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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
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    Citations

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    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. Daniel J. Benjamin & David Cesarini & Patrick Turley & Alexander Strudwick Young, 2024. "Social-Science Genomics: Progress, Challenges, and Future Directions," NBER Working Papers 32404, National Bureau of Economic Research, Inc.
    6. 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.
    7. 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.
    8. Mattia Marchi & Anne Alkema & Charley Xia & Chris H. L. Thio & Li-Yu Chen & Winni Schalkwijk & Gian M. Galeazzi & Silvia Ferrari & Luca Pingani & Hyeokmoon Kweon & Sara Evans-Lacko & W. David Hill & M, 2024. "Investigating the impact of poverty on mental illness in the UK Biobank using Mendelian randomization," Nature Human Behaviour, Nature, vol. 8(9), pages 1771-1783, September.
    9. 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.
    10. James P. Pirruccello & Paolo Achille & Seung Hoan Choi & Joel T. Rämö & Shaan Khurshid & Mahan Nekoui & Sean J. Jurgens & Victor Nauffal & Shinwan Kany & Kenney Ng & Samuel F. Friedman & Puneet Batra , 2024. "Deep learning of left atrial structure and function provides link to atrial fibrillation risk," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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