MTAG relies on bivariate LD score regression (and by extension its assumptions) to estimate the correlation in GWAS estimation error due to sample overlap. To gauge MTAG’s performance, we simulate an extreme case of sample overlap using real data from the UK Biobank (UKB). We run three GWASs of height, each using two-thirds of the data, with 50% overlap between each pair of GWAS samples. Then we run MTAG on the three GWASs. Figure 2a is a scatterplot of the resulting MTAG z-statistics against the z-statistics from a single GWAS run on the entire UKB sample. Figure 2b is the scatterplot from an analogous analysis of DEP in UKB. The regression slope and R2 are both essentially one for both phenotypes, indicating that MTAG generates the correct z-statistics in these cases. The results are similar when we repeat this analysis using four other phenotypes (Online Methods).
