MiXeR is extended to a bivariate context by assuming that, for a pair of traits, common genetic variants can be described as a mixture of four components – shared ‘causal’ variants (1), unique ‘causal’ variants for trait 1 (2) and trait 2 (3), and non-causal variants (4). Informed by the model parameters from univariate MiXeR for each trait, bivariate MiXeR estimates the shared component’s polygenicity irrespective of effect directions and correlation of effect sizes (rgs). The genome-wide genetic correlation (rg) and proportion of shared variants with concordant effects are derived from these model parameters. See supplementary methods and supplementary fig. 1 for further details.
