Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates

English version

Autoři: Jessica M. B. Rees ^aff001; Angela M. Wood ^aff001; Frank Dudbridge ^aff003; Stephen Burgess ^aff001
Působiště autorů: Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom ^aff001; Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH16 4UX, United Kingdom ^aff002; Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, United Kingdom ^aff003; MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, United Kingdom ^aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222362

Souhrn

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.

Klíčová slova:

Simulation and modeling – Alzheimer's disease – Medical risk factors – Schizophrenia – Lipoproteins – Instrumental variable analysis – Genetic predisposition – Research errors

Zdroje

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Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates

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