MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner’s curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.

Related collections

Most cited references 32

Record: found
Abstract: found
Article: not found

Avoiding bias from weak instruments in Mendelian randomization studies.

Stephen Burgess, Mitchell G. Thompson, Mika Kivimaki (2011)

Mendelian randomization is used to test and estimate the magnitude of a causal effect of a phenotype on an outcome by using genetic variants as instrumental variables (IVs). Estimates of association from IV analysis are biased in the direction of the confounded, observational association between phenotype and outcome. The magnitude of the bias depends on the F-statistic for the strength of relationship between IVs and phenotype. We seek to develop guidelines for the design and analysis of Mendelian randomization studies to minimize bias. IV analysis was performed on simulated and real data to investigate the effect on bias of size of study, number and choice of instruments and method of analysis. Bias is shown to increase as the expected F-statistic decreases, and can be reduced by using parsimonious models of genetic association (i.e. not over-parameterized) and by adjusting for measured covariates. Using data from a single study, the causal estimate of a unit increase in log-transformed C-reactive protein on fibrinogen (μmol/l) is shown to increase from -0.005 (P = 0.99) to 0.792 (P = 0.00003) due to injudicious choice of instrument. Moreover, when the observed F-statistic is larger than expected in a particular study, the causal estimate is more biased towards the observational association and its standard error is smaller. This correlation between causal estimate and standard error introduces a second source of bias into meta-analysis of Mendelian randomization studies. Bias can be alleviated in meta-analyses by using individual level data and by pooling genetic effects across studies. Weak instrument bias is of practical importance for the design and analysis of Mendelian randomization studies. Post hoc choice of instruments, genetic models or data based on measured F-statistics can exacerbate bias. In particular, the commonly cited rule of thumb that F > 10 avoids bias in IV analysis is misleading.

0 comments Cited 982 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

George Davey Smith, Shah Ebrahim (2003)

Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.

0 comments Cited 968 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

Olena Yavorska, Stephen Burgess (2017)

Abstract MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype—phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3).

0 comments Cited 720 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Ashish Patel: Role: MethodologyRole: SoftwareRole: Writing – Review & Editing

Ting Ye: Role: MethodologyRole: SoftwareRole: Writing – Review & Editing

Haoran Xue: Role: MethodologyRole: SoftwareRole: Writing – Review & Editing

Zhaotong Lin: Role: MethodologyRole: SoftwareRole: Writing – Review & Editing

ORCID: https://orcid.org/0000-0001-8723-4392

Siqi Xu: Role: MethodologyRole: SoftwareRole: Writing – Review & Editing

Benjamin Woolf: Role: Writing – Review & Editing

Amy M. Mason: Role: Writing – Review & Editing

ORCID: https://orcid.org/0000-0002-8019-0777

Stephen Burgess: Role: ConceptualizationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – Original Draft PreparationRole: Writing – Review & Editing

ORCID: https://orcid.org/0000-0001-5365-8760

Journal

Journal ID (nlm-ta): Wellcome Open Res

Journal ID (iso-abbrev): Wellcome Open Res

Title: Wellcome Open Research

Publisher: F1000 Research Limited (London, UK )

ISSN (Electronic): 2398-502X

Publication date (Electronic): 12 October 2023

Publication date Collection: 2023

Volume: 8

Electronic Location Identifier: 449

Affiliations

[1 ]MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK

[2 ]Department of Biostatistics, University of Washington, Seattle, Washington, USA

[3 ]Department of Biostatistics, City University of Hong Kong, Hong Kong, Hong Kong

[4 ]Division of Biostatistics, School of Public Health, University of Minnesota Duluth, Duluth, Minnesota, USA

[5 ]Department of Statistics, Florida State University, Tallahassee, Florida, USA

[6 ]Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong

[7 ]Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, England, UK

[8 ]School of Psychological Science, University of Bristol, Bristol, England, UK

[9 ]British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, England, UK

[10 ]Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, England, UK

[1 ]Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan

[1 ]Department of Twin Research and Genetic Epidemiology, King's College London, London, England, UK

Author notes

[a ] sb452@ 123456medschl.cam.ac.uk

No competing interests were disclosed.

Competing interests: No competing interests were disclosed.

Author information

Zhaotong Lin https://orcid.org/0000-0001-8723-4392

Amy M. Mason https://orcid.org/0000-0002-8019-0777

Stephen Burgess https://orcid.org/0000-0001-5365-8760

Article

DOI: 10.12688/wellcomeopenres.19995.1

PMC ID: 10616660

PubMed ID: 37915953

SO-VID: e43993c1-028a-4e60-b468-8f616551133a

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date accepted : 25 September 2023

Funding

Funded by: National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Behaviour

Award ID: NIHR203337

Funded by: NIHR Cambridge Biomedical Research Centre

Award ID: NIHR203312

Funded by: EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart

Award ID: 116074

Funded by: British Heart Foundation

Award ID: RG/18/13/33946

Funded by: Wellcome Trust

Award ID: 225790

Funded by: Wellcome Trust

Award ID: 204623

Funded by: UK Research and Innovation

Award ID: MC_UU_00002/7

This research was supported by the Wellcome Trust (225790/Z/22/Z), the United Kingdom Research and Innovation Medical Research Council (MC_UU_00002/7), the British Heart Foundation (RG/18/13/33946), and the National Institute for Health Research Cambridge Biomedical Research Centre (NIHR203312). AMM is funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Behaviour (NIHR203337) and the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant (116074). The views expressed are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data

Read this article at

Abstract

Related collections

Genome Engineering using CRISPR

Most cited references 32

Avoiding bias from weak instruments in Mendelian randomization studies.

'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

Author and article information

Contributors

Journal

Affiliations

Author notes

Author information

Article

History

Funding

Categories

Comments

Comment on this article

Similar content 144

Cited by 6

Most referenced authors 327