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Evaluation of exome filtering techniques for the analysis of clinically relevant genes
Author(s) -
Kernohan Kristin D.,
Hartley Taila,
Alirezaie Najmeh,
Robinson Peter N.,
Dyment David A.,
Boycott Kym M.
Publication year - 2018
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23374
Subject(s) - exome , exome sequencing , omim : online mendelian inheritance in man , in silico , computational biology , biology , phenotype , genetics , genetic heterogeneity , gene , bioinformatics
A significant challenge facing clinical translation of exome sequencing is meaningful and efficient variant interpretation. Each exome contains ∼500 rare coding variants; laboratories must systematically and efficiently identify which variant(s) contribute to the patient's phenotype. In silico filtering is an approach that reduces analysis time while decreasing the chances of incidental findings. We retrospectively assessed 55 solved exomes using available datasets as in silico filters: Online Mendelian Inheritance in Man (OMIM), Orphanet, Human Phenotype Ontology (HPO), and Radboudumc University Medical Center curated panels. We found that personalized panels produced using HPO terms for each patient had the highest success rate (100%), while producing considerably less variants to assess. HPO panels also captured multiple diagnoses in the same individual. We conclude that custom HPO‐derived panels are an efficient and effective way to identify clinically relevant exome variants.