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.