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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01337

Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat through Genome-wide Association Study and Genomic Selection

  • 1Washington State University, United States

Snow mold is a yield-limiting disease of wheat in the Pacific Northwest (PNW) region of the US where there is prolonged snow cover. The objectives of this study were to identify genomic regions associated with snow mold tolerance in a diverse panel of PNW winter wheat lines in a genome-wide association study (GWAS) and to evaluate the usefulness of genomic selection (GS) for snow mold tolerance. An association mapping panel (AMP; N=458 lines) was planted in Mansfield and Waterville, WA in 2017 and 2018 and genotyped using the Illumina® 90K Single Nucleotide Polymorphism (SNP) array. GWAS identified 100 significant markers across 17 chromosomes, where SNPs on chromosomes 5A and 5B coincided with major freezing tolerance and vernalization loci. Increased number of favorable alleles was related to improved snow mold tolerance. Independent predictions using the AMP as a training population (TP) to predict snow mold tolerance of breeding lines evaluated between 2015 and 2018 resulted in a mean accuracy of 0.36 across models and marker sets. Modeling non-additive effects improved accuracy even in the absence of a close genetic relatedness between the TP and selection candidates. Selecting lines based on genomic estimated breeding values and tolerance scores resulted in a 24% increase in tolerance. The identified genomic regions associated with snow mold tolerance demonstrated the genetic complexity of this trait and the difficulty in selecting tolerant lines using markers. GS was validated and showed potential for use in PNW winter wheat for selecting on complex traits such tolerance to snow mold.

Keywords: GWAS - genome-wide association study, genomic selection (GS), Ridge regression best linear unbiased prediction, Reproducing Kernal Hilbert space, Reproducing kernel Hilbert space (RKHS), genomic best linear unbiased prediction (GBLUP), snow mold tolerance, winter wheat

Received: 18 Jan 2019; Accepted: 25 Sep 2019.

Copyright: © 2019 Lozada, Godoy, Murray and Carter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Arron Carter, Washington State University, Pullman, United States, ahcarter@wsu.edu