We applied MiXeR36 to our ADHD GWAS summary statistics and GWAS from a selection of complex traits showing high genetic correlation with ADHD: ASD86, SCZ54, BMI87, educational attainmet88, age at first birth89, smoking initiation85, insomnia90 and a new GWAS meta-analysis of depression including 371,184 cases and 978,703 controls91 (Supplementary Table 19) to quantify (i) the number of variants influencing each trait and (ii) the genetic overlap between ADHD and each of the other traits. We used MiXeR with default settings (https://github.com/precimed/mixer) in a two-step process. First, we ran a univariate model for each trait to estimate the number of common variants having a non-zero genetic additive impact on the phenotype. The univariate model generates estimates of “polygenicity” (i.e., the proportion of non-null variants) and “discoverability” (i.e., the variance of effect sizes of non-null SNPs). Second, the variance estimates from the univariate step were used to run a bivariate model in a pairwise fashion (i.e. ADHD vs. each of the other traits), which produced estimates of SNPs with a specific effect on the first or on the second trait, and SNPs
