Yunlong Pang, Yuye Wu, Chunxia Liu, Wenhui Li, Paul St. Amand, Amy Bernardo, Danfeng Wang, Lei Dong, Xiufang Yuan, Huirui Zhang, Meng Zhao, Linzhi Li, Liming Wang, Fang He, Yunlong Liang, Qiang Yan, Yue Lu, Yu Su, Hongming Jiang, Jiajie Wu, Anfei Li, Lingrang Kong, Guihua Bai & Shubing Liu

Theoretical and Applied Genetics September 2021; vol. 134:2857–2873

Key message

High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding.

Abstract

Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat.

See: https://link.springer.com/article/10.1007/s00122-021-03863-6