DeepWAS: Directly integrating regulatory information into GWAS using deep learning supports master regulator MEF2C as risk factor for major depressive disorder

BioRxiv : the Preprint Server for Biology
Gökcen EraslanNikola S. Mueller

Abstract

Background: Genome-wide association studies (GWAS) identify genetic variants predictive of common diseases but this does not directly inform on molecular mechanisms. The recently developed deep learning-based method DeepSEA uses DNA sequences to predict regulatory effects for up to 1000 functional units, namely regulatory elements and chromatin features in specific cell-types from the ENCODE project. Results: We here describe "DeepWAS", a conceptually new GWAS approach that integrates these predictions to identify SNP sets per functional units prior to association analysis based on multiple regression. To test the power of this approach, we use genotype data from a major depressive disorder (MDD) case/control sample (total N=1,537). DeepWAS identified 177 regulatory SNPs moderating 122 functional units. MDD regulatory SNPs were located mostly in promoters, intronic and distal intergenic regions and validated with public data. Blood regulatory SNPs were experimentally annotated with methylation quantitative trait loci (QTLs), expression quantitative trait methylation loci and expression QTLs and replicated in an independent cohort. Joint integrative analysis of regulatory SNPs and the independently identified annotations were co...Continue Reading

Related Concepts

Mental Disorders
Blood
Chromatin
Mental Depression
Genome
Learning
Methylation
Reversal Learning
Transcription Factor
Promoter

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