A genome-wide and brain-wide statistical investigation into the cellular and genetic architectures of mood disorders and suicidal behaviors

Mood disorders including major depressive disorder (MDD) and bipolar disorder are leading causes of disease burden worldwide. Mood disorders are also strongly linked to risk of death by suicide. Due to challenges posed by their heterogeneous, comorbid, polygenic, and environmentally-influenced nature, the etiologies of mood disorders remain poorly understood. In recent years, human genome-wide association studies (GWAS) have linked hundreds of genetic loci to mood disorder phenotypes; the current challenge, then, is to uncover the biological mechanisms implicated by these loci. Here, we present a robust statistical approach that combines landmark GWAS and single-nucleus RNA sequencing (snRNAseq) gene expression datasets. Specifically, MAGMA v1.10 software was used to conduct gene property and conditional analysis on well-powered European-ancestry GWAS summary statistics for MDD (n=807,553; loci=102), bipolar disorder (n= 413,466; loci=64), and suicidal behaviors (n=815,178; loci=4) to identify significant independent associations with cell types as defined by the most comprehensive human brain snRNAseq dataset available to date (461 cell types, derived from 105 brain regions and 3,369,219 individual nuclei), published in 2023 by Siletti et al. Using this methodology, we mapped polygenic risk for MDD, bipolar disorder, and suicidal behavior to specific human brain cell types and their anatomical locations. Results demonstrated distinct cellular profiles for the three phenotypes that not only identified cell types and brain areas concordant with prior findings, but also identified novel cell type associations. Further analyses conducted on cell type gene expression and gene specificity data with NeuronChat and WEBGESTALT drug over-representation analysis predicted molecular interactions between implicated cell types and suggested novel drug repurposing and development opportunities. This data-driven project is “unbiased” with respect to researcher-driven hypotheses about which cell types should be associated with mood disorders, and reveals novel associations and a greater degree of cellular and molecular specificity regarding mood disorder and suicidal behavior etiology than ever previously available.