When GWAS meets the Connectivity Map: drug repositioning for seven psychiatric disorders

Abstract

Our knowledge of disease genetics has advanced rapidly during the past decade, with the advent of high-throughput genotyping technologies such as genome-wide association studies (GWAS). However, few methodologies were developed and systemic studies performed to identify novel drug candidates utilizing GWAS data. In this study we focus on drug repositioning, which is a cost-effective approach to shorten the developmental process of new therapies. We proposed a novel framework of drug repositioning by comparing GWAS-imputed transcriptome with drug expression profiles from the Connectivity Map. The approach was applied to 7 psychiatric disorders. We discovered a number of novel repositioning candidates, many of which are supported by preclinical or clinical evidence. We found that the predicted drugs are significantly enriched for known psychiatric medications, or therapies considered in clinical trials. For example, drugs repurposed for schizophrenia are strongly enriched for antipsychotics (p = 4.69E-06), while those repurposed for bipolar disorder are enriched for antipsychotics (p = 2.26E-07) and antidepressants (p = 1.17E-05).These findings provide support to the usefulness of GWAS signals in guiding drug discoveries and the validity of our approach in drug repositioning. We also present manually curated lists of top repositioning candidates for each disorder, which we believe will serve as a useful resource for researchers.

Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis

“…The current results do not provide sufficient evidence for the 410 association of these drugs and their effects on the PF-ranked genes found for PD; in fact, no gene-411 drug association was found either at the individual drug level, or at the drug class level. Differently 412 from previous reports (So, 2017;So et al, 2016), which support drug repositioning from genes 413 extracted from GWAS for anxiety and mood disorders, our findings do not support such proposals. 414 Extensive methodological differences are probably responsible for this discrepancy.…”

“…These techniques have been applied to GWAS analyses of psychiatric 57 disorders to not only make sense of the huge amount of data, but also to inform future research on 58 the pathophysiology and pharmacology of these diseases. For example, So and colleagues (So, 59 2017;So et al, 2016So et al, , 2017 used gene-set analyses to propose drug repositioning for depression and 60 anxiety disorders, with good predictive value. Lotan et al (2014) analyzed single nucleotide 61 polymorphisms (SNPs) derived from GWAS for attention-deficit/hyperactivity disorder, anxiety 62 disorders, autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia, 63 and found that 22% the genes associated with those SNPs are shared between at least two of those 64 groups; they also found that the shared genes are enriched in the postsynaptic density, expressed in 65 immune tissues, and co-expressed in the developing human brain.…”

Translating GWAS Findings Into Therapies For Depression And Anxiety Disorders: Drug Repositioning Using Gene-Set Analyses Reveals Enrichment Of Psychiatric Drug Classes

“…In this study we employed the GSA approach to drug repositioning. The current study is complementary to our recent repositioning attempt by a novel methodology in which the drug-induced transcriptome are compared against GWAS-imputed expression profiles 10 . Each of these two methods has their own advantages and disadvantages.…”

Association of Hydroxylmethyl Glutaryl Coenzyme A Reductase Inhibitors, L-Type Calcium Channel Antagonists, and Biguanides With Rates of Psychiatric Hospitalization and Self-Harm in Individuals With Serious Mental Illness

“…Many drugs have been identified as having potential for repurposing in these disorders. 1 , 2 We cross-referenced these drugs with the most commonly prescribed drugs in the general population. 3 We chose to investigate the following 3 classes of drugs for which we might expect clinical benefit: hydroxylmethyl glutaryl coenzyme A reductase inhibitors (HMG-CoA RIs; ie, statins), L-type calcium channel (LTCC) antagonists (eg, verapamil hydrochloride), and biguanides (eg, metformin hydrochloride).…”