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

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  • Taylor, Amy E.
  • Davies, Neil M.
  • Ware, Jennifer J.
  • VanderWeele, Tyler
  • Smith, George Davey
  • Munafò, Marcus R.

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.

Suggested Citation

  • Taylor, Amy E. & Davies, Neil M. & Ware, Jennifer J. & VanderWeele, Tyler & Smith, George Davey & Munafò, Marcus R., 2014. "Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates," Economics & Human Biology, Elsevier, vol. 13(C), pages 99-106.
  • Handle: RePEc:eee:ehbiol:v:13:y:2014:i:c:p:99-106
    DOI: 10.1016/j.ehb.2013.12.002
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    References listed on IDEAS

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    1. Carslake, David & Fraser, Abigail & Davey Smith, George & May, Margaret & Palmer, Tom & Sterne, Jonathan & Silventoinen, Karri & Tynelius, Per & Lawlor, Debbie A. & Rasmussen, Finn, 2013. "Associations of mortality with own height using son's height as an instrumental variable," Economics & Human Biology, Elsevier, vol. 11(3), pages 351-359.
    2. Paul S. Clarke & Frank Windmeijer, 2012. "Instrumental Variable Estimators for Binary Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1638-1652, December.
    3. Fletcher, Jason M. & Lehrer, Steven F., 2011. "Genetic lotteries within families," Journal of Health Economics, Elsevier, vol. 30(4), pages 647-659, July.
    4. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    5. Wehby, George L. & Murray, Jeffrey C. & Wilcox, Allen & Lie, Rolv T., 2012. "Smoking and body weight: Evidence using genetic instruments," Economics & Human Biology, Elsevier, vol. 10(2), pages 113-126.
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    Cited by:

    1. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    2. Nicola Barban & Elisabetta De Cao & Sonia Oreffice & Climent Quintana-Domeque, 2016. "Assortative Mating: A Genetic Assessment," Working Papers 2016-034, Human Capital and Economic Opportunity Working Group.

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