We compared AnnoPred with several commonly used risk prediction methods based on summary data of association studies. PRSsig and PRSall were both calculated as the inner product of marginal effect size estimates and the corresponding genotypes. PRSall used all the SNPs that are shared between training and testing datasets while PRSsig only used SNPs with p-values below 5 × 10−8 in the training set. PRSP+T used SNPs passing both LD pruning and p-value thresholding. The thresholds are tuned in an independent dataset over a grid (0, 0.1, 0.2, … 0.9 for LD; 1, 0.3, 0.1, 0.03, 0.01, 3E-3, 1E-3, 3E-4, 1E-4, 3E-5, 1E-5, 1E-6, 1E-7, 5E-8, 1E-8 for p-value). LDpred can be viewed as a special case of AnnoPred, assuming the whole genome as the only functional annotation. This is because when enrichment is constant (i.e. causal variants are uniformly distributed across the genome), per-SNP heritability estimates would be nearly constant and therefore results in similar performance to LDpred. We have performed an additional simulation to demonstrate this using WTCCC genotype data with ~15K individuals and ~330K variants. We randomly
