Making the most of Clumping and Thresholding for polygenic scores

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, people usually test several p-value thresholds to maximize predictive ability of derived polygenic scores. Along with this p-value threshold, we propose to tune 3 other hyper-parameters for C+T. We implement an efficient way to derive C+T scores corresponding to many different sets of hyper-parameters. For example, you can now derive thousands of different C+T scores for 300K individuals and 1M variants in less than one day. We show that tuning 4 hyper-parameters of C+T consistently improves its predictive performance in both simulations and real data applications as compared to tuning only the p-value threshold.Using this grid of computed C+T scores, we further extend C+T with stacking. More precisely, instead of choosing one set of hyper-parameters that maximizes prediction in some training set, we propose to learn an optimal linear combination of all these C+T scores using an efficient penalized regression. We call this method Stacked Clumping and Thresholding (SCT) and show that this makes C+T more flexible. When the training set is large enough, SCT can provide much larger predictive performance as compared to any of the C+T scores individually.

Screening Human Embryos for Polygenic Traits Has Limited Utility

“…Finally, statistical approaches to calculating PSs from GWASs are becoming increasingly sophisticated (Khera et al, 2018;Privé et al, 2019a;Torkamani et al, 2018). Most notably, the application of penalized regression methods to the generation of PSs holds a potential for rapid gains in r 2 ps without requiring any additional data collection in either GWAS datasets or imputation reference panels (Mak et al, 2017;Privé et al, 2019b).…”

Software as a Service for the Genomic Prediction of Complex Diseases

“…2. Better statistical methods to identify the most predictive set of variants to estimate risk for a given disease [10,11] 3. The availability of genome-wide data from hundreds of thousands of people linked with thousands of environmental and physiological measurement, such as the UK Biobank, an innovative project developed by the UK Government to collect genetic, health and lifestyle data from 500,000 people that has been made available to scientists and companies.…”

Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data

“…Many PRS methods proposed recently estimate SNP effects with GWAS summary statistics. One of the simplest is clumping and thresholding (C+T) [8] [9][10] [11] [12][13] [14], in which linkage disequilibrium (LD) clumping is applied to the SNPs that pass a p-value threshold. Another related method is pruning and thresholding (P+T), which only includes the SNPs whose pvalues exceed a threshold after LD pruning.…”