Fig. 1

Schematic overview of RSS-E, a model-based enrichment analysis method for GWAS summary statistics. RSS-E combines three types of public data: GWAS summary statistics (1.1), external LD estimates (1.2), and predefined SNP sets (1.3). GWAS summary statistics consist of a univariate effect size estimate (\(\hat \beta _j\)) and corresponding standard error (\(\hat s_j\)) for each SNP, which are routinely generated in GWAS. External LD estimates are obtained from an external reference panel with ancestry matching the population of GWAS cohorts. SNP sets here are derive from gene sets based on biological pathways or sequencing data. We combine these three types of data by fitting a Bayesian multiple regression (2.1–2.2) under two models about the enrichment parameter (θ): the baseline model (2.3) that each SNP has equal chance of being associated with the trait (M₀: θ = 0), and the enrichment model (2.4) that SNPs in the SNP set are more often associated with the trait (M₁: θ > 0). To test enrichment, RSS-E computes a Bayes factor (BF) comparing these two models (3.1). RSS-E also automatically prioritizes loci within an enriched set by comparing the posterior distributions of genetic effects (β) under M₀ and M₁ (3.2). Here we summarize the posterior of β as P₁, the posterior probability that at least one SNP in a locus is trait-associated. Differences between P₁ estimated under M₀ and M₁ reflect the influence of enrichment on genetic associations, which can help identify new trait-associated genes (3.2)