Enhancing Genomic Selection for Soybean Drought Tolerance via Integration of Epistasis and AlphaFold2 Prediction

Soybean is a globally important economic and food crop, whose production is often constrained by drought stress, posing a serious threat to yield and quality. Genomic selection (GS) has become a core technology in modern breeding, effectively enhancing breeding efficiency. However, conventional prediction models mainly rely on additive genetic effects and fail to adequately incorporate non-additive factors such as epistasis, limiting further improvements in prediction accuracy. In this study, a genome-wide epistatic analysis of soybean drought tolerance identified 3594 protective interaction pairs. Incorporating significant epistatic SNP pairs into six genomic prediction models resulted in comparable and substantial improvements in prediction accuracy across all models (by 24%). Furthermore, integration of AlphaFold2-based protein structure prediction and transcriptional regulatory analyses validated the biological reliability of protective epistatic pairs, effectively reducing the risk of false positives. Network construction and functional enrichment analyses further revealed that these epistatic pairs participate in coordinated protein structural interactions and are enriched in key biological pathways. Haplotype analysis confirmed the critical regulatory role of non-additive effects in soybean drought tolerance. Collectively, this study establishes a comprehensive evidence chain from molecular mechanisms to breeding applications, demonstrating that integrating epistasis into GS can effectively enhance prediction performance for drought tolerance in soybean. These findings provide novel research strategies for the genetic analysis of complex traits and efficient breeding.