STATISTICS COLLOQUIUM
Ying Wei, Professor
Department of Biostatistics
Mailman School of Public Health
Columbia University
Integrated Quantile RAnk Test (iQRAT) for gene-level associations
Abstract
Genetic association studies often evaluate the combined group-wise effects of rare and common genetic variants on
phenotypes at gene level. Many approaches have been proposed for group-wise association tests, such as the widely used
burden tests and sequence kernel association tests with sequencing data. Most of these approaches focus on identifying
mean effects. As thegenetic associations are complex, we propose an efficient integrated rank test to investigate the
genetic effect across the entire distribution/quantile function of a phenotype. The resulting test complements the mean-
based analysis and improve efficiency and robustness. The proposed testintegrates the rank score test statistics over
quantile levels while incorporating Cauchy combination test scheme and Fisher's method to maximize the power. It
generalized the classical quantile-specific rank-score test. 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. This is joint work with Tianying Wang and Iuliana Ionita-Laza.
Bio: Ying Wei is a statistician and a Professor of Biostatistics in the Columbia University Mailman School of Public Health, working primarily on quantile regression, semiparametric models of longitudinal data, and their applications.
Wei earned her Ph.D. in statistic from the University of Illinois at Urbana–Champaign in 2004 and has been a faculty member of Biostatistics in the Columbia University, and also an affiliated member of the Data Science Institute ever since.
In 2011, Wei received the Noether Young Scholar Award of the American Statistical Association, "for outstanding early contributions to nonparametric statistics." In 2015, Wei was elected as a Fellow of the American Statistical Association. Wei is also an elected member of the International Statistical Institute. In 2020 she was named as a Fellow of the Institute of Mathematical Statistics "for contributions to the development, dissemination, and application of mathematical statistics".
For more information, contact: Tracy Burke at tracy.burke@uconn.edu