Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

Burgess, Stephen; Butterworth, Adam S.; Thompson, John R.

doi:10.1016/j.jclinepi.2015.08.001

Cited by 85 publications

(81 citation statements)

References 48 publications

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“…Burgess and colleagues suggested that variants with known biology are better for use in MR studies [49]. The underlying biology of some of the SNPs (those selected from functional literature) included in our analysis is known, and linked to changes in alcohol metabolism, and as such, would be better for use in a PGRS MR analysis.…”

Section: Discussionmentioning

confidence: 99%

Exploration of a Polygenic Risk Score for Alcohol Consumption: A Longitudinal Analysis from the ALSPAC Cohort

et al. 2016

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BackgroundUncertainty remains about the true extent by which alcohol consumption causes a number of health outcomes. Genetic variants, or combinations of variants built into a polygenic risk score (PGRS), can be used in an instrumental variable framework to assess causality between a phenotype and disease outcome of interest, a method known as Mendelian randomisation (MR). We aimed to identify genetic variants involved in the aetiology of alcohol consumption, and develop a PGRS for alcohol.MethodsRepeated measures of alcohol consumption from mothers and their offspring were collected as part of the Avon Longitudinal Study of Parents and Children. We tested the association between 89 SNPs (identified from either published GWAS data or from functional literature) and repeated measures of alcohol consumption, separately in mothers (from ages 28–48) and offspring (from ages 15–21) who had ever reported drinking. We modelled log units of alcohol using a linear mixed model and calculated beta coefficients for each SNP separately. Cross-validation was used to determine an allelic score for alcohol consumption, and the AVENGEME algorithm employed to estimate variance of the trait explained.ResultsFollowing correction for multiple testing, one SNP (rs1229984) showed evidence for association with alcohol consumption (β = -0.177, SE = 0.042, p = <0.0001) in the mothers. No SNPs showed evidence for association in the offspring after correcting for multiple testing. The optimal allelic score was generated using p-value cut offs of 0.5 and 0.05 for the mothers and offspring respectively. These scores explained 0.3% and 0.7% of the variance.ConclusionOur PGRS explains a modest amount of the variance in alcohol consumption and larger sample sizes would be required to use our PGRS in an MR framework.

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Section: Discussionmentioning

confidence: 99%

Exploration of a Polygenic Risk Score for Alcohol Consumption: A Longitudinal Analysis from the ALSPAC Cohort

et al. 2016

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“…A full discussion on adjustment of covariates in IV analysis is beyond the scope of this manuscript; further information is available elsewhere [23]. Although these assumptions are restrictive, we note that even if these parametric assumptions are not satisfied, a Mendelian randomization investigation can still be interpreted as a test of the causal null hypothesis, even if the magnitude of the causal effect estimate does not have an interpretation [24,25]. Hence, while these assumptions are necessary to ensure the same causal effect parameter is identified by all IVs, and so that the methods provide consistent estimates of a causal parameter (even in a two-sample setting), causal inferences from the methods (that is, rejection or otherwise of the null hypothesis of no causal effect) are valid under much weaker assumptions.…”

Section: Summarized Data Methods (Correlated Ivs)mentioning

confidence: 99%

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

2015

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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.

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“…In the context of Mendelian randomization, this means that not only should the magnitude of a causal estimate not be overinterpreted [10,36], but even the direction of the causal estimate may be a false guide as to whether the exposure should be increased or decreased unless further assumptions are made.…”

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Predicting the Direction of Causal Effect Based on an Instrumental Variable Analysis: A Cautionary Tale

Burgess

Small

2016

Journal of Causal Inference

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An instrumental variable can be used to test the causal null hypothesis that an exposure has no causal effect on the outcome, by assessing the association between the instrumental variable and the outcome. Under additional assumptions, an instrumental variable can be used to estimate the magnitude of causal effect of the exposure on the outcome. In this paper, we investigate whether these additional assumptions are necessary in order to predict the direction of the causal effect, based on the direction of association between the instrumental variable and the outcome, or equivalently based on the standard (Wald) instrumental variable estimate. We demonstrate by counterexample that if these additional assumptions (such as monotonicity of the instrument-exposure association) are not satisfied, then the instrumental variable-outcome association can be in the opposite direction to the causal effect for all individuals in the population. Although such scenarios are unlikely, in most cases, a definite conclusion about the direction of causal effect requires similar assumptions to those required to estimate a causal effect.

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Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

Cited by 85 publications

References 48 publications

Exploration of a Polygenic Risk Score for Alcohol Consumption: A Longitudinal Analysis from the ALSPAC Cohort

Exploration of a Polygenic Risk Score for Alcohol Consumption: A Longitudinal Analysis from the ALSPAC Cohort

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

Predicting the Direction of Causal Effect Based on an Instrumental Variable Analysis: A Cautionary Tale

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