Eigen-Epistasis for detecting Gene-Gene interactions

TL;DR

This paper introduces Eigen-Epistasis, a novel gene-level method for detecting gene-gene interactions in GWAS, using Eigen-Epistasis components and Group Lasso to identify significant interactions without prior filtering.

Contribution

The paper presents a new approach for gene-level epistasis detection that does not rely on filtering for significant genes, using Eigen-Epistasis components and penalized regression.

Findings

Outperforms recent methods on synthetic data

Successfully detects new gene-gene interactions in GWAS datasets

Demonstrates robustness across different settings

Abstract

A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects.In this paper we propose an original approach for detecting epistasis at the gene level, without systematically filtering on significant genes. We first compute interaction variables for each gene pair by finding its Eigen-Epistasis component, defined as the linear combination of Gene SNPs having the highest correlation with the phenotype. The selection of significant effects is done using a penalized regression method based on Group Lasso controlling the False Discovery Rate.The method is tested against two recent alternative proposals from the literature using synthetic data, and shows good performances in different settings. We demonstrate the power of our approach by detecting new gene-gene interactions on three genome-wide association studies.