Avoiding bias from weak instruments in Mendelian randomization studies
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Open Access
- 16 March 2011
- journal article
- research article
- Published by Oxford University Press (OUP) in International Journal of Epidemiology
- Vol. 40 (3), 755-764
- https://doi.org/10.1093/ije/dyr036
Abstract
Background Mendelian randomization is used to test and estimate the magnitude of a causal effect of a phenotype on an outcome by using genetic variants as instrumental variables (IVs). Estimates of association from IV analysis are biased in the direction of the confounded, observational association between phenotype and outcome. The magnitude of the bias depends on the F-statistic for the strength of relationship between IVs and phenotype. We seek to develop guidelines for the design and analysis of Mendelian randomization studies to minimize bias. Methods IV analysis was performed on simulated and real data to investigate the effect on bias of size of study, number and choice of instruments and method of analysis. Results Bias is shown to increase as the expected F-statistic decreases, and can be reduced by using parsimonious models of genetic association (i.e. not over-parameterized) and by adjusting for measured covariates. Using data from a single study, the causal estimate of a unit increase in log-transformed C-reactive protein on fibrinogen (μmol/l) is shown to increase from −0.005 (P = 0.99) to 0.792 (P = 0.00003) due to injudicious choice of instrument. Moreover, when the observed F-statistic is larger than expected in a particular study, the causal estimate is more biased towards the observational association and its standard error is smaller. This correlation between causal estimate and standard error introduces a second source of bias into meta-analysis of Mendelian randomization studies. Bias can be alleviated in meta-analyses by using individual level data and by pooling genetic effects across studies. Conclusions Weak instrument bias is of practical importance for the design and analysis of Mendelian randomization studies. Post hoc choice of instruments, genetic models or data based on measured F-statistics can exacerbate bias. In particular, the commonly cited rule of thumb that F > 10 avoids bias in IV analysis is misleading.Keywords
This publication has 31 references indexed in Scilit:
- ‘Mendelian randomization’ equals instrumental variable analysis with genetic instrumentsStatistics in Medicine, 2008
- Mendelian randomization: Using genes as instruments for making causal inferences in epidemiologyStatistics in Medicine, 2008
- Mendelian randomization as an instrumental variable approach to causal inferenceStatistical Methods in Medical Research, 2007
- Estimation of Bias in Nongenetic Observational Studies Using “Mendelian Triangulation”Annals of Epidemiology, 2006
- What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?BMJ, 2005
- Mendelian randomization: prospects, potentials, and limitationsInternational Journal of Epidemiology, 2004
- ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*International Journal of Epidemiology, 2003
- The Distribution of the Instrumental Variables Estimator and Its $t$-Ratio When the Instrument is a Poor OneThe Journal of Business, 1990
- The Exact Sampling Distribution of Ordinary Least Squares and Two-Stage Least Squares EstimatorsJournal of the American Statistical Association, 1969
- The Exact Distribution of a Structural Coefficient EstimatorJournal of the American Statistical Association, 1968