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

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Item Type:Preprint
Title:DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
Creators Name:Arloth, J. and Eraslan, G. and Andlauer, T.F.M. and Martins, J. and Iurato, S. and Kühnel, B. and Waldenberger, M. and Frank, J. and Gold, R. and Hemmer, B. and Luessi, F. and Nischwitz, S. and Paul, F. and Wiendl, H. and Gieger, C. and Heilmann-Heimbach, S. and Kacprowski, T. and Laudes, M. and Meitinger, T. and Peters, A. and Rawal, R. and Strauch, K. and Lucae, S. and Müller-Myhsok, B. and Rietschel, M. and Theis, F.J. and Binder, E.B. and Mueller, N.S.
Abstract:Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) 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) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.
Source:bioRxiv
Publisher:Cold Spring Harbor Laboratory Press (U.S.A.)
Article Number:069096
Date:5 December 2018
Official Publication:https://doi.org/10.1101/069096

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