Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

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 acc...

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Bibliographic Details
Published in:American journal of human genetics 2021-06, Vol.108 (6), p.1001-1011
Main Authors: Albiñana, Clara, Grove, Jakob, McGrath, John J., Agerbo, Esben, Wray, Naomi R., Bulik, Cynthia M., Nordentoft, Merete, Hougaard, David M., Werge, Thomas, Børglum, Anders D., Mortensen, Preben Bo, Privé, Florian, Vilhjálmsson, Bjarni J.
Format: Article
Language:English
Subjects:
Case-Control Studies
complex traits
Disease - genetics
genetic prediction
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Medicin och hälsovetenskap
meta-analysis
Models, Statistical
Multifactorial Inheritance
Phenotype
polygenic risk scores
Polymorphism, Single Nucleotide
PRS
psychiatric disorders
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Summary: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.
ISSN:0002-9297
1537-6605
1537-6605
DOI:10.1016/j.ajhg.2021.04.014