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Zixuan Wang

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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. · Plants · 0 citations

Aug 2026

Soybean Fine-Tunes Defense-Growth Trade-offs via Transcriptional Reprogramming During Beneficial P. chlororaphis Colonization.

Rhizosphere-associated plant growth-promoting rhizobacteria (PGPR) critically enhance plant defense and growth. Our previous study identified Pseudomonas chlororaphis IRHB3 from the soybean rhizosphere and demonstrated its efficacy in suppressing soil-borne disease and promoting plant growth. However, the molecular mechanisms underlying IRHB3 colonization of soybean roots remain poorly characterized. In this study, spatio-temporal colonization dynamics revealed that IRHB3 rapidly adhered to the root surfaces and colonized into endosphere by the root tip, with cortical proliferation coinciding with lateral root formation. Transcriptional profiling indicated that early colonization activated pattern-triggered immunity (PTI) and differentially regulated genes associated with transmembrane signaling receptor kinase signaling, MAPK cascade, reactive oxygen species (ROS) burst, and phytohormone signaling. Following endosphere colonization, IRHB3 reprogrammed host transcriptional priorities toward developmental processes, upregulating photosynthesis-related genes and phytohormone pathways that facilitate root morphogenesis. Notably, multiple transcription factor (TF) families were dynamically induced during colonization. Crucially, transient overexpression of early adhesion-responsive GmWRKY22 or GmWRKY29 in soybean hairy roots suppressed IRHB3 colonization and dynamically modulated ROS biosynthesis-related RBOHs expression, whereas RNAi-mediated silencing of either gene enhanced bacterial colonization and attenuated ROS responses. Collectively, our findings demonstrate that soybean co-opts PTI machinery for the early detection of beneficial rhizobacteria while dynamically balancing defense-growth trade-offs. This work provides a mechanistic framework for optimizing PGPR applications in legume cultivation systems.

Dengqin Wei, Ling Chen, Yuping Li et al. · Plant Physiology · 0 citations