Molecular Breeding

, 39:29 | Cite as

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

  • Craig T. Beil
  • Victoria A. Anderson
  • Alexey Morgounov
  • Scott D. HaleyEmail author


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.


Low-temperature tolerance Photoperiod Genomic selection Ridge regression best linear unbiased prediction Vernalization 



The authors thank the USDA-ARS National Small Grains Collection for the timely delivery of seed used as controls for KASP analysis, Dr. Sarah Grogan for her help in compiling KASP primers, and Emily Hudson-Arns for helping to optimize KASP assays. The IWWIP is financially supported by Ministry of Food, Agriculture and Livestock of the Republic of Turkey and CRP WHEAT.

Authors’ contributions

CB designed the experiments, carried out collection of field phenotypes, conducted KASP marker analyses, performed all statistical analyses, and drafted the manuscript. VA conducted GBS library preparation and bioinformatics for SNP allele calling, coordinated KASP assay design, and provided editorial suggestions on the draft manuscript. SH helped to design and implement the experiments, assisted with data analysis, and provided editorial input on the draft manuscript. AM provided the germplasm used in these experiments and editorial input on the draft manuscript. All authors read and approved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11032_2018_925_MOESM1_ESM.docx (338 kb)
ESM 1 (DOCX 337 kb)


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Soil and Crop SciencesColorado State UniversityFort CollinsUSA
  2. 2.CIMMYT, P.K. 39 EmekAnkaraTurkey

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