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. 2020 Jan 17;11(1):376.
doi: 10.1038/s41467-019-14156-4.

A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Stephen Burgess  1   2 , Christopher N Foley  3 , Elias Allara  4   5 , James R Staley  4   6 , Joanna M M Howson  4   7   8
Affiliations

A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Stephen Burgess et al. Nat Commun. .

Abstract

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

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Conflict of interest statement

While the manuscript was under review, JMMH became a full time employee of Novo Nordisk. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Comparison of the methods based on mean squared error criterion.
The mean squared error of the various methods is plotted in each scenario with a null causal effect. The corresponding plot with a positive causal effect was practically identical. The contamination mixture method has the best overall performance according to this measure, particularly in scenarios where 40 or 60 out of the 100 genetic variants were invalid instruments. The vertical axis is plotted on a logarithmic scale.
Fig. 2
Fig. 2. Scatter plot of genetic associations.
Genetic associations with HDL-cholesterol (standard deviation units) against genetic associations with CHD risk (log odds ratios). These association estimates are the inputs for the contamination mixture method. Error bars for genetic associations are 95% confidence intervals. Heavy black line is the causal estimate from the contamination mixture method with the strongest signal, lighter black line is the causal estimate from the secondary peak. The grey area is the 95% confidence interval for the causal effect; this comprises two ranges as the likelihood is bimodal.
Fig. 3
Fig. 3. Variants having same directions of associations with blood cell traits.
Details of genetic variants, nearest gene, beta-coefficients (standard errors, SE) for associations with HDL-cholesterol (HDL-c), triglycerides (TG), LDL-cholesterol (LDL-c), coronary heart disease (CHD) risk, mean corpuscular haemoglobin concentration (MCHC), platelet distribution width (PDW), and red cell distribution width (RCDW) for 11 genetic variants in 9 distinct gene regions having a distinct pattern of associations. All associations are orientated to the HDL-cholesterol increasing allele. Red indicates that the association is positive; blue for negative. The brightness of the colouring corresponds to the p-value for the strength of association from a Z-test; brighter colours correspond to lower p-values.
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