Huang Yen-Tsung, Liang Liming, Moffatt Miriam F, Cookson William O C M, Lin Xihong
Departments of Epidemiology and Biostatistics, Brown University, Providence, Rhode Island, United States of America.
Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Genet Epidemiol. 2015 Jul;39(5):347-56. doi: 10.1002/gepi.21905. Epub 2015 May 22.
Genome-wide association studies (GWAS) have been a standard practice in identifying single nucleotide polymorphisms (SNPs) for disease susceptibility. We propose a new approach, termed integrative GWAS (iGWAS) that exploits the information of gene expressions to investigate the mechanisms of the association of SNPs with a disease phenotype, and to incorporate the family-based design for genetic association studies. Specifically, the relations among SNPs, gene expression, and disease are modeled within the mediation analysis framework, which allows us to disentangle the genetic effect on a disease phenotype into two parts: an effect mediated through a gene expression (mediation effect, ME) and an effect through other biological mechanisms or environment-mediated mechanisms (alternative effect, AE). We develop omnibus tests for the ME and AE that are robust to underlying true disease models. Numerical studies show that the iGWAS approach is able to facilitate discovering genetic association mechanisms, and outperforms the SNP-only method for testing genetic associations. We conduct a family-based iGWAS of childhood asthma that integrates genetic and genomic data. The iGWAS approach identifies six novel susceptibility genes (MANEA, MRPL53, LYCAT, ST8SIA4, NDFIP1, and PTCH1) using the omnibus test with false discovery rate less than 1%, whereas no gene using SNP-only analyses survives with the same cut-off. The iGWAS analyses further characterize that genetic effects of these genes are mostly mediated through their gene expressions. In summary, the iGWAS approach provides a new analytic framework to investigate the mechanism of genetic etiology, and identifies novel susceptibility genes of childhood asthma that were biologically meaningful.
全基因组关联研究（GWAS）已成为识别疾病易感性单核苷酸多态性（SNP）的标准方法。我们提出了一种新方法，称为整合GWAS（iGWAS），它利用基因表达信息来研究SNP与疾病表型关联的机制，并将基于家系的设计纳入遗传关联研究。具体而言，SNP、基因表达和疾病之间的关系在中介分析框架内建模，这使我们能够将对疾病表型的遗传效应分解为两部分：通过基因表达介导的效应（中介效应，ME）和通过其他生物学机制或环境介导机制的效应（替代效应，AE）。我们开发了对ME和AE的综合检验，这些检验对潜在的真实疾病模型具有稳健性。数值研究表明，iGWAS方法能够促进发现遗传关联机制，并且在检验遗传关联方面优于仅使用SNP的方法。我们对儿童哮喘进行了基于家系的iGWAS，整合了遗传和基因组数据。iGWAS方法使用错误发现率小于1%的综合检验识别出六个新的易感基因（MANEA、MRPL53、LYCAT、ST8SIA4、NDFIP1和PTCH1），而仅使用SNP分析时，在相同的截止值下没有基因通过检验。iGWAS分析进一步表明，这些基因的遗传效应大多通过它们的基因表达介导。总之，iGWAS方法提供了一个新的分析框架来研究遗传病因机制，并识别出具有生物学意义的儿童哮喘新易感基因。
