Compared to the many existing multi-trait methods,1–5 MTAG has a unique combination of four features that make it potentially useful in many settings. First, it can be applied to GWAS summary statistics (without access to individual-level data) from an arbitrary number of traits. Second, the summary statistics need not come from independent discovery samples: MTAG uses bivariate linkage disequilibrium (LD) score regression6 to account for (possibly unknown) sample overlap between the GWAS results for different traits. Third, MTAG generates trait-specific effect estimates for each single-nucleotide polymorphism (SNP). Finally, even when applied to many traits, MTAG is computationally quick because every step has a closed-form solution.
