HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts

作者：Shubham Chaudhary, A. Voigts, Michael D. Bereket, Matthew L. Albert, Kristina Schwamborn, E. Zeggini, F. P. Casale · 发表于：Genome Biology · 年份：2026 · DOI：10.1186/s13059-026-04031-z · 被引用次数：3 · 研究领域：Medicine

Understanding how genetic variation shapes tissue structure is crucial for disease biology, yet scalable, general-purpose frameworks for genetic analysis of histology traits are lacking. We present HistoGWAS, a framework for genome-wide association studies of histology data that leverages foundation models for automated trait definition, variance component models for efficient association testing, and generative models for variant effect interpretation. Applied to 11 tissues from the Genotype-Tissue Expression project, HistoGWAS identifies four genome-wide significant loci associated with tissue histology—tissue quantitative trait loci (tissueQTLs)—which we link to molecular changes and complex traits. Power analyses demonstrate scalability to population-scale histology cohorts. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-026-04031-z.