Huang Yen-Tsung
Department of Epidemiology, Brown University, Providence, RI 02912, USA.
Biostatistics. 2014 Oct;15(4):587-602. doi: 10.1093/biostatistics/kxu014. Epub 2014 Apr 4.
Genome-wide association studies (GWASs) and expression-/methylation-quantitative trait loci (eQTL/mQTL) studies constitute popular approaches for investigating the association of single nucleotide polymorphisms (SNPs) with disease and expression/methylation, respectively. Here, we propose to integrate QTL studies to more powerfully test the SNP effect on disease in GWASs when they are conducted among different subjects. We propose a model for the joint effect of SNPs, methylation, and gene expression on disease risk and obtain the marginal model for SNPs by integrating out methylation and expression. We characterize all possible causal relations among SNPs, methylation, and expression and study the corresponding null hypotheses of no SNP effect in terms of the regression coefficients in the joint model. We develop a score test for variance components of regression coefficients to evaluate the genetic effect. We further propose an omnibus test to accommodate different models. We illustrate the utility of the proposed method in an asthma GWAS study, a brain tumor study, and numerical simulations.
全基因组关联研究（GWAS）以及表达/甲基化定量性状位点（eQTL/mQTL）研究分别是用于探究单核苷酸多态性（SNP）与疾病以及表达/甲基化之间关联的常用方法。在此，我们建议整合QTL研究，以便在不同受试者中进行GWAS时，更有力地检验SNP对疾病的影响。我们提出了一个关于SNP、甲基化和基因表达对疾病风险的联合效应模型，并通过对甲基化和表达进行积分得到SNP的边际模型。我们刻画了SNP、甲基化和表达之间所有可能的因果关系，并根据联合模型中的回归系数研究了无SNP效应的相应原假设。我们开发了一种用于回归系数方差分量的得分检验，以评估遗传效应。我们进一步提出了一种综合检验，以适应不同模型。我们在哮喘GWAS研究、脑肿瘤研究和数值模拟中说明了所提方法的实用性。
