Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

• Published in: PLoS genetics Vol. 17; no. 11; p. e1009918
• Main Authors: Ng, Bernard, Casazza, William, Kim, Nam Hee, Wang, Chendi, Farhadi, Farnush, Tasaki, Shinya, Bennett, David A, De Jager, Philip L, Gaiteri, Christopher, Mostafavi, Sara
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.11.2021; Public Library of Science (PLoS)
• Subjects: Alleles; Biology and Life Sciences; DNA methylation; Epigenetic inheritance; Epigenome - genetics; Gene expression; Gene regulation; Genetic Diseases, Inborn - genetics; Genetic Diseases, Inborn - pathology; Genetic diversity; Genetic Predisposition to Disease; Genetic regulation; Genetic variation; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genotype & phenotype; Health aspects; Humans; Hypotheses; Medicine and Health Sciences; Methods; Phenotype; Phenotypes; Polymorphism, Single Nucleotide - genetics; Quantitative Trait Loci - genetics; Statistical analysis; Transcriptome - genetics; Transcriptomes; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1009918

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.