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
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
4972-4974Subventions
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
Nat Biotechnol. 2013 Dec;31(12):1102-10
pubmed: 24270849
Science. 2018 Mar 16;359(6381):1233-1239
pubmed: 29590070
Nucleic Acids Res. 2015 Jan;43(Database issue):D789-98
pubmed: 25428349
J Am Med Inform Assoc. 2019 Dec 1;26(12):1437-1447
pubmed: 31609419
Nucleic Acids Res. 2020 Jan 8;48(D1):D835-D844
pubmed: 31777943
Nucleic Acids Res. 2021 Jan 8;49(D1):D1207-D1217
pubmed: 33264411
Nat Rev Drug Discov. 2020 Feb;19(2):77-78
pubmed: 32020066
Annu Rev Biomed Data Sci. 2021 Jul 20;4:1-19
pubmed: 34465180
Eur J Hum Genet. 2020 Feb;28(2):165-173
pubmed: 31527858