Graphical Model--Based Multiple Testing with Applications to Genome-Wide Association Studies

• Activity Number: 629
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Mental Health Statistics Section
• Author(s): Chunming Zhang*+ and Jie Liu and Page David
• Companies: University of Wisconsin - Madison and University of Wisconsin-Madison and University of Wisconsin-Madison
• Keywords: simultaneous inference ; dependence ; p-value ; false discovery rate

Abstract

Large-scale multiple testing tasks often exhibit dependence, and leveraging the dependence between individual tests is one challenging and important problem in statistics. We propose a multiple testing procedure based on a Markov-random-field-coupled mixture model. Simulation studies show that the numerical performance of multiple testing can be improved substantially by using our procedure. We apply the procedure to a real-world genome-wide association study on breast cancer, and we identify several SNPs with strong association evidence.