Test for Rare Variant Effects on Secondary Traits in Case-Control Sequencing Studies

• Activity Number: 248
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Godwin Yung* and Xihong Lin and Seunggeun Lee
• Companies: Harvard School of Public Health and Harvard School of Public Health and University of Michigan
• Keywords: sequencing studies ; generalized estimating equations ; inverse probability weighting ; ascertainment bias ; variance component ; case-control

Abstract

Sequencing studies are increasingly being conducted to identify rare variants associated with complex traits. Because these studies require considerable investment, researchers often study multiple traits collected on the same subjects to maximize return. However, limited power of classical single marker association analysis for rare variants poses a central challenge. In addition, many sequencing studies have adopted a case-control design; improperly accounting for case-control ascertainment can lead to biased estimates of association between markers and secondary traits. We propose the sequence kernel association test with inverse-probability-of-sampling weights (IPW-SKAT), a supervised, flexible, and computationally efficient regression method to test for marker-secondary trait association studies in case-control sequencing studies. As a score-based variance component test, IPW-SKAT can quickly calculate p-values and be easily applied to sequencing data. It offers an advantage over naive methods by protecting the type I error rate in general situations. Our principles are justified theoretically and via simulations, and illustrated by a real sequencing study.