title: BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies.
abstract: 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.
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wan
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yang
authors.forename: Hong
authors.initials: H
authors.lastname: Xue
authors.forename: Xiaodan
authors.initials: X
authors.lastname: Fan
authors.forename: Nelson L S
authors.initials: NL
authors.lastname: Tang
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000235:genetics; D016022:Case-Control Studies; D002896:Chromosomes, Human, Pair 6 / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D004843:Epistasis, Genetic; D018740:Genetic Heterogeneity; D056426:Genetic Loci / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
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