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bioRxiv · 10.1101/2019.12.18.881326

MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

Foley, C. N.·Kirk, P. D.·Burgess, S.
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Abstract

MotivationMendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes null and junk clusters, to provide protection against the detection of spurious clusters. ResultsOur algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and ImplementationMR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Contactsb452@medschl.cam.ac.uk or christopher.foley@mrc-bsu.cam.ac.uk Supplementary InformationSupplementary Material is included in the submission.

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Foley, C. N., Kirk, P. D., Burgess, S.. 2019-12-19. MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates. https://doi.org/10.1101/2019.12.18.881326

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