STATISTICS COLLOQUIUM
Zuoheng Wang
Assistant Professor of Public Health (Biostatistics)
Yale School of Public Health
Longitudinal SNP Set Association Analysis of Quantitative Phenotypes
ABSTRACT
Traditional association analyses of genetic variants mainly focus on cross-sectionally measured phenotypes. Many health studies have collected phenotypes at multiple time points. Repeated measurements from the same subject over a period of time not only provide a more accurate assessment of disease condition, but also allow us to explore the genetic influence on disease development and progression. Thus there is growing interest in studying longitudinally measured data. Here we propose LSKAT (Longitudinal SNP-set Kernel Association Test), a robust association test of rare and common variants with longitudinal phenotypes, which extends the SKAT method for a single measurement to repeated measurements. LSKAT uses several variance components to account for the within-subject correlation in longitudinal data and the contributions from all genetic variants (rare and common) in a region. In simulation studies, we evaluate the performance of LSKAT, and demonstrate that LSKAT has an improved power, by making full use of repeated measurements, as comparing to previously proposed tests on a single measurement or average measurements for each subjects. Finally, we apply LSKAT to test for association in veteran cohort with both HIV-infected and uninfected patients to understand the genetic risk underlying alcohol dependence.
DATE: Wednesday, February 24, 2016
TIME: 4:00 p.m. -5:00 p.m.
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 p.m. in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu