A Multivariate Phenotype Quasi-Likelihood Score Test for Genetic Association in Samples with Known or Cryptic Structure

• Activity Number: 648
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Timothy Thornton* and Mingdong Liu
• Companies: University of Washington and University of Washington
• Keywords: GWAS ; pleiotropy ; multivariate score test ; relatedness ; genetic association ; population structure

Abstract

Pleiotropy is the phenomenon of a single gene influencing multiple, and sometimes seemingly unrelated traits. Recently, there has been an increased interest in the identification of genetic variants that are associated with multiple phenotypes to gain a better understanding of the underpinnings of complex traits. A variety of statistical approaches have been proposed for multivariate trait analysis in unrelated samples. Many genetic studies, however, include related individuals. We consider the problem of genetic association testing with general quantitative and binary traits in samples with known or cryptic structure. We propose the multivariate phenotype quasi-likelihood (MPQ) score test. In simulation studies with multivariate traits and samples with unrelated and related individuals, we demonstrate that the MPQ represents an overall improvement over existing approaches, including GEE, in terms of both power and type-1 error. We further demonstrate the utility of the MPQ test for the identification of pleiotropic effects for inflammatory response phenotypes in a genome-wide association study of 3,548 Hispanic American postmenopausal women from the Women's Health Initiative.