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

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 from SNPs to disease, e.g., gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which are the combined direct 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 χ2 distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that is determined by SNPs only, SNPs and gene expression, or includes also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to re-analyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

“…Huang, Vanderweele, and Lin (2014) developed a method that integrates SNP and gene expression data, treating gene expression as the mediator in the causal mechanism from SNPs to the disease outcome (Figure 2). They used a logistic regression model $$\text{logit }false[Pfalse(Y=1false|X,G,Efalse)false]=G^{\mathrm{T}}{\beta }_G+E{\beta }_E+{\mathit{\text{EG}}}^{\mathrm{T}}{\beta }_{\mathit{\text{GE}}}+X^{\mathrm{T}}{\beta }_X$$to characterize the dependence of the disease outcome on a set of SNPs G , the expression E of a gene, and other covariates X .…”

“…To overcome this problem, Huang et al (2014) proposed a variance component test. They assumed that the components in the vector β G are independent and follow an arbitrary distribution with mean 0 and variance τ G , and that the components in β GE are independent and follow an arbitrary distribution with mean 0 and variance GE .…”

“…The disease model (1) hence becomes a logistic mixed-effect model, and the test of hypothesis (3) becomes a joint test of variance components and a scalar regression coefficient: $$H_0:{\tau }_G=0,{\tau }_{\mathit{\text{GE}}}=0,{\beta }_E=0.$$Therefore, the proposed variance component test is insensitive to the number of SNPs in G . As the true disease model is unknown and can be different from (1), e.g., without the interaction term, Huang et al (2014) further proposed an omnibus test that accommodates different possible disease models.…”

MOPED 2.5—An Integrated Multi-Omics Resource: Multi-Omics Profiling Expression Database Now Includes Transcriptomics Data

“…Building on the best practices used in different fields, MOPED integrates heterogeneous data into a unified public resource. The integration of multi-omics data can be essential for scientific discovery (Efron and Tibshirani, 2007;Huang, 2014;Huang et al, 2014;Olex et al, 2014), thus by providing a platform for multi-omics data, MOPED can act as a launching point for scientific discoveries.…”

Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes

“…Combined Annotation Dependent Depletion (CADD) and Genome Wide Annotation of Variants (GWAVA) are machine learning based approaches that work along the same line by integrating diverse resources to identify functional non-coding variants [135,136]. Recently, large numbers of genome-wide studies have successfully integrated highthroughput omic measurements, including gene expression and epigenetic variation data to increase the power for discovery of causal genes and to better understand the possible molecular and cellular mechanisms of the disease [137][138][139][140].…”