| Title: | Making the Most of Clumping and Thresholding for Polygenic Scores |
| Journal: | American Journal of Human Genetics |
| Published: | 21 Nov 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31761295/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2019.11.001 |
| URL: | http://www.cell.com/article/S0002929719304227/pdf |
Publication 5353
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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.</p>
4 Authors
- Florian Privé
- Bjarni J Vilhjálmsson
- Hugues Aschard
- Michael G B Blum
Enabling scientific discoveries that improve human health