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Detecting genetic interactions for quantitative traits with U ‐statistics
Author(s) -
Li Ming,
Ye Chengyin,
Fu Wenjiang,
Elston Robert C.,
Lu Qing
Publication year - 2011
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20594
Subject(s) - multifactor dimensionality reduction , statistic , test statistic , gene–environment interaction , genetic association , biology , single nucleotide polymorphism , genetics , gene , quantitative trait locus , computational biology , genome wide association study , candidate gene , statistical hypothesis testing , statistics , computer science , mathematics , genotype
Abstract The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U ‐Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene‐gene/gene‐environment interactions. In this new approach, a U ‐Statistic‐based forward algorithm is first used to select potential disease‐susceptibility loci and then a weighted U ‐statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U ‐Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high‐dimensional gene‐gene/gene‐environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene‐gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2 , jointly associated with the level of ND ( P ‐value = 5.31e−7). The association, which involves essential interaction, is replicated in two independent datasets with P ‐values of 1.08e−5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products. Genet. Epidemiol . 2011. © 2011 Wiley‐Liss, Inc. 35: 457‐468, 2011