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Implementation and implications for polygenic risk scores in healthcare
Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various com...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290135/ https://www.ncbi.nlm.nih.gov/pubmed/34284826 http://dx.doi.org/10.1186/s40246-021-00339-y |
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author | Slunecka, John L. van der Zee, Matthijs D. Beck, Jeffrey J. Johnson, Brandon N. Finnicum, Casey T. Pool, René Hottenga, Jouke-Jan de Geus, Eco J. C. Ehli, Erik A. |
author_facet | Slunecka, John L. van der Zee, Matthijs D. Beck, Jeffrey J. Johnson, Brandon N. Finnicum, Casey T. Pool, René Hottenga, Jouke-Jan de Geus, Eco J. C. Ehli, Erik A. |
author_sort | Slunecka, John L. |
collection | PubMed |
description | Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs. |
format | Online Article Text |
id | pubmed-8290135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82901352021-07-20 Implementation and implications for polygenic risk scores in healthcare Slunecka, John L. van der Zee, Matthijs D. Beck, Jeffrey J. Johnson, Brandon N. Finnicum, Casey T. Pool, René Hottenga, Jouke-Jan de Geus, Eco J. C. Ehli, Erik A. Hum Genomics Review Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs. BioMed Central 2021-07-20 /pmc/articles/PMC8290135/ /pubmed/34284826 http://dx.doi.org/10.1186/s40246-021-00339-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Slunecka, John L. van der Zee, Matthijs D. Beck, Jeffrey J. Johnson, Brandon N. Finnicum, Casey T. Pool, René Hottenga, Jouke-Jan de Geus, Eco J. C. Ehli, Erik A. Implementation and implications for polygenic risk scores in healthcare |
title | Implementation and implications for polygenic risk scores in healthcare |
title_full | Implementation and implications for polygenic risk scores in healthcare |
title_fullStr | Implementation and implications for polygenic risk scores in healthcare |
title_full_unstemmed | Implementation and implications for polygenic risk scores in healthcare |
title_short | Implementation and implications for polygenic risk scores in healthcare |
title_sort | implementation and implications for polygenic risk scores in healthcare |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290135/ https://www.ncbi.nlm.nih.gov/pubmed/34284826 http://dx.doi.org/10.1186/s40246-021-00339-y |
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