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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)
Verlagsversion Forschungsdaten 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 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
Zeitschrift Genome Biology
Quellenangaben Band: 27, Heft: 1, Seiten: , Artikelnummer: 122 Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Institute of Translational Genomics (ITG)
Helmholtz Pioneer Campus (HPC)
Förderungen Free State of Bavaria's Hightech Agenda
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)