Haplotype-based genome-wide association increases the predictability of leaf rust (Puccinia triticina) resistance in wheat. (22nd August 2020)

• Record Type: Journal Article
• Authors: Liu, Fang; Jiang, Yong; Zhao, Yusheng; Schulthess, Albert W; Reif, Jochen C
• Editors: Uauy, Cristobal
• Is Part Of: Journal of experimental botany. Volume 71:Number 22(2020)
• Page Start: 6958
• Page End: 6968
• Publication Date: 2020-08-22
• Subjects: Association mapping -- FH-based GWAS -- functional haplotype -- hybrid wheat -- independent validation -- leaf rust -- predictability -- SNP-based GWAS; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/eraa387
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : Functional haplotypes constructed based on epistatic interactions increased the predictability of leaf rust resistance in hybrid wheat as compared with single-marker effect estimates. Abstract: Resistance breeding is crucial for sustainable control of wheat leaf rust and single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) are widely used to dissect leaf rust resistance. Unfortunately, GWAS based on SNPs often explained only a small proportion of the genetic variation. We compared SNP-based GWAS with a method based on functional haplotypes (FH) considering epistasis in a comprehensive hybrid wheat mapping population composed of 133 parents plus their 1574 hybrids and characterized with 626 245 high-quality SNPs. In total, 2408 and 1 139 828 significant associations were detected in the mapping population by using SNP-based and FH-based GWAS, respectively. These associations mapped to 25 and 69 candidate regions, correspondingly. SNP-based GWAS highlighted two already-known resistance genes, Lr22a and Lr34-B, while FH-based GWAS detected associations not only on these genes but also on two additional genes, Lr10 and Lr1 . As revealed by a second hybrid wheat population for independent validation, the use of detected associations from SNP-based and FH-based GWAS reached predictabilities of 11.72% and 22.86%, respectively. Therefore, FH-based GWAS is not only more powerful for detecting associations, but also improves the accuracy of marker-assisted selection compared with the SNP-based approach.