Automated phenotyping and interpretation of image-variation phenotypes for efficient  genetic dissection (PRJCA045399)
Traditional Genome-Wide Association Studies (GWAS) rely on human-defined phenotypes, but their scarcity and dependence on prior knowledge limit genetic discovery power. Advanced digital phenotyping and representation learning enhance genetic discovery power yet lack intuitive interpretation of gene-associated digital phenotypes, stalling progress toward biological insights. We present AIPheno, a novel generative AI-driven genetic discovery framework integrating an Encoder for automated image phenotyping and a Generator for interpretative image generation. Results demonstrate that AIPheno enhanced genetic discovery power across humans, domestic pigeons, rice, and swine, especially in the human retinal fundus dataset, where 22 of 106 identified loci are novel. Intriguingly, AIPheno's interpretability validates reported gene-phenotype associations with robust evidence, refines these established associations by clarifying previously elusive nuanced mechanisms, and further uncovers biological insights into novel genes. Overall, AIPheno bridges digital phenotypes to genes, and genes to biological insights, establishing a novel paradigm for genetic dissection of complex traits.