Epiformer: epistasis detection by genome language model and dual-channel network

Epistasis plays a crucial role in explaining missing heritability in complex traits, yet most detection methods fail to effectively capture local and global interaction patterns and long-range dependencies critical to complex trait architecture. We propose Epiformer that leverages genome language model Evo 2 to capture long-range dependencies from genetic background, and a dual-channel network to jointly model local and global epistasis and additive effects. Epiformer can identify key SNPs and their interactions from complex genomic data to empower phenotype prediction with interpretability, which reinforces epistasis detection. Epiformer performs robustly across species, revealing biologically meaningful patterns and offering new insights into genetic architecture.