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Am J Hum Genet. 2018 Jul 5;103(1):58-73. doi: 10.1016/j.ajhg.2018.05.010. Epub 2018 Jun 28.

Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes.

Author information

1
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
2
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
3
Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
4
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55901, USA.
5
Division of Nephrology, Department of Medicine, Columbia University, New York, NY 10032, USA.
6
Department of Pediatrics and Medicine, Columbia University, New York, NY 10032, USA.
7
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address: cw2384@cumc.columbia.edu.
8
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: wangk@email.chop.edu.

Abstract

Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10-16), supporting the value of phenotype-driven gene prioritization in diagnostic sequence interpretation. To assess the generalizability, we replicated this finding on an independent EHR dataset of ten individuals with a positive diagnosis from a different institution. We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.

KEYWORDS:

biomedical informatics; diagnosis; electronic health records; exome; genome; knowledge engineering; natural language processing; next-generation sequencing; phenotyping; precision medicine

PMID:
29961570
PMCID:
PMC6035281
DOI:
10.1016/j.ajhg.2018.05.010
[Indexed for MEDLINE]
Free PMC Article

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