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Am J Hum Genet. 2014 Apr 3;94(4):599-610. doi: 10.1016/j.ajhg.2014.03.010.

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

Author information

1
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
2
Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Utah, Salt Lake City, UT 84112, USA.
3
Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA; ARUP Institute for Clinical and Experimental Pathology, 500 Chipeta Way, Salt Lake City, UT 84108, USA.
4
Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, University of Utah, Salt Lake City, UT 84112, USA.
5
ARUP Institute for Clinical and Experimental Pathology, 500 Chipeta Way, Salt Lake City, UT 84108, USA.
6
Utah Science, Technology, and Research Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.
7
Omicia Inc., 1625 Clay Street, Oakland, CA 94612, USA.
8
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA; Utah Science, Technology, and Research Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA.
9
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, P.O. Box 301439, Houston, TX 77230, USA.
10
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA; Utah Science, Technology, and Research Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: myandell@genetics.utah.edu.

Abstract

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

PMID:
24702956
PMCID:
PMC3980410
DOI:
10.1016/j.ajhg.2014.03.010
[Indexed for MEDLINE]
Free PMC Article

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