1 vote

Using Ontology Fingerprints to evaluate genome-wide association study results

Lam C. Tsoi¹, Michael Boehnke², Richard L. Klein³ & W. Jim Zheng¹

Correspondence: (Login to view email address)

1. Department of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina, Charleston, SC 29425
2. Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
3. Division of Endocrinology, Metabolism, and Medical Genetics, Department of Medicine, Medical University of South Carolina & Research Service, Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC

DOC (171.5 KB) PDF (66.6 KB)

Document Type:

Manuscript

Date:

Received 14 August 2009 20:36 UTC; Posted 14 August 2009

Subjects:

Genetics & Genomics, Bioinformatics

Tags:

• ontology fingerprints
• ontology
• gene ontology
• genome-wide association studies
• GWAS

Abstract:

We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation.

Collection:

International Conference on Biomedical Ontology

Discussion

Votes:

1 vote

(Login to vote)

Comments:

0 comments

(Login to post a comment)

Share:

(Login to share with a colleague)

Additional information

License:

This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Tsoi, Lam, Boehnke, Michael, Klein, Richard, and Zheng, W. Jim. Using Ontology Fingerprints to evaluate genome-wide association study results. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2009.3615.1> (2009)

Version info:

Other versions of this document in Nature Precedings

None.

Other versions of this document elsewhere on the web

None known.

Submit a new version link