Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

Abstract

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel-machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, since it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel-machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.

Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

“…Detailed proofs are in Supplementary Materials. Following the Previous works 11 – 13 , 15 , 21 – 24 , test statistics of the FGE-SKAT are based on the second term of the likelihood function.…”

“…Tests for gene-environment interactions using one SNP and one environmental factor have been proposed 14 . For better statistical power, such types of interactions using genomic regions are also discussed 15 , 16 . Recent studies also showed surprising findings with gene-environment interactions 17 , 18 .…”

A review of kernel methods for genetic association studies

“…Lin, Lee, Christiani, and Lin (2013) proposed aggregative GxE testing by treating the main genetic and environmental effects as fixed and testing the significance of random GxE interaction effects using a score test. Broadaway et al (2015) provided an alternative formulation of GESAT, which jointly tests the genetic, environmental, and GxE interaction effects using the joint weighted two-way interaction kernel. Kernel methods outside of the kernel regression framework, such as kernel canonical correlation analysis (Larson et al, 2014;Yuan et al, 2012), have also been developed for GxG association analyses in case-control studies.…”

Composite Kernel Machine Regression Based on Likelihood Ratio Test for Joint Testing of Genetic and Gene–Environment Interaction Effect

“…A variance component score test is usually used for statistical significance. In the problem of testing the overall genetic effect, a two‐way interaction kernel has been used that showed improved power compared to traditional joint testing (Broadaway et al, ).…”