Mendelian Randomization Analysis in Observational Epidemiology
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초록
Mendelian randomization (MR) in epidemiology is the use of genetic variants as instrumental variables (IVs) in non-experimental design to make causality of a modifiable exposure on an outcome or disease. It assesses the causal effect between risk factor and a clinical outcome. The main reason to approach MR is to avoid the problem of residual confounding. There is no association between the genotype of early pregnancy and the disease, and the genotype of an individual cannot be changed. For this reason, it results with randomly assigned case-control studies can be set by regressing the measurements. IVs in MR are used genetic variants for estimating the causality. Usually an outcome is a disease and an exposure is risk factor, intermediate phenotype which may be a biomarker. The choice of the genetic variable as IV (Z) is essential to a successful in MR analysis. MR is named ‘Mendelian deconfounding’ as it gives to estimate of the causality free from biases due to confounding (C). To estimate unbiased estimation of the causality of the exposure (X) on the clinically relevant outcome (Y), Z has the 3 core assumptions (A1-A3). A1) Z is independent of C; A2) Z is associated with X; and A3) Z is independent of Y given X and C; The purpose of this review provides an overview of the MR analysis and is to explain that using an IV is proposed as an alternative statistical method to estimate causal effect of exposure and outcome under controlling for a confounder.
Abstract
Mendelian randomization (MR) in epidemiology is the use of genetic variants as instrumental variables (IVs) in non-experimental design to make causality of a modifiable exposure on an outcome or disease. It assesses the causal effect between risk factor and a clinical outcome. The main reason to approach MR is to avoid the problem of residual confounding. There is no association between the genotype of early pregnancy and the disease, and the genotype of an individual cannot be changed. For this reason, it results with randomly assigned case-control studies can be set by regressing the measurements. IVs in MR are used genetic variants for estimating the causality. Usually an outcome is a disease and an exposure is risk factor, intermediate phenotype which may be a biomarker. The choice of the genetic variable as IV (Z) is essential to a successful in MR analysis. MR is named ‘Mendelian deconfounding’ as it gives to estimate of the causality free from biases due to confounding (C). To estimate unbiased estimation of the causality of the exposure (X) on the clinically relevant outcome (Y), Z has the 3 core assumptions (A1-A3). A1) Z is independent of C; A2) Z is associated with X; and A3) Z is independent of Y given X and C; The purpose of this review provides an overview of the MR analysis and is to explain that using an IV is proposed as an alternative statistical method to estimate causal effect of exposure and outcome under controlling for a confounder.
참고문헌 (39)
- Sekula P, "Mendelian randomization as an approach to assess causality using observational data", J Am Soc Nephrol, 27, pp.3253~3265 (2016)
- 인준용, "Randomization in clinical studies", Korean Journal of Anesthesiology, 72(3), pp.221~232 (2019), 대한마취통증의학회
- Burgess S, "Mendelian randomization: methods for using genetic variants in causal estimation" (2015), CRC Press
- Tobin MD, "Commentary: development of Mendelian randomization:from hypothesis test to ‘Mendelian deconfounding’", Int J Epidemiol, 33, pp.26~29 (2004)
- Gray R, "How to avoid bias when comparing bone marrow transplantation with chemotherapy", Bone Marrow Transplant, 7(Suppl 3), pp.9~12 (1991)
- Wheatley K, "Commentary: Mendelian randomization--an update on its use to evaluate allogeneic stem cell transplantation in leukaemia", Int J Epidemiol, 33, pp.15~17 (2004)
- Jansen H, "Mendelian randomization studies in coronary artery disease", Eur Heart J, 35, pp.1917~1924 (2014)
- Ference BA, "Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis", J Am Coll Cardiol, 60, pp.2631~2639 (2012)
- 부소영, "Genetically Mediated Lipid Metabolism and Risk of Insulin Resistance: Insights from Mendelian Randomization Studies", 지질·동맥경화학회지, 8(2), pp.132~143 (2019), 한국지질동맥경화학회[DOI]
- Smith GD, "Mendelian randomization for strengthening causal inference in observational studies:application to gene × environment interactions", Perspect Psychol Sci, 5, pp.527~545 (2010)
- Greenland S, "An introduction to instrumental variables for epidemiologists", Int J Epidemiol, 29, pp.722~729 (2000)
- Wright S, "The tariff on animal and vegetable oils" (1928), Macmillan
- Goldberger AS, "Structural equation methods in the social sciences", Econometrica, 40, pp.979~1001 (1972)
- Ogbuanu IU, "Can we apply the Mendelian randomization methodology without considering epigenetic effects?", Emerg Themes Epidemiol, 6, pp.3~ (2009)
- Almon R, "Associations between lactase persistence and the metabolic syndrome in a cross-sectional study in the Canary Islands", Eur J Nutr, 49, pp.141~146 (2010)
- Bech BH, "Stillbirth and slow metabolizers of caffeine:comparison by genotypes", Int J Epidemiol, 35, pp.948~953 (2006)
- Chen L, "Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach", PLoS Med, 5, pp.e52~ (2008)
- Mumby HS, "Mendelian randomisation study of childhood BMI and early menarche", J Obes, 2011, pp.180729~ (2011)
- von Hinke Kessler Scholder S, "Genetic markers as instrumental variables: an application to child fat mass and academic achievement" (2010), The Centre for Market and Public Organisation
- Ebrahim S, "Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?", Hum Genet, 123, pp.15~33 (2008)
- Timpson NJ, "C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization", Int J Obes, 35, pp.300~308 (2011)
- Kivimäki M, "Does high C-reactive protein concentration increase atherosclerosis? The Whitehall II Study", PLoS One, 3, pp.e52~ (2008)
- Allin KH, "C-reactive protein and the risk of cancer: a mendelian randomization study", J Natl Cancer Inst, 102, pp.202~206 (2010)
- Timpson NJ, "C-reactive protein and its role in metabolic syndrome: mendelian randomisation study", Lancet, 366, pp.1954~1959 (2005)
- Trompet S, "Apolipoprotein e genotype, plasma cholesterol, and cancer: a Mendelian randomization study", Am J Epidemiol, 170, pp.1415~1421 (2009)
- Casas JP, "Homocysteine and stroke: evidence on a causal link from mendelian randomisation", Lancet, 365, pp.224~232 (2005)
- Ding W, "The impact of poor health on academic performance: New evidence using genetic markers", J Health Econ, 28, pp.578~597 (2009)
- Wehby GL, "‘Mendelian randomization’ equals instrumental variable analysis with genetic instruments", Stat Med, 27, pp.2745~2749 (2008)
- Thomas DC, "Commentary: the concept of ‘Mendelian randomization’", Int J Epidemiol, 33, pp.21~25 (2004)
- Burgess S, "A review of instrumental variable estimators for Mendelian randomization", Stat Methods Med Res, 26, pp.2333~2355 (2017)
- Pearl J, "Causality" (2000), Cambridge University Press
- Nitsch D, "Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials", Am J Epidemiol, 163, pp.397~403 (2006)
- Teumer A, "Common methods for performing mendelian randomization", Front Cardiovasc Med, 5, pp.51~ (2018)
- Didelez V, "Mendelian randomization as an instrumental variable approach to causal inference", Stat Methods Med Res, 16, pp.309~330 (2007)
- Lawlor DA, "Mendelian randomization: using genes as instruments for making causal inferences in epidemiology", Stat Med, 27, pp.1133~1163 (2008)
- Greco M FD, "Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome", Stat Med, 34, pp.2926~2940 (2015)
- Lim HR, "Effect of blood lead concentration on attention deficit hyperactivity disorder in Korean children:a Mendelian randomization study", pp.2017
- "Package ‘MendelianRandomization’" (2019)[URL]
- Yavorska OO, "MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data", Int J Epidemiol, 46, pp.1734~1739 (2017)