Further elucidation of GMPPB as a risk gene for depression through integrative multi-omics analyses

Depression, a prevalent and recurrent mental disorder, significantly impairs the quality of life and social functioning. Traditional genome-wide association studies GWAS is difficult to accurately locate causative genes due to linkage disequilibrium and tissue-specific expression quantitative trait loci (eQTL) effects, while single-tissue transcriptome-wide association study (TWAS) may overlook cross-tissue regulatory heterogeneity or produce spurious associations. To address these limitations, the combined sparse canonical correlation analysis (sCCA) with the Aggregated Cauchy Association Test (ACAT) analysis was employed for capturing shared expression patterns across multiple tissues, reducing spurious associations and enhancing its power to detect genes with heterogeneous regulatory effects. The present investigation employed sCCA-ACAT, integrating two GWAS datasets with eQTL data from 49 tissues of the Genotype-Tissue Expression (GTEx) project, constructing a cross-tissue gene expression signature for depression. Four susceptibility genes were identified through FUSION and the Multi-marker Analysis of GenoMic Annotation (MAGMA) analysis. GMPPB was further validated as a robust risk gene via Mendelian randomization and colocalization analysis in the amygdala and anterior cingulate cortex. Functional annotation suggested that GMPPB might regulate inflammatory responses through glycosylation pathways to increase the risk of depression, though this hypothesis requires experimental validation. This study found that GMPPB in the amygdala and anterior cingulate cortex is associated with depression and might regulate inflammatory responses through glycosylation modifications, increasing the risk of depression. This finding provides new insights for further mechanistic studies, prevention, and treatment of depression.