A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning.

ABSTRACT: Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our frame

SUBMITTER: Bennett BD 

PROVIDER: S-EPMC3441565 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature