Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Related collections

Most cited references 32

Record: found
Abstract: found
Article: not found

Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.

Jennifer E. Posey, Tamar Harel, Pengfei Liu … (2017)

Background Whole-exome sequencing can provide insight into the relationship between observed clinical phenotypes and underlying genotypes. Methods We conducted a retrospective analysis of data from a series of 7374 consecutive unrelated patients who had been referred to a clinical diagnostic laboratory for whole-exome sequencing; our goal was to determine the frequency and clinical characteristics of patients for whom more than one molecular diagnosis was reported. The phenotypic similarity between molecularly diagnosed pairs of diseases was calculated with the use of terms from the Human Phenotype Ontology. Results A molecular diagnosis was rendered for 2076 of 7374 patients (28.2%); among these patients, 101 (4.9%) had diagnoses that involved two or more disease loci. We also analyzed parental samples, when available, and found that de novo variants accounted for 67.8% (61 of 90) of pathogenic variants in autosomal dominant disease genes and 51.7% (15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7% (17 of 38) of patients with two monoallelic variants. Causal copy-number variants were found in 12 patients (11.9%) with multiple diagnoses. Phenotypic similarity scores were significantly lower among patients in whom the phenotype resulted from two distinct mendelian disorders that affected different organ systems (50 patients) than among patients with disorders that had overlapping phenotypic features (30 patients) (median score, 0.21 vs. 0.36; P=1.77×10(-7)). Conclusions In our study, we found multiple molecular diagnoses in 4.9% of cases in which whole-exome sequencing was informative. Our results show that structured clinical ontologies can be used to determine the degree of overlap between two mendelian diseases in the same patient; the diseases can be distinct or overlapping. Distinct disease phenotypes affect different organ systems, whereas overlapping disease phenotypes are more likely to be caused by two genes encoding proteins that interact within the same pathway. (Funded by the National Institutes of Health and the Ting Tsung and Wei Fong Chao Foundation.).

0 comments Cited 308 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

Sebastian Köhler, Leigh C Carmody, Nicole Vasilevsky … (2019)

Abstract The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

0 comments Cited 299 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

Frederick E Dewey, Michael Murray, John Overton … (2016)

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

0 comments Cited 249 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-journal-id): 101731738

Journal ID (pubmed-jr-id): 47837

Journal ID (nlm-ta): NPJ Digit Med

Journal ID (iso-abbrev): NPJ Digit Med

Title: NPJ digital medicine

ISSN (Electronic): 2398-6352

Publication date Nihms-submitted: 11 May 2019

Publication date (Electronic): 2 May 2019

Publication date (Print): 2019

Publication date PMC-release: 20 May 2019

Volume: 2

Electronic Location Identifier: 32

Affiliations

[1 ]The Jackson Laboratory for Genomic Medicine, Farmington CT 06032, USA

[2 ]Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239, USA

[3 ]Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97239, USA

[4 ]Library, Oregon Health and Science University, Portland, OR 97239, USA

[5 ]Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045, USA

[6 ]Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

[7 ]North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

[8 ]Genetics Department, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

[9 ]School of Information and Library Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

[10 ]Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

[11 ]Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA

[12 ]Institute for Clinical and Translational Research, Johns Hopkins University, Baltimore, MD 21202, USA

[13 ]Charité Centrum für Therapieforschung, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany

[14 ]Einstein Center Digital Future, Berlin 10117, Germany

[15 ]Linus Pauling Institute and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA

[16 ]Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA

[17 ]Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA

[18 ]Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN 46202, USA

[19 ]Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, University of North Carolina, Chapel Hill, NC 27599, USA

[20 ]University of North Carolina Center for Environmental Medicine, Asthma and Lung Biology, University of North Carolina, Chapel Hill, NC 27599, USA

[21 ]Department of Pediatrics, Section of Pediatric Critical Care, University of Colorado School of Medicine, Aurora, CO 80045, USA

[22 ]Adult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO 80045, USA

[23 ]Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA

Author notes

Correspondence: Peter N. Robinson ( peter.robinson@ 123456jax.org )

AUTHOR CONTRIBUTIONS

X.A.Z.: software engineering, curation, data analysis, and interpretation; A.Y.: software engineering; N.V., J.P.G., L.C.C., TJ.C., P.N.R.: data curation; D.D.: software engineering; M.P.J., V.R., K.R.: data analysis; E.R.P., J.C., K.F.: data collection and interpretation; D.B.P., H.X., R.Z., J.R., N.A.W., S.K., C.M., D.V., C.G.C., L.H., C.J.M., M.A.H.: data interpretation; T.D.B., J.A.F., B.M., A.L.S.: manual verification of curated annotations; X.A.Z., P.N.R.: designed study, wrote manuscript.

Author information

Xingmin Aaron Zhang http://orcid.org/0000-0002-7284-3950

Amy Yates http://orcid.org/0000-0002-0664-7185

Nicole Vasilevsky http://orcid.org/0000-0001-5208-3432

Tiffany J. Callahan http://orcid.org/0000-0002-8169-9049

Leigh C. Carmody http://orcid.org/0000-0001-7941-2961

Daniel Danis http://orcid.org/0000-0003-0900-3411

Karamarie Fecho http://orcid.org/0000-0002-6704-9306

Sebastian Köhler http://orcid.org/0000-0002-5316-1399

Daniel J. Vreeman http://orcid.org/0000-0001-5119-6531

Tellen D. Bennett http://orcid.org/0000-0003-1483-4236

James A. Feinstein http://orcid.org/0000-0003-3074-8805

Lawrence E. Hunter http://orcid.org/0000-0003-1455-3370

Christopher G. Chute http://orcid.org/0000-0001-5437-2545

Peter N. Robinson http://orcid.org/0000-0002-0736-9199

Article

Manuscript ID: NIHMS1028600

DOI: 10.1038/s41746-019-0110-4

PMC ID: 6527418

PubMed ID: 31119199

SO-VID: 3ce2e1e3-7503-450c-a666-0492a0e05358

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linkto the Creative Commons license, and indicate ifchanges were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To viewa copy ofthis license, visit http://creativecommons.org/licenses/by/4.0/.

History

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Read this article at

Abstract

Related collections

Laboratory Phonology

Most cited references 32

Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

Author and article information

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 49

Cited by 21

Most referenced authors 761