TY - JOUR
T1 - Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome
AU - Zemojtel, Tomasz
AU - Köhler, Sebastian
AU - Mackenroth, Luisa
AU - Jäger, Marten
AU - Hecht, Jochen
AU - Krawitz, Peter
AU - Graul-Neumann, Luitgard
AU - Doelken, Sandra
AU - Ehmke, Nadja
AU - Spielmann, Malte
AU - Øien, Nancy Christine
AU - Schweiger, Michal R.
AU - Krüger, Ulrike
AU - Frommer, Götz
AU - Fischer, Björn
AU - Kornak, Uwe
AU - Flöttmann, Ricarda
AU - Ardeshirdavani, Amin
AU - Moreau, Yves
AU - Lewis, Suzanna E.
AU - Haendel, Melissa
AU - Smedley, Damian
AU - Horn, Denise
AU - Mundlos, Stefan
AU - Robinson, Peter N.
N1 - Publisher Copyright:
© 2014 American Association for the Advancement of Science. All rights reserved.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - 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.
AB - 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.
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U2 - 10.1126/scitranslmed.3009262
DO - 10.1126/scitranslmed.3009262
M3 - Article
C2 - 25186178
AN - SCOPUS:84907284564
SN - 1946-6234
VL - 6
JO - Science translational medicine
JF - Science translational medicine
IS - 252
M1 - 252ra123
ER -