Showing papers by "Scott D. Haley published in 2019"

Genotype imputation in winter wheat using first-generation haplotype map SNPs improves genome-wide association mapping and genomic prediction of traits.

Abstract: Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-sequencing of low-copy genomic regions in the reference panel of 62 lines to impute marker genotypes in a diverse panel of winter wheat cultivars from the U.S. Great Plains. The IBD segments between the reference population and winter wheat cultivars were identified based on SNP genotyped using the 90K iSelect wheat array and genotyping by sequencing (GBS). A genome-wide association study and genomic prediction of resistance to stripe rust in winter wheat cultivars showed that an increase in marker density achieved by imputation improved both the power and precision of trait mapping and prediction. The majority of the most significant marker-trait associations belonged to imputed genotypes. With the vast amount of SNP variation data accumulated for wheat in recent years, the presented imputation framework will greatly improve prediction accuracy in breeding populations and increase resolution of trait mapping hence, facilitate cross-referencing of genotype datasets available across different wheat populations.

Genomic selection for winter survival ability among a diverse collection of facultative and winter wheat genotypes

Abstract: Winter survival ability is important for autumn sown winter wheat (Triticum aestivum L.) in regions with cold winters. Wheat vernalization and photoperiod genes influence adaptation by regulating the timing of the transition from vegetative to reproductive growth to protect the floral meristem from cold temperatures. We evaluated winter injury of 287 genotypes from the Facultative and Winter Wheat Observation Nursery (FAWWON) in six field environments over 3 years (2014 to 2016) in Colorado. Entries were genotyped using single-nucleotide polymorphisms (SNPs) obtained by genotyping by sequencing (GBS) and at known vernalization (Vrn-A1, Vrn-B1, and Vrn-D1) and photoperiod (Ppd-B1 and Ppd-D1) loci using Kompetitive Allele Specific PCR (KASP) assays. Winter injury was observed and visually scored in five of the six environments. Mean GS prediction accuracies across the five environments, obtained through ridge regression best linear unbiased prediction (RR-BLUP) using 23,269 SNPs alone as random effects, ranged from 0.26 ± 0.01 to 0.74 ± 0.00. Incorporation of alleles at Vrn-A1, Vrn-B1, and Vrn-D1 loci as fixed effects in the GS models together with GBS markers as random effects provided the highest prediction accuracy with mean GS accuracies ranging from 0.34 ± 0.01 to 0.78 ± 0.00 across the five environments. Genomic selection models incorporating photoperiod alleles as fixed effects rarely improved GS prediction accuracy of winter injury. Genomic selection models that incorporate both major and minor genetic factors that influence low-temperature tolerance improved the model predictions for identifying genotypes that are best adapted to regions where cold winter temperatures are an important production constraint.