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S. Didorenko

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Open access Sep 2026

Multi-Season Multi-Model GWAS Prioritizes Stable Genomic Loci and Candidate Genes for Six Agronomic Traits in Soybean Under Conditions in Southeastern Kazakhstan

Reliable identification of genomic regions controlling complex agronomic traits across variable growing seasons remains a major challenge in soybean genetics and breeding. Here, a diverse panel of 252 soybean accessions was evaluated over six consecutive growing seasons (2018–2023) for flowering time, maturity, plant height, number of seeds per plant, seed yield per plant, and thousand-seed weight. Whole-genome resequencing and variant filtering yielded 2,019,772 high-quality SNPs, and association signals were evaluated using Inclusive Integrative Input Multiple-locus Random-SNP-effect Mixed Linear Model (IIIVmrMLM), Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Multi-Locus Mixed Model (MLMM) together with linkage disequilibrium (LD)-based locus consolidation. Cross-model prioritization retained 21 high-confidence loci supported by all three GWAS models and distributed across 10 chromosomes. Among the identified loci, 18 overlapped or co-localized with previously reported SoyBase genes and QTLs, whereas three (q.VER2.13-1, q.YP.01-1, and q.TSW.15-1) showed no positional overlap with known genes and QTLs and were therefore considered presumably novel. These three loci were associated with flowering time, yield per plant, and thousand-seed weight, accounting for 1.60%, 2.13%, and 5.53% of phenotypic variation, respectively. Ten loci co-localized with genomic regions containing established soybean regulators, including E2, E3, GmDt2, and POWR1, support the biological plausibility of the association results. Integration of genomic position, functional annotation, and tissue-expression evidence prioritized 112 candidate genes across 18 loci. These findings provide a focused set of genomic loci and candidate genes for independent validation and further investigation of the genetic basis of soybean adaptation and yield formation under variable continental growing conditions.

A. Zatybekov, Y. Genievskaya, C. Fang et al. · 0 citations
Open access Aug 2026

Integrative haplotype and SNP-based GWAS supports the identification of stable genomic loci controlling yield-related traits in soybean

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.

Shynar Mazkirat, S. Didorenko, K. Bulatova et al. · 0 citations

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