Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Exploring and Mobilizing the Gene Bank Biodiversity for Wheat Improvement

  • Deepmala Sehgal,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Prashant Vikram,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Carolina Paola Sansaloni,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Cynthia Ortiz,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Carolina Saint Pierre,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Thomas Payne,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Marc Ellis,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Ahmed Amri,

    Affiliation: International Centre for Agricultural Research in the Dry Areas (ICARDA), Bashir El Kassar Street, Verdun, Beirut, 1108–2010, Lebanon

  • César Daniel Petroli,

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

  • Peter Wenzl,

    Affiliation: DivSeek, Global Crop Diversity Trust, Platz der Vereinten Nationen, 53113, Bonn, Germany

  • Sukhwinder Singh

    suk.singh@cgiar.org

    Affiliation: International Maize and Wheat Improvement Center (CIMMYT), Km. 45 Carretera México-Veracruz, Colonia El Batán, Texcoco, Edo. de México, CP, 56130, Mexico

Exploring and Mobilizing the Gene Bank Biodiversity for Wheat Improvement

  • Deepmala Sehgal, 
  • Prashant Vikram, 
  • Carolina Paola Sansaloni, 
  • Cynthia Ortiz, 
  • Carolina Saint Pierre, 
  • Thomas Payne, 
  • Marc Ellis, 
  • Ahmed Amri, 
  • César Daniel Petroli, 
  • Peter Wenzl
PLOS
x

Abstract

Identifying and mobilizing useful genetic variation from germplasm banks to breeding programs is an important strategy for sustaining crop genetic improvement. The molecular diversity of 1,423 spring bread wheat accessions representing major global production environments was investigated using high quality genotyping-by-sequencing (GBS) loci, and gene-based markers for various adaptive and quality traits. Mean diversity index (DI) estimates revealed synthetic hexaploids to be genetically more diverse (DI= 0.284) than elites (DI = 0.267) and landraces (DI = 0.245). GBS markers discovered thousands of new SNP variations in the landraces which were well known to be adapted to drought (1273 novel GBS SNPs) and heat (4473 novel GBS SNPs) stress environments. This may open new avenues for pre-breeding by enriching the elite germplasm with novel alleles for drought and heat tolerance. Furthermore, new allelic variation for vernalization and glutenin genes was also identified from 47 landraces originating from Iraq, Iran, India, Afghanistan, Pakistan, Uzbekistan and Turkmenistan. The information generated in the study has been utilized to select 200 diverse gene bank accessions to harness their potential in pre-breeding and for allele mining of candidate genes for drought and heat stress tolerance, thus channeling novel variation into breeding pipelines. This research is part of CIMMYT’s ongoing ‘Seeds of Discovery’ project visioning towards the development of high yielding wheat varieties that address future challenges from climate change.

Introduction

Grain production needs to be doubled to feed an increasing world population which is estimated to reach approximately 9 billion by 2050 [1]. The existing trends in wheat yield increase are inadequate to meet this projected demand [2]. Bread wheat (Triticum aestivum subsp. aestivum) is one of the most important crops providing one-fifth of the total calories for the world’s population. Breeding gains rely on access to useful genetic variations from crops’ gene pools. Gene banks are the repositories of beneficial gene(s)/alleles from crop’s primary, secondary or tertiary gene pools which should be harnessed for present and future wheat genetic improvement programs [3]. Under-utilized but useful gene bank variation, when channeled into elite breeding materials using effective pre-breeding strategies, can provide diverse benefits including increased stress tolerance, yield potential and improving nutritional and processing quality [4].

During the Green Revolution era, global increases in wheat yield potential were achieved by deploying plant height genes (Rht1 and Rht2; [5]), as well as numerous genes for disease resistances. The semi-dwarf, fertilizer responsive, lodging resistant and high yielding green revolution varieties replaced landraces and traditional varieties grown by the farmers [6]. As a consequence, the genetic diversity in most of the world’s wheat producing regions became limited. Even today this remains as one of the major challenges for wheat improvement [7, 8, 9] as modern high-yielding wheat cultivars possess genes or gene combinations pyramided by breeders using well-adapted cultivars. There is need to introgress new variations and gene combinations from landraces and wild species (via synthetics). In this direction, CIMMYT has enormously expanded the utilization of widely adapted germplasm which is genetically diverse, and over years have made elite gene pool almost as diverse as landraces [10, 11]. However, introgression of additional variation hidden in genetic resources is necessary to further improve wheat and to enable the continued development of high yielding cultivars which can cope well with a wide range of environmental fluctuations and stresses.

To achieve this objective, gene banks such as those at CIMMYT (International Maize and Wheat Improvement Center) and ICARDA (International Center for Agricultural Research in the Dry Areas), can play a significant role. A project currently being pursued at CIMMYT—Seeds of Discovery (SeeD; http://seedsofdiscovery.org) is centered towards characterizing and mobilizing under-utilized genetic variations from maize and wheat gene banks into breeding pipelines. Wheat accessions are being characterized for genetic diversity and phenotypic performance using the state-of-the-art genotyping and phenotyping technologies [12]. Genotyping-by-sequencing (GBS) is an advanced next generation sequencing approach for genotyping which provides a rapid, high-throughput, and cost-effective tool for performing genome-wide analysis of genetic diversity [13, 14, 15, 16]. Further, characterization of the wheat gene bank accessions for adaptive and quality trait genes has the potential to reveal novel alleles useful for breeding. Assessing genome-wide and gene-specific diversity will not only provide a robust estimate of the diversity but will also reveal the germplasm containing novel alleles which may be useful for wheat breeding programs. This will help in achieving the overarching goal to improve wheat for different environments, ecosystems and stress situations.

The present study was conducted to characterize different sets of gene bank accessions and identify useful variations that can be efficiently utilized in wheat breeding. Specific objectives of the present investigation were: (1) to quantify the molecular diversity of a set of 1,423 bread wheat accessions including specific sets of landraces assembled through a trait-based approach called focused identification of germplasm strategy (FIGS), synthetic hexaploids and elite germplasm (S1 Table) using the DArTseq-GBS approach; (2) to assess the gene-based diversity of the collection for important adaptive and quality traits; and (3) to identify novel alleles that can be deployed for wheat breeding.

Results

GBS diversity in different germplasm sets

DArT-based GBS SNPs was used to investigate four germplasm sets, namely, the FIGS Drought set (FD; drought tolerant landrace accessions identified through FIGS approach, received from ICARDA), the Australia Hot set (AH; landrace accessions identified as heat tolerant, received from Australian gene bank, Horsham, Victoria), synthetic hexaploids (SH) and elite lines (E). A total of 29 K GBS SNP markers were available for the FD, AH, SH, and E lines. After removing markers with missing data > 20%, minor allele frequency < 0.05 and unknown map positions, 11K markers were used for diversity analysis. S1 Fig shows GBS markers specific to each group and shared among the four germplasm groups.

Nei’s diversity index (DI) was calculated for each germplasm group (Table 1). It ranged from ranged from 0.182–0.285, 0.182–0.305, 0.204–0.406 and 0.172–0.315 with mean values of 0.242, 0.248, 0.284 and 0.267 in FD, AH, SH and E, respectively. The mean within group genetic distance estimates (Table 1) in FD, AH, SH and E were 0.094, 0.105, 0.181 and 0.125, respectively. These results revealed the highest diversity in synthetic hexaploids followed by elites and landraces. To ascertain that the obtained trend is not due to sample size differences, DI was also calculated by taking an equal number of samples (211) randomly from each group, and a similar pattern of diversity was observed (S2 Table). The distribution of DIs in the germplasm sets revealed that a higher percentage of markers in both synthetic hexaploids and elite germplasm have DI between 0.4 and 0.5 as compared to landraces where maximum percentage of markers was in the group with DI ≤0.1 (Fig 1). In both landraces and elite germplasm, the D sub-genome was less diverse than the A and B sub-genomes (Figs a and b in S2 Fig), whereas in synthetic hexaploids the diversity of the D sub-genome was not only higher than its A and B sub-genomes but also the D sub-genomes of both landraces and elite lines (Table 1, Fig c in S2 Fig).

thumbnail
Fig 1. Nei’s diversity index (DI) distribution across FIGS Drought (a), Australia Hot (b), synthetic hexaploids (c) and elites (d).

Each column represents percentage of markers having DI either equal to or less than the value shown on X-axis.

http://dx.doi.org/10.1371/journal.pone.0132112.g001

thumbnail
Table 1. Nei’s diversity index (DI) and within group mean genetic distance (GD) in landraces (FIGS Drought and Australia Hot), synthetic hexaploids and elite lines.

http://dx.doi.org/10.1371/journal.pone.0132112.t001

Gene-specific marker diversity in different germplasm sets

The allele frequency for 39 investigated genes (S3 Table) was highly variable in the germplasm groups (Table 2). The gene for grain protein content (GPC), photoperiod insensitivity and vernalization gene alleles PpdA1a and VrnB1b, and the 1RS:1BL translocations were absent in landraces. GPC and PpdA1a were present in synthetic hexaploids and VrnB1b and 1RS:1BL translocations in elite lines, albeit with low frequencies (Table 2). The vernalization gene allele VrnA1c and all seven investigated alleles of the powdery mildew resistance gene (Pm3) were absent in the tested elites. The VrnA1c allele, which has rarely been found in other wheat collections [17, 18] was found to be present in landraces from Afghanistan, India, Iran and Pakistan (S4 Table). The four powdery mildew resistance alleles (Pmb, Pmc, Pmf and Pmg) were present in landraces with frequencies ranging from 0.022 to 0.419 (Table 2). Although with low frequency, two Pm (Pm3f and Pm3g) alleles were also present in SH. The whole collection was devoid of three Pm alleles (Pm3a, Pm3d and Pm3e), and the stem rust gene Sr36 and fusarium head blight gene Fhb1. Mean DI based on 39 gene-based markers revealed elite germplasm (DI = 0.15) to be less diverse than FD (DI = 0.16) and AH (DI = 0.17) but more diverse than SH (DI = 0.13).

thumbnail
Table 2. Allele frequencies of genes across landraces, synthetic hexaploids and elite lines.

http://dx.doi.org/10.1371/journal.pone.0132112.t002

GBS and gene-based marker diversities in landraces from different geographic regions

The distribution of DIs for landraces from Afghanistan, India, Iran, Iraq, and Pakistan revealed that the latter two groups had the highest percentage of markers with DI between 0.4 and 0.5 (Fig 2a–2e). Mean DI and polymorphic information content (PIC) values revealed that landraces from Iraq formed the most diverse group followed by those from Pakistan (Fig 2f). Diversity estimates based on gene-based markers also showed the highest diversity in landraces from Iraq (S3 Fig). S4 Table presents the frequency of 39 gene-specific alleles in landraces from five countries.

thumbnail
Fig 2. Nei’s diversity index (DI) distribution across Afghanistan (a), India (b), Iran (c), Iraq (d) and Pakistan (e).

Each column in a-e represents percentage of markers having DI either equal to or less than the value shown on X-axis. Part f of figure represents mean DI and PIC across all countries.

http://dx.doi.org/10.1371/journal.pone.0132112.g002

Neighbor joining dendrogram

The neighbor joining (NJ) tree divided the four germplasm sets (FD, AH, SH and E) into six groups (Fig 3). Eighty five percent of landraces from the FD and AH groups formed one group and the remaining 15% of landraces (mainly from the AH group) dispersed in two mixed groups composed of landraces, SH and E. The SH were divided into two groups; one bigger group with 581 SH made by crossing durum wheat (T. turgidum ssp. durum) and Ae. tauschii, and, the other one with only 47 genotypes made by crossing emmer wheat (T. dicoccon) and Ae. tauschii. The remaining SH were dispersed in two mixed groups. The elite germplasm was dispersed in four different groups in the dendrogram. The group labelled as Elite (Fig 3) was the biggest group of elites constituting 163 (77.2%) accessions. The remaining elites were either dispersed in the two mixed groups (14.2%) or were part of the bigger SH group (8.5%). The second NJ tree (Fig 4) shows the geographic origin of landraces. The landraces from Iraq were predominant in one of the mixed groups and those from Afghanistan, India and Pakistan were predominant in the second mixed group. A few landraces were also present in the group that contained the 163 elites (Elite group). In the landrace group, majority of the genotypes from Afghanistan and Iran clustered separately, whereas genotypes from India and Pakistan formed one mixed group. We compared the levels of diversity in the two groups of SH obtained in the dendrograms which revealed non-significant differences between them (S4 Fig).

thumbnail
Fig 3. Neighbor joining dendrogram based on GBS markers using Euclidean distance showing genetic relationships among four germplasm sets.

http://dx.doi.org/10.1371/journal.pone.0132112.g003

thumbnail
Fig 4. Neighbor joining dendrogram based on GBS markers using Euclidean distance showing genetic relationships of landraces from different geographic origins with elites and synthetic hexaploids.

http://dx.doi.org/10.1371/journal.pone.0132112.g004

Coefficient of gene differentiation and gene flow

Estimates of the coefficient of gene differentiation (Gst) revealed that there was more divergence between the elite germplasm and landraces than between landraces of different geographic origins (Table 3). These results were confirmed by gene flow (Nm) analyses. Gene flow between elite germplasm and landraces was lower than among landraces of different origins (Table 3).

thumbnail
Table 3. Apportionment of genetic variation between different germplasm sets.

http://dx.doi.org/10.1371/journal.pone.0132112.t003

Novel alleles for known genes of agronomic importance

Screening of germplasm sets with the known allele-specific markers for vernalization and glutenin genes (S3 Table) revealed novel bands for Vrn-A1c, vrn-B3, GluA3b, GluB3g and GluB3i in the landraces of different origins. Screening of landraces for the Vrn-A1c allele revealed an expected band of 1170 bp in 13% of landraces. However, in some landraces originating from Iraq (9), Iran (4) and Afghanistan (2), a band of ~ 600 bp was obtained, thus indicating a deletion in the Vrn-A1c allele (Fig a in S5 Fig). Similarly, a band of ~ 1900–2000 bp was observed in the landraces from Pakistan (2), Iran (7), Uzbekistan (1), Turkmenistan (1) and Afghanistan (2) instead of an expected band size of 1140 bp for allele vrnB3, indicating an insertion of approximately 800–900 bp (Fig-b in S5 Fig). Novel alleles for GluA3b, GluB3g and GluB3i were also identified with greater band sizes than expected (bands of 894, 853 and 621 bp, respectively) in 19 landraces originating from Pakistan, Afghanistan, India and Turkmenistan (Figs a, b and c in S6 Fig).

Sequence variation analysis of novel alleles

The new allelic bands of Vrn genes were cloned, sequenced and aligned with known vrn-A1, Vrn-A1c and vrn-B3 alleles (17, 18). The sequence alignment of 600 bp band with recessive winter allele vrn-A1 (AY747600) and Vrn-A1c (AY747599) revealed a novel deletion of 5997 bp in intron 1 of winter allele vrn-A1. This novel deletion represents an additional deletion of 493 bp in intron 1 of winter allele vrn-A1, along with a 5504 bp deletion in Vrn-A1c (Fig 5). The new Vrn-A1 allele is named as Vrn-A1f and submitted to NCBI GenBank (Accession no. KR824429). Similarly, the alignment of the new Vrn-B3 allele with the recessive vrn-B3 allele revealed a large insertion of 890 bp into the 5′untranslated region (UTR) of vrn-B3, and an additional deletion of 1 bp and three SNPs outside this large insertion (S7 Fig). Sequence analysis of new Glu alleles is underway.

thumbnail
Fig 5. Schematic diagram of vrn-A1 gene, Vrn-A1c and new allelic variant Vrn-A1f.

The eight exons are show as red boxes with numbers E1 to E8 and introns are shown as In1 to In 7. The novel deletion is shown in intron 1 between 1807 and 7804 bp relative to recessive allele vrn-A1 (AY747600) and dominant allele Vrn-A1c (AY747599). Nucleotide numbers are based on the sequence of vrn-A1 (AY747600).

http://dx.doi.org/10.1371/journal.pone.0132112.g005

Discussion

As humanity confronts the nexus of ever-rising food demands and climate change, the need to exploit the full potential of wheat genetic resources to accelerate performance gains has become more urgent. Wheat genetic resources from gene banks need to be characterized to channelize useful genetic variation into modern elite gene pools. This is the first report of genetic characterization of a very large (1,423 accessions) set of wheat germplasm using GBS and gene-specific markers. Recently, Manickavelu et al. [19] reported diversity of 446 Afghan wheat landraces with GBS markers. The GBS technology has the potential to provide an in-depth and a robust diversity estimate with much reduced ascertainment bias as compared to other whole-genome-genotyping technologies [20]. It can also unveil new and favorable genetic variations in gene bank accessions, thus enabling a targeted choice of accessions with high value for pre-breeding [21]. A number of genetic diversity studies have been conducted for wheat using marker systems other than GBS [10, 2235].

The landrace and elite materials investigated in the study represented major spring wheat growing environments of the world. Particularly, the landraces were collected from drought and heat prone environments using a specific trait-based FIGS approach adopted by ICARDA gene bank. It is a highly innovative approach based on the assumption that drought- and themostress- tolerant landraces are prevalent in areas where stress has been most severe—the phenomenon of co-evolution [36]. The accessions originating from such regions are collected and pursued further and only the highest potential accessions are then confirmed in field experiments. Using this approach novel sources of resistance in wheat to drought, heat, salinity and to several diseases and insect pests have been successfully identified [37].

We identified thousands of new SNP variations specific to drought and heat tolerant accessions (S1 Fig). Some of these SNP variations can be incorporated to elite genotypes after investigating their allelic effects with genome wide association analysis (GWAS). The novel superior alleles thus identified in GWAS can be fitted into genomic prediction models to realize genetic gains through genomic selection. This approach is currently being followed in the SeeD-Wheat project at CIMMYT. This has opened new avenues for enriching the elite germplasm with novel drought and heat tolerant genes and for further broadening the diversity of elite germplasm. Among landrace accessions, those from Iraq were the most diverse followed by Pakistan (Fig 2). The higher diversity in Iraq and Pakistan, even greater than Iran, is unexpected from known evolutionary history of bread wheat as Iran is one of the main centers of evolution of wheat. However, it should be noted that only drought and heat tolerant genotypes were collected from Iran in the FD and AH sets, respectively. Thus, they are not representative of entire geographical diversity in Iran. Although this trait-based selection has limited the overall diversity in the tested landraces (Table 1), it has provided useful alleles and resources to breed for heat and drought prone environments.

In the NJ dendrogram, 77% of the elite lines and 85% of the landraces formed separate clusters; 96% SH also grouped into two clusters separate from elites (Figs 3 and 4). Further, high genetic differentiation (Gst) between elites and landraces was observed (Table 3). These results explicitly indicate a) the divergence of the tested elites from the landraces and SH and b) landraces and SH as two different pools of genetic variation for further broadening the genetic base of elite germplasm. Two groups were evident in SH which were divided according to the tetraploid parent used in the crosses; durum wheat x Ae. tauschii and emmer wheat x Ae. tauschii. In the CIMMYT wide crossing program most synthetics were produced using modern durum wheats (T. turgidum subsp. durum), while only a few dozen combinations included emmer wheat (T. dicoccon) [38]. Diversity estimates in two groups of SH did not differ significantly (S4 Fig), which indicates that both tetraploid parents have contributed equally to the diversity of SH. The diversity information of landraces and SH from two different origins (landraces: FD and AH; SH: T. turgidum- and T. dicoccon-based) has been integrated into the wheat breeding pipelines to introgress novel variations into high yielding and widely adapted elite backgrounds. More than 200 diverse accessions have been identified for pre-breeding and for allele mining of candidate genes for drought and heat tolerance.

The higher number of GBS SNP markers specific to SH than landraces (S1 Fig) was not unexpected considering that such gain of novel DNA fragments is common after polyploid formation [39]. Several mechanisms such as homoeologous recombination, point mutation, transposon activation and gene conversion-like events have been reported to generate novel genetic changes in polyploids [39]. From a breeder’s perspective, these results are significant as some of this variation may provide novel alleles to wheat breeders for traits not yet tapped in the primary gene pool of wheat. Dreisigacker et al. [11] also reported several novel bands in SH with SSR markers which were stably inherited in synthetics-derived backcrossed lines. Thus, a detailed scrutiny of the novel GBS SNP tags in SH is required to identify the genes worth introgressing into elite germplasm.

Comparison of the diversity of the tested elite germplasm vis-á-vis previous reports (S5 Table) with SNP markers on elite germplasm of other breeding programs revealed that the elite lines of the present study are more diverse than most other breeding programs [4044]. This result supports previous conclusions that CIMMYT breeders successfully broadened the genetic diversity of the elite germplasm through incorporation of primary synthetics into the breeding programs [10, 11, 45] and also via consistent introductions of exotic materials from all over the world [10, 46, 47].

The diversity pattern obtained from 39 allele-specific markers for different adaptive and quality traits’ genes showed an order: landraces>elites>SH. This order is opposite to what was observed with GBS-based diversity (Table 1). These results were expected as most of the adaptive and quality genes have been fixed in the elite germplasm through years of breeding (Table 2). Genic diversity analysis further demonstrated that landraces from Iraq are the most diverse (S3 Fig), which is in accordance with the GBS marker-based results. The most significant output of assessing genic diversity was the identification of novel alleles for various agronomically important genes (Fig 5, S5 and S6 Figs). Two allelic variations in Vrn-A1 and Vrn-B3 genes, associated with deletions and insertions, respectively, were identified. The sequence differences in the promoter region and large insertions or deletions in the intron I of the Vrn-1 locus have been reported to be associated with spring vs. winter growth habit [17, 18]. Allele Vrn-A1c carries a large 5504 bp deletion in intron I of recessive allele vrn-A1 [17]. We detected a novel deletion of 5997 bp, named Vrn-A1f, in the intron I region of recessive gene vrn-A1 which extended 440 bp further downstream and 53 bp upstream from the deletion in Vrn-A1c (Fig 5). Vrn-A1f was observed in landraces from Iran, Iraq and Afghanistan, thus pointing to a Middle East and/or near eastern origin of this allele. Similarly, we detected an insertion of 890 bp in the 5’ UTR region of promotor of vrn-B3, and an additional 1-bp deletion and three SNPs outside this large insertion in the promotor region. Derakhshan et al. [48] reported a similar size insertion in the vrn-B3 gene from Iranian landraces. The authors, however, did not sequence the band. Chen et al. [49] cloned the 2000 bp band and reported a 890 bp insertion in a Chinese cultivar Chadianhong, and named it as Vrn-B3b. Comparison of the sequences of insertions in Chadianhong and the landraces of this study revealed an identical 890 bp insertion (S7 Fig), thus indicating the presence of Vrn-B3b allele in landraces of the present study. Vrn-B3b was identified in the landraces from Pakistan, Iran, Uzbekistan, Turkmenistan and Afghanistan, thus indicating a wide distribution of this allele. Preliminary evidence suggests that Vrn-A1f promotes flowering by six to seven days (Fig a in S8 Fig), and Vrn-B3b delays flowering by ten days (Fig b in S8 Fig) as also reported in Chadianhong [49].

Heading time is a major determinant of wheat’s adaptation to different environments, and critical in minimizing the risk of frost, heat, and drought for reproductive development. In future climate change scenarios, the interplay of Ppd and Vrn genes will have important implications for improving yield by controlling flowering time [50]. An in-depth crop modelling simulation study taking into account 35 possible climate scenarios revealed that photoperiod-sensitive cultivars of millet and sorghum are more resilient to future climate conditions than modern photoperiod-insensitive cultivars [50]. In this regard, Vrn-A1f and Vrn-B3b alleles identified in photoperiod-sensitive landraces adapted to heat and drought prone environments could be very efficiently utilized for developing climate smart wheat varieties. The effects of Vrn-B3b allele on yield have not been yet investigated (49). Responses of Vrn-A1f and Vrn-B3b alleles on grain yield are currently under investigation for their efficient utilization in the wheat breeding. We are also analyzing the interactions of the above said alleles with previously reported ones to determine a suitable combination for introgression into elite wheat genotypes. New alleles of Glu genes were also observed in landraces from Pakistan, Iran, Turkmenistan, India and Afghanistan (Figs a, b and c in S6 Fig). Allelic variations at the Glu-3 loci (encoding low molecular weight glutenin subunits) have a pronounced effect on the visco-elastic properties of wheat dough [51]. The effect of these novel variations on visco-elastic properties is also under investigation, particularly the novel Glu-B3g allele (Fig-a in S6 Fig), as positive effect of Glu-B3g on peak mixing time (a parameter of strong dough) has already been established [52].

The agronomically important alleles controlling highly heritable traits such as heading, height and pre-harvest sprouting (Ppd-D1a, Vrn-B1a, Vrn-D1, Rht-B1b, Vp-B1) were almost fixed in the tested elite germplasm (Table 2). Of the various diseases of wheat, resistance to soil borne mosaic virus is highly heritable being controlled by a single locus, Sbm1 [53]. This gene was also fixed in the elite lines (Table 2). The genes/gene alleles controlling less heritable traits (resistance to leaf rust, powdery mildew, fusarium head blight) were present either in moderate frequency (LR34) or were absent (seven Pm alleles, Fhb1) in the tested elite lines. It is noteworthy, however, that elite germplasm display significant resistance for fusarium head blight and powdery mildew, which could have resulted from selection of yet unknown or uninvestigated genes/alleles. Among the quality traits, grain hardness is extremely important and forms the basis of differentiating within the world trade of wheat grain. The trait is related to the variation in two puroindolines (Pin A and Pin B) encoded by Pina and Pinb genes, respectively. The absence or mutation of either of these genes results in hard texture (54). The tested elite lines showed almost fixed Pina gene (Pina-D1b frequency 92.5%), whereas the frequency of the Pinb gene (Pinb-D1b) was only 1.9% (Table 2). Previous studies have reported significant advantage of Pinb-D1b allele over Pina-D1b for milling and bread quality traits [54]. The Pinb-D1b allele was identified in 16 landraces from diverse origins (S4 Table) and 4 SH in this study which can be introgressed into elite germplasm to increase the allelic variability of this locus. This study has confirmed the potential benefits related to the use of landraces and synthetic wheats as exotic parents to introduce new allelic diversity into breeding programs. Germplasms resources are freely available for the global wheat community.

Conclusions

The results of this study suggest that there is significant unexploited variation in landraces and SH that can be channeled into modern cultivars. This genetic variation, when combined with existing genetic variation in the elite wheat gene pool, will further improve stress adaptation and quality traits and also enrich it with novel drought and heat tolerance genes. Efforts are being made to maximize variation for heat and drought tolerance alleles in elite genotypes to complement wheat improvement activities. Based on the marker information generated in this study, more than two hundred landraces and synthetic hexaploids are being used for pre-breeding and generating bridging germplasm. An ‘allele-mining panel’ has also been assembled for allele mining of candidate genes for drought and heat stress tolerance. The new allelic variation identified for vernalization and glutenin genes will be incorporated into breeding program once their effects on yield and quality parameters are validated. The lines carrying the new alleles can be made available to the researchers worldwide on request.

Materials and Methods

Plant material

A total of 1,423 wheat germplasm accessions were characterized in this study (S1 Table). These included 561 landrace accessions representing three geographic regions (Near East, Middle East and South West Asia), 651 synthetic hexaploids developed at CIMMYT by crossing durum wheat (T. turgidum subsp. durum) or emmer wheat (T. dicoccon) with diverse Aegilops tauschii accessions, as well as 211 cultivars and elite lines. Of the 561 landrace accessions, 280 landraces were obtained from ICARDA. These landraces were identified as drought tolerant using a focused identification of germplasm strategy (FIGS) approach (http://www.icarda.org/tools/figs) and were denoted as ‘FIGS Drought’ (FD) in this study. Remaining 281 landrace accessions were obtained from Australian gene bank, Horsham, Victoria. These landraces were identified as heat tolerant. This set was denoted as ‘Australia Hot’ (AH).

Genotypic characterization

Genomic DNA was extracted from fresh leaves collected from a single individual plant per accession using a modified CTAB (cetyltrimethylammonium bromide) method [55] and quantified using NanoDrop 8000 spectrophotometer V 2.1.0. For genotypic characterization, a next-generation sequencing technique called DArTseq was employed. A complexity reduction method including two enzymes was used to generate a genome representation of the set of samples. PstI-RE site specific adapter was tagged with 96 different barcodes enabling multiplexing a plate of DNA samples to run within a single lane on Illumina HiSeq2500 instrument (Illumina Inc., San Diego, CA). The successful amplified fragments were sequenced up to 77 bases, generating approximately 500,000 unique reads per sample. Thereafter the FASTQ files (full reads of 77bp) were quality filtered using a Phred quality score of 30, which represent a 90% of base call accuracy for at least 50% of the bases. More stringent filtering was also performed on barcode sequences using a Phred quality score of 10, which represent 99.9% of base call accuracy for at least 75% of the bases. A proprietary analytical pipeline developed by DArT P/L was used to generate allele calls for SNP and presence/absence variation (PAV) markers. Then, a set of filtering parameter was applied to select high quality markers for this specific study. One of the most important parameters is the average reproducibility of markers in technical replicates for a subset of samples, which in this specific study was set at 99.5%. Another critical quality parameter is call rate. This is the percentage of targets that could be scored as '0' or '1', the threshold was set at 50%. PAV’s markers were not used in this study.

Gene-based marker genotyping

Sequence tagged site (STS) markers reported on the MASWheat (http://maswheat.ucdavis.edu/protocols/index.htm) database for various agronomic traits, as well as for quality and disease resistance genes were used for genotyping using PCR protocols and gel electrophoresis procedures described in this database. In addition, genotyping was done using SNP markers designed from wheat gene sequences, reported on CerealsDB (http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/kasp_download.php?URL=), using the KASPar genotyping system (KBiosciences, UK). The allele-specific gene-based STS and SNP markers used in this study are listed in S3 Table and their primer sequences are described on MASWheat and CerealsDB databases.

Cloning, nucleotide sequencing and analysis

The novel bands were cloned and sequenced using the commercial service provided by the Molecular Biology Service Centre, Simon Fraser University, Vancouver, BC, Canada. A standard T/A cloning procedure using pGEM-T Easy vector (Promega) was used [56]. Sequencing chromatograms were analyzed using Chromas Version 1.4.5. Sequencing data of novel vernalization-gene fragments were aligned with sequences of vrn-A1 (AY747600), Vrn-A1c (AY747599) and vrn-B3 (DQ890162) genes using the BLAST2 sequences option of BLASTN program available at NCBI (http://www.ncbi.nlm.nih.gov/), database and the CLUSTAL X programme [57]. A default setting with a fixed gap penalty of 6.66, and a 0.5 DNA transition weight in the multiple alignment parameter option was opted for alignment.

Diversity analysis

The map positions of GBS SNP markers were obtained from a 64K consensus map provided by DArT Pvt. Ltd., Australia. The number of mapped markers for the A, B and D genomes was 3964 (33.3%), 4294 (36.1%) and 3616 (30.4%), respectively. Before diversity analysis, markers were filtered using the criterion; missing data < 20% and minor allele frequency > 0.05.

Two diversity parameters, Nei’s diversity index (DI) and polymorphic information content (PIC), were calculated to characterize the genetic diversity of A, B and D genome-based GBS markers, gene-based markers, and of different germplasm sets using the “Genetics” package in R (http://www.r-project.org/) and POPGENE version 1.32 [58]. To compare the diversity of landraces from different geographic origins, countries with minimum 20 representatives (Iran, Iraq, India, Pakistan and Afghanistan) were included in analysis.

Nei’s genetic diversity statistics [59] was used to measure total genetic diversity (Ht) as well as intra-population (Hs) genetic diversity. The coefficient of gene differentiation (GST) was calculated as GST = 1—Hs / Ht. Gene flow was estimated as Nm = 0.5 x (1—Gst)/Gst. Genetic relationships were inferred by obtaining a distance matrix (using Euclidean distance) with GBS SNP markers using a custom R function and then using the distance matrix for constructing a neighbor joining dendrogram. The confidence interval of the genetic relationships among the accessions was determined by performing 1000 bootstraps. The genetic groupings were confirmed using DARwin v 5.0.158 [60].

Supporting Information

S1 Fig. GBS marker distribution in different germplasm sets.

The underlined number represent markers specific to each group and markers alongside arrows represent those shared exclusively between two groups. Markers shared among any three and all four groups are not shown. FD; FIGS Drought, AH; Australia Hot, SH; Synthetic Hexaploids, E; Elite germplasm.

doi:10.1371/journal.pone.0132112.s001

(TIFF)

S2 Fig. Comparison of diversities of A, B and D sub-genomes across FIGS Drought (FD), Australia Hot (AH), synthetic hexaploids (SH) and elite (E) using all samples (Fig a), using 211 samples in each group (Fig b) and DIAB: DID in landraces (FD+AH), SH and E (Fig c).

doi:10.1371/journal.pone.0132112.s002

(TIFF)

S3 Fig. Mean DI (blue) and PIC (red) across all countries based on gene-based markers.

doi:10.1371/journal.pone.0132112.s003

(TIFF)

S4 Fig. Mean DI and PIC in two sets of synthetic hexaploids.

doi:10.1371/journal.pone.0132112.s004

(TIFF)

S5 Fig. Polymerase chain amplification with Vrn-A1c (Fig a) and vrn-B3 (Fig b) allele specific primers in landraces.

The amplified bands in three (Fig a) and two landraces (Fig b) are smaller and larger, respectively, than the expected sizes (1170 and 1140 bp for Vrn-A1c and vrn-B3, respectively).

doi:10.1371/journal.pone.0132112.s005

(TIFF)

S6 Fig. Polymerase chain amplification with GluB3g (Fig a), GluA3b (Fig b) and GluB3i (Fig c) allele specific primers in landraces.

The amplified bands in a few landraces are larger than the expected sizes (853, 894 and 621bp for GluB3g, GluA3b and GluB3i, respectively).

doi:10.1371/journal.pone.0132112.s006

(TIFF)

S7 Fig. Alignment of Vrn-B3 vernalization allele identified in landraces of present study (Vrn-B3L) with recessive vrn-B3 and Vrn-B3b.

doi:10.1371/journal.pone.0132112.s007

(DOC)

S8 Fig. Phenotypic effects of novel alleles Vrn-A1f (Fig a) and Vrn-B3b (Fig b) on flowering time.

The landrace accession carrying Vrn-A1f deletion (Fig a; left pot) flowers six to seven days earlier than the line without this deletion (Fig a; right pot). The landrace accession carrying Vrn-B3b insertion flowers ten days later (Fig b; left pot) than the line without this insertion (Fig b; right pot).

doi:10.1371/journal.pone.0132112.s008

(TIFF)

S1 Table. Landraces, Synthetic hexaploids and elites used in the study.

doi:10.1371/journal.pone.0132112.s009

(XLSX)

S2 Table. Nei’s diversity index (DI) in landraces (FIGS Drought and Australia Hot), synthetic hexaploids and elite lines using 211 samples in each group.

doi:10.1371/journal.pone.0132112.s010

(DOCX)

S3 Table. Thirty nine allele specific gene-based SNP and STS markers used in the present study.

doi:10.1371/journal.pone.0132112.s011

(DOCX)

S4 Table. Allele frequencies of gene alleles in the landrace germplasm from different countries.

doi:10.1371/journal.pone.0132112.s012

(DOCX)

S5 Table. Review of wheat diversity studies using different marker systems.

doi:10.1371/journal.pone.0132112.s013

(XLSX)

Acknowledgments

The authors acknowledge the financial support received from the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA) through the project Seeds of Discovery-Sustainable Modernization of Traditional Agriculture project (MasAgro). The authors also thank Drs. Greg Grimes and Kenneth Street for providing heat and drought tolerant landraces. Authors duly acknowledge the support from CIMMYT scientists- Drs. Kate Dreher, Kevin Pixley, and Ravi Singh for their critical review of the manuscript.

Author Contributions

Conceived and designed the experiments: SS DS PV. Performed the experiments: DS SS PV CO CPS CSP. Analyzed the data: DS PV. Contributed reagents/materials/analysis tools: TP ME AA CDP PW. Wrote the paper: DS PV SS PW.

References

  1. 1. FAO repository (2009) Global agriculture towards 2050 Rome FAO. Available: http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf.
  2. 2. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8: (6), e66428. pmid:23840465 doi: 10.1371/journal.pone.0066428
  3. 3. Hoisington D, Khairallah M, Reeves T, Ribaut JM, Skovmand B, Taba S, et al. (1999) Plant genetic resources: what can they contribute toward increased crop productivity? Proc Natl Acad Sci USA, 96: 5937–5943. pmid:10339521 doi: 10.1073/pnas.96.11.5937
  4. 4. Trethowan RM, Mujeeb-Kazi A (2008) Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Sci 48: 1255–1265. doi: 10.2135/cropsci2007.08.0477
  5. 5. Kihara H (1983) Origin and history of ‘Daruma’, a parental variety of Norin 10 In: Proceedings of 6th International Wheat Genetics Symposium, Plant Germplasm Institute, University of Kyoto, Kyoto, Japan.
  6. 6. Smale M, Reynolds MP, Warburton M, Skovmand B, Trethowan R, Singh RP, et al. (2002) Dimensions of diversity in modern spring bread wheat in developing countries from 1965. Crop Sci 42: 1766–1779. doi: 10.2135/cropsci2002.1766
  7. 7. Brisson N, Gate P, Gouache D, Charmet G, Oury F-X, Huard F (2010) Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Res 119: 201–212. doi: 10.1016/j.fcr.2010.07.012
  8. 8. Graybosch RA, Peterson CJ (2010) Genetic improvement in winter wheat yields in the great plains of North America, 1959–2008. Crop Sci 50: 1882–1890. doi: 10.2135/cropsci2009.11.0685
  9. 9. Keilwagen J, Kilian B, Özkan H, Babben S, Perovic D, Mayer KFX, et al. (2014) Separating the wheat from the chaff—a strategy to utilize plant genetic resources from ex situ gene banks. Scientific Rep 4: 5231. doi: 10.1038/srep05231
  10. 10. Warburton ML, Crossa J, Franco J, Kazi M, Trethowan R, Rajaram S, et al. (2006) Bringing wild relatives back into the family: recovering genetic diversity in CIMMYT improved wheat germplasm. Euphytica 149: 289–301. doi: 10.1007/s10681-005-9077-0
  11. 11. Dreisigacker S, Kishii M, Lage J, Warburton M (2008) Use of synthetic hexaploid wheat to increase diversity for CIMMYT bread wheat improvement. Aust J Agric Res 59: 413–420. doi: 10.1071/ar07225
  12. 12. Singh S, Carolina SP, Vikram P, Juan Andres BF, Huihui L, Chen C, et al. (2014) Mining of the global wheat genetic resources. Plant and Animal genome conference, San Diego, California, USA, Jan 11–15, p207.
  13. 13. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6: e19379. doi: 10.1371/journal.pone.0019379. pmid:21573248
  14. 14. Fu YB, Peterson GW (2011) Genetic diversity analysis with 454 pyrosequencing and genomic reduction confirmed the eastern and western division in the cultivated barley gene pool. Plant Gen 4: 226–237. doi: 10.3835/plantgenome2011.08.0022
  15. 15. Huan X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, et al. (2009) High throughput genotyping by whole-genome resequencing. Genome Res 19: 1068–1076 doi: 10.1101/gr.089516.108. pmid:19420380
  16. 16. Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Gen 5: 92–102. doi: 10.3835/plantgenome2012.05.0005
  17. 17. Fu DL, Szucs P, Yan LL, Helguera M, Skinner JS, von Zitzewitz J, et al. (2005) Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat. Mol Genet Genom 273, 54–65. doi: 10.1007/s00438-004-1095-4
  18. 18. Yan L, Loukoianov A, Blechl A, Tranquilli G, Ramakrishna W, SanMiguel P, et al. (2004) The wheat VRN2 gene is a flowering repressor down-regulated by vernalization. Science, 303: 1640–1644. pmid:15016992 doi: 10.1126/science.1094305
  19. 19. Manickavelu A, Jighly A, Ban T (2014) Molecular evaluation of orphan Afghan common wheat (Triticum aestivum L.) landraces collected by Dr. Kihara using single nucleotide polymorphic markers. BMC Plant Biol 14:320. doi: 10.1186/s12870-014-0320-5. pmid:25432399
  20. 20. Heslot N, Rutkoski J, Poland J, Jannick J-L, Sorrells ME (2013) Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity. PLoS One 8: e74612. doi: 10.1371/journal.pone.0074612. pmid:24040295
  21. 21. Kilian B, Graner A (2012) NGS technologies for analyzing germplasm diversity in gene banks. Brief Func Genom 11: 38–50. doi: 10.1093/bfgp/elr046
  22. 22. Christiansen MJ, Andersen SB, Ortiz R (2002) Diversity changes in an intensively bred wheat germplasm during the 20th century. Mol Breed 9: 1–11.
  23. 23. Chen X, Min D, Yasir TA, Hu Y-G (2012) Genetic diversity, population structure and linkage disequilibrium in elite Chinese winter wheat investigated with SSR markers. PLoS One 7: e44510. doi: 10.1371/journal.pone.0044510. pmid:22957076
  24. 24. Donini P, Law JR, Koebner RM, Reeves JC, Cooke RJ (2000) Temporal trends in the diversity of UK wheat. Theor Appl Genet 100: 912–917. doi: 10.1007/s001220051370
  25. 25. Dreisigacker S, Shewayrga H, Crossa J, Arief VN, DeLacy IH, Singh RP, et al. (2012) Genetic structures of the CIMMYT international yield trial targeted to irrigated environments. Mol Breed 29: 529–541. doi: 10.1007/s11032-011-9569-7
  26. 26. Huang Q, Borner A, Roder S, Ganal W (2002) Assessing genetic diversity of wheat (Triticum aestivum L) germplasm using microsatellite markers. Theor Appl Genet 105: 699–707. pmid:12582483 doi: 10.1007/s00122-002-0959-4
  27. 27. Koebner RMD, Donini P, Reeves JC, Cooke RJ, Law JR (2003) Temporal flux in the morphological and molecular diversity of UK barley. Theor Appl Genet 106, 550–558. pmid:12589556
  28. 28. Lu H, Bernardo R (2001) Molecular marker diversity among current and historical maize inbreds. Theor Appl Genet 103: 613–617. doi: 10.1007/pl00002917
  29. 29. Manifesto MM, Schlatter AR, Hopp HE, Suarez EY, Dubcovsky J (2001) Quantitative evaluation of genetic diversity in wheat germplasm using molecular markers. Crop Sci 41: 682–690. doi: 10.2135/cropsci2001.413682x
  30. 30. Nielsen NH, Backes G, Stougaard J, Andersen SU, Jahoor A (2014) Genetic diversity and population structure analysis of European hexaploid bread wheat (Triticum aestivum L) varieties. PLoS One 9: e94000. doi: 10.1371/journal.pone.0094000. pmid:24718292
  31. 31. Reif JC, Zhang P, Dreisigacher S, Warburton ML, Ginkel MV, Hoisington D, et al. (2005) Wheat genetic diversity trends during domestication and breeding. Theor Appl Genet 110: 859–864. pmid:15690175 doi: 10.1007/s00122-004-1881-8
  32. 32. Roussel V, Koenig J, Bechert M, Balfourier F (2004) Molecular diversity in French bread wheat accessions related to temporal trends and breeding programs. Theor Appl Genet 108: 920–930. pmid:14614567 doi: 10.1007/s00122-003-1502-y
  33. 33. Roussel V, Leisova K, Exbrayat F, Stehno Z, Balfourier F (2005) SSR allelic diversity changes in 480 European bread wheat varieties released from 1840 to 2000. Theor Appl Genet 111: 162–170. pmid:15887038 doi: 10.1007/s00122-005-2014-8
  34. 34. Russell JR, Ellis RP, Thomas WTB, Waugh R, Provan J, Booth A, et al. (2000) A retrospective analysis of spring barley germplasm development from ‘foundation genotypes’ to currently successful cultivars. Mol Breed 6: 553–568.
  35. 35. Zhang LY, DongCheng L, XiaoLi G, WenLong Y, JiaZhu S, DaoWen W, et al. (2011) Investigation of genetic diversity and population structure of common wheat cultivars in northern China using DArT markers. BMC Genet 12: 42. doi: 10.1186/1471-2156-12-42. pmid:21569312
  36. 36. Bouhssini ME, Street M, Amri K, Mackay A, Ogbonnaya M, Omran FC, et al. (2011) Sources of resistance in bread wheat to Russian wheat aphid (Diuraphis noxia) in Syria identified using the Focused Identification of Germplasm Strategy (FIGS). Plant Breed 130: 96–97. doi: 10.1111/j.1439-0523.2010.01814.x
  37. 37. Solh M, van Ginkel M (2014) Drought preparedness and drought mitigation in the developing world's drylands. Weather Climate Extrem 3: 62–66. doi: 10.1016/j.wace.2014.03.003
  38. 38. Van Ginkel M, Ogbonnaya F (2007) Novel genetic diversity from synthetic wheats in breeding cultivars for changing production conditions. Field Crops Res 104: 86–94. doi: 10.1016/j.fcr.2007.02.005
  39. 39. Osborn TC, Pires JC, Birchler JA, Auger DL, Chen ZJ, Lee HS, et al. (2003) Understanding mechanisms of novel gene expression in polyploids. Trends Genet 19: 141–147. pmid:12615008 doi: 10.1016/s0168-9525(03)00015-5
  40. 40. Cavanagh CR, Chao S, Wang S, Huang BE, Stephen S, Kiani S, et al. (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad Sci USA, 110: 8057–8062. doi: 10.1073/pnas.1217133110. pmid:23630259
  41. 41. Chao S, Zhang W, Akhunov E, Sherman J, Ma Y, Luo MC, et al. (2009) Analysis of gene-derived SNP marker polymorphism in US wheat (Triticum aestivum L) cultivars. Mol Breed 23: 23–33. doi: 10.1007/s11032-008-9210-6
  42. 42. Chao S, Dubcovsky J, Dvorak J, Luo MC, Baenziger SP, Matnyazov R, et al. (2010) Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L). BMC Genom 11: 727. doi: 10.1186/1471-2164-11-727
  43. 43. Somers DJ, Kirkpatrick R, Moniwa M, Walsh A (2003) Mining single-nucleotide polymorphisms from hexaploid wheat ESTs. Genome 49: 431–437. doi: 10.1139/g03-027
  44. 44. Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE et al. (2014) Characterization of polyploid wheat genomic diversity using a high density 90000 single nucleotide polymorphism array. Plant Biotech J 12: 787–796. doi: 10.1111/pbi.12183
  45. 45. Zhang P, Dreisigacker S, Melchinger AE, Van Ginkel M, Hoisington D, Warburton ML (2005) Quantifying novel sequence variation in CIMMYT synthetic hexaploid wheats and their backcross-derived lines using SSR markers. Mol Breed 15: 1–10. doi: 10.1007/s11032-004-1167-5
  46. 46. Rajaram S, van Ginkel M (2001) Mexico: 50 years of international wheat breeding In: The World Wheat Book: A History of Wheat Breeding (Bonjean AP and Angus WJ, eds), pp 579–608 Paris, Lavoisier Publishing.
  47. 47. Rajaram S, Borlaug NE, van Ginkel M (2002) CIMMYT international wheat breeding In: Bread Wheat Improvement and Production (Curtis BC, Rajaram S and Macpherson HG, eds), pp 103–117 FAO, Rome, Plant Production and Protection Series No 30.
  48. 48. Derakhshan B, Mohammadi SA, Moghaddam M, Jalal Kamali MR (2013) Molecular characterization of vernalization genes in Iranian wheat landraces. Crop Breed J: 3(1) 11–14.Chen F, Gao M, Zhang J, Zuo A, Shang X, Cui D (2013) Molecular characterization of vernalization and response genes in bread wheat from the Yellow and Huai Valley of China. BMC Plant Biol 13: 199.
  49. 49. Chen F, Gao M, Zhang J, Zuo A, Shang X, Cui D (2013) Molecular characterization of vernalization and response genes in bread wheat from the Yellow and Huai Valley of China. BMC Plant Biol 13: 199. doi: 10.1186/1471-2229-13-199. pmid:24314021
  50. 50. Sultan B, Roudier P, Quirion P, Alhassane A, Muller B, Dingkuhn M, et al. (2013) Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa. Environ Res Lett 8: 014040. doi: 10.1088/1748-9326/8/1/014040
  51. 51. Liu L, Ikeda TM, Branlard G, Pena RJ, Rogers WJ, Lerner SE, et al. (2010) Comparison of low molecular weight glutenin subunits identified by SDS-PAGE, 2-DE, MALDI-TOF-MS and PCR in common wheat. BMC Plant Biol 10: 124–147. doi: 10.1186/1471-2229-10-124. pmid:20573275
  52. 52. Miwako I, Sachiko F, Wakako M-F, Tatsuya IM, Zenta N, Koichi N, et al. (2011) Effect of allelic variation in three glutenin loci on dough properties and bread making qualities of winter wheat. Breed Sci 61: 281. doi: 10.1270/jsbbs.61.281
  53. 53. Bass C, Handley R, Adams MJ, Hammond-Kosack KE, Kanyuka K (2006) The Sbm1 locus conferring resistance to Soil-borne cereal mosaic virus maps to a gene-rich region on 5DL in wheat. Genome 49: 1140–1148 pmid:17110994 doi: 10.1139/g06-064
  54. 54. Martin JM, Frohberg RC, Morris CF, Talbert LE, Giroux MJ (2001) Milling and bread baking traits associated with puroindoline sequence type in hard red spring wheat. Crop Sci 41: 228–234. doi: 10.2135/cropsci2001.411228x
  55. 55. Hoisington D, Khairallah M, Gonzalez-de-Leon D (1994) Laboratory protocols, CIMMYT Applied Molecular Genetics Laboratory, 2nd edn CIMMYT, Mexico, DF
  56. 56. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual, 2nd edition, New York, Cold Spring Harbor Laboratory.
  57. 57. Thompson JD, Gibson TJ, Plewniak F, Mougin FJ, Higgins DG (1997) The Clustal X Windows interface, flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 25: 4876–4882. pmid:9396791 doi: 10.1093/nar/25.24.4876
  58. 58. Yeh FC, Young RC, Timothy B, Boyle TB, Ye ZH, Mao JX (1997) POPGENE: the user friendly shareware for population genetic analysis Molecular Biology and Biotechnology Centre, University of Alberta, Canada.
  59. 59. Nei M (1973) Analysis of genetic diversity in subdivided populations. Proc Natl Acad Sci USA 70: 3321–3323. pmid:4519626 doi: 10.1073/pnas.70.12.3321
  60. 60. Perrier X, Jacquemoud-Collet JP (2006) DARwin software. Available: http://darwinciradfr.