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mdd-wave3-meta

PGC MDD Wave 3 Meta-analysis

https://github.com/psychiatric-genomics-consortium/mdd-wave3-meta

Science Score: 75.0%

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    CITATION.cff file
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    Found .zenodo.json file
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    Found 12 DOI reference(s) in README
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    Links to: zenodo.org
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    Organization psychiatric-genomics-consortium has institutional domain (www.med.unc.edu)
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    Low similarity (13.7%) to scientific vocabulary
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Repository

PGC MDD Wave 3 Meta-analysis

Basic Info
  • Host: GitHub
  • Owner: psychiatric-genomics-consortium
  • License: mit
  • Language: Shell
  • Default Branch: main
  • Homepage:
  • Size: 254 MB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 2
Created about 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

PGC MDD3 Meta-analysis

Published paper Summary statistics Archived software repository

The wave 3 meta-analysis ("MDD3") by the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. See table of included cohorts.

Current version: v3.49.46.01 [DIV]

MDD Manhattan plot

Project overview

Meta-analysis of cohorts for genome-wide association studies of Major Depressive Disorder.

Analysis conducted on LISA/Snellius.

Getting started

Step 1

Clone the repository

git clone git@github.com:psychiatric-genomics-consortium/mdd-wave3-meta.git cd mdd-meta You will need to ensure you have generated and added SSH keys between your machine (OS/Windows/Linux) and your github account. This video gives instructions for how to do this.

Step 2

Install Anaconda.

Linux: wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh sh Miniconda3-latest-Linux-x86_64.sh

MacOS: bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)" brew install anaconda

Install Python 3.8, Snakemake 5.32, and the basic project dependencies

conda activate base conda install python=3.8 conda install -c conda-forge mamba mamba install -c bioconda -c conda-forge snakemake-minimal==5.32.2 mamba install dropbox mamba install pandas

Step 3

Configure the analysis workflow. Make a copy of the configuration file

cp config.yaml-template config.yaml

Then edit and fill in config.yaml with the required parameters for the analysis to be conducted.

Step 4

Run the meta analysis for the required population. If you are only doing downstream analysis on meta-analysed sumstats, go to Step 5.

A meta-analysis can be run for each ancestries group. For example, the European ancestries meta analysis can be run with:

snakemake -j1 --use-conda postimp_eur

See more about adding additional cohorts to the meta-analysis. The meta analysis workflow is stored in rules/meta.smk.

Step 5

Prepare for running downstream analysis.

Download the required summary statistics "MDD3 202X excluding 23andMe (European Ancestries)" from the PGC website. Move the file into the location

results/distribution/daner_pgc_mdd_no23andMe_eur_hg19_v3.TBD.TBD.gz

Depending on the version of conda you have installed on your machine, you may need to use a package called 'pulp'. This can be installed using pip

pip install pulp

Run downstream analysis

Check the rules directory for the analyses to be run. See more on how to contribute.

Built With

Lead Analysts

  • Mark James Adams - analyst - Edinburgh
  • Swapnil Awasthi - analyst - Broad
  • Fabian Strait - analyst - CIMH
  • Xiangrui Meng - analyst UCL
  • David Howard - analyst - KCL
  • Jonathan Coleman - analyst – KCL
  • Oliver Pain - analyst - KCL
  • Xueyi Shen - analyst - Edinburgh
  • Shuyang Yao - analyst – Karolinska
  • V Kartik Chundru - analyst Wellcome Sanger
  • Karmel Choi - analyst – Harvard/MGH
  • Karoline Kuchenbaecker - analytical group lead - UCL
  • Naomi Wray - analytical group director - Queensland
  • Stephan Ripke - analytical group director - Broad
  • Cathryn Lewis - workgroup chair - KCL
  • Andrew McIntosh - workgroup chair - Edinburgh

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

The PGC has received funding from the US National Institute of Mental Health (5 U01MH109528-04). Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (NWO 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam.

Owner

  • Name: Psychiatric Genomics Consortium
  • Login: psychiatric-genomics-consortium
  • Kind: organization

The PGC unites investigators around the world to conduct meta- and mega-analyses of genomic data for psychiatic disorders.

Citation (CITATION.cff) 

# YAML 1.2
---
authors:
  -
    affiliation: "University of Edinburgh"
    family-names: "Adams"
    given-names: "Mark James" 
    orcid: https://orcid.org/0000-0002-3599-6018
  -
    name: "Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium"
cff-version: "1.2.0"
doi: "10.5281/zenodo.11935052"
abstract: "In a genome-wide association study meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory and medium spiny neurons and involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment."
references:
    - type: data
      title: "Genome-wide association study summary statistics for Major Depressive Disorder"
      authors:
        -
          name: "Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium"
      url: https://www.med.unc.edu/pgc/download-results/
      doi: "10.6084/m9.figshare.27061255"
    - type: article
      status: in-press
      journal: "Cell"
      title: "Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell-types and pharmacotherapies"
      doi: "10.1016/j.cell.2024.12.002"
      authors:
        -
          name: "Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium"
    - type: article
      status: preprint
      title: "Genome-wide study of major depression in 685,808 diverse individuals identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies"
      authors:
      -
        family-names: "Adams"
        given-names: "Mark James"
      -
        name: "Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium"
      -
        family-names: "Lewis"
        given-names: "Cathryn M"
      - family-names: "McIntosh"
        given-names: "Andrew M" 
      doi: "10.1101/2024.04.29.24306535"
keywords: 
  - "major depressive disorder"
  - "major depression"
  - "genome-wide association study"
  - "psychiatric genetics"
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/psychiatric-genomics-consortium/mdd-wave3-meta"
title: "PGC MDD2025"
version: "3.49.46.01"
date-released: 2025-02-06
...

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