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<p>Soybean (<i>Glycine max</i> (L.) Merr.) seed weight is a critical yield component that varies significantly across Canadian growing regions due to environmental factors. Dissecting the genetic basis of this trait is challenging, as phenotypic plasticity often obscures stable regulatory mechanisms. This study aimed to identify stable gene co-expression networks associated with seed weight using multi-year transcriptomic data. We performed weighted gene co-expression network analysis (WGCNA) on RNA-seq data from leaf tissue collected from ten soybean genotypes grown across Eastern and Western Canada over four years (2018–2021). We identified five gene modules that were significantly correlated with seed weight and highly preserved across years. Functional analysis linked these modules to source-sink relationships, specifically circadian rhythm, photosynthesis, and lipid metabolism. Hub gene analysis identified known regulators <i>Reveille 1</i> and <i>Wrinkled 1</i>, validating the biological relevance of the networks. Furthermore, 18 of the top 50 hub genes co-localized with known quantitative trait loci (QTLs) for seed weight. Differential expression analysis revealed 11 hub genes with consistent regionally-linked expression patterns, suggesting potential targets for region-specific breeding. By focusing on network stability through module preservation analysis, we filtered out environmental noise to reveal core regulatory pathways connecting leaf gene expression during seed filling to seed weight at harvest. These stable hub genes represent high-confidence targets for breeding programs aiming to improve soybean yield stability across different geographies.</p>

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Conserved regulatory modules and hub genes linking leaf transcriptomes to soybean seed weight

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月28日

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  • Jakob Bruggink,
  • Julia Wozny,
  • Ashkan Golshani,
  • Elroy Cober,
  • Bahram Samanfar

摘要

Soybean (Glycine max (L.) Merr.) seed weight is a critical yield component that varies significantly across Canadian growing regions due to environmental factors. Dissecting the genetic basis of this trait is challenging, as phenotypic plasticity often obscures stable regulatory mechanisms. This study aimed to identify stable gene co-expression networks associated with seed weight using multi-year transcriptomic data. We performed weighted gene co-expression network analysis (WGCNA) on RNA-seq data from leaf tissue collected from ten soybean genotypes grown across Eastern and Western Canada over four years (2018–2021). We identified five gene modules that were significantly correlated with seed weight and highly preserved across years. Functional analysis linked these modules to source-sink relationships, specifically circadian rhythm, photosynthesis, and lipid metabolism. Hub gene analysis identified known regulators Reveille 1 and Wrinkled 1, validating the biological relevance of the networks. Furthermore, 18 of the top 50 hub genes co-localized with known quantitative trait loci (QTLs) for seed weight. Differential expression analysis revealed 11 hub genes with consistent regionally-linked expression patterns, suggesting potential targets for region-specific breeding. By focusing on network stability through module preservation analysis, we filtered out environmental noise to reveal core regulatory pathways connecting leaf gene expression during seed filling to seed weight at harvest. These stable hub genes represent high-confidence targets for breeding programs aiming to improve soybean yield stability across different geographies.