Leveraging functional annotations in genetic risk prediction for human complex diseases.

Evaluating the effect of sample size on prediction accuracy in simulation.Traits were simulated using SNPs of chromosome 1, chromosome 1 and 2, chromosome 1 to 4 and the whole genome while keeping the same proportion of causal variants and heritability to mimic the situation of increasing sample size. In the figure, logNr = logNMMs, where N is the number of individuals, M is the total number of variants and Ms is the number of variants used in simulation. In total four settings were simulated for each effective sample size: h2 = 0.25, p = 0.001; h2 = 0.25, p = 0.01; h2 = 0.5, p = 0.001; h2 = 0.5, p = 0.01, where p represents the proportion of causal variants. Each dot represent the mean COR of 50 replicates in one simulation setting and error bar represents the standard error.

Evaluating effectiveness of annotations and empirical effect size prior.(A) GWAS signal enrichment across GenoCanyon and tissue-specific GenoSkyline annotations. The horizontal lines mark p-value cutoffs of 0.05 and Bonferroni corrected significance level. (B) Comparing signal strength of SNPs with high priors and low priors in independent validation cohorts. SNPs with higher priors have significantly stronger associations across three independent and well-powered testing datasets (N>2,000). P-values were calculated using one-sided Kolmogorov-Smirnov test. (C) Comparing consistency of SNPs’ effect direction between training and testing datasets. Each bar quantifies the proportion of SNPs with consistent effect directions. P-values were calculated using one-sided two-sample binomial test.