E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics.

ABSTRACT:

Motivation

Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information.

Results

We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes wer

SUBMITTER: Gerring ZF 

PROVIDER: S-EPMC8388029 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature