|Title||Analysis of gene-gene interactions using gene-trait similarity regression.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Wang, Xin, Michael P. Epstein, and Jung-Ying Tzeng|
|Keywords||Algorithms, Computer Simulation, Epistasis, Genetic, Genotype, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Principal Component Analysis, Regression Analysis|
OBJECTIVE: Gene-gene interactions (G×G) are important to study because of their extensiveness in biological systems and their potential in explaining missing heritability of complex traits. In this work, we propose a new similarity-based test to assess G×G at the gene level, which permits the study of epistasis at biologically functional units with amplified interaction signals.METHODS: Under the framework of gene-trait similarity regression (SimReg), we propose a gene-based test for detecting G×G. SimReg uses a regression model to correlate trait similarity with genotypic similarity across a gene. Unlike existing gene-level methods based on leading principal components (PCs), SimReg summarizes all information on genotypic variation within a gene and can be used to assess the joint/interactive effects of two genes as well as the effect of one gene conditional on another.RESULTS: Using simulations and a real data application to the Warfarin study, we show that the SimReg G×G tests have satisfactory power and robustness under different genetic architecture when compared to existing gene-based interaction tests such as PC analysis or partial least squares. A genome-wide association study with approx. 20,000 genes may be completed on a parallel computing system in 2 weeks.
|Alternate Journal||Hum Hered|
|Original Publication||Analysis of gene-gene interactions using gene-trait similarity regression.|
|PubMed Central ID||PMC4115296|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 HG007508 / HG / NHGRI NIH HHS / United States
R01 MH074027 / MH / NIMH NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
Analysis of gene-gene interactions using gene-trait similarity regression.