Speaker: XIAOWEI WU (University of Chicago

Title: Genetic Association Testing for a Longitudinally-Measured Quantitative Trait in Samples with Related Individuals

Abstract: Although genome-wide association studies have shown some success in identifying genetic variants associated with disease risk, few published studies have investigated the contribution of genetic variants to disease susceptibility over time. For analysis of a longitudinally-measured trait, it is important to account for the time-dependence among observations for a given individual. When the sample includes related individuals, there is also dependence across individuals, due to the effects of genetics as well as possible environmental effects. We consider the problem of association testing for a longitudinally-measured trait in samples with related individuals. We propose a quasi-likelihood score test for genotypes conditional on longitudinal phenotype measurements and covariates. The conditional model for genotypes is derived from a phenotype model that includes components of variance for polygenic effects, inter-individual variation caused by non-genetic effects, time-varying correlation of observations within individuals and measurement errors. This approach has a key feature in that it incorporates two types of individuals with missing data into the analysis: (1) individuals with missing phenotypes but nonmissing genotypes and (2) individuals with missing genotypes but nonmissing phenotypes, provided that they have at least one genotyped relative. Simulation studies demonstrate that the proposed method is able to gain additional power for detecting association. We apply the method to the whole genome analysis of longitudinal systolic blood pressure measurements from the Framingham SHARe data.