The bivariate causal mixture model (MiXeR)19 and local analysis of covariant association (LAVA) can shed light on this “missing dimension”. MiXeR circumvents the need to identify all ‘causal’ variants by inferring the total number of ‘causal’ variants for each trait (univariate), and the total number of shared and unique ‘causal’ variants for a pair of traits (bivariate).19 MiXeR also estimates the genetic correlation of shared variants (rgs), in addition to rg (fig. 1b). Using MiXeR, we have demonstrated extensive genetic overlap across several mental disorders and related traits with mixed effect directions.13,18,21 The relevance of mixed effects has been further emphasised by LAVA, which calculates local genetic correlations across the genome.22 Despite employing a distinct statistical framework, LAVA revealed widespread local genetic correlations across somatic and mental traits with mixed effect directions, even in the presence of minimal rg.22 However, neither MiXeR nor LAVA have been systematically applied across mental disorders, cognitive and personality traits using largest-to-date GWAS.
