Sparse Simultaneous Signal Detection for Identifying Genetically Controlled Disease Genes

• Published in: Journal of the American Statistical Association Vol. 112; no. 519; pp. 1032 - 1046
• Main Authors: Zhao, Sihai Dave, Cai, T. Tony, Cappola, Thomas P., Margulies, Kenneth B., Li, Hongzhe
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 03.07.2017; Taylor & Francis Group,LLC
• Subjects: Applications and Case Studies; death; eQTL; equations; etiology; gene expression regulation; genes; genetic variation; genome-wide association study; genomics; GWAS; heart failure; Higher criticism; Integrative genomics; linkage disequilibrium; quantitative trait loci; risk; single nucleotide polymorphism; statistical analysis; United States; United States; GWAS; eQTL; Higher criticism; Integrative genomics
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2016.1270825

Genome-wide association studies (GWAS) and differential expression analyses have had limited success in finding genes that cause complex diseases such as heart failure (HF), a leading cause of death in the United States. This article proposes a new statistical approach that integrates GWAS and expression quantitative trait loci (eQTL) data to identify important HF genes. For such genes, genetic variations that perturb its expression are also likely to influence disease risk. The proposed method thus tests for the presence of simultaneous signals: SNPs that are associated with the gene's expression as well as with disease. An analytic expression for the p-value is obtained, and the method is shown to be asymptotically adaptively optimal under certain conditions. It also allows the GWAS and eQTL data to be collected from different groups of subjects, enabling investigators to integrate public resources with their own data. Simulation experiments show that it can be more powerful than standard approaches and also robust to linkage disequilibrium between variants. The method is applied to an extensive analysis of HF genomics and identifies several genes with biological evidence for being functionally relevant in the etiology of HF. It is implemented in the R package ssa . Supplementary materials for this article are available online.