ArticleFrontiers in plant science2026
Integrative haplotype and SNP-based GWAS supports the identification of stable genomic loci controlling yield-related traits in soybean.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Multi-Season Multi-Model GWAS Prioritizes Stable Genomic Loci and Candidate Genes for Six Agronomic Traits in Soybean Under Conditions in Southeastern Kazakhstan.Plants (Basel, Switzerland) · 2026Article
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10 authors.
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Abstract
Soybean yield is vulnerable to environmental variation, therefore, it is important to detect and implement stable genomic regions associated with yield-related traits in soybean breeding programs. In this study, SNP and haplotype-based GWAS were conducted to reveal important candidate genomic regions and putative candidate genes associated with soybean yield-related traits. This study demonstrates that the integration of haplotype and SNP-based GWAS could improve the detection of genomic regions associated with complex traits, enhance statistical power, and facilitate the identification of biologically relevant candidate genes. Ten stable haplotype blocks and six stable SNPs were detected based on the integration of haplotype and SNP-based GWAS, respectively. Furthermore, multiple candidate genes associated with the yield-related traits were identified. For instance, six genes were identified as transporters, including Glyma.15G092800, encoding serine-type endopeptidase activity, Glyma.15G203300 encoding a major facilitator superfamily (MFS) sugar transporter, Glyma.04G163000, transmembrane transporter, and Glyma.04G164100, leucine-rich repeat receptor-like protein kinase (LRR-RLK), as the most promising candidate genes. Additionally, three genes involved in signaling and pathways of various phytohormones can be promising candidates for increasing seed yield through improving plant architecture in soybean plants. The identified superior haplotypes with favourable alleles will be useful for marker-assisted selection in future breeding programs in soybean.
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