Abstract
The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.
Highlights
Genome wide association studies (GWAS) have discovered tens of thousands of common genetic variants (SNPs) associated with complex traits and disease susceptibility [1]
cascading epigenomic analysis for GWAS (CEWAS) entails learning a set of Cascading epigenomic analysis for identifying disease genes genome wide association studies (GWAS) variants models for predicting DNA methylation (DNAm) levels from genotype data and a set of models for predicting gene expression from predicted DNAm levels (Fig 1A, Methods). The former set of models are applied to GWAS summary statistics to generate epigenomic level statistics, which are combined using the latter set of models on a gene-by-gene basis
We proposed CEWAS for integrating genotype, DNAm, and gene expression data to model the cascading effects of GWAS SNPs on DNAm, gene expression, and eventually phenotypes
Summary
Genome wide association studies (GWAS) have discovered tens of thousands of common genetic variants (SNPs) associated with complex traits and disease susceptibility [1]. The conventional approach is to apply univariate association analysis to map each SNP to its target gene based on the correlation between SNP dosages and gene expression levels (i.e. expression quantitative trait loci (eQTL) studies) [3]. A few studies have begun to investigate epigenomic modifications by combining the effects of GWAS SNPs on phenotypes and DNA methylation (mQTLs), in addition to gene expression (eQTLs) [10,11,12]. Along these lines, the use of epigenomic annotations to guide selection of expression-predictive SNPs has been proposed [13]. A recent approach attempts to go beyond modeling cis effects by incorporating trans SNPs associated with epigenomic mediators of gene expression [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
