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Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways

Author

Listed:
  • David M. Howard

    (University of Edinburgh)

  • Mark J. Adams

    (University of Edinburgh)

  • Masoud Shirali

    (University of Edinburgh)

  • Toni-Kim Clarke

    (University of Edinburgh)

  • Riccardo E. Marioni

    (University of Edinburgh)

  • Gail Davies

    (University of Edinburgh
    University of Edinburgh)

  • Jonathan R. I. Coleman

    (King’s College London
    South London and Maudsley NHS Trust)

  • Clara Alloza

    (University of Edinburgh)

  • Xueyi Shen

    (University of Edinburgh)

  • Miruna C. Barbu

    (University of Edinburgh)

  • Eleanor M. Wigmore

    (University of Edinburgh)

  • Jude Gibson

    (University of Edinburgh)

  • Saskia P. Hagenaars

    (King’s College London
    South London and Maudsley NHS Trust)

  • Cathryn M. Lewis

    (King’s College London
    South London and Maudsley NHS Trust)

  • Joey Ward

    (University of Glasgow)

  • Daniel J. Smith

    (University of Glasgow)

  • Patrick F. Sullivan

    (Karolinska Institutet
    University of North Carolina
    University of North Carolina)

  • Chris S. Haley

    (University of Edinburgh)

  • Gerome Breen

    (King’s College London
    South London and Maudsley NHS Trust)

  • Ian J. Deary

    (University of Edinburgh
    University of Edinburgh)

  • Andrew M. McIntosh

    (University of Edinburgh
    University of Edinburgh)

Abstract

Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P

Suggested Citation

  • David M. Howard & Mark J. Adams & Masoud Shirali & Toni-Kim Clarke & Riccardo E. Marioni & Gail Davies & Jonathan R. I. Coleman & Clara Alloza & Xueyi Shen & Miruna C. Barbu & Eleanor M. Wigmore & Jud, 2018. "Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03819-3
    DOI: 10.1038/s41467-018-03819-3
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    Cited by:

    1. Fartein Ask Torvik & Espen Moen Eilertsen & Laurie J. Hannigan & Rosa Cheesman & Laurence J. Howe & Per Magnus & Ted Reichborn-Kjennerud & Ole A. Andreassen & Pål R. Njølstad & Alexandra Havdahl & Eiv, 2022. "Modeling assortative mating and genetic similarities between partners, siblings, and in-laws," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Joey Ward & Nicholas Graham & Rona J Strawbridge & Amy Ferguson & Gregory Jenkins & Wenan Chen & Karen Hodgson & Mark Frye & Richard Weinshilboum & Rudolf Uher & Cathryn M Lewis & Joanna Biernacka & D, 2018. "Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-8, September.
    3. Aleksandr Talishinsky & Jonathan Downar & Petra E. Vértes & Jakob Seidlitz & Katharine Dunlop & Charles J. Lynch & Heather Whalley & Andrew McIntosh & Fidel Vila-Rodriguez & Zafiris J. Daskalakis & Da, 2022. "Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression," Nature Communications, Nature, vol. 13(1), pages 1-20, December.

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