Efficient Two-step Testing of Gene-gene Interactions in Genome-wide Association Studies
Overview
Public Health
Authors
Affiliations
Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.
Stamp J, DenAdel A, Weinreich D, Crawford L G3 (Bethesda). 2023; 13(8).
PMID: 37243672 PMC: 10484060. DOI: 10.1093/g3journal/jkad118.
Improved two-step testing of genome-wide gene-environment interactions.
Kawaguchi E, Kim A, Lewinger J, Gauderman W Genet Epidemiol. 2022; 47(2):152-166.
PMID: 36571162 PMC: 9974838. DOI: 10.1002/gepi.22509.
Kawaguchi E, Li G, Lewinger J, Gauderman W Stat Med. 2022; 41(9):1644-1657.
PMID: 35075649 PMC: 9007892. DOI: 10.1002/sim.9319.
A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.
Wen J, Ford C, Janies D, Shi X Bioinformatics. 2020; 36(12):3803-3810.
PMID: 32227194 PMC: 7320619. DOI: 10.1093/bioinformatics/btaa216.
Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.
Liu Y, Huang J, Urbanowicz R, Chen K, Manduchi E, Greene C Genet Epidemiol. 2019; 44(1):52-66.
PMID: 31583758 PMC: 6980207. DOI: 10.1002/gepi.22262.