We estimated the distribution of genetic effect-sizes of addiction-rf (Genomic SEM) and the 4 input GWAS (PAU, PTU, CUD, OUD) using Genetic effect-size distribution inference from summary-level data (GENESIS). GENESIS is a likelihood-based approach56. In this approach, GWAS summary statistics and an external panel of linkage disequilibrium (in our case, the 1000 Genomes Phase 3 reference panel) are used to estimate a projected distribution of SNP effect sizes. A flexible normal mixture model based on the number of tagged SNPs and LD scores is estimated. A 3-component model is fit, where SNP effect sizes are estimated to belong to one of three components based on bins of effect sizes (large, medium, small). If the distribution of SNPs is multivariate normal, the estimation of the SNPs with large and medium effect sizes can be done via their independent effect sizes. The third component represents SNPs with null and small effect sizes, and these should follow a similar distribution. Therefore, this model reweights SNPs and generates a projected distribution of effect sizes, and from this projection, we can draw conclusions about the distribution of effect sizes54.
