Sparse Simultaneous Signal Detection and Its Applications in Genomics
主 题: Sparse Simultaneous Signal Detection and Its Applications in Genomics
报告人: Hongzhe Li ( University of Pennsylvania Perelman School of Medicine)
时 间: 2016-06-28 14:00 - 15:00
地 点: 理科一号楼 1114
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 new procedure to test for the existence of these simultaneous signals, show that the test statistic is asymptotically optimal under certain conditions and provide a procedure to obtain finite-sample p-values. I will apply the proposed test to a heart failure study at Penn (MAGNet) to identify potentially important transcripts that are mechanistically associated with human heart failure. Finally, I will also briefly present a related problem of optimal detection of weak positive dependence between two mixture distributions and show its application in gene set enrichment analysis.
