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

By Xiang Wan, Can Yang, Qiang Yang, H. Xue, Xiaodan Fan, Nelson L. S. Tang, Weichuan Yu. American Journal of Human Genetics, 2010

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Gene-gene interactions have long been recognized to be fundamentally important for understanding 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). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of 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 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the 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, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/10694a39afb734f25347b433f84c74c57aac7cea

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Citations in the corpus

Second-generation PLINK: rising to the challenge of larger and richer datasets (2014, methodology): “’s BOOST software for fast epistasis testing [30], • Ueki, Cordell, and Howey’s -fast-epistasis variance correction and joint-effects test [31,32], • Taliun, Gamper, and Pattaro’s optimizations to Gabriel et al.”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “Some examples include, BOolean Operation-based Screening and Testing ” (BOOST), FastEpistasis (Schüpbach et al., 2010), and Tree-based Epistasis Association Mapping (TEAM) (Zhang et al., 2010).”