| 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 |
| Citations: | 145 (70 in last 2 years) as of 8 Aug 2024 |
Publication 5353
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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