Identification of QTLs and candidate genes for 100-seed weight trait using PyBSASeq algorithm in soybean

Abstract

<p id="C3">The PyBSASeq algorithm, based on quantifying the enrichment of likely trait-associated SNPs in a chromosomal interval, was proved more suitable for genetic analysis of complex quantitative traits in bulked segregant analysis. In this study, to identify the loci and candidate genes of 100-seed weight (SW) in soybean using the PyBSASeq algorithm, 149 RILs derived from the hybrid of ‘Huapidou’ and ‘Qihuang 26’ were used as materials. As a result, 11 candidate regions closely associated with SW were identified by mining the 100-grain weight related loci of soybean, which were located on chromosomes 1, 2, 4, 7, 9, 14, and 16, respectively. Among these regions, <italic>qSW4-1</italic>, <italic>qSW9-1</italic>, <italic>qSW9-2</italic>, and <italic>qSW7-1</italic> were consistent with the QTLs for 100-seed weight reported previously, and a total of 218 coding genes were included in the candidate regions. Among these genes, <italic>Glyma.02G075000</italic> and <italic>Glyma.04G082500 </italic>were predicted to be candidate genes using gene expressional and haplotype analysis. Bioinformatics analysis showed that the candidate genes were mainly involved in sugar transport and biosynthesis of Vitamin E. The results will be helpful to elucidate the genetic mechanism of seed weight regulation in soybean and provide a reference for the study of quantitative trait based on BSA-Seq method.