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

Jung Hoon Son, Gangcai Xie, Chi Yuan, Lyudmila Ena, Ziran Li, Andrew Goldstein, Lulin Huang, Liwei Wang, Feichen Shen, Hongfang Liu, Karla Mehl, Emily E. Groopman, Maddalena Marasa, Krzysztof Kiryluk, Ali G. Gharavi, Wendy K. Chung, George Hripcsak, Carol Friedman, Chunhua Weng, Kai Wang

Research output: Contribution to journalArticle

24 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)58-73
Number of pages16
JournalAmerican journal of human genetics
Volume103
Issue number1
DOIs
StatePublished - Jul 5 2018

Keywords

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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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    Son, J. H., Xie, G., Yuan, C., Ena, L., Li, Z., Goldstein, A., Huang, L., Wang, L., Shen, F., Liu, H., Mehl, K., Groopman, E. E., Marasa, M., Kiryluk, K., Gharavi, A. G., Chung, W. K., Hripcsak, G., Friedman, C., Weng, C., & Wang, K. (2018). Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes. American journal of human genetics, 103(1), 58-73. https://doi.org/10.1016/j.ajhg.2018.05.010