250 000 base pair positions [33]. Age, age [2], sex, first five ancestry-based principal components, a SNP-array variable, and interaction terms between sex and age, and sex and age [2] were defined as fixed effects. To account for relatedness, prediction was performed using generalized equation estimation (GEE) as implemented in the “gee” package (version 4.13–19) in R (version 3.5.3). GEE applies a sandwich correction over the standard errors to account for clustering in the data [34]. To correct for multiple testing, we applied an FDR correction at α = 0.05 for 16 tests. QIMR excluded SNPs with low imputation quality (r2 = 0.6) and MAF below 1% and selected the most significant independent SNPs using PLINK1.9 [35] (criteria linkage disequilibrium r2 = 0.1 within windows of 10 MBp). We calculated different PGS for seven P value thresholds (P < 1e–5, P < 0.001, P < 0.01, P < 0.05, P < 0.1, P < 0.5, and P < 1.0) of the GWAS summary statistics. PGS were calculated from the imputed genotype dosages to the 1000 Genomes (Phase 3 Release 5) reference panel. We fitted linear mixed models, which controlled for relatedness using a Genetic Relatedness Matrix (GRM) and covariates sex,
