HistoGWAS: An AI Framework for Automated and Interpretable Genetic Analysis of Tissue Phenotypes

Understanding how genetic variation affects tissue structure and function is crucial for deciphering disease mechanisms, yet comprehensive methods for genetic analysis of tissue histology are lacking. We address this gap with HistoGWAS, a framework integrating AI tools for representation learning and image generation with fast variance component models to enable scalable and interpretable genome-wide association studies of histological traits. HistoGWAS employs histology foundation models for automated trait characterization and generative models to visually interpret the genetic influences on these traits. Applied to eleven tissue types from the GTEx cohort, HistoGWAS identifies four genome-wide significant loci, which we linked to specific tissue histological and gene expression changes. A power analysis confirms the effectiveness of HistoGWAS in analyses of large-scale histological data, underscoring its potential to transform imaging genetic studies.