Improving disease gene prioritization using the semantic similarity of Gene Ontology terms.
BACKGROUND
Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level.


RESULTS
Here, we introduce MedSim, a novel approach for ranking candidate genes for a particular disease based on functional comparisons involving the Gene Ontology. MedSim uses functional annotations of known disease genes for assessing the similarity of diseases as well as the disease relevance of candidate genes. We benchmarked our approach with genes known to be involved in 99 diseases taken from the OMIM database. Using artificial quantitative trait loci, MedSim achieved excellent performance with an area under the ROC curve of up to 0.90 and a sensitivity of over 70% at 90% specificity when classifying gene products according to their disease relatedness. This performance is comparable or even superior to related methods in the field, albeit using less and thus more easily accessible information.


BACKGROUND
MedSim is offered as part of our FunSimMat web service (http://www.funsimmat.de).
Schlicker
Andreas
A
Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics, Saarbrücken, Germany.
Lengauer
Thomas
T
Albrecht
Mario
M
D000818:Animals; D019985:Benchmarking; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005796:Genes; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D006801:Humans; D051379:Mice; D020412:Multifactorial Inheritance; D011506:Proteins / Q000235:genetics; D012660:Semantics; D012984:Software
D011506:Proteins
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