Sparse Simultaneous Signal Detection for Identifying Genetically Controlled Disease Genes. Issue 519 (3rd July 2017)

• Record Type: Journal Article
• Authors: Zhao, Sihai Dave; Cai, T. Tony; Cappola, Thomas P.; Margulies, Kenneth B.; Li, Hongzhe
• Is Part Of: Journal of the American Statistical Association. Volume 112:Issue 519(2017)
• Page Start: 1032
• Page End: 1046
• Publication Date: 2017-07-03
• Subjects: eQTL -- GWAS -- Higher criticism -- Integrative genomics; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2016.1270825
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: 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 packagessa . Supplementary materials for this article are available online.