1. Genetic dissection of and genomic selection for seed weight, pod length, and pod width in soybean.
- Author
-
Yijie Chen, Yajun Xiong, Huilong Hong, Gang Li, Jie Gao, Qingyuan Guo, Rujian Sun, Honglei Ren, Fan Zhang, Jun Wang, Jian Song, and Lijuan Qiu
- Subjects
SOYBEAN yield ,PLANT genetics ,VEGETATION mapping ,PLANT hormones ,SINGLE nucleotide polymorphisms - Abstract
A biparental soybean population of 364 recombinant inbred lines (RILs) derived from Zhongdou 41 ( ZYD 02.878 was used to identify quantitative trait loci (QTL) associated with hundred-seed weight (100-SW), pod length (PL), and pod width (PW). 100-SW, PL, and PW showed moderate correlations among one another, and 100-SW was correlated most strongly with PW (0.64–0.74). Respectively 74, 70, 75 and 19 QTL accounting for 38.7%–78.8% of total phenotypic variance were identified by inclusive composite interval mapping, restricted two-stage multi-locus genome-wide association analysis, 3 variancecomponent multi-locus random-SNP-effect mixed linear model analysis, and conditional genome-wide association analysis. Of these QTL, 189 were novel, and 24 were detected by multiple methods. Six loci were associated with 100-SW, PL, and PW and may be pleiotropic loci. A total of 284 candidate genes were identified in colocalizing QTL regions, including the verified gene Seed thickness 1 (ST1). Eleven genes with functions involved in pectin biosynthesis, phytohormone, ubiquitin-protein, and photosynthesis pathways were prioritized by examining single nucleotide polymorphism (SNP) variation, calculating genetic differentiation index, and inquiring gene expression. The prediction accuracies of genomic selection (GS) for 100-SW, PL, and PW based on single trait-associated markers reached 0.82, 0.76, and 0.86 respectively, but selection index (SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy. These results shed light on the genetic basis of 100-SW, PL, and PW and provide GS models for these traits with potential application in breeding programs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF