A New Estimating Equation Approach for Secondary Trait Analyses in Genetic Case-Control Studies

• Activity Number: 254
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Xiaoyu Song* and Iuliana Ionita-Laza and Ying Wei
• Companies: Columbia University and Columbia University and Columbia University
• Keywords: secondary trait analysis ; estimating equations ; case-control studies ; GWAS

Abstract

In this manuscript, we propose a new estimating equation based approach that provides unbiased secondary traits analysis in genetic case-control studies. In genetic studies, analysis on secondary traits is an important way to discover potential disease pathways. When data are collected from case-control designs, direct analyses are often biased. Several methods have been proposed to address this issue, including the inverse-probability-of-sampling-weighted approach and the maximum likelihood approach. Comparing to the existing ones, the proposed estimating equation approach enjoys the following properties. One, it creates a general framework that is applicable to a wide range of genetic models. It could be used to model various types phenotypes and SNPs, and is also easy to incorporate covariates. Second, it is computationally simple and straightforward. We compared our method with the existing ones in both numerical studies and a stroke GWAS data. The proposed method was shown to be less sensitive to the sampling scheme and underlying disease model. For these reasons, we believe that our new methods complement the existing approaches, and are useful to analyze secondary traits.