To infer credible causal associations between intelligence and traits that are genetically correlated with intelligence, we performed Generalized Summary-data based Mendelian Randomization29 (GSMR; see URLs). This method utilizes summary-level data to test for causal associations between a putative risk factor (exposure) and an outcome by using independent genome-wide significant SNPs as instrumental variables. HEIDI-outlier detection was used to filter genetic instruments that show clear pleiotropic effects on both the exposure phenotype and the outcome phenotype. We used a threshold p-value of 0.01 for the outlier detection analysis in HEIDI which removes 1% of SNPs by chance if there is no pleiotropic effect. To test for a potential causal effect of intelligence on various outcomes, we selected traits in non-overlapping samples that showed significant genetic correlations (rg) with intelligence. We tested for bi-directional causation by repeating the analyses while switching the role of each correlated phenotype as an exposure and intelligence as the outcome. For each trait, we selected independent (r2=<0.1), GWS lead SNPs as instrumental variables in the analyses. For traits with less than 10 GWS lead SNPs (i.e. the
