A computational system to select candidate genes for complex human traits

MOTIVATION Identification of the genetic variation underlying complex traits is challenging. The wealth of information publicly available about the biology of complex traits and the function of individual genes permits the development of informatics-assisted methods for the selection of candidate genes for these traits. RESULTS We have developed a computational system named CAESAR that ranks all annotated human genes as candidates for a complex trait by using ontologies to semantically map natural language descriptions of the trait with a variety of gene-centric information sources. In a test of its effectiveness, CAESAR successfully selected 7 out of 18 (39%) complex human trait susceptibility genes within the top 2% of ranked candidates genome-wide, a subset that represents roughly 1% of genes in the human genome and provides sufficient enrichment for an association study of several hundred human genes. This approach can be applied to any well-documented mono- or multi-factorial trait in any organism for which an annotated gene set exists. AVAILABILITY CAESAR scripts and test data can be downloaded from http://visionlab.bio.unc.edu/caesar/