Integrative Genomic Analysis via Sparse Simultaneous Signal Detection

• Activity Number: 5
• Type: Invited
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Hongzhe Li*
• Companies: University of Pennsylvania
• Keywords: Detection boundary ; eQTL ; Sparsity ; Genome-wide studies ; Genetical genomics

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 and genetic variants 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. Based on this model, I will discuss several interesting integrative genomic problems that can be formulated as sparse simultaneous signal detection problems and present methods for detecting such signals. I will apply the proposed tests to the heart failure study to identify potentially important transcripts and biological pathways that are mechanistically associated with human heart failure.