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Knauer-Arloth, J. ; Eraslan, G. ; Andlauer, T.F.M.* ; Martins, J.* ; Iurato, S.* ; Kühnel, B. ; Waldenberger, M. ; Frank, J.* ; Gold, R.* ; Hemmer, B.* ; Luessi, F.* ; Nischwitz, S.* ; Paul, F.* ; Wiendl, H.* ; Gieger, C. ; Heilmann-Heimbach, S.* ; Kacprowski, T.* ; Laudes, M.* ; Meitinger, T. ; Peters, A. ; Rawal, R. ; Strauch, K. ; Lucae, S.* ; Müller-Myhsok, B.* ; Rietschel, M.* ; Theis, F.J. ; Binder, E.B.* ; Müller, N.S.

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

PLoS Comput. Biol. 16:e1007616 (2020)

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Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Transcription Factor-binding; Risk; Discovery; Variants; Impact

ISSN (print) / ISBN 1553-734X

e-ISSN 1553-7358

Journal PLoS Computational Biology

Quellenangaben Volume: 16, Issue: 2, Article Number: e1007616

Publisher Public Library of Science (PLoS)

Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)
Institute of Epidemiology II (EPI2)
Institute of Human Genetics (IHG)
Institute of Genetic Epidemiology (IGE)