Genome-Wide Association Study and Genomic Prediction of Soybean Mosaic Virus Resistance

文献类型： 外文期刊

第一作者： He, Di

作者： He, Di;Wu, Xintong;Liu, Zhi;Yang, Qing;Shi, Xiaolei;Yan, Long;He, Di;Yan, Long;Song, Qijian;Shi, Ainong;Li, Dexiao

作者机构：

关键词： soybean mosaic virus (SMV); genome-wide association study (GWAS); genomic prediction (GP); quantitative trait locus (QTL); marker-assisted selection (MAS)

期刊名称：INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES （ 影响因子：4.9； 五年影响因子：5.7 ）

ISSN： 1661-6596

年卷期： 2025 年 26 卷 5 期

页码：

收录情况： SCI

摘要： Soybean mosaic virus (SMV), a pathogen responsible for inducing leaf mosaic or necrosis symptoms, significantly compromises soybean seed yield and quality. According to the classification system in the United States, SMV is categorized into seven distinct strains (G1 to G7). In this study, we performed a genome-wide association study (GWAS) in GAPIT3 using four analytical models (MLM, MLMM, FarmCPU, and BLINK) on 218 soybean accessions. We identified 22 SNPs significantly associated with G1 resistance across chromosomes 1, 2, 3, 12, 13, 17, and 18. Notably, a major quantitative trait locus (QTL) spanning 873 kb (29.85-30.73 Mb) on chromosome 13 exhibited strong association with SMV G1 resistance, including the four key SNP markers: Gm13_29459954_ss715614803, Gm13_29751552_ss715614847, Gm13_30293949_ss715614951, and Gm13_30724301_ss715615024. Within this QTL, four candidate genes were identified: Glyma.13G194100, Glyma.13G184800, Glyma.13G184900, and Glyma.13G190800 (3Gg2). The genomic prediction (GP) accuracies ranged from 0.60 to 0.83 across three GWAS-derived SNP sets using five models, demonstrating the feasibility of GP for SMV-G1 resistance. These findings could provide a useful reference in soybean breeding targeting SMV-G1 resistance.

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