3655-4A wPt-7926-4A
barc135-4A# gwm1251-4A
wmc232-4A
wPt-9551-4A
gwm274-4A
80
wPt-2129-4A
cfa2149-4B
wPt-0105-4A wPt-3766-4A wPt-6214-4A wPt-9833-4A
wPt-5749-4A
wPt-4596-4A#
kbo-0058-4A
wPt-7193-4A wPt-3584-4A
barc70-4A
kbo-0376-4A
kbo-0306-4A wPt-3596-4A
wmc500-4A
wPt-1743-4A
wPt-0538-4A
wPt-8144-4A
wPt-731406-4A
wPt-0668-4A wPt-2331-4A# wPt-9162-4A wPt-9196-4A#
wPt-667406-4A wPt-7168-4A
wPt-2780-4A wPt-2794-4A# wPt-2985-4A# wPt-3108-4A# wPt-3796-4A#
wPt-669319-4A wPt-731311-4A wPt-8167-4A# wPt-8271-4A# wPt-8886-4A#
wPt-9183-4A
gwm1694-4A
wPt-5099-4A wPt-9305-4A cfd30-4A
wPt-5055-4A
kbo-0357-4A
kbo-0356-4A wPt-6502-4A# wPt-7821-4A#
wPt-3506-4A# wPt-4241-4A
wPt-3793-4A barc78-4A#
TC85050-4A
wPt-3449-4A#
wPt-0798-4A
barc327-4A
wPt-7354-4A#
wPt-663849-4A
psr573a-4A wmc624-4A
wPt-744404-4A
wPt-3876-4A
wPt-666551-4A
kbo-0096-4A kbo-0150-4A
wPt-4634-4A
wPt-1155-4A# wPt-4424-4A# wPt-9418-4A# wmc646-4A wPt-9150-4A
wPt-5434-4A wPt-0833-4A
wmc776-4A
wmc219-4A#
psr573b-4A wmc722-4A
wPt-0992-4A wPt-664252-4A wPt-669103-4A wPt-743193-4A wPt-743510-4A
wPt-744818-4A
wPt-1007-4A wPt-2836-4A wPt-3729-4A# wPt-5544-4A wPt-6390-4A#
wPt-669526-4A wPt-732309-4A
wPt-0764-4A wPt-7876-4A# wPt-8657-4A# wPt-8784-4A wPt-9059-4A
wmc723-4A
wPt-8171-4A
barc52-4A wmc313-4A# wPt-0560-4A wPt-2151-4A# wPt-2371-4A
wPt-731166-4A wPt-7891-4A wPt-8139-4A
wPt-0763-4A wPt-6176-4A
rPt-0238-4A# wPt-5172-4A#
wPt-11661-4A#
wPt-667136-4A wPt-743738-4A
wPt-9103-4A wPt-9598-4A
100
120
gwm192-4B# gwm165-4B# wmc657-4B
wPt-2273-4B
wPt-2994-4B
gwm251-4B barc227-4B gwm375-4B gwm149-4B
barc236-4A
wmc161-4A# wPt-6515-4A#
wPt-0162-4A
kbo-0125-4A
BE446304_110-4B
gwm3072-4B
gwm368-4B# kbo-0086-4B
gwm856-4B#
barc20-4B#
wPt-8892-4B#
gwm888-4B barc125-4B
wPt-3991-4B
wPt-5334-4B wPt-5497-4B
tPt-5342-4B wPt-0915-4B
wPt-8134-4B
wmc89-4B# wmc48-4B# ksm62-4B
psp3030-4B
barc1045-4B barc1054-4B gdm61-4B
wPt-5655-4B
kbo-0041-4B
tPt-7156-4B
wPt-0872-4B# wPt-4931-4B
wPt-3101-4B tPt-0602-4B wPt-3908-4B
dupw23-4B
barc337-4B
gwm113-4B
gwm540-4B BE443190_94-4B BE443253_414-4B
wPt-1491-4B
barc340-4B#
wmc238-4B
gwm513-4B#
barc1174-4B
gwm781-4B
gwm495-4B#
40
wPt-7355-4A#
80
QTL44
30
gwm894-4A# wPt-8443-4A
wPt-2221-4A
50
40
50
QTL23
40
Chr. 7A
Chr. 6B
wmc710-4B#
barc193-4B#
wmc617-4B
gwm1278-4B# kbo-0064-4B#
gwm857-4B
10
barc344-3B
-10
90
wPt-8054-5B# wPt-5928-5B# wPt-3763-5B# wPt-5604-5B#
cfd7-5B
wPt-6989-5B wPt-4367-5B
wmc75-5B
gwm408-5B#
wPt-5896-5B# wPt-3030-5B wPt-6014-5B
wPt-3049-5B
wPt-2707-5B wPt-6191-5B
barc275-5B
gwm604-5B gdm133-5B
wmc593-5B
wPt-5803-5B
barc140-5B
barc308-5B#
rPt-7889-5B
kbo-0309-5B
wmc160-5B# wPt-3329-5B
tPt-1253-5B
barc142-5B# gwm1246-5B# kbo-0117-5B wmc99-5B
barc232-5B# gdm116-5B
wmc235-5B
wPt-8931-5B wPt-3076-5B wPt-664788-5B
wPt-11579-5B
barc349-5B wmc118-5B
wPt-8125-5B#
wPt-6880-5B#
wPt-1907-5B
cfd86-5B
311891-5B
wPt-3213-5B
kbo-0220-5B
gwm1016b-5B
wmc640-5B
wPt-7006-5B
barc59-5B
gdm101-5B
wPt-7400-5B
gwm814-5B
CD373602_310-5B
gwm497-5B#
wPt-11578-5B
wPt-0837-5B
wPt-0927-5B#
wPt-667015-5B barc243b-5B gwm118-5B
barc243a-5B#
wPt-2829-6A wPt-9832-6A
wPt-0689-6A wPt-3524-6A
wPt-5395-6A# wPt-8443-6A
wPt-9382-6A wPt-4907-6A
wPt-741904-6A wPt-1742-6A wPt-666927-6A wPt-5633-6A wPt-6656
wPt-731861-6A
wPt-7475-6A# wPt-667740-6A
tPt-6710-6A wPt-669271-6A
wPt-669247-6A
wPt-671987-6A
wPt-732683-6A wPt-1285-6A wPt-9687-6A
wPt-730977-6A wPt-2870-6A wPt-732176-6A wPt-732730-6A wPt-73
wPt-732075-6A
wPt-7330-6A
wPt-732504-6A
wPt-4017-6A
wPt-4016-6A# wPt-6520-6A# wPt-4270-6A wPt-7938-6A wPt-3468-6
wPt-2153-6A
gwm459-6A#
wPt-2426-6A wPt-0864-6A
gwm334-6A#
wPt-8006-6A
wPt-9572-6A
wPt-4589-6A
wPt-5964-6A
wPt-7663-6A
gwm719-6A
wPt-7486-6A
kbo-0136-6A wPt-0259-6A
kbo-0132-6A kbo-0130-6A kbo-0131-6A
wPt-2822-6A
wPt-5652-6A wPt-7616-6A
rPt-9065-6A tPt-2833-6A wPt-6904-6A tPt-0877-6A
wPt-3965-6A wPt-7623-6A
barc23-6A#
kbo-0109-6A wmc630-6A gwm1573-6A
ksum98-6A
gwm1009-6A#
kbo-0079-6A
wPt-8833-6A wPt-3091-6A kbo-0383-6A wPt-0357-6A
gwm1040-6A
wPt-7599-6A wPt-3202-6A
kbo-0053-6A
wPt-9584-6A
wPt-9829-6A wPt-3043-6A
gwm1296-6A
barc3-6A
wPt-2077-6A
psp3009.3-6A
wPt-0139-6A
wPt-731442-6A
wPt-5834-6A
gwm82-6A
barc37-6A
barc171-6A
barc195-6A
barc146-6A#
gwm132-6A# cfd80-6A barc1071-6A
gwm786-6A#
BF483631-6A
gwm4675-6A
wmc748-6A
barc118-6A
gwm356-6A#
barc1077-6A
barc107-6A
barc1165-6A
BJ261821-6A
cfa2164-6A
wmc163-6A
barc113-6A#
wmc256-6A#
gwm1150-6A#
wPt-2525-4B
gwm1320-4B
LpxB1_1-4B
rht-B1-4B
gwm610-4A
20
wPt-3524-6B wPt-0689-6B
wPt-6048-5A
QTL24
gwm1354-2B
wmc659-2B gwm526-2B gdm93-2B
gwm4020-2B
gwm4888-2B
gwm1399-2B
CK162551-2B
gwm4110-2B gwm1027-2B gpw4474-2B gwm1325-2B
CJ577969-2B
304741-2B
gwm846-2B
BG313362-2B
wPt-6522-2B# wPt-3701-2B
wPt-0049-2B# wPt-3378-2B wPt-6973-2B wPt-8916-2B tPt-1366-2B
gwm4828-2B#
rPt-6122-2B wPt-2135-2B# wPt-7360-2B# wPt-3705-2B wPt-5009-2B
wPt-2724-2B
wPt-6643-2B
wPt-2929-2B# wPt-2502-2B# wPt-4892-2B#
gdm87-2B
wPt-0749-2B wPt-0891-2B
BF474250-2B
kbo-0367-2B
wmc361-2B# barc159-2B# kbo-0080-2B kbo-0081-2B wPt-4426-2B
wPt-3632-2B wmc167-2B
wmc445-2B
wPt-6894-2B
cfd50-2B
0
gwm695-4A
130
180
kbo-0254-1B#
tPt-6091-1B
wPt-1973-1B#
wPt-4651-1B
wPt-1770-1B wPt-1313-1B
kbo-0407-1B
wPt-6142-1B# wPt-3950-1B# wPt-734314-1B
wPt-5915-1B# kbo-0260-1B wPt-2180-1B wPt-2968-1B wPt-2384-1B
wPt-4497-1B kbo-0135-1B wPt-3954-1B
gwm1139-1B
wmc728-1B#
kbo-0211-1B kbo-0051-1B
210
10
gwm192-4A
cfd283-3B
190
gwm443-5A#
cfa2187-5A
gwm154-5A#
gwm1574-5A# barc316-5A#
wPt-6964-5A
cfa2104-5A
wmc654-5A
wmc489-5A#
wPt-6892-4B
gwm165-4A#
140
barc51-3A wPt-6422-3A
wPt-5125-3A#
tPt-7492-3A# wPt-5133-3A#
cfa2076-3A#
gwm1038-3A#
kbo-0180-3A
wPt-1036-3A
wPt-8876-3A
wPt-4545-3A# wPt-3978-3A
wPt-1888-3A#
wPt-0147-3A wPt-0549-3A
tPt-6376-3A# wPt-732237-3A
wPt-4584-3A
barc1113-3A#
wPt-8203-3A# wPt-5658-3A
kbo-0092-3A#
wPt-9393-4B tPt-5519-4B
-10
wPt-5173-3A#
120
Chr. 4B
gwm1093-4A#
10
kbo-0290-3A
-10
wmc44-1B#
wPt-5217-1B
wmc830-1B
wPt-4702-1B
wPt-4361-1B
Chr. 5A
0
barc115-3B#
wPt-1339-3A
wPt-2622-2B wPt-744181-2B
0
cft132-3B
wPt-7264-3B
gwm389-3B# cfp3236-3B
cfp132-3B
wPt-11419-3B
gwm1034-3B# kbo-0075-3B#
cft5057-3B cft5014-3B
wPt-7984-3B
cft5034-3B# cft5008-3B cft5055-3B cft5021-3B cft5006-3B
cft5005-3B
cft5000-3B#
wPt-2426-3B
tPt-1079-3B wPt-667640-3B
barc147-3B
wPt-9303-3B
cft5036-3B# 305799-3B
barc133-3B#
wPt-666738-3B wPt-742222-3B wPt-10291-3B
kbo-0416-3B
wPt-3260-3B wPt-744251-3B
cfb6008-3B umn10-3B cfb6011-3B
cfb6060-3B
cfb6018-3B cfp5075-3B
cfb6057-3B cfb6116-3B cfb6127-3B cfb6058-3B cfp5080-3B
cfb6059-3B cfb6045-3B wPt-2757-3B wPt-1081-3B
cfb6043-3B cfb6128-3B cfp5078-3B
cfb6021-3B
cfp78-3B cfp60-3B
cs-ssr7-3B
gwm493-3B# BF293133_75-3B
gpw7774-3B
wPt-3536-3B wPt-0302-3B
cfa2226-3B
gpw3248-3B
cfd79-3B#
barc102-3B#
ksum76b-3B
barc218-3B
wPt-0267-3B
wPt-4842-3B
wPt-5473-3B
wmc500-3B
wPt-8828-3B
ksm45-3B
wPt-0250-3B
kbo-0402-3B
wmc808-3B
wPt-1691-3B wPt-0086-3B
wPt-2830-3B
wPt-9088-3B wPt-2720-3B
wPt-731754-3B wPt-8140-3B
wmc43-3B# wmc78-3B
wPt-730063-3B
wPt-2766-3B
wPt-8297-3B
wPt-6474-3B
wPt-7472-3B
wPt-6239-3B wPt-2143-3B wPt-8592-3B
wPt-2013-3B wPt-4476-3B wPt-1191-3B
wPt-1612-3B
CA499601b-3B
tPt-1366-3B barc75-3B wPt-6973-3B
wPt-6235-3B rPt-3056-3B wPt-6604-3B
wPt-1159-3B# wPt-10130-3B
wPt-10530-3B
rPt-5396-3B
gwm779-3B
wPt-6945-3B#
wmc505-3B# wmc540-3B wmc777-3B
wmc612-3B
wPt-11570-3B
wPt-5390-3B# wPt-11295-3B
wmc762-3B wmc533-3B gwm144-3B
gwm420-3B
wPt-9432-3B# wPt-9510-3B#
cfd4-3B
gwm762-3B
gwm685-3B# gwm107-3B
gwm77-3B wmc643-3B
wmc625-3B# barc73-3B gwm285-3B wmc689-3B wmc693-3B
cfd219-3B
gwm376-3B
gwm1015-3B#
gwm582c-3B
wPt-6047-3B kbo-0335-3B cfa2134-3B
wPt-5939-3B
gwm540b-3B
wmc1-3B
wPt-0446-3B wPt-0544-3B
barc203-3B
wmc527-3B barc268-3B
gwm1029-3B
gwm1005-3B
barc164-3B#
wPt-0212-3B
wmc418-3B#
gwm131-3B
gwm802-3B#
120
120
cfa2147-1B kbo-0034-1B
150
Chr. 4A
Chr. 3B
barc310-3A wPt-7485-3A 304419-3A
wPt-3094-3A# kbo-0026-3A
wPt-1331-3A wPt-0546-3A wPt-4480-3A
barc57-3A# wPt-9634-3A wPt-0419-3A wPt-2810-3A
wPt-0714-3A wPt-745076-3A
wPt-6204-3A barc294-3A
wPt-3348-3A wPt-1923-3A wPt-1652-3A
wPt-9369-3A# wPt-6854-3A# wPt-7992-3A#
barc12-3A#
wPt-7955-3A
wPt-1111-3A wPt-2938-3A wPt-1655-3A
wPt-9222-3A wPt-2729-3A
gwm369-3A wmc532-3A
QTL13
cfa2219-1B
140
Chr. 3A
Chr. 2B
barc1138-2A
barc212-2A#
wmc407-2A#
barc124-2A
gwm636-2A# wPt-7049-2A#
wPt-4197-2A# tPt-1041-2A# wPt-4533-2A# kbo-0102-2A# wPt-5647-2A#
wPt-2401-2A kbo-0395-2A
wPt-6245-2A# kbo-0295-2A kbo-0010-2A wPt-744759-2A wPt-741412-2A
wPt-667588-2A
kbo-0351-2A
barc128-2A
wmc667-2A
cfd36-2A
wPt-666658-2A
kbo-0247-2A# wmc630-2A
wmc25b-2A
wPt-9624-2A# wPt-7626-2A#
wPt-4021-2A
wmc25-2A
tPt-5584-2A
wmc636-2A
wPt-5839-2A
QTL9
QTL7
40
50
10
PPD-A1
QTL2
QTL3
40
10
0
190
200
wPt-9027-7B wPt-8660-7B
372439-7B wPt-2283-7B
wPt-8211-7B# wPt-0837-7B
wPt-4843-7B#
wPt-7975-7B# wPt-5846-7B# wPt-3147-7B#
wPt-5283-7B
barc1005-7B# barc1068-7B
wPt-0276-7B
wmc323-7B# wmc606-7B
barc279-7B
gwm169-7B
wPt-7318-7B
wmc597-7B
cfa2114-7B
CA594434-7B
gwm537-7B#
dupw167-7B
gwm400-7B#
wmc405-7B BJ239878-7B
wPt-7064-7B
gwm951-7B
wPt-671900-7B wPt-664965-7B
ari4-7B
wmc182-7B#
gwm573-7B# wmc426-7B
kbo-0093-7B
kbo-0406-7B
gwm1184-7B#
gwm3019-7B#
wmc546-7B
kbo-0029-7B kbo-0152-7B
gwm46-7B# barc23-7B
wPt-8273-7B
wPt-2737-7B#
barc231d-7B
barc72-7B# wmc758-7B
gwm297-7B#
wmc218-7B
wPt-11565-7B
barc267-7B wmc662-7B wmc476-7B
gwm871-7B
gwm1173-7B
gwm540a-7B
ubc807.1-7B
gwm333-7B#
gwm897-7B
kbo-0366-7B
wPt-7934-7B# wPt-6498-7B#
gwm983-7B
kbo-0389-7B#
kbo-0255-7B
wPt-740752-7B
BF474379_496-7B
wmc396-7B#
TC74583-7B
barc176-7B# kbo-0146-7B cfa2174b-7B
cfa2106-7B
wPt-11598-7B
barc278-7B#
wPt-2305-7B# wPt-3730-7B#
BE498985_42-7B
wPt-8919-7B#
wmc195-7B
wPt-0920-7B
wPt-3271-7B
wPt-11511-7B
wPt-4025-7B# gwm112-7B
wPt-4323-7B wPt-1817-7B wPt-0980-7B
wPt-9665-7B
gwm361b-7B
wPt-6162-7B
cfd6-7B
gwm132-7B# kbo-0019-7B
wPt-2308-7B
gwm767-7B
wmc517-7B#
wPt-1330-7B wPt-3533-7B
barc315-7B# kbo-0390-7B kbo-0209-7B barc258-7B
wPt-5922-7B
wPt-9987-7B
wPt-5892-7B wPt-7887-7B
wPt-5975-7B
wPt-8689-7B
kbo-0087-7B
wPt-5343-7B# wPt-8615-7B
kbo-0372-7B
wPt-2668-7B#
wPt-4869-7B# wPt-7351-7B# tPt-7362-7B# wPt-4298-7B# wPt-4010-7B#
TC69382-7B tPt-5747-7B
rPt-3887-7B
wPt-8417-7B#
wPt-4814-7B#
wPt-4045-7B
wPt-8233-7B
wPt-11630-7B
gwm6-7B
gwm611-7B#
wPt-744652-7B
wPt-3086-7B# wPt-4300-7B#
gpw1045-7B
barc10b-7B
gwm577-7B#
wPt-6104-7B# wPt-7295-7B#
wPt-3939-7B# wPt-8938-7B
TC88833-7B
wPt-6156-7B#
gwm783-7B#
wPt-5646-7B
wPt-8040-7B gdm36-7B
wPt-8007-7B
wmc276-7B#
wmc273-7B# wmc581-7B
cfa2040-7B# wPt-7720-7B kbo-0325-7B
barc1073-7B
wPt-5228-7B# wPt-1957-7B# wPt-5462-7B# barc32-7B# wPt-1075-7B
gpw3112-7B
barc50-7B
TC70722-7B
barc182-7B# wPt-744987-7B wPt-3004-7B wPt-11540-7B barc82-7B
gwm344b-7B wmc500-7B barc1162-7B
wPt-0530-7B
wPt-665293-7B
wPt-5138-7B
gwm1061-7B
wPt-2449-7B# wPt-0884-7B# wPt-8387-7B# wPt-7108-7B# barc340-7B#
ubw14-7B
gwm146-7B#
gpw8090-7B# mag1811-7B
ubw22-7B
Psy-B1-7B#
gwm344a-7B#
wPt-7387-7B
wPt-4038-7B# wPt-9515-7B wPt-0504-7B wmc10-7B ubw-1085-7B
wPt-5585-7B
wPt-0020-7B
wPt-7219-7B# tPt-3700-7B# wPt-6869-7B# wPt-2592-7B wPt-2878-7B
wPt-5377-7B wPt-3439-7B wPt-4743-7B
wPt-4259-7B
wPt-9992-7B
wPt-304724-7B
ubw15-7B
ubw18-7B# ubw19-7B ubw31-7B
ubw26-7B
wPt-6276-7B wPt-0477-7B
cfa2257-7B#
wPt-5677-7B
Additional files provided with this submission:
Additional file 1: 1407458002130799_add1.pptx, 2956K
http://www.biomedcentral.com/imedia/9331105051444514/supp1.pptx
Additional file 2: 1407458002130799_add2.xlsx, 83K
http://www.biomedcentral.com/imedia/2044927835144451/supp2.xlsx
Additional file 7: 1407458002130799_add7.docx, 46K
http://www.biomedcentral.com/imedia/1767860873144451/supp7.docx

A consensus framework map of durum wheat Triticum durum

Transcription

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