Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction
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Dokumenter
- Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction
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The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | American Journal of Human Genetics |
| Vol/bind | 108 |
| Udgave nummer | 6 |
| Sider (fra-til) | 1001-1011 |
| Antal sider | 11 |
| ISSN | 0002-9297 |
| DOI | |
| Status | Udgivet - 2021 |
Bibliografisk note
Funding Information:
This study was funded by grants from The Lundbeck Foundation (R102-A9118, R155-2014-1724, R335-2019-2339, and R248-2017-2003) and The Danish National Research Foundation (Niels Bohr Professorship to Prof. John J. McGrath). The Anorexia Nervosa Genetics Initiative (ANGI) was an initiative of the Klarman Family Foundation. The authors gratefully acknowledge the Psychiatric Genomics Consortium (PGC) and the research participants and employees of 23andMe, Inc. for providing the summary statistics. All of the computing for this project was performed on the GenomeDK cluster. We would like to thank GenomeDK and Aarhus University for providing computational resources and support that contributed to these research results. This research has been conducted using the UK Biobank Resource under Application Number 41181.
Funding Information:
This study was funded by grants from The Lundbeck Foundation ( R102-A9118 , R155-2014-1724 , R335-2019-2339 , and R248-2017-2003 ) and The Danish National Research Foundation (Niels Bohr Professorship to Prof. John J. McGrath). The Anorexia Nervosa Genetics Initiative (ANGI) was an initiative of the Klarman Family Foundation. The authors gratefully acknowledge the Psychiatric Genomics Consortium (PGC) and the research participants and employees of 23andMe, Inc. for providing the summary statistics. All of the computing for this project was performed on the GenomeDK cluster. We would like to thank GenomeDK and Aarhus University for providing computational resources and support that contributed to these research results. This research has been conducted using the UK Biobank Resource under Application Number 41181.
Publisher Copyright:
© 2021 The Authors
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