Testing Genetic Effects of Rare and Common Variants Together in Association Studies

• Activity Number: 537
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Renfang Jiang*+ and Jianping Dong and Yilin Dai
• Companies: Michigan Tech University and Michigan Technological University and Michigan Technological University
• Keywords: genetic association study ; rare variants ; common variants ; collapsing ; common diseases

Abstract

Traditional methods have been used to find association between many common variants and rare or common diseases. After the development of the next generation sequencing technologies, many tests for detecting rare variants have been proposed. We consider the situations where both rare and common variants are associated with a disease, but their effects are too weak to be detected separately. We propose a test to detect them together. The proposed test finds causal variants by a forward selection procedure, and collapses causal rare and common variants with weights separately. It also differentiates the effects of deleterious and protective sources. Simulations show that comparing with existing methods, the new method has higher power when both common and rare variants are associated with a disease but their effects cannot be detected by other methods separately. Covariates such as environmental factors can be included since the test is based on a logistic regression model. It can also deal with quantitative traits by using a linear regression model.