摘要：We proposed a faster pedigree-based generalized multifactor dimensionality reduction algorithm, called PedGMDR II (PII), to detect gene-gene interactions underlying complex traits. Inherited from our previous framework of PedGMDR (PI), PII can handle both dichotomous and continuous traits in pedigree-based designs and allows for covariate adjustment. Compared with PI, this faster version can theoretically halve the computing burden and memory requirement. To evaluate the performance of PII, we performed comprehensive simulations across a wide variety of experimental scenarios, in which we considered two study designs, discordant sib pairs and mixed families of varying size, and, for each study design, we considered five common factors that may potentially affect statistical power: minor allele frequency, missing rate of parental genotypes, covariate effect, gene-gene interaction, and scheme to adjust phenotypic outcomes. Simulations showed that PII gave well controlled type I error rates against population admixture. Under a total of 4,096 scenarios simulated, PII, in general, had a higher average power than PI for both dichotomous and continuous traits, and the advantage was more pronounced for continuous traits. PII also appeared to be less sensitive than PI to changes in the other four factors than the magnitude of genetic effects considered in this study. Applied to the Mid-South Tobacco Family study, PII detected a significant interaction with a $p$ value of $5.4 × 10^{−5}$ between two taster receptor genes, $TAS2R16$ and $TAS2R38$, responsible for nicotine dependence. In conclusion, PII is a faster supplementary version of our previous PI for detecting multifactor interactions.
关键词：gene-gene interaction; pedigree-based design; GMDR; population admixture; statistical power
