Joint Analysis of SNP and Gene Expression/DNA Methylation Data in Genetic Association Studies of Complex Diseases Using Mediation Analysis

• Activity Number: 532
• Type: Invited
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Caucus for Women in Statistics
• Author(s): Xihong Lin*+ and Yen-Tsung Huang and Tyler VanderWeele
• Companies: Harvard School of Public Health and Brown University and Harvard
• Keywords: GWAS ; Integrative Genetics and Genomics ; Mediation Analysis ; Assoication Analysis

Abstract

Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism between SNPs and disease, e.g., gene expressions or DNA methylation data, are usually neglected in these association studies. In this paper, we propose to exploit gene expression/DNA methylation information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions/DNA methylation and diseases. We propose a variance component test for the total effects of SNPs and a gene expression/DNA methylation on disease risk. We cast the test within the causal mediation analysis framework with a gene expression as a potential mediator. For eQTL/mQTL SNPs, we show that the use of gene expression information can enhance power to test for the total effects of a SNP-set, which are the combined direct effect and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of $\chi^2$ distributions, which can b