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Bioinformatics Advance Access first published online on January 19, 2007
This version published online on February 21, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm001
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A computational system to select candidate genes for complex human traits

Kyle J Gaulton 1,2,3,*, Karen L Mohlke 3 and Todd J Vision 4

1Curriculum in Genetics and Molecular Biology, 2Bioinformatics and Computational Biology Training Program, Departments of 3Genetics and 4Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516

*to whom correspondence should be addressed. Kyle J Gaulton, E-mail: kgaulton{at}email.unc.edu


  Abstract

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 8 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/.

Associate Editor: John Quackenbush

Received on October 30, 2006; revised on January 2, 2007; accepted on January 8, 2007

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