Article
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.
PLoS computational biology - 1 Feb 2020
Arloth Janine, Eraslan Gökcen, Andlauer Till F M, Martins Jade, Iurato Stella, Kühnel Brigitte, Waldenberger Melanie, Frank Josef, Gold Ralf, Hemmer Bernhard, Luessi Felix, Nischwitz Sandra, Paul Friedemann, Wiendl Heinz, Gieger Christian, Heilmann-Heimbach Stefanie, Kacprowski Tim, Laudes Matthias, Meitinger Thomas, Peters Annette, Rawal Rajesh, Strauch Konstantin, Lucae Susanne, Müller-Myhsok Bertram, Rietschel Marcella, Theis Fabian J, Binder Elisabeth B, Mueller Nikola S
Abstract excerpt
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...
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