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Review
. 2019 Dec;10(4):486-496.
doi: 10.1002/jrsm.1346. Epub 2019 Apr 23.

Meta-analysis and Mendelian randomization: A review

Jack Bowden  1 , Michael V Holmes  1   2   3   4
Affiliations
Review

Meta-analysis and Mendelian randomization: A review

Jack Bowden et al. Res Synth Methods. 2019 Dec.

Abstract

Mendelian randomization (MR) uses genetic variants as instrumental variables to infer whether a risk factor causally affects a health outcome. Meta-analysis has been used historically in MR to combine results from separate epidemiological studies, with each study using a small but select group of genetic variants. In recent years, it has been used to combine genome-wide association study (GWAS) summary data for large numbers of genetic variants. Heterogeneity among the causal estimates obtained from multiple genetic variants points to a possible violation of the necessary instrumental variable assumptions. In this article, we provide a basic introduction to MR and the instrumental variable theory that it relies upon. We then describe how random effects models, meta-regression, and robust regression are being used to test and adjust for heterogeneity in order to improve the rigor of the MR approach.

Keywords: Mendelian randomization; meta-analysis; pleiotropy; two-sample summary data MR.

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Conflict of interest statement

The author reported no conflict of interest.

Figures

Figure 1
Figure 1
Causal directed acyclic graph (DAG) representing the hypothetical relationship between genetic variant G, exposure X, and outcome Y, in the presence of unobserved confounding, U. Solid arrows represent allowed relationships between the variables. Dashed lines represent relationships that are forbidden for G to qualify as a valid instrumental variable (IV). The G‐X and X‐Y arrows are parameterized by γ and β, with the latter denoting the causal effect of X on Y
Figure 2
Figure 2
Meta‐analysis of the association of rs1205 with C‐reactive protein (left) and heart disease (right) in studies contributing towards the C‐reactive protein coronary heart disease genetics collaboration.7 Estimates reflect the mean difference in log CRP per allele (left) and odds ratio of CHD per allele (right). CHD, coronary heart disease; CRP, C‐reactive protein
Figure 3
Figure 3
Scatter plot of single nucleotide polymorphism (SNP)‐outcome associations versus SNP‐exposure associations for a fictional Mendelian randomization (MR) analysis using 13 variants. Vertical and horizontal lines centered at each data point show 95% confidence intervals for the associations. The slope joining each data point to the origin represents the ratio estimate of a given SNP. IVW, inverse variance weighted
Figure 4
Figure 4
A, Hypothetical scatter plot with directional pleiotropy. Consequently, MR‐Egger estimates a nonzero intercept. B, Hypothetical funnel plot. Directional pleiotropy is seen to induce asymmetry. The MR‐Egger estimate can be interpreted as the value that would have been obtained if the funnel plot were symmetrical.
Figure 5
Figure 5
A, Scatter plot of the SBP data horizontal and vertical dashed lines show 95% confidence interval for each association. IVW, MR‐Egger, weighted median slope, and MBE slope are also shown. B, Funnel plot of the SBP data. Data on variant rs17249754 are represented by a square in each plot. CHD, coronary heart disease; IVW, inverse variance weighted; MBE, mode‐based estimate; MR, Mendelian randomization; SNP, single nucleotide polymorphism
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References

    1. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1‐22. - PubMed
    1. Pearl J. Causality: Models, Reasoning, and Inference. New York, NY, USA: 2nd edn: Cambridge University Press; 2009.
    1. Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26(5):2333‐2355. - PMC - PubMed
    1. Vansteelandt S, Bowden J, Babanezhad M, Goetghebeur E. On instrumental variables estimation of causal odds ratios. Statistical Science. 2011;26(3):403‐422.
    1. Zhao Q, Wang J, Hemani G, Bowden J, Small D. Statistical inference in two‐sample summary‐data Mendelian randomization using robust adjusted profile score. ArXiv 2018. https://arxiv.org/pdf/1801.09652.pdf

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