Mol Psychiatry. 2026 Jul 1. doi: 10.1038/s41380-026-03730-0. Online ahead of print.
ABSTRACT
Depression arises from dynamic interactions among genetic predisposition, brain alterations, and environmental stressors. Despite genome-wide association studies (GWAS) identifying risk loci, the mechanisms translating genetic variation into brain changes remain elusive. Imaging-derived phenotypes (IDPs) were the intermediate traits linking genetic architecture to neural circuit dysfunction. Here, we collected large-scale GWAS summary statistics of depression and IDPs across European (EUR; N = 1,293,933 and 33,224, respectively) and East Asian (EAS; N = 82,874 and 7058, respectively). In the multiple-trait analysis between depression and IDPs, we clarified their genetic correlation through MTAG, identified the pleiotropic single nucleotide variants (SNVs) and genes with functional insight, and established the causal relationship through Mendelian randomization via TwoSampleMR in EUR and EAS ancestry, respectively. To discern the heterogeneous genetic drivers, we selected independent SNVs from the multiple-trait analyses to perform unsupervised clustering. Six clusters delineated distinct biological pathways for metabolic regulation, neurotransmitter dynamics, and neuroimmune interactions, with tissue/cell type specificity through MAGMA. Finally, we dissected relationships between depression and polygenic risk score, IDPs, and modifiable lifestyle factors, and introduced a machine learning framework to refine risk stratification (N = 16,166). Our study advanced the understanding of the multiscale etiology of depression while providing dynamic depression risk stratification for precision prevention.
PMID:42387104 | DOI:10.1038/s41380-026-03730-0
