STATISTICS COLLOQUIUM
Tianying Wang, Ph.D.
Postdoctoral Research Scientist
Department of Biostatistics
Mailman School of Public Health
Columbia University
Integrated Quantile Rank Test (iQRAT) for gene-level associations
in Sequencing Studies
Abstract
Sequence-based association studies often evaluate the group-wise effects of rare and common genetic variants within a gene on a phenotype of interest. Many such approaches have been proposed, such as the widely used burden and sequence kernel association tests. These approaches focus on identifying genetic effects on the phenotypic mean. As the genetic associations can be complex, we propose here an efficient rank test to investigate the genetic effects across the entire distribution of a phenotype. The proposed test generalizes the classical quantile-specific rank-score test, by integrating the rank score test statistics over quantile levels while incorporating Cauchy combination test scheme and Fisher's method to maximize the power. We show that the resulting test complements the mean-based analysis and improves efficiency and robustness. Using simulations studies and real Metabochip data on lipid traits, we investigated the performance of the new test in comparison with the burden tests and sequence kernel association tests in multiple scenarios.
DATE: Wednesday, September 4, 2019
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu