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

ABSTRACT: 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 10,39

SUBMITTER: Arloth J 

PROVIDER: S-EPMC7043350 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature