Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods

By Stephen Burgess, Frank Dudbridge, S. Thompson. Statistics in Medicine, 2015

1,022 citations recorded by the source corpus.

Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual‐level data on the IVs are aggregated into a univariate score, which is used as a single IV, and (ii) a summary statistic method, in which causal estimates calculated from each IV using summarized data are combined in an inverse‐variance weighted meta‐analysis. To avoid bias from weak instruments, unweighted and externally weighted allele scores have been recommended. Here, we propose equivalent approaches using summarized data and also provide extensions of the methods for use with correlated IVs. We investigate the impact of different choices of weights on the bias and precision of estimates in simulation studies. We show that allele score estimates can be reproduced using summarized data on genetic associations with the risk factor and the outcome. Estimates from the summary statistic method using external weights are biased towards the null when the weights are imprecisely estimated; in contrast, allele score estimates are unbiased. With equal or external weights, both methods provide appropriate tests of the null hypothesis of no causal effect even with large numbers of potentially weak instruments. We illustrate these methods using summarized data on the causal effect of low‐density lipoprotein cholesterol on coronary heart disease risk. It is shown that a more precise causal estimate can be obtained using multiple genetic variants from a single gene region, even if the variants are correlated. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/16aa1fec71bbe54854d83d0fb8e0949571196780

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Citations in the corpus

An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, background): “However, if this assumption is violated the causal null will still be respected and it will still be possible to identify whether the exposures are causally associated with the outcome.(21-23) Throughout this analysis we assume linearity of the relationship between the exposures and the outcome, how”
Meta‐analysis and Mendelian randomization: A review (2019, methodology): “In that case, themodel parameters must be estimated using generalized least squares by specifying a correlation matrix for the set of SNPs.(34) Extensions for the weighted median and MBE for correlated variants have yet to be explored and is an interesting avenue for further research.”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, methodology): “(3) using generalized weighted linear regression [14].”
Bias due to participant overlap in two‐sample Mendelian randomization (2016, background): “The IVs are modeled as independently distributed biallelic single nucleotide polymorphisms (SNPs) with a minor allele frequency of 0.3.”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “analogy to RCTs, each instrumenting SNP is considered an independent experiment (in the sense that they independently modify the exposure), and as such the results from each experiment can be meta-analysed to give an overall estimate (7,46,47).”
Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic (2016, positive): “These ideas naturally complement allele score approaches that have been shown to successfully mitigate weak instrument bias when performing standard two-stage least squares or IVW analyses.(18,28) In conclusion, assessing the strength of NOME violation is an important prerequisite to performing caus”