PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics

Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics fr...

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Autores principales: Zhao, Zijie, Yi, Yanyao, Song, Jie, Wu, Yuchang, Zhong, Xiaoyuan, Lin, Yupei, Hohman, Timothy J., Fletcher, Jason, Lu, Qiongshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419981/
https://www.ncbi.nlm.nih.gov/pubmed/34488838
http://dx.doi.org/10.1186/s13059-021-02479-9
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author Zhao, Zijie
Yi, Yanyao
Song, Jie
Wu, Yuchang
Zhong, Xiaoyuan
Lin, Yupei
Hohman, Timothy J.
Fletcher, Jason
Lu, Qiongshi
author_facet Zhao, Zijie
Yi, Yanyao
Song, Jie
Wu, Yuchang
Zhong, Xiaoyuan
Lin, Yupei
Hohman, Timothy J.
Fletcher, Jason
Lu, Qiongshi
author_sort Zhao, Zijie
collection PubMed
description Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02479-9.
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spelling pubmed-84199812021-09-09 PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics Zhao, Zijie Yi, Yanyao Song, Jie Wu, Yuchang Zhong, Xiaoyuan Lin, Yupei Hohman, Timothy J. Fletcher, Jason Lu, Qiongshi Genome Biol Method Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02479-9. BioMed Central 2021-09-06 /pmc/articles/PMC8419981/ /pubmed/34488838 http://dx.doi.org/10.1186/s13059-021-02479-9 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 Method
Zhao, Zijie
Yi, Yanyao
Song, Jie
Wu, Yuchang
Zhong, Xiaoyuan
Lin, Yupei
Hohman, Timothy J.
Fletcher, Jason
Lu, Qiongshi
PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title_full PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title_fullStr PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title_full_unstemmed PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title_short PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
title_sort pumas: fine-tuning polygenic risk scores with gwas summary statistics
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419981/
https://www.ncbi.nlm.nih.gov/pubmed/34488838
http://dx.doi.org/10.1186/s13059-021-02479-9
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