Several methods can be used to calculate polygenic risk scores. These include ‘clumping/pruning and thresholding’ methods, where a reduced set of genetic variants is identified through pruning on linkage disequilibrium, and accounting for evidence of association with the trait being studied (clumping). Polygenic risk scores are then calculated summing over all SNPs meeting a p value threshold, or set of thresholds, as implemented in PRSice [11] and PLINK [12]. In contrast, other methods assess the best prediction genome-wide by explicitly modelling the correlation structure between variants without attempting to identify a minimal subset of SNPs for prediction; the most widely used implementation is the Bayesian LDpred approach [13]. Many novel risk score methods are under development and may have increased power in comparison with our current methods (for example, SBayesR [14]). We will use the term ‘polygenic risk scores’ to cover all methods that sum genetic data to provide individual risk measures and will assume that these are transformed to have a standard normal distribution. The measures used to assess the predictive ability of a PRS are summarised in Table
