The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
31 10 2022
Historique:
received: 10 06 2022
revised: 06 09 2022
accepted: 08 09 2022
pubmed: 10 9 2022
medline: 3 11 2022
entrez: 9 9 2022
Statut: ppublish

Résumé

Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patient's clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian disease-like phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approach's usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e. ICD codes and phecodes), human phenotype ontology terms and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate PheRSs, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS's transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 36083022
pii: 6694842
doi: 10.1093/bioinformatics/btac619
pmc: PMC9620826
doi:

Banques de données

figshare
['10.6084/m9.figshare.20016728']

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

4972-4974

Subventions

Organisme : NLM NIH HHS
ID : R01 LM010685
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35GM124685
Pays : United States
Organisme : NLM NIH HHS
ID : R01LM010685
Pays : United States

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

Références

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Auteurs

Layla Aref (L)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Lisa Bastarache (L)

Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.

Jacob J Hughey (JJ)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.

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