Abstract:

Advances in genome-wide association studies (GWAS) and integrative genomic analyses have enabled the identification of shared genetic architecture across diverse brain-related phenotypes. However, systematically prioritizing pleiotropic loci and translating association signals into biologically meaningful candidate genes remain significant challenges. To address this, we developed a multi-trait GWAS fine-mapping framework and applied it to 30 GWAS datasets representing more than 20 brain-related phenotypes and over 10 million participants. The analytical pipeline integrated Bayesian fine-mapping, cross-trait analyses, linkage disequilibrium and haplotype mapping, transcriptomic validation, pharmacogenomic annotation, protein–protein interaction network analysis, and pathway enrichment. Across all datasets, 999 tag causal single nucleotide polymorphisms (SNPs) and 860 primary gene entries were identified, yielding 474 unique genes, including 193 pleiotropic genes shared across multiple traits. Cross-trait analyses identified 54 recurrent SNPs and 608 overlapping genomic loci, with two major genomic regions showing extensive overlap across phenotypes. Downstream functional analyses prioritized 97 genes with significant brain expression patterns and consistently identified the dopamine D2 receptor gene (DRD2) as a central hub within interconnected protein interaction networks. Enrichment analyses highlighted dopaminergic signaling and substance use–related pathways among the most significant biological processes. These findings demonstrate the utility of large-scale multi-trait fine mapping for uncovering shared genetic mechanisms across neurobehavioral phenotypes and provide a framework for prioritizing candidate genes for future functional and translational investigation.