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    • HistoGWAS: An AI-enabled Framework for Automated Genetic Analysis of Tissue Phenotypes in Histology Cohorts

    Shubham Chaudhary, Almut Voigts,Michael Bereket,Matthew L. Albert, Kristina Schwamborn,Eleftheria Zeggini,Francesco Paolo Casale

    crossref（2024）

    Institute of AI for Health | Department of Computer Science | Octant Biosciences | Institute of Pathology | TUM School of Medicine and Health

    Cited 0|Views6
    Abstract
    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 currently lacking. We address this gap with HistoGWAS, a framework that merges AI-driven tissue characterization with fast variance component models for scalable genetic association testing. This integration enables automated, genome-wide assessments of variant effects on tissue histology and facilitates the visualization of phenotypes linked to significant genetic loci. Applying HistoGWAS to eleven tissue types from the GTEx cohort, we identified four genome-wide significant loci, which we linked to distinct tissue histological and gene expression changes. Ultimately, a power analysis confirms HistoGWAS’s effectiveness in large-scale histology cohorts, underscoring its transformative potential in studying the effects of genetic variations on tissue and their role in health and disease. ### Competing Interest Statement MA is an employee of Octant and in the scientific advisory board of HI-Bio. The other authors declare that they have no competing interests.
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