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RápidoPGS: a rapid polygenic score calculator for summary GWAS data without a test dataset.

Accepted version
Peer-reviewed

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Article

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Authors

Vigorito, Elena 
Kelemen, Martin 

Abstract

MOTIVATION: Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning. RESULTS: We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case-control datasets, with median r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. AVAILABILITY AND IMPLEMENTATION: Our method is available with a GPL license as an R package from CRAN and GitHub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Description

Keywords

Genotype, Genome-Wide Association Study, Multifactorial Inheritance, Polymorphism, Single Nucleotide

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
Medical Research Council (MC_UU_00002/4)
Wellcome Trust (107881/Z/15/Z)
Wellcome Trust (203950/Z/16/Z)
National Institute for Health and Care Research (IS-BRC-1215-20014)
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