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Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

Nature Genetics volume 50, pages 693–698 (2018)Cite this article

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A Publisher Correction to this article was published on 02 July 2018

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Abstract

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from –131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances.

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Fig. 1: Description of the MR-PRESSO method.
Fig. 2: Distribution of the distortion of causal estimates before and after correction for horizontal pleiotropy using the MR-PRESSO distortion test.

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Change history

  • 02 July 2018

    In the version of this article initially published, the Supplementary Text and Figures file was missing Supplementary Tables 4, 6, 8 and 10–14. The correct file has now been provided online.

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Acknowledgements

We thank the various GWA consortia for generously sharing the GWA summary statistics. We thank A. Dobbyn and D. Jordan for critical comments on the manuscript. R.D. is supported by R35GM124836 from the National Institute Of General Medical Sciences of the US National Institutes of Health, R01HL139865 from the National Heart, Lung, Blood Institute of the US National Institutes of Health, research grants from AstraZeneca and Goldfinch Bio and, previously, an American Heart Association Cardiovascular Genome-Phenome Discovery grant (15CVGPSD27130014). B.N. is supported by 1R01MH094469, 1R01MH107649-01 and 5U01HG009088-02 from the US National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Marie Verbanck and Chia-Yen Chen.

  2. These authors jointly supervised this work: Benjamin Neale and Ron Do.

Authors and Affiliations

  1. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marie Verbanck & Ron Do

  2. The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marie Verbanck & Ron Do

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marie Verbanck & Ron Do

  4. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Chia-Yen Chen & Benjamin Neale

  5. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    Chia-Yen Chen & Benjamin Neale

  6. Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA

    Chia-Yen Chen & Benjamin Neale

Authors
  1. Marie Verbanck
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  2. Chia-Yen Chen
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  3. Benjamin Neale
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  4. Ron Do
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Contributions

M.V. and C.-Y.C. contributed to study conception, data analysis, interpretation of the results and drafting of the manuscript; and R.D. and B.N. contributed to study conception, interpretation of the results and critical revision of the manuscript.

Corresponding authors

Correspondence to Benjamin Neale or Ron Do.

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Competing interests

B.N. is a member of Deep Genomics Scientific Advisory Board, has received travel expenses from Illumina, and also serves as a consultant for Avanir and Trigeminal solutions; R.D. has received research support from AstraZeneca and Goldfinch Bio.

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Integrated supplementary information

Supplementary Figure 1 General framework for Mendelian randomization with violation of the exclusion restriction criterion (assumption 3).

a) Direct horizontal pleiotropic effect. b) Mediated horizontal pleiotropic effect. The genetic variant Gj has a significant effect on the outcome Y, which is not mediated by exposure E1 which is called a horizontal pleiotropic effect. Figure a shows a direct horizontal pleiotropic effect of the instrumental variable (IV) on the outcome. Figure b shows a horizontal pleiotropic effect of the IV on the outcome, through a second exposure (E2) causal to the outcome.

Supplementary Figure 2 Representative example of effect sizes of body mass index (BMI) and C-reactive protein.

The red data point represents a single nucleotide variant (rs2075650) that was detected as an outlier by the MR-PRESSO outlier test. The slope of the two lines represent the causal estimates from the naïve inverse variance weighted (IVW) regression (blue; n = 93) and IVW regression after removal of the outlier variant, rs2075650 (red; n = 92).

Supplementary Figure 3 General framework for standard Mendelian randomization.

The general Mendelian randomization framework infers causality of the exposure E1 on the outcome Y using the Gj variants as IVs, given the confounders U. The validity of Mendelian randomization relies on three assumptions: 1) The genetic variant is associated with the exposure E1; 2) The genetic variant is independent of confounders U; 3) The genetic variant is independent of the outcome Y conditional on the exposure E1 and confounders U. This third assumption is usually referred to as the ‘exclusion restriction’ criterion. Confounders are unmeasured in this model.

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Verbanck, M., Chen, CY., Neale, B. et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50, 693–698 (2018). https://doi.org/10.1038/s41588-018-0099-7

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