Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders

Importance Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear. Objective To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders. Design, Setting, and Participants Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders—attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder—one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024. Main Outcomes and Measures Gene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis–testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments. Results Incorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder ( P permutation [ P -perm] = 7 × 10 −4 ; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence. Conclusions and Relevance In this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.