IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-019-14156-4.html
   My bibliography  Save this article

A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Author

Listed:
  • Stephen Burgess

    (University of Cambridge
    University of Cambridge)

  • Christopher N Foley

    (University of Cambridge)

  • Elias Allara

    (University of Cambridge
    University of Cambridge)

  • James R Staley

    (University of Cambridge
    University of Bristol)

  • Joanna M. M. Howson

    (University of Cambridge
    University of Cambridge and Cambridge University Hospitals
    Novo Nordisk Research Centre Oxford, Innovation Building - Old Road Campus, Roosevelt Drive)

Abstract

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

Suggested Citation

  • Stephen Burgess & Christopher N Foley & Elias Allara & James R Staley & Joanna M. M. Howson, 2020. "A robust and efficient method for Mendelian randomization with hundreds of genetic variants," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-14156-4
    DOI: 10.1038/s41467-019-14156-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-14156-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-019-14156-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qing Cheng & Xiao Zhang & Lin S. Chen & Jin Liu, 2022. "Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Yuanyuan Yu & Lei Hou & Xu Shi & Xiaoru Sun & Xinhui Liu & Yifan Yu & Zhongshang Yuan & Hongkai Li & Fuzhong Xue, 2022. "Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods," PLOS Genetics, Public Library of Science, vol. 18(3), pages 1-21, March.
    3. Yihe Yang & Noah Lorincz-Comi & Xiaofeng Zhu, 2023. "Unbiased estimation and asymptotically valid inference in multivariable Mendelian randomization with many weak instrumental variables," Papers 2301.05130, arXiv.org, revised Feb 2024.
    4. Shitong Xiang & Tianye Jia & Chao Xie & Wei Cheng & Bader Chaarani & Tobias Banaschewski & Gareth J. Barker & Arun L. W. Bokde & Christian Büchel & Sylvane Desrivières & Herta Flor & Antoine Grigis & , 2023. "Association between vmPFC gray matter volume and smoking initiation in adolescents," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Lingyan Chen & James E. Peters & Bram Prins & Elodie Persyn & Matthew Traylor & Praveen Surendran & Savita Karthikeyan & Ekaterina Yonova-Doing & Emanuele Angelantonio & David J. Roberts & Nicholas A., 2022. "Systematic Mendelian randomization using the human plasma proteome to discover potential therapeutic targets for stroke," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    6. Zhaotong Lin & Yangqing Deng & Wei Pan, 2021. "Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model," PLOS Genetics, Public Library of Science, vol. 17(11), pages 1-25, November.
    7. Ruoyu Wang & Qihua Wang & Wang Miao, 2023. "A robust fusion-extraction procedure with summary statistics in the presence of biased sources," Biometrika, Biometrika Trust, vol. 110(4), pages 1023-1040.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-14156-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.