title: Analysis of gene-gene interactions using gene-trait similarity regression.
abstract: 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.
authors: Bioinformatics Research Center, North Carolina State University, Raleigh, N.C., USA.
authors: Xin
authors: X
authors: Wang
authors: Michael P
authors: MP
authors: Epstein
authors: Jung-Ying
authors: JY
authors: Tzeng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004843:Epistasis, Genetic; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D012044:Regression Analysis