Linking GWAS to pharmacological treatments for psychiatric disorders

Large-scale genome-wide association studies (GWAS) have identified multiple disease-associated genetic variations across different psychiatric dis-orders raising the question of how these genetic variants relate to the corresponding pharmacological treatments. Here we investigated 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 drugs for psychiatric disor-ders. We introduce a novel measure of weighted similarity between gene targets for pharmacological treatments and GWAS risk variants for psychiatric disorders according to SNP position, gene distance on the protein interaction network (PPI), brain eQTL, and gene expression pattern across the brain. Focusing on four psychiatric disorders—ADHD, bipolar disorder, schizophrenia, and major de-pressive disorder—we assess relationships between the gene targets of drug treatments and GWAS hits across these weighted similarity metrics. Our results indicate that while incorporating information derived from functional bioinformatics data, such as the PPI network and spatial gene expression, revealed links for bipolar disorder, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeds null expectations. This relatively low degree of correspondence across modalities suggests that the genetic mechanisms driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms used for targeting symptom manifestations through pharmacological treatments and that novel approaches for under-standing and treating psychiatric disorders may be required.