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Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates.

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posted on 2019-10-02, 13:05 authored by Jessica M. B. Rees, Angela M. Wood, Frank Dudbridge, Stephen Burgess
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates under weaker assumptions than the standard instrumental variable assumptions. The median-based estimator and MR-Egger are examples of such methods. However, these methods can be sensitive to genetic variants with heterogeneous causal estimates. Such heterogeneity may arise from over-dispersion in the causal estimates, or specific variants with outlying causal estimates. In this paper, we develop three extensions to robust methods for Mendelian randomization with summarized data: 1) robust regression (MM-estimation); 2) penalized weights; and 3) Lasso penalization. Methods using these approaches are considered in two applied examples: one where there is evidence of over-dispersion in the causal estimates (the causal effect of body mass index on schizophrenia risk), and the other containing outliers (the causal effect of low-density lipoprotein cholesterol on Alzheimer's disease risk). Through an extensive simulation study, we demonstrate that robust regression applied to the inverse-variance weighted method with penalized weights is a worthwhile additional sensitivity analysis for Mendelian randomization to provide robustness to variants with outlying causal estimates. The results from the applied examples and simulation study highlight the importance of using methods that make different assumptions to assess the robustness of findings from Mendelian randomization investigations with multiple genetic variants.

Funding

Stephen Burgess is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z).

History

Citation

PLoS ONE, 2019, 14(9): e0222362

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

  • VoR (Version of Record)

Published in

PLoS ONE

Publisher

Public Library of Science

eissn

1932-6203

Acceptance date

2019-08-27

Copyright date

2019

Available date

2019-10-02

Publisher version

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222362

Notes

All data can be found in the PhenoScanner database (http://www.phenoscanner.medschl.cam.ac.uk/). R code for performing the approaches outlined in the paper, and extracting genetic association estimates are found in S1 Appendix. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222362#pone.0222362.s001

Language

en

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