Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

By Stephen Burgess, R. Scott, Nicholas J. Timpson, George Davey Smith, S. Thompson. European Journal of Epidemiology, 2015

1,618 citations recorded by the source corpus.

Abstract Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/5065289ba8b3ea33991dbded5fa4b10e50b6c51d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “However, including more than one variant in each gene region can improve precision of the causal estimate [48].”
Bias due to participant overlap in two‐sample Mendelian randomization (2016, background): “1Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (Email: sb452@medschl.cam.ac.uk) 2MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK 3School of Social and Community Medicine, University of Bristol, Bristol, UK Correspondence Stephen Burgess, D”
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, methodology): “The above summary-data Mendelian randomization design has wide applicability in practice (Burgess et al., 2015) and there is a lot of ongoing efforts in developing public databases and software platforms (Hemani et al., 2018).”
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator (2016, methodology): “If there is no intercept term in the regression model, then the MR-Egger slope estimate β̂E will equal the IVW estimate (Burgess et al., 2015a).”
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data (2017, methodology): “Increasingly, Mendelian randomization analyses are being performed using summarized data, in particular the associations (beta-coefficients and standard errors) of each genetic variant with the exposure and outcome variables.(3) Such associations are often made publicly available by studies or conso”
A review of instrumental variable estimators for Mendelian randomization (2015, background): “This makes two-sample Mendelian randomization using summarized data a worthwhile analysis strategy from the perspective of power, and one that should be encouraged in practice, particularly if there is available summarized data on genetic associations with the outcome.(133) The only caveat is that t”
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, methodology): “Extensions to allow for correlated IVs have been discussed in the context of the likelihood-based method [18] and can be implemented by allowing for a joint multivariate distribution of the βXk and βYk estimates, with the variance–covariance matrix incorporating the correlation between the IVs (see ”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, background): “Given the increasing number of publicly available summary statistics from large GWAS consortia, summary data MR methods enable many causal hypotheses to be rapidly interrogated without the administrative burden and cooperation required to perform equivalent individual-level data analyses.(6,7)”
Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016, background): “Now genetic epidemiologists have shown us how to provide complete open-access summary data, and it is likely that over the coming decade important and impactful use will be made of these data.(4) A decade before the first paper proposing the use of MR,(2) Lau and colleagues demonstrated that, had a ”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, methodology): “A particularly attractive proposal is to automate the causal inference by using published GWAS data [14], and a large database and software platform is currently being developed [28].”