Mendel’s laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues
TLDR
This work responds to criticisms of Mendelian randomization by Mukamal, Stampfer and Rimm, demonstrating that their strictures with respect to population stratification, confounding, weak instrument bias, pleiotropy and confounding have been addressed.Abstract:
We respond to criticisms of Mendelian randomization (MR) by Mukamal, Stampfer and Rimm (MSR). MSR consider that MR is receiving too much attention and should be renamed. We explain how MR links to Mendel’s laws, the origin of the name and our lack of concern regarding nomenclature. We address MSR’s substantive points regarding MR of alcohol and cardiovascular disease, an issue on which they dispute the MR findings. We demonstrate that their strictures with respect to population stratification, confounding, weak instrument bias, pleiotropy and confounding have been addressed, and summarise how the field has advanced in relation to the issues they raise. We agree with MSR that “the hard problem of conducting high-quality, reproducible epidemiology” should be addressed by epidemiologists. However we see more evidence of confrontation of this issue within MR, as opposed to conventional observational epidemiology, within which the same methods that have demonstrably failed in the past are simply rolled out into new areas, leaving their previous failures unexamined.read more
Citations
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The MRC IEU OpenGWAS data infrastructure
Ben Elsworth,Matthew Lyon,Matthew Lyon,Tessa Alexander,Yi Liu,Peter Matthews,Jon Hallett,Phil Bates,Phil Bates,Tom Palmer,Valeriia Haberland,George Davey Smith,Jie Zheng,Philip C Haycock,Tom R. Gaunt,Tom R. Gaunt,Gibran Hemani +16 more
TL;DR: The OpenGWAS database is presented, an open source, open access, scalable and high-performance cloud-based data infrastructure that imports and publishes complete GWAS summary datasets and metadata for the scientific community.
Journal ArticleDOI
Mendelian randomization
Bryon J.T. Morgan,Jane Richtsmeier,Oscar Giménez,S. P. Brooks,Sophia Green,John Benedetti,Lawrence H. Moulton,Harry Wynn,Anatoly Zhigljavsky +8 more
TL;DR: Mendelian randomization (MR) as discussed by the authors is a technique for using genetic variation to examine the causal effect of a modifiable exposure on an outcome such as disease status.
Mendelian randomization
Eleanor Sanderson,M. Maria Glymour,Michael C. Holmes,Hyunseung Kang,J. Morrison,Marcus R. Munafò,Tom Palmer,C. Mary Schooling,Chris Wallace,Qingyuan Zhao,George Davey Smith +10 more
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Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development.
Michael V. Holmes,Tom G. Richardson,Brian A. Ference,Neil M Davies,Neil M Davies,George Davey Smith +5 more
TL;DR: This Review compares and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits and explains how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.
References
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Journal ArticleDOI
Marginal Structural Models and Causal Inference in Epidemiology
TL;DR: In this paper, the authors introduce marginal structural models, a new class of causal models that allow for improved adjustment of confounding in observational studies with exposures or treatments that vary over time, when there exist time-dependent confounders that are also affected by previous treatment.
Book ChapterDOI
Testing for Weak Instruments in Linear IV Regression
James H. Stock,Motohiro Yogo +1 more
TL;DR: This paper proposed quantitative definitions of weak instruments based on the maximum IV estimator bias, or the maximum Wald test size distortion, when there are multiple endogenous regressors, and tabulated critical values that enable using the first-stage F-statistic (or, for instance, the Cragg-Donald (1993) statistic) to test whether give n instruments are weak.
Journal ArticleDOI
‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?
George Davey Smith,Shah Ebrahim +1 more
TL;DR: 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.
Journal ArticleDOI
Vitamin E Consumption and the Risk of Coronary Heart Disease in Men
Eric B. Rimm,Meir J. Stampfer,Alberto Ascherio,Edward Giovannucci,Graham A. Colditz,Walter C. Willett +5 more
TL;DR: Evidence is provided of an association between a high intake of vitamin E and a lower risk of coronary heart disease in men, and public policy recommendations with regard to the use ofitamin E supplements should await the results of additional studies.
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