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

Janine Arloth, Gökcen Eraslan, Till F.M. Andlauer, Jade Martins, Stella Iurato, Brigitte Kühnel, Melanie Waldenberger, Josef Frank, Ralf Gold, Bernhard Hemmer, Felix Luessi, Sandra Nischwitz, Friedemann Paul, Heinz Wiendl, Christian Gieger, Stefanie Heilmann-Heimbach, Tim Kacprowski, Matthias Laudes, Thomas Meitinger, Annette PetersRajesh Rawal, Konstantin Strauch, Susanne Lucae, Bertram Müller-Myhsok, Marcella Rietschel, Fabian J. Theis, Elisabeth B. Binder, Nikola S. Mueller

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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 cellmapslab/DeepWAS.

Original languageEnglish
Article numbere1007616
JournalPLoS Computational Biology
Issue number2
StatePublished - Feb 2020


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