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Econ Hum Biol. 2014 Mar;13:99-106. doi: 10.1016/j.ehb.2013.12.002. Epub 2013 Dec 13.

Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates.

Author information

1
MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK. Electronic address: amy.taylor@bristol.ac.uk.
2
MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
3
MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK; MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
4
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
5
MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK.

Abstract

Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. The main purpose of this paper is to demonstrate how results can be biased if researchers select genetic variants on the basis of their association with the exposure in their own dataset, as often happens in candidate gene analyses. This can lead to estimates that indicate apparent "causal" relationships, despite there being no true effect of the exposure. In addition, we discuss the potential bias in estimates of magnitudes of effect from Mendelian randomization analyses when the measured exposure is a poor proxy for the true underlying exposure. We illustrate these points with specific reference to tobacco research.

KEYWORDS:

Causal inference; Instrumental variable; Mendelian randomization; Smoking; Tobacco

PMID:
24388127
PMCID:
PMC3989031
DOI:
10.1016/j.ehb.2013.12.002
[Indexed for MEDLINE]
Free PMC Article

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