leverages the common genetic information across multiple correlated traits to boost the power of each individual trait44. MTAG is also powerful at disentangling spurious sources of genetic correlation, such as due to overlapping samples, using Linkage Disequilibrium Score Regression45. Meta-analysis across the EA and AA samples was performed using inverse-variance weighting and genomic control in METAL46. Sex and log-transformed age (at the time of EEG recording) were included as covariates in the model, as each of these variables were associated with theta EEG (p<0.001).
