Genome-Wide Association Study and Genomic Prediction of Essential Agronomic Traits in Diversity Panel of Soybean Varieties

文献类型： 外文期刊

第一作者： Dong, Qianli

作者： Dong, Qianli;Cheng, Yuting;Li, Yiyang;Tong, Yan;Yu, Jiaxin;Liu, Bao;Xu, Chunming;Liu, Dazhuang;Ding, Xiaoyang;Zhao, Na

作者机构：

关键词： soybean; GWAS; genomic selection; yield

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： Soybean, a globally important crop, is a typical short-day and thermophilic plant. Continuous efforts are necessary to elucidate the genetic basis of its essential traits. In this study, we assembled a collection of 203 soybean varieties, all of which are well suited for cultivation in the northeastern region of China. We assessed 15 agronomic traits under three distinct environments, noting substantial phenotypic variations in the panel and stable correlations among traits. The population structure analysis, based on genotyping-by-sequencing (GBS) data, revealed seven subpopulations within the panel and significant gene flows among these subpopulations. Through genome-wide association studies (GWASs), we identified 64 significantly associated loci (SALs) for 15 traits and unveiled the genetic interconnections between yield and related traits. Additionally, we highlighted a few candidate genes within SALs for yield and related traits. Finally, we evaluated the genomic prediction performances of four distinct methods across the three environments, revealing the significant influence of environmental factors on predictive accuracies. We found that rrBLUP is suitable for most traits, though specific traits may benefit from more complex machine learning models. Our findings establish a foundation for the future research of genetic mechanisms of soybean agronomic traits and the application of genomic selection in soybean breeding.

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