Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry

Abstract

Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.

Post‐GWAS in Psychiatric Genetics: A Developmental Perspective on the “Other” Next Steps

“…The justification for pursuing these large-scale gene identification efforts, which are costly endeavors that require coordination and collaboration across hundreds of scientific groups, 20 is often that identifying genes influencing disorder will help advance understanding of the underlying biology, 21 and be useful in developing new therapeutic drugs. 26,27 This argument can be found in both the scientific literature, 28 and in lay descriptions about the importance of genetic studies of psychiatric disorders. 29 One of the challenges with drug development for psychiatric outcomes is the limited understanding of underlying biology, and likely complex heterogeneity of etiological factors.…”

“…The justification for pursuing these large-scale gene identification efforts, which are costly endeavors that require coordination and collaboration across hundreds of scientific groups [The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011], is often that identifying genes influencing disorder will help advance understanding of the underlying biology (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), and be useful in developing new therapeutic drugs (Sanseau et al ., 2012, So et al ., 2017). This argument can be found in both the scientific literature (Breen et al , 2016), and in lay descriptions about the importance of genetic studies of psychiatric disorders (Yilmaz, 2016).…”

From gene networks to drugs: systems pharmacology approaches for AUD

“…One study approached this problem in an innovative way for psychiatric illnesses and could be considered a hybrid approach since the authors inferred gene expression data from genotype data (So et al 2017). The approach relied on an algorithm called MetaXcan (Barbeira et al 2016), that incorporated GTEx data to build statistical models for predicting expression levels from SNPs in a reference transcriptome data set, and these prediction models were used to impute the expression z -scores (i.e., z -statistics derived from association tests of expression changes with disease status) based on GWAS summary statistics.…”

“…To compare the disease and drug signatures the KS-like statistic (as described by (Lamb et al 2006; Subramanian et al 2017) is the most frequently used similarity metric, although several studies also use Spearman or Pearson correlation coefficients (Azim et al 2017; Siavelis et al 2016; So et al 2017) or Fischer’s Exact Test (Delahaye-Duriez et al 2016). Most studies operate under the transcriptional “reversal hypothesis”, which assumes that drugs with negative connectivity scores (i.e., with gene expression signatures that revert the disease’s effects on gene expression to the control state) would ameliorate disease phenotype.…”

“…Another surrogate for brain gene expression could be in peripherally accessible cell types, like PBMCs, especially considering the impressive evidence for immune involvement in AUD (for reviews see (Crews et al 2017; de Timary et al 2017; Mayfield and Harris 2017)). Another option that does not require access to brain tissue is imputing gene expression from GWAS summary data as discussed above (So et al 2017). However, the latter approach has yet to be validated in vivo and will likely improve as the databases used to make the imputations improve, for example, by increasing samples in GTEx to better detect eQTLs.…”