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Genetic association studies have been a popular approach for assessing theassociation between common Single Nucleotide Polymorphisms (SNPs) and complexdiseases. However, other genomic data involved in the mechanism from SNPs todisease, for example, gene expressions, are usually neglected in theseassociation studies. In this paper, we propose to exploit gene expressioninformation to more powerfully test the association between SNPs and diseasesby jointly modeling the relations among SNPs, gene expressions and diseases. Wepropose a variance component test for the total effect of SNPs and a geneexpression on disease risk. We cast the test within the causal mediationanalysis framework with the gene expression as a potential mediator. For eQTLSNPs, the use of gene expression information can enhance power to test for thetotal effect of a SNP-set, which is the combined direct and indirect effects ofthe SNPs mediated through the gene expression, on disease risk. We show thatthe test statistic under the null hypothesis follows a mixture of $\chi^2$distributions, which can be evaluated analytically or empirically using theresampling-based perturbation method. We construct tests for each of threedisease models that are determined by SNPs only, SNPs and gene expression, orinclude also their interactions. As the true disease model is unknown inpractice, we further propose an omnibus test to accommodate differentunderlying disease models. We evaluate the finite sample performance of theproposed methods using simulation studies, and show that our proposed testperforms well and the omnibus test can almost reach the optimal power where thedisease model is known and correctly specified. We apply our method toreanalyze the overall effect of the SNP-set and expression of the ORMDL3 geneon the risk of asthma.

Joint analysis of SNP and gene expression data in genetic association studies of complex diseases