Abstract
Polygenic scores (PGS) aim to predict complex traits at an individual level based on genetic data. Computation of PGS is based on simply ascertained genome-wide association summary statistics but typically requires an independent test dataset to tune PGS parameters, and for the more sophisticated methods, the computation of LD matrices. Internally tuned methods have recently been proposed that obviate the need for a test dataset, but they remain computationally intensive to run. Here we present RápidoPGS, a flexible and fast method to compute PGS without the need to compute LD-matrices, requiring only summary-level GWAS datasets. Based on fine-mapping principles, RápidoPGS computes the posterior probability that each variant is causal, which in turn is used to shrink effect sizes adaptively as a function of LD and strength of association. We show by summary and individual-level validation that RápidoPGS performs well in comparison with another well-established internally-trained method (median AUC difference [RápidoPGS - LDpred2-auto] = −0.02205, range = [−0.1184, 0.0217]), with at least 50-fold improved speed. RápidoPGS is implemented in R, and can work with user-supplied summary statistics or download them directly from the GWAS catalog. We propose that RápidoPGS can be used to rapidly screen a set of candidate traits for utility, before more computationally intensive methods are applied to selected traits.
Competing Interest Statement
The authors have declared no competing interest.
