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Polygenic risk scores for cardiovascular diseases and type 2 diabetes

Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual’s risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known r...

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Autores principales: Wong, Chi Kuen, Makalic, Enes, Dite, Gillian S., Whiting, Lawrence, Murphy, Nicholas M., Hopper, John L., Allman, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718402/
https://www.ncbi.nlm.nih.gov/pubmed/36459520
http://dx.doi.org/10.1371/journal.pone.0278764
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author Wong, Chi Kuen
Makalic, Enes
Dite, Gillian S.
Whiting, Lawrence
Murphy, Nicholas M.
Hopper, John L.
Allman, Richard
author_facet Wong, Chi Kuen
Makalic, Enes
Dite, Gillian S.
Whiting, Lawrence
Murphy, Nicholas M.
Hopper, John L.
Allman, Richard
author_sort Wong, Chi Kuen
collection PubMed
description Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual’s risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.
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spelling pubmed-97184022022-12-03 Polygenic risk scores for cardiovascular diseases and type 2 diabetes Wong, Chi Kuen Makalic, Enes Dite, Gillian S. Whiting, Lawrence Murphy, Nicholas M. Hopper, John L. Allman, Richard PLoS One Research Article Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual’s risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex. Public Library of Science 2022-12-02 /pmc/articles/PMC9718402/ /pubmed/36459520 http://dx.doi.org/10.1371/journal.pone.0278764 Text en © 2022 Wong et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wong, Chi Kuen
Makalic, Enes
Dite, Gillian S.
Whiting, Lawrence
Murphy, Nicholas M.
Hopper, John L.
Allman, Richard
Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title_full Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title_fullStr Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title_full_unstemmed Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title_short Polygenic risk scores for cardiovascular diseases and type 2 diabetes
title_sort polygenic risk scores for cardiovascular diseases and type 2 diabetes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718402/
https://www.ncbi.nlm.nih.gov/pubmed/36459520
http://dx.doi.org/10.1371/journal.pone.0278764
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