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Am J Hum Genet. 2019 Dec 5;105(6):1213-1221. doi: 10.1016/j.ajhg.2019.11.001. Epub 2019 Nov 21.

Making the Most of Clumping and Thresholding for Polygenic Scores.

Author information

1
Laboratoire TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, La Tronche, France; Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark. Electronic address: florian.prive.21@gmail.com.
2
Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark.
3
Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France.
4
Laboratoire TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, La Tronche, France. Electronic address: michael.blum@univ-grenoble-alpes.fr.

Abstract

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.

KEYWORDS:

C+T; PRS; UK Biobank; clumping and thresholding; complex traits; polygenic risk scores; stacking

PMID:
31761295
PMCID:
PMC6904799
[Available on 2020-06-05]
DOI:
10.1016/j.ajhg.2019.11.001

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