Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.

Background: Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored.

Methods: We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK Biobank and BD using GWAS summary statistics. We carried out a suite of sensitivity analyses and examined phenotypic categories with the greatest effect on BD. We applied a novel inverse sparse regression model which accounts for covariance between sets of correlated effects to estimate 'direct causal effects' (DCE), representing the effect of one phenotype conditional on all other effects. We used DCE weights to create causal scores for BD using neuroimaging data from three clinical cohorts.

Results: We found 28 significant causal relationship pairs after multiple testing corrections containing BD as a term, 27 of which described neuroimaging phenotype effects on BD. White matter tract phenotypes have larger absolute effects on BD than vice versa in MR tests and estimated direct causal effect solutions. We found that white matter phenotypes had significantly larger out-degrees than non-white matter tract phenotypes across network solutions. A causal score constructed using neuroimaging causal estimates was a significant predictor of BD in an adolescent cohort (O.R.=0.79).

Conclusion: Mendelian randomization analyses suggest that neuroanatomical variation, specifically in white matter tracts such as the longitudinal fasciculi, is likely a cause rather than a consequence of BD. Verification of estimated causal relationships requires replication and triangulation of evidence approaches using other study designs.