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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.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Economics & Human Biology.

    Volume (Year): 13 (2014)
    Issue (Month): C ()
    Pages: 99-106

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    Handle: RePEc:eee:ehbiol:v:13:y:2014:i:c:p:99-106

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    Web page: http://www.elsevier.com/locate/inca/622964

    Related research

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

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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. 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 518-29, October.
    4. 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.
    5. Fletcher, Jason M. & Lehrer, Steven F., 2011. "Genetic lotteries within families," Journal of Health Economics, Elsevier, vol. 30(4), pages 647-659, July.
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