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

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Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. / Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium.

In: Translational psychiatry, Vol. 9, No. 1, 117, 01.12.2019, p. 117.

Research output: Contribution to journalArticle

Harvard

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium 2019, 'Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder', Translational psychiatry, vol. 9, no. 1, 117, pp. 117. https://doi.org/10.1038/s41398-019-0451-4

APA

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2019). Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Translational psychiatry, 9(1), 117. [117]. https://doi.org/10.1038/s41398-019-0451-4

Vancouver

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Translational psychiatry. 2019 Dec 1;9(1):117. 117. https://doi.org/10.1038/s41398-019-0451-4

Author

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. / Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. In: Translational psychiatry. 2019 ; Vol. 9, No. 1. pp. 117.

Bibtex Download

@article{37f2b8b13a1e4b749ac1b3bdbf9a5a46,
title = "Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder",
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.",
author = "Gaspar, {H{\'e}l{\'e}na A} and Zachary Gerring and Christopher H{\"u}bel and {Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium} and Middeldorp, {Christel M} and Derks, {Eske M} and Gerome Breen",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s41398-019-0451-4",
language = "English",
volume = "9",
pages = "117",
journal = "Translational psychiatry",
issn = "2158-3188",
publisher = "Nature Publishing Group",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder

AU - Gaspar, Héléna A

AU - Gerring, Zachary

AU - Hübel, Christopher

AU - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

AU - Middeldorp, Christel M

AU - Derks, Eske M

AU - Breen, Gerome

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

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U2 - 10.1038/s41398-019-0451-4

DO - 10.1038/s41398-019-0451-4

M3 - Article

C2 - 30877270

VL - 9

SP - 117

JO - Translational psychiatry

JF - Translational psychiatry

SN - 2158-3188

IS - 1

M1 - 117

ER -

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