There are increasing evidence of shared genetics between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Here, we demonstrate how multivariate analyses can help reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. We first conducted univariate and multivariate genome-wide association studies (GWAS) for eight MRI-derived brain volume phenotypes in 20K UK Biobank participants. We performed various enrichment analysis to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autisms, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Univariate MRI GWAS display only negligible genetic correlation with psychiatric disorders at all levels we investigated. Multitrait GWAS identified multiple new associations and show significant enrichment for variants associated with both ADHD and schizophrenia variants. We further clustered top associated variants based on their MRI multitrait association using an optimized k-medoids approach and detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses pointed multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia. Altogether our results show that multitrait association signature can be used to infer genetically driven latent MRI variables associated with psychiatric disorder, opening paths for future biomarker developments.

Linking the genetic structure of neuroanatomical phenotypes with psychiatric disorders