Prioritizing causal disease genes using unbiased genomic features.

ABSTRACT:

Background

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits.

Results

To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towa

SUBMITTER: Deo RC 

PROVIDER: S-EPMC4279789 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature