High-Resolution Genome-wide Association Study Identifies Genomic Regions and Candidate Genes for Important Agronomic Traits in Wheat

• Published in: Molecular plant 2020-09, Vol.13 (9), p.1311-1327
• Main Authors: Pang, Yunlong, Liu, Chunxia, Wang, Danfeng, St. Amand, Paul, Bernardo, Amy, Li, Wenhui, He, Fang, Li, Linzhi, Wang, Liming, Yuan, Xiufang, Dong, Lei, Su, Yu, Zhang, Huirui, Zhao, Meng, Liang, Yunlong, Jia, Hongze, Shen, Xitong, Lu, Yue, Jiang, Hongming, Wu, Yuye, Li, Anfei, Wang, Honggang, Kong, Lingrang, Bai, Guihua, Liu, Shubing
• Format: Article
• Language: English
• Subjects: agronomic traits; candidate genes; Chromosomes, Plant - genetics; genome-wide association; Genome-Wide Association Study - methods; Quantitative Trait Loci - genetics; Triticum - genetics; wheat
• ISSN: 1674-2052; 1752-9867
• DOI: 10.1016/j.molp.2020.07.008

Wheat (Triticum aestivum) is a major staple food crop worldwide. Genetic dissection of important agronomic traits is essential for continuous improvement of wheat yield to meet the demand of the world's growing population. We conducted a large-scale genome-wide association study (GWAS) using a panel of 768 wheat cultivars that were genotyped with 327 609 single-nucleotide polymorphisms generated by genotyping-by-sequencing and detected 395 quantitative trait loci (QTLs) for 12 traits under 7 environments. Among them, 273 QTLs were delimited to ≤1.0-Mb intervals and 7 of them are either known genes (Rht-D, Vrn-B1, and Vrn-D1) that have been cloned or known QTLs (TaGA2ox8, APO1, TaSus1-7B, and Rht12) that were previously mapped. Eight putative candidate genes were identified for three QTLs that enhance spike seed setting and grain size using gene expression data and were validated in three bi-parental populations. Protein sequence analysis identified 33 putative wheat orthologs that have high identity with rice genes in QTLs affecting similar traits. Large r2 values for additive effects observed among the QTLs for most traits indicated that the phenotypes of these identified QTLs were highly predictable. Results from this study demonstrated that significantly increasing GWAS population size and marker density greatly improves detection and identification of candidate genes underlying a QTL, solidifying the foundation for large-scale QTL fine mapping, candidate gene validation, and developing functional markers for genomics-based breeding in wheat. A panel of 768 diverse wheat accessions genotyped with 327 609 GBS-derived SNPs was used to conduct GWAS for 12 agronomic traits measured under multiple environments. A large set of QTLs and candidate genes were identified with three QTLs validated using bi-parental populations, solidifying the foundation for QTL fine mapping, candidate gene validation, and genomics-based breeding.