Potential risk proteins for suicide attempts: multi-omics analysis based on European population biobanks

Suicide is defined as an intentional act of ending one's own life. Suicide attempt (SA) is a significant risk factor for suicide death. Research on SA has progressed from socio-psychological perspectives to the molecular and genetic levels. While the biological mechanisms underlying genome-wide association studies (GWAS) identified risk loci remain largely unclear. To investigate the potential risk mechanisms, we constructed a systematic analytic pipeline using brain protein quantitative trait locus (pQTL) datasets (Banner, N=152; ROSMAP, N=376), a brain expression quantitative trait locus (eQTL) datasets (N=452), and SA GWAS summary statistics (N_case=35,786, N_control=779,392). We performed proteome-wide association study (PWAS), Mendelian randomization (MR), Bayesian colocalization analysis, transcriptome-wide association study (TWAS), and multi-marker analysis of genomic annotation (MAGMA) to systematically identify and screen for novel genetically supported candidate proteins related to the biological mechanism of SA in the brain. For functional annotation, we used the GeneMANIA to bulid a functional prediction network integrating co-expression, physical interactions, and pathway colocalization to identify core proteins. PWAS identified three brain proteins whose genetically predicted abundance was significantly associated with SA. Among them, GMPPB was prioritized as putative causal protein, supported by MR analysis (false discovery rate, FDR<0.05) and Bayesian colocalization analysis (posterior probability PPH₄≥0.8). Specifically, higher genetically predicted GMPPB protein levels were associated with increased risk of SA. Although our analyses primarily relied on datasets from European-ancestry populations, the shared genetic architecture across populations and the generalizability of genome-wide data analytical approaches suggest that our findings may still provide useful insights into the biological mechanisms underlying SA and help inform the development of intervention strategies and genetic counseling in Chinese populations.