Methods Advancement in Complex Association Analysis

• Activity Number: 164
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Arnab Maity*+ and Patrick F. Sullivan and Jung-Ying Tzeng
• Companies: North Carolina State University and The University of North Carolina at Chapel Hill and North Carolina State University
• Keywords: Hypothesis testing ; Kernel machine regression ; Multivariate regression ; Restricted maximum likelihood ; Score based test

Abstract

Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, in this work, we construct a multivariate regression framework based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. This framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes such as multiple univariate kernel machine tests with original phenotypes or with their principal components. We find that none of these approaches has uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger.