Evaluation and extension of a kernel-based method for gene-gene interaction tests of common variants
Interaction is likely to play a signiﬁcant role in complex diseases, and various methods are available for identifying interactions between variants in genome-wide association studies (GWAS). Kernel-based variance component methods such as SKAT are ﬂexible and computationally eﬃcient methods for identifying marginal associations. A kernel-based variance component method, called the Gene-centric Gene-Gene Interaction with Smoothing-sPline ANOVA model (SPA3G) was proposed to identify gene-gene interactions for a quantitative trait. For interaction testing, the SPA3G method performs better than some SNP-based approaches under many scenarios. In this thesis, we evaluate the properties of the SPA3G method and extend SPA3G using alternative p-value approximations and interaction kernels. This thesis focuses on common variants only. Our simulation results show that the allele matching interaction kernel, combined with the method of moments p-value approximation, leads to inﬂated type I error in small samples. For small samples, we propose a Principal Component (PC)-based interaction kernel and computing p-values with a 3-moment adjustment that yield more appropriate type I error. We also propose a weighted PC kernel that has higher power than competing approaches when interaction eﬀects are sparse. By combining the two proposed kernels, we develop omnibus methods that obtain near-optimal power in most settings. Finally, we illustrate how to analyze the interaction between selected gene pairs on the age at natural menopause (ANM) from the Framingham Heart Study.