Evaluation of genome-wide association study results through development of ontology fingerprints
Year: 2009
Type: article
Abstract: MOTIVATION: Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important. RESULTS: We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint--the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are 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, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant 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.
Source: Bioinformatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funders: Pharmaceutical Research and Manufacturers of America Foundation, Medical University of South Carolina, University of South Carolina, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine, National Center for Research Resources
Awards: Identifying Genes for Type 2 Diabetes: FUSION, G3468304963, Training of Toolmakers for Bio-Medical Informatics (TTB*, Identifying Genes for Type 2 Diabetes: FUSION, Design and Analysis of Human Gene Mapping Studies, G5774308675, South Carolina COBRE for Oral Health Research, G8950521765, Identifying Genes for Type 2 Diabetes: FUSION
OpenAlex ID: W2107761256