Integrative Genomic Modeling and Data Analysis in Major Depressive Disorder

• Activity Number: 55
• Type: Invited
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: WNAR
• Author(s): George C Tseng*+
• Companies: University of Pittsburgh
• Keywords: meta-analysis ; integrative analysis ; genomic data ; major depressive disorder

Abstract

Major depressive disorder (MDD) is a heterogeneous illness with mostly uncharacterized underlying genetic mechanisms. In this talk, we describe our long term investigation on an integrative analysis of eleven post-mortem transcriptomic studies, covering multiple brain regions and multiple case-control cohorts with confounding variables. In the first part, we focus on candidate marker detection by a random intercept model with variable selection and meta-analysis. Meta-regression is then further applied to detect gender and brain region dependent biomarkers. In the second part, we discuss a meta-clustering approach to combine studies and generate co-expression gene modules. Integration with public GWAS results and pathway databases identifies a module highly associated with mental disorders. Finally, we present a meta-analysis combining direct Pearson correlation and liquid association technique to identify co-expression networks and identify novel gene regulatory mechanisms in MDD.