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Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort

With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R(2) criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statisti...

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Autores principales: Duhazé, Julianne, Jantzen, Rodolphe, Payette, Yves, De Malliard, Thibault, Labbé, Catherine, Noisel, Nolwenn, Broët, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193029/
https://www.ncbi.nlm.nih.gov/pubmed/32391062
http://dx.doi.org/10.3389/fgene.2020.00408
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author Duhazé, Julianne
Jantzen, Rodolphe
Payette, Yves
De Malliard, Thibault
Labbé, Catherine
Noisel, Nolwenn
Broët, Philippe
author_facet Duhazé, Julianne
Jantzen, Rodolphe
Payette, Yves
De Malliard, Thibault
Labbé, Catherine
Noisel, Nolwenn
Broët, Philippe
author_sort Duhazé, Julianne
collection PubMed
description With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R(2) criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statistic derived under a two-component mixture model. It evaluates the effect of the PRS on both the propensity to experience the event and on the dynamic of the event among the susceptible subjects. Simulation results show that our index has good properties. We compared our index to other implemented pseudo-R(2) for survival data. Along with our index, two other indices have comparable good behavior when the PRS has a non-null propensity effect, and our index is the only one to detect when the PRS has only a dynamic effect. We evaluated the 5-year predictivity of an 18-single-nucleotide-polymorphism PRS for incident breast cancer cases on the CARTaGENE cohort using several pseudo-R(2) indices. We report that our index, which summarizes both a propensity and a dynamic effect, had the highest predictive accuracy. In conclusion, our proposed pseudo-R(2) is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies.
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spelling pubmed-71930292020-05-08 Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort Duhazé, Julianne Jantzen, Rodolphe Payette, Yves De Malliard, Thibault Labbé, Catherine Noisel, Nolwenn Broët, Philippe Front Genet Genetics With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R(2) criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statistic derived under a two-component mixture model. It evaluates the effect of the PRS on both the propensity to experience the event and on the dynamic of the event among the susceptible subjects. Simulation results show that our index has good properties. We compared our index to other implemented pseudo-R(2) for survival data. Along with our index, two other indices have comparable good behavior when the PRS has a non-null propensity effect, and our index is the only one to detect when the PRS has only a dynamic effect. We evaluated the 5-year predictivity of an 18-single-nucleotide-polymorphism PRS for incident breast cancer cases on the CARTaGENE cohort using several pseudo-R(2) indices. We report that our index, which summarizes both a propensity and a dynamic effect, had the highest predictive accuracy. In conclusion, our proposed pseudo-R(2) is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies. Frontiers Media S.A. 2020-04-24 /pmc/articles/PMC7193029/ /pubmed/32391062 http://dx.doi.org/10.3389/fgene.2020.00408 Text en Copyright © 2020 Duhazé, Jantzen, Payette, De Malliard, Labbé, Noisel and Broët. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Duhazé, Julianne
Jantzen, Rodolphe
Payette, Yves
De Malliard, Thibault
Labbé, Catherine
Noisel, Nolwenn
Broët, Philippe
Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title_full Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title_fullStr Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title_full_unstemmed Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title_short Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort
title_sort quantifying the predictive accuracy of a polygenic risk score for predicting incident cancer cases : application to the cartagene cohort
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193029/
https://www.ncbi.nlm.nih.gov/pubmed/32391062
http://dx.doi.org/10.3389/fgene.2020.00408
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