Making the Most of Clumping and Thresholding for Polygenic Scores

• Published in: American journal of human genetics 2019-12, Vol.105 (6), p.1213-1221
• Main Authors: Privé, Florian, Vilhjálmsson, Bjarni J., Aschard, Hugues, Blum, Michael G.B.
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatics; Biological Specimen Banks; C+T; Case-Control Studies; clumping and thresholding; complex traits; Computer Science; Computer Simulation; Disease; Disease - genetics; Genetic Predisposition to Disease; Genetics; Genome-Wide Association Study; Humans; Life Sciences; Methodology; Models, Genetic; Multifactorial Inheritance; Multifactorial Inheritance - genetics; polygenic risk scores; Polymorphism, Single Nucleotide; Populations and Evolution; PRS; Santé publique et épidémiologie; stacking; Statistics; UK Biobank; United Kingdom
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2019.11.001

Polygenic prediction has the potential to contribute to precision medicine. Clumping and thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, several p value thresholds are tested to maximize predictive ability of the derived polygenic scores. Along with this p value threshold, we propose to tune three other hyper-parameters for C+T. We implement an efficient way to derive thousands of different C+T scores corresponding to a grid over four hyper-parameters. For example, it takes a few hours to derive 123K different C+T scores for 300K individuals and 1M variants using 16 physical cores. We find that optimizing over these four hyper-parameters improves the predictive performance of C+T in both simulations and real data applications as compared to tuning only the p value threshold. A particularly large increase can be noted when predicting depression status, from an AUC of 0.557 (95% CI: [0.544–0.569]) when tuning only the p value threshold to an AUC of 0.592 (95% CI: [0.580–0.604]) when tuning all four hyper-parameters we propose for C+T. We further propose stacked clumping and thresholding (SCT), a polygenic score that results from stacking all derived C+T scores. Instead of choosing one set of hyper-parameters that maximizes prediction in some training set, SCT learns an optimal linear combination of all C+T scores by using an efficient penalized regression. We apply SCT to eight different case-control diseases in the UK biobank data and find that SCT substantially improves prediction accuracy with an average AUC increase of 0.035 over standard C+T.