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Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics

A Publisher Correction to this article was published on 29 May 2020

This article has been updated

Abstract

Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.

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Fig. 1: Assumptions of traditional MR and the CAUSE model.
Fig. 2: Performance of CAUSE and other MR methods in simulated data.
Fig. 3: False positives resulting from reverse causal effects.
Fig. 4: Effect-size estimates and variant-level contribution to CAUSE test statistics for four trait pairs.
Fig. 5: Tests for causal effects of blood cell composition on immune-mediated traits.

Data availability

All of the data analyzed are publicly available with the exception of blood pressure summary statistics from Ehret et al.28. These are available through dbGaP Accession phs000585.v2.p1. Download links for all other datasets are available in Supplementary Table 11. Instructions and code for formatting and processing data and reproducing CAUSE analysis results can be found on the website https://jean997.github.io/cause/.

Code availability

All software and analysis code is publicly available. The CAUSE method is implemented in an R package available through GitHub. The website https://jean997.github.io/cause/ includes pipelines and instructions for replicating all results presented in this paper. The CAUSE software (R package) can be found at https://github.com/jean997/cause. The simulations software (R package) can be found out https://github.com/jean997/causeSims.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grants MH110531 (to X.H.) and HG002585 (to M.S.), and a research grant from the March of Dimes (to X.H.).

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Authors

Contributions

J.M. and X.H. conceived and designed the model. J.M. designed the algorithm, implemented the software, conducted analyses and performed simulations. J.M., X.H. and M.S. contributed to writing the manuscript. J.H.M. contributed to preparing GWAS data. N.K. contributed to software development and data preparation and computed LD.

Corresponding author

Correspondence to Xin He.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 False positive-power trade-offs for different proportions of correlated pleiotropic variants.

We compare the power when \(\gamma = \sqrt {0.05}\) and q = 0 to the false positive rate when γ = 0, q varies from 0 to 0.5 and \(\eta = \sqrt {0.05}\). There are 100 simulations each in the causal and non-causal scenarios. Curves are created by varying the significance threshold. Points indicate the power and false positive rate achieved at a threshold of p = 0.05.

Extended Data Fig. 2 Tests for casual effects of risk factors on diseases.

Each cell summarizes the results of six methods for a pair of traits. In the left column of the cell, methods from bottom to top are CAUSE, IVW regression, and Egger regression. In the right column, methods from bottom to top are weighted median, weighted mode, and MR-PRESSO. Filled symbols indicate a nominally significant p < 0.05.

Extended Data Fig. 3 Tests for casual effects of disease outcomes on risk factors.

Tests for casual effects of disease outcomes on mediators. Each cell summarizes the results of six methods for a pair of traits. In the left column of the cell, methods from bottom to top are CAUSE, IVW regression, and Egger regression. In the right column, methods from bottom to top are weighted median, weighted mode, and MR-PRESSO. Filled symbols indicate a nominally significant p < 0.05.

Extended Data Fig. 4 Workflow of a CAUSE analysis.

Dashed boxes represent input data. Each solid box is an analysis step completed by the given function in the cause R package. LD pruning can be parallelized over chromosomes. Text at the bottom of boxes indicates user provided parameters and their default values. All analyses presented are run with default parameters.

Supplementary information

Supplementary Information

Supplementary Tables 1, 2, 6 and 7 and Note

Reporting Summary

Supplementary Tables

Supplementary Tables 3–5 and 8–11

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Morrison, J., Knoblauch, N., Marcus, J.H. et al. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet 52, 740–747 (2020). https://doi.org/10.1038/s41588-020-0631-4

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