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Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We p...

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Autores principales: Truong, Buu, Hull, Leland E., Ruan, Yunfeng, Huang, Qin Qin, Hornsby, Whitney, Martin, Hilary, van Heel, David A., Wang, Ying, Martin, Alicia R., Lee, S. Hong, Natarajan, Pradeep
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980241/
https://www.ncbi.nlm.nih.gov/pubmed/36865265
http://dx.doi.org/10.1101/2023.02.21.23286110
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author Truong, Buu
Hull, Leland E.
Ruan, Yunfeng
Huang, Qin Qin
Hornsby, Whitney
Martin, Hilary
van Heel, David A.
Wang, Ying
Martin, Alicia R.
Lee, S. Hong
Natarajan, Pradeep
author_facet Truong, Buu
Hull, Leland E.
Ruan, Yunfeng
Huang, Qin Qin
Hornsby, Whitney
Martin, Hilary
van Heel, David A.
Wang, Ying
Martin, Alicia R.
Lee, S. Hong
Natarajan, Pradeep
author_sort Truong, Buu
collection PubMed
description Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10(−5)) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10(−6)), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10(−6)) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10(−7)) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10(−4)). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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spelling pubmed-99802412023-03-03 Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases Truong, Buu Hull, Leland E. Ruan, Yunfeng Huang, Qin Qin Hornsby, Whitney Martin, Hilary van Heel, David A. Wang, Ying Martin, Alicia R. Lee, S. Hong Natarajan, Pradeep medRxiv Article Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10(−5)) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10(−6)), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10(−6)) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10(−7)) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10(−4)). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population. Cold Spring Harbor Laboratory 2023-03-23 /pmc/articles/PMC9980241/ /pubmed/36865265 http://dx.doi.org/10.1101/2023.02.21.23286110 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Truong, Buu
Hull, Leland E.
Ruan, Yunfeng
Huang, Qin Qin
Hornsby, Whitney
Martin, Hilary
van Heel, David A.
Wang, Ying
Martin, Alicia R.
Lee, S. Hong
Natarajan, Pradeep
Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title_full Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title_fullStr Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title_full_unstemmed Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title_short Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
title_sort integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980241/
https://www.ncbi.nlm.nih.gov/pubmed/36865265
http://dx.doi.org/10.1101/2023.02.21.23286110
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