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Chaudhary, S. ; Voigts, A. ; Bereket, M.* ; Albert, M.L.* ; Schwamborn, K.* ; Zeggini, E. ; Casale, F.P.

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

Genome Biol. 27:122 (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Colocalization ; Generative Models ; Genome-wide Association Studies ; Histology ; Kernel Methods ; Semantic Autoencoder ; Variance Component Test; Association; Models; Tests; Foxe1; Features; Targets
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Journal Genome Biology
Quellenangaben Volume: 27, Issue: 1, Pages: , Article Number: 122 Supplement: ,
Publisher Springer
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Institute of Translational Genomics (ITG)
Helmholtz Pioneer Campus (HPC)
Grants Free State of Bavaria's Hightech Agenda
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)