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Gräf, L. ; Sens, D.W. ; Shilova, L. ; Casale, F.P.

Disease risk predictions with differentiable mendelian randomization.

In: (Research in Computational Molecular Biology). Berlin [u.a.]: Springer, 2024. 385-389 (Lect. Notes Comput. Sc. ; 14758 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Predicting future disease onset is crucial in preventive healthcare, yet longitudinal datasets linking early risk factors to subsequent health outcomes are scarce. To address this challenge, we introduce Differentiable Mendelian Randomization (DMR), an extension of the classical Mendelian Randomization framework for disease risk predictions without longitudinal data. To do so, DMR leverages risk factors and genetic profiles from a healthy cohort, along with results from genome-wide association studies (GWAS) of diseases of interest. In this work, we describe the DMR framework and confirm its reliability and effectiveness in simulations and an application to a type 2 diabetes (T2D) cohort.
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Publication type Article: Conference contribution
Corresponding Author
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Research in Computational Molecular Biology
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14758 LNCS, Issue: , Pages: 385-389 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)