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

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), 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.

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

“…The HPO is widely used in rare disease diagnostics, but one bottleneck is that in many settings, HPO terms need to be entered manually into the analysis software. A recent study used text-mining to extract detailed patient phenotypes through natural language processing of clinical narratives in EHR, and used the resulting lists of HPO terms for genomic diagnostics 11 . Our tool could supplement such tools by providing a computational representation of laboratory findings.…”

“…The outcome of a FHIR observation can be represented by a term in the Human Phenotype Ontology (HPO), which is a logically defined vocabulary for describing human abnormal phenotypes 9 . The HPO has become the de facto standard for computational phenotype analysis in genomics and rare disease [9][10][11] . The HPO currently contains 13,608 terms including a comprehensive representation of laboratory abnormalities such as Hyperglycemia, Thrombocytopenia, and Increased urine alpha-ketoglutarate concentration.…”

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

“…We, and others, hypothesize that expanding cohort size by assigning a probability of disease may improve the power of heritability and genome-wide association studies. [25][26][27][28][29][30] Utilizing the structured framework present in many current EHRs, along with machine learning models may provide a generalizable approach for expanding research study cohort size. Supplementary Table 1 for definitions of sets.…”

Deep Phenotyping in the -Omic Era: Methodological Issues Facing Neuropsychiatric Disorders

“…Clinical terms reported at different points in time or in different epochs may not have the same connotation, but may result in being clustered together. However, Son et al [52] used EHR-Phenolyzer, an algorithm for extracting and analyzing phenotypes from heterogeneous EHR narratives. Results from their phenotypic analyses identified genes associated with confirmed monogenic disorders in 16/28 patients assessed.…”