BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

TL;DR

BOOST is a novel, efficient method for detecting gene-gene interactions in genome-wide case-control studies, enabling rapid analysis of all pairwise interactions and uncovering new genetic insights into complex diseases.

Contribution

The paper introduces BOOST, a fast and simple method for genome-wide gene-gene interaction detection, significantly reducing computational time compared to existing approaches.

Findings

Identified distinct interaction patterns in type 1 diabetes and rheumatoid arthritis datasets.

Discovered new gene interactions in the MHC region related to type 1 diabetes.

Analysis completed in less than 60 hours on standard desktop hardware.

Abstract

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named `BOolean Operation based Screening and Testing'(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool.