High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genomewide association studies in wheat.SNP genotyping | polyploid wheat | selection scans | wheat improvement | breeding history

Because of polyploidy and large genome size, deletion stocks of bread wheat are an ideal material for physically allocating ESTs and genes to small chromosomal regions for targeted mapping. To enhance the utility of deletion stocks for chromosome bin mapping, we characterized a set of 84 deletion lines covering the 21 chromosomes of wheat using 725 microsatellites. We localized these microsatellite loci to 94 breakpoints in a homozygous state (88 distal deletions, 6 interstitial), and 5 in a heterozygous state representing 159 deletion bins. Chromosomes from homoeologous groups 2 and 5 were the best covered (126 and 125 microsatellites, respectively) while the coverage for group 4 was lower (80 microsatellites). We assigned at least one microsatellite in up to 92% of the bins (mean 4.97 SSR/bin). Only a few discrepancies concerning marker order were observed. The cytogenetic maps revealed small genetic distances over large physical regions around the centromeres and large genetic to physical map ratios close to the telomeres. As SSRs are the markers of choice for many genetic and breeding studies, the mapped microsatellite loci will be useful not only for deletion stock verifications but also for allocating associated QTLs to deletion bins where numerous ESTs that could be potential candidate genes are currently assigned.

Fusarium head blight (FHB) resistance is quantitative and diffi cult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used U.S. cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (MLR) (fi xed effects). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including genomewide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. Genomic selection accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON, marker plus trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genomewide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be used to improve accuracies in some cases.