Rare-Variant Kernel Machine Test for Longitudinal Data for Population and Family Samples

• Activity Number: 540
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Qi Yan* and Wei Chen and Nianjun Liu
• Companies: and University of Pittsburgh Children's Hospital of Pittsburgh and The University of Alabama at Birmingham
• Keywords: rare variant analysis ; longitudinal data ; quantitative traits ; linear mixed model ; linear kernel function ; family samples

Abstract

The recent development of sequencing technology allows the identification of the association between rare genetic variants and complex diseases. However, the discovery of disease associated novel variants critically depends on appropriate statistical methods. Among the existing methods, the kernel machine test as a set-based approach has been shown to perform well for association test of rare variants in different scenarios. Many studies have been conducted to measure the phenotypes at multiple time points, but the standard kernel machine methodology only considers the phenotype measurement at one time point. In addition, family-based design has been widely used in genetic association studies, thus the familial relatedness needs to be appropriately handled in the data analysis. In this paper, we introduce a framework for association test of rare variants, which uses multiple phenotype measurements for each subject from either population or family samples. We propose an analytical method using kernel machine regression, which is applicable to longitudinal data with quantitative phenotype from either population (denoted as L-KM) or family samples (LF-KM).