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

SUMMARY: 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 resem...

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Autores principales: Aref, Layla, Bastarache, Lisa, Hughey, Jacob J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620826/
https://www.ncbi.nlm.nih.gov/pubmed/36083022
http://dx.doi.org/10.1093/bioinformatics/btac619
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author Aref, Layla
Bastarache, Lisa
Hughey, Jacob J
author_facet Aref, Layla
Bastarache, Lisa
Hughey, Jacob J
author_sort Aref, Layla
collection PubMed
description SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-96208262022-11-01 The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants Aref, Layla Bastarache, Lisa Hughey, Jacob J Bioinformatics Applications Notes SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-09-09 /pmc/articles/PMC9620826/ /pubmed/36083022 http://dx.doi.org/10.1093/bioinformatics/btac619 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Aref, Layla
Bastarache, Lisa
Hughey, Jacob J
The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title_full The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title_fullStr The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title_full_unstemmed The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title_short The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants
title_sort phers r package: using phenotype risk scores based on electronic health records to study mendelian disease and rare genetic variants
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620826/
https://www.ncbi.nlm.nih.gov/pubmed/36083022
http://dx.doi.org/10.1093/bioinformatics/btac619
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