Polygenic scores can encompass thousands of individual genetic variants spread throughout the genome and include a mixture of true genetic association signal and noise from statistical artifact and stochastic error (Maher, 2015). Conventional polygenic scoring methods have typically accounted for less than 2% of the genetic liability underlying complex traits, although this improves as the discovery sample sizes increases. Simulations indicate that tens of thousands of subjects may still be needed to achieve clinically meaningful prediction with these methods (Dudbridge, 2013). Efforts to amplify true genetic signal and reduce noise could enhance the predictive power of polygenic scores. Although some methods have been developed to improve polygenic scores, as of yet there has been no attempt to use information beyond the discovery GWAS (i.e. p value thresholds for filtering the inclusion of SNPs or linkage disequilibrium structure for weighting SNPs) to further refine the creation of such scores.
