Nucleic Acids Research Advance Access published online on February 7, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn007
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Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies
1Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294 and 2Bioinformatics Research Center, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA
*To whom correspondence should be addressed. Tel: 704-687-8541; Fax: 704-687-6610;Email: aloraine{at}uncc.edu
Received November 5, 2007. Revised December 21, 2007. Accepted January 7, 2008.
Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.
Present address: Tesfaye M. Baye, Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI 53226 USA Shiju Zhang, Department of Mathematics, Texas A&M University – Kingsville, Kingsville, TX 78363 USA
Presented in part at the Annual Meeting of The Obesity Society, 20–24 October 2007 in New Orleans, LA, USA.
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