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Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome

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Item Type:Article
Title:Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome
Creators Name:Zemojtel, T. and Koehler, S. and Mackenroth, L. and Jaeger, M. and Hecht, J. and Krawitz, P. and Graul-Neumann, L. and Doelken, S. and Ehmke, N. and Spielmann, M. and Oien, N.C. and Schweiger, M.R. and Krueger, U. and Frommer, G. and Fischer, B. and Kornak, U. and Floettmann, R. and Ardeshirdavani, A. and Moreau, Y. and Lewis, S.E. and Haendel, M. and Smedley, D. and Horn, D. and Mundlos, S. and Robinson, P.N.
Abstract:Less than half of patients with suspected genetic disease receive a molecular diagnosis. We have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical data into an effective diagnostic workflow. We used variants in the 2741 established Mendelian disease genes [the disease-associated genome (DAG)] to develop a targeted enrichment DAG panel (7.1 Mb), which achieves a coverage of 20-fold or better for 98% of bases. Furthermore, we established a computational method [Phenotypic Interpretation of eXomes (PhenIX)] that evaluated and ranked variants based on pathogenicity and semantic similarity of patients' phenotype described by Human Phenotype Ontology (HPO) terms to those of 3991 Mendelian diseases. In computer simulations, ranking genes based on the variant score put the true gene in first place less than 5% of the time; PhenIX placed the correct gene in first place more than 86% of the time. In a retrospective test of PhenIX on 52 patients with previously identified mutations and known diagnoses, the correct gene achieved a mean rank of 2.1. In a prospective study on 40 individuals without a diagnosis, PhenIX analysis enabled a diagnosis in 11 cases (28%, at a mean rank of 2.4). Thus, the NGS of the DAG followed by phenotype-driven bioinformatic analysis allows quick and effective differential diagnostics in medical genetics.
Keywords:Computational Biology, Exome, Human Genome, Inborn Genetic Diseases, Mutation, Phenotype, Prospective Studies, Reproducibility of Results, Retrospective Studies
Source:Science Translational Medicine
ISSN:1946-6234
Publisher:American Association for the Advancement of Science
Volume:6
Number:252
Page Range:252ra123
Date:3 September 2014
Official Publication:https://doi.org/10.1126/scitranslmed.3009262
PubMed:View item in PubMed

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