Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Nyine, Moses; Wang, Shichen; Kiani, Kian; Jordan, Katherine; Liu, Shuyu; Byrne, Patrick; Haley, Scott; Baenziger, Stephen; Chao, Shiaoman; Bowden, Robert; Akhunov, Eduard
• Is Part Of: G3. Volume 9:Issue 1(2019)
• Page Start: 125
• Page End: 133
• Publication Date: 2019-01-01
• Subjects: Wheat HapMap -- SNP -- Imputation -- GWAS -- Genomic Prediction -- GenPred -- Shared Data Resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.118.200664
• Languages: English
• ISSNs: 2160-1836

Abstract

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.