Evidence map›Paper›PMID 30877270›Full record

ArticleTranslational psychiatry2019

Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder.

Héléna A Gaspar, Zachary Gerring, Christopher Hübel, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Christel M Middeldorp, Eske M Derks, Gerome Breen

Open access · goldAbstract read
In one paragraph

Article in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
6.6field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 4 countries.

Héléna A GasparKing's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK. helena.gaspar@kcl.ac.uk.ORCID http://orcid.org/0000-0002-5540-2707
Zachary GerringTranslational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia.
Christopher HübelKing's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK.ORCID http://orcid.org/0000-0002-1267-8287
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
Christel M MiddeldorpChild Health Research Centre, University of Queensland, South Brisbane, QLD 4072, Australia.ORCID http://orcid.org/0000-0002-6218-0428
Eske M DerksTranslational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia.ORCID http://orcid.org/0000-0002-6292-6883
Gerome BreenKing's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK.
National Health Service · GBQIMR Berghofer Medical Research Institute · AUThe University of Queensland · AU

Funding

3/7 Psychiatric Genomics Consortium: Finding actionable variationU01MH109536 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHARNEY, ALEXANDER W, HUCKINS, LAURA MARIANNE · 2016 to 2020
$2.7M
1/7 Psychiatric Genomics Consortium: Finding actionable variationU01MH109528 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SULLIVAN, PATRICK F · 2016 to 2020
$2.5M
NIMH NIH HHS U01 MH109528NIMH NIH HHS U01 MH109536Wellcome Trust
6 · The paper itself

Abstract

The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics, and genetically predicted expression levels in different tissues, using the online tool Drug Targetor ( drugtargetor.com ). We also investigated drug-target relationships that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 153 protein-coding genes are significantly associated with MDD in MAGMA after multiple testing correction; among these, five are predicted to be down or upregulated in brain regions and 24 are known druggable genes. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics, and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings not only require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new-and better-treatment options.

Indexed as

Gene Regulatory NetworksAntipsychotic AgentsBrainCase-Control StudiesDrug Delivery SystemsDrug DiscoveryGenome-Wide Association StudyHumansMajor Depressive DisorderAntipsychotic Agents

Identifiers

PMID30877270
PMCPMC6420656
OpenAlexW2798107019

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.