Gene-Disease Associations via Sparse Simultaneous Signal Detection

• Activity Number: 603
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Sihai Dave Zhao*+ and Tony Cai and Hao Li and Hongzhe Li
• Companies: University of Illinois at Urbana-Champaign and University of Pennsylvania and University of California, San Francisco and University of Pennsylvania
• Keywords: Higher criticism ; Integrative genomics ; Simultaneous detection

Abstract

A common approach to identifying genes that are regulated by disease variants is to consider genes located close to or containing the variants. However, genomic proximity does not always imply regulation, and in addition some disease genes may be regulated by distant variants. One promising approach is to combine genome-wide association data and eQTL data to directly search for genes whose expressions are regulated by disease variants, by testing whether there are SNPs that are simultaneously associated with both disease and gene expression. In this paper a method is proposed to detect such simultaneous associations. The method allows the SNP-expression and SNP-disease associations to be calculated in independent datasets, can detect weaker associations than existing procedures, and is quick to compute and easy to implement. In addition, it is shown that the proposed method is asymptotically optimal under certain conditions. In simulations it is shown that the proposed procedure is more powerful than standard enrichment approaches, and in data analysis the procedure is used to identify genes whose regulation may play a role in Crohn's disease.