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Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Carlos Cinelli, Nathan LaPierre, View ORCID ProfileBrian L. Hill, Sriram Sankararaman, Eleazar Eskin
doi: https://doi.org/10.1101/2020.10.21.347773
Carlos Cinelli
1Department of Statistics, University of California, Los Angeles, CA, USA
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  • For correspondence: carloscinelli@ucla.edu
Nathan LaPierre
2Department of Computer Science, University of California, Los Angeles, CA, USA
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Brian L. Hill
2Department of Computer Science, University of California, Los Angeles, CA, USA
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Sriram Sankararaman
2Department of Computer Science, University of California, Los Angeles, CA, USA
3Department of Human Genetics, University of California, Los Angeles, CA, USA
4Department of Computational Medicine, University of California, Los Angeles, CA, USA
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Eleazar Eskin
2Department of Computer Science, University of California, Los Angeles, CA, USA
3Department of Human Genetics, University of California, Los Angeles, CA, USA
4Department of Computational Medicine, University of California, Los Angeles, CA, USA
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ABSTRACT

Mendelian Randomization (MR) exploits genetic variants as instrumental variables to estimate the causal effect of an “exposure” trait on an “outcome” trait from observational data. However, the validity of such studies is threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to partially mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large genetic databases. Here, we describe a suite of sensitivity analysis tools for MR that enables investigators to properly quantify the robustness of their findings against these (and other) unobserved validity threats. Specifically, we propose the routine reporting of sensitivity statistics that can be used to readily quantify the robustness of a MR result: (i) the partial R2 of the genetic instrument with the exposure and the outcome traits; and, (ii) the robustness value of both genetic associations. These statistics quantify the minimal strength of violations of the MR assumptions that would be necessary to explain away the MR causal effect estimate. We also provide intuitive displays to visualize the sensitivity of the MR estimate to any degree of violation, and formal methods to bound the worst-case bias caused by violations in terms of multiples of the observed strength of principal components, batch effects, as well as putative pleiotropic pathways. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings, by showing that the MR estimate of the causal effect of body mass index (BMI) on diastolic blood pressure is relatively robust, whereas the MR estimate of the causal effect of BMI on Townsend deprivation index is relatively fragile.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted October 21, 2020.
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Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
Carlos Cinelli, Nathan LaPierre, Brian L. Hill, Sriram Sankararaman, Eleazar Eskin
bioRxiv 2020.10.21.347773; doi: https://doi.org/10.1101/2020.10.21.347773
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Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
Carlos Cinelli, Nathan LaPierre, Brian L. Hill, Sriram Sankararaman, Eleazar Eskin
bioRxiv 2020.10.21.347773; doi: https://doi.org/10.1101/2020.10.21.347773

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