Abstract
Gene-based and single-nucleotide polymorphism (SNP) set association studies provide an important complement to SNP analysis. Kernel-based nonparametric regression has recently emerged as a powerful and flexible tool for this purpose. Our goal is to explore whether this approach can be extended to incorporate and test for interaction effects, especially for genes containing rare variant SNPs. Here, we construct nonparametric regression models that can be used to include a gene-environment interaction effect under the framework of the least-squares kernel machine and examine the performance of the proposed method on the Genetic Analysis Workshop 17 unrelated individuals data set. Two hundred simulated replicates were used to explore the power for detecting interaction. We demonstrate through a genome scan of the quantitative phenotype Q1 that the simulated gene-environment interaction effect in the data can be detected with reasonable power by using the least-squares kernel machine method.
Highlights
There is continuing interest in the investigation of interactions in human genetics, including gene-environment and gene-gene interactions, on the assumption that they play an important role in understanding complex traits
In this report we focus on the analysis of quantitative phenotype Q1 in the Genetic Analysis Workshop 17 (GAW17) data set with an least-squares kernel machine (LSKM)-based method that shows the greatest promise
We answer the two separate questions asked in the introduction: (1) What is the power of the LSKMbased method to detect a gene-environment interaction effect per se, based on models (11) and (12); and (2) does incorporating interaction terms into the LSKM improve the power of detecting a true gene with interaction effects, based on models (13) and (14)?
Summary
There is continuing interest in the investigation of interactions in human genetics, including gene-environment and gene-gene interactions, on the assumption that they play an important role in understanding complex traits. Interaction is traditionally a departure from additivity incorporated into a linear regression model (logistic regression for binary traits) as one or more product terms. Where yi is the quantitative trait outcome of the ith individual, xji are binary indicator variables of genotypes or exposures, b1 and b2 are regression coefficients of the main effects of genotypes or exposures, and b3 is an interaction effect term. We usually wish to achieve two purposes by incorporating such an interaction term: first, improving the power to detect a causal gene with interaction effects; and, second, detecting an interaction effect per se, which hopefully will allow us to elucidate biological interaction. Given the complex nature of interaction effects, it may be necessary to consider a more flexible parameterization of statistical interaction (which nonparametric regression allows) than just the product of first-order terms
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