Time：2019/7/1   10:00-11:00

Location：Mingde Main Building 1031

Topic：A scalable and powerful test based on the Cauchy distribution, with applications to genetic association studies

Abstract:

With the advent of massive DNA sequencing data, there is a strong need for methods that can achieve statistical and computational efficiency simultaneously. For example, ongoing large-scale whole genome sequencing (WGS) studies are currently producing hundreds of terabytes of WGS data from tens of thousands of individuals with a wide spectrum of phenotypes. In this talk, I present a novel p-value combination test based on the Cauchy distribution. This test is very general and enjoys many good properties, such as simple, computationally efficient, resistant to correlation, and powerful against sparse alternatives. The effectiveness of the proposed test is demonstrated by a WGS analysis of blood-related phenotypes.

Resume：

Yaowu Liu currently is a postdoctoral fellow at the Harvard School of Public Health. He obtained his Ph.D. in statistics at Purdue University. His research interests include scalable methods for large-scale statistical inference, high-dimensional data analysis, and statistical genetics.