Improving polygenic risk prediction from summary statistics by an empirical Bayes approach
Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach req...
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ftpubmed:oai:pubmedcentral.nih.gov:5286518 2023-05-15T17:42:35+02:00 Improving polygenic risk prediction from summary statistics by an empirical Bayes approach So, Hon-Cheong Sham, Pak C. 2017-02-01 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286518/ http://www.ncbi.nlm.nih.gov/pubmed/28145530 https://doi.org/10.1038/srep41262 en eng Nature Publishing Group http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286518/ http://www.ncbi.nlm.nih.gov/pubmed/28145530 http://dx.doi.org/10.1038/srep41262 Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ CC-BY Article Text 2017 ftpubmed https://doi.org/10.1038/srep41262 2017-02-12T01:08:30Z Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach requires testing over a range of p-value thresholds, which are often chosen arbitrarily. The prediction error estimated from the optimized threshold may also be subject to an optimistic bias. To improve genomic risk prediction, we proposed new empirical Bayes approaches to recover the underlying effect sizes and used them as weights to construct PRS. We applied the new PRS to twelve cardio-metabolic traits in the Northern Finland Birth Cohort and demonstrated improvements in predictive power (in R2) when compared to standard PRS at the best p-value threshold. Importantly, for eleven out of the twelve traits studied, the predictive performance from the entire set of genome-wide markers outperformed the best R2 from standard PRS at optimal p-value thresholds. Our proposed methodology essentially enables an automatic PRS weighting scheme without the need of choosing tuning parameters. The new method also performed satisfactorily in simulations. It is computationally simple and does not require assumptions on the effect size distributions. Text Northern Finland PubMed Central (PMC) Scientific Reports 7 1 |
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Article So, Hon-Cheong Sham, Pak C. Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
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Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach requires testing over a range of p-value thresholds, which are often chosen arbitrarily. The prediction error estimated from the optimized threshold may also be subject to an optimistic bias. To improve genomic risk prediction, we proposed new empirical Bayes approaches to recover the underlying effect sizes and used them as weights to construct PRS. We applied the new PRS to twelve cardio-metabolic traits in the Northern Finland Birth Cohort and demonstrated improvements in predictive power (in R2) when compared to standard PRS at the best p-value threshold. Importantly, for eleven out of the twelve traits studied, the predictive performance from the entire set of genome-wide markers outperformed the best R2 from standard PRS at optimal p-value thresholds. Our proposed methodology essentially enables an automatic PRS weighting scheme without the need of choosing tuning parameters. The new method also performed satisfactorily in simulations. It is computationally simple and does not require assumptions on the effect size distributions. |
format |
Text |
author |
So, Hon-Cheong Sham, Pak C. |
author_facet |
So, Hon-Cheong Sham, Pak C. |
author_sort |
So, Hon-Cheong |
title |
Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
title_short |
Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
title_full |
Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
title_fullStr |
Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
title_full_unstemmed |
Improving polygenic risk prediction from summary statistics by an empirical Bayes approach |
title_sort |
improving polygenic risk prediction from summary statistics by an empirical bayes approach |
publisher |
Nature Publishing Group |
publishDate |
2017 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286518/ http://www.ncbi.nlm.nih.gov/pubmed/28145530 https://doi.org/10.1038/srep41262 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286518/ http://www.ncbi.nlm.nih.gov/pubmed/28145530 http://dx.doi.org/10.1038/srep41262 |
op_rights |
Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
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CC-BY |
op_doi |
https://doi.org/10.1038/srep41262 |
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Scientific Reports |
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