We used MTAG100 to conduct multivariable GWAS analyses, reporting GWAS results for each of the ascertainment-specific subgroups. Through this approach, we aimed to address potential concerns about heterogeneity in genetic liability for individual subgroups following different ascertainment strategies. MTAG is a multi-trait analysis that is usually used to combine different but related traits into one meta-analysis by leveraging the shared heritability among the different traits and thereby gaining power. In this case, our aim was to generate ascertainment-specific estimates, while boosting power by leveraging the high shared heritability between the subgroups. The MTAG analysis resulted in four different GWAS summary statistics, one for each subgroup (clinical, comorbid, biobanks, 23andMe). We performed maxFDR analyses to approximate the upper bound on the FDR of MTAG results.
