LEVERAGING GENETICS AND MOLECULAR ANALYSES TO UNCOVER DRUG TARGETS AND DRUG REPURPOSING OPPORTUNITIES FOR SUICIDALITY

Background At present, pharmacological treatment options for suicidality are extremely limited. While some drugs have shown promise for treating suicidality independent of their effects on mood, their mechanisms of action are not fully understood, and furthermore they are only indicated in the context of specific psychiatric disorders. It is well established that drugs that have genetic support for their target or mechanism of action have a higher chance of success, underscoring the value of genetically informed drug target discovery. Suicidal ideation (SI) and suicidal behavior (SB) are known heritable traits, providing a strong basis for leveraging genetics to guide drug discovery and drug repurposing. This study leverages the largest available multi-ancestry genome-wide association studies (GWAS) of SI (N=1,569,690) and SB (N=1,359,285) from the Psychiatric Genomics Consortium to uncover novel drug targets and drug repurposing opportunities for suicidality treatment. Method We performed drug-gene set analyses via the DRUGSETS pipeline and GSA-MiXeR tool using 1,939 drug-gene sets from DGIdb including FDA-approved drugs and compounds in Phase 3 clinical trials with known mechanisms of action. Subsequently, we used summary-data-based Mendelian Randomization (SMR) analyses and the largest available expression (eQTL) and protein (pQTL) quantitative trait loci datasets from brain and blood to evaluate causal relationships between molecular traits and suicidality phenotypes. Bonferroni correction and HEIDI tests were applied. Finally, drug‑induced gene expression data from the Connectivity Map will be compared with MR‑derived effect size to prioritize compounds predicted to counteract molecular changes associated with suicidality. Results DRUGSETS identified 149 nominally significant FDA-approved drug-gene sets for SB and 81 for SI (p < 0.05), and three phase-3 compound-gene sets each for SB and SI. Enrichment analyses using GSA-MiXeR revealed 85 drug-gene sets (AIC > 0) for SB and 47 drug-gene sets (AIC > 0) for SI. Drugs were prioritized based on enrichment using both DRUGSETS (p < 0.05) and GSA-MiXeR (AIC > 0), resulting in 48 candidate drugs for SB, and 18 candidate drugs for SI. Annotation of implicated drug–gene sets using ATC classifications revealed significant enrichment for hormonal drug indications for SB (p=3.14 × 10-7), and psychotropic drug indications for both SB (p= 1.71 × 10-5) and SI (p=0.005). SMR analyses leveraging brain eQTLs of the cortex identified four credible genes for SB (FURIN, TIAF1, GMPPB, PPP6C) and four credible genes for SI (TRMT61A, FURIN, LINC01954, EXD3). Ongoing SMR analyses incorporating pQTLs from brain and blood tissue will further refine molecular targets for suicidality. Discussion These findings demonstrate that integrating molecular trait data with large‑scale GWAS has the potential to support genetically and biologically-informed drug repurposing opportunities for suicidality.