DEPARTMENT OF STATISTICS
Sihai Dave Zhao
Department of Biostatistics and Epidemiology
University of Pennsylvania
Perelman School of Medicine
Statistical inference for finding disease-associated transcripts by integrating genomic data using sparse simultaneous signal detection
ABSTRACT
The increasing availability of large-scale genomic data has made possible an integrative approach to studying disease. Such research seeks to uncover disease mechanisms by combining multiple types of genomic information, which may be collected on multiple sets of patients. I will focus on a study that integrates GWAS and eQTL data collected from two different sets of subjects to find transcripts potentially functionally relevant to human heart failure. I will first formalize a model that defines important transcripts as those whose expression levels are associated with SNPs that are simultaneously associated with disease. I will then propose a procedure to test for the existence of these simultaneous signals, show that my test statistic is asymptotically optimal under certain conditions, provide a procedure to obtain finite-sample p-values, and show in simulations that my proposal can be more powerful than existing methods. Finally, I will apply my proposed test to the heart failure study to identify potentially important transcripts.
DATE: Friday, January 31, 2014
TIME: 11:00 a.m.
PLACE: Philip E. Austin Building – Room 344
For more information, contact: Tracy Burke at tracy.burke@uconn.edu