RT Journal Article
SR Electronic
T1 Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome
JF Science Translational Medicine
FD American Association for the Advancement of Science
SP 252ra123
OP 252ra123
DO 10.1126/scitranslmed.3009262
VO 6
IS 252
A1 Zemojtel, Tomasz
A1 Köhler, Sebastian
A1 Mackenroth, Luisa
A1 Jäger, Marten
A1 Hecht, Jochen
A1 Krawitz, Peter
A1 Graul-Neumann, Luitgard
A1 Doelken, Sandra
A1 Ehmke, Nadja
A1 Spielmann, Malte
A1 Øien, Nancy Christine
A1 Schweiger, Michal R.
A1 Krüger, Ulrike
A1 Frommer, Götz
A1 Fischer, Björn
A1 Kornak, Uwe
A1 Flöttmann, Ricarda
A1 Ardeshirdavani, Amin
A1 Moreau, Yves
A1 Lewis, Suzanna E.
A1 Haendel, Melissa
A1 Smedley, Damian
A1 Horn, Denise
A1 Mundlos, Stefan
A1 Robinson, Peter N.
YR 2014
UL http://stm.sciencemag.org/content/6/252/252ra123.abstract
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.