posterior mean causal effect sizes under a point-normal prior77, and for BLUP methods by estimating (joint-fit) posterior mean causal effect sizes under a normal mixture prior81,82. Although polygenic risk scores must await even larger training sample sizes to attain clinical utility, appreciable prediction accuracies have been achieved for some traits, including a Nagelkerke R2 of 0.25 (AUC: 75%) for schizophrenia77. An important caveat is that it is critical when constructing and evaluating polygenic risk scores to avoid non-independence of training and validation samples (e.g. due to cryptic relatedness or shared population stratification), which could cause prediction accuracy to be overstated relative to what could be achieved in an independent validation sample77,83.
