Multi-SNP mediation intersection-union test

Abstract

Summary Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up. Availability and implementation The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT. Supplementary information Supplementary data are available at Bioinformatics online.

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

“…Our simulations included scenarios where the assumptions for valid causal inference (using MR and/or BN) were violated, as we were interested in investigating the extent to which this would result in erroneous or misleading inferences. We also applied two recently-proposed methods, LCV [29] and SMUT [48], along with BN, MR and a recent MR extension [25], to data generated under a more complex simulation scenario involving extreme pleiotropy.…”

“…This included 4 metabolites (Fig 10, left hand https://doi.org/10.1371/journal.pgen.1008198.g007 (Fig 10, right hand panels) with a reverse effect (so that Y influenced the metabolite). We applied the recently proposed SMUT [48] method, along with BN, MR and a recent MR extension (MR-BMA) [25]. Bayesian networks for each of the four scenarios (A-D) used for the simulated binary data.…”

“…We also applied a recently-proposed multi-SNP mediation intersection-union test known as SMUT [48]. SMUT tests the joint mediation effects of multiple (potentially correlated) genetic variants on a trait through a single mediator, effectively generating a hypothesis test for mediation but with a multivariate genetic exposure.…”

“…Weighted allele score variables were used as instrumental variables and were re-constructed within each simulation replicate using SNPs passing a p-value threshold of p < 5 × 10 −6 of association with the appropriate metabolite or with Y, using the estimated regression coefficients as weights. The same SNPs were also used as the genetic variants in SMUT (using the R package SMUT [48]), which was also performed between every individual metabolite and Y (or vice versa for reverse causal effects).…”

MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity

“…Evidence for mediation is provided by randomized genetic variation used to perturb gene expression and observing the expression effects propagated to traits. With access to genotype, expression, and trait data, classical mediation techniques can be employed, as in the methods CIT [17] and SMUT [18], while MR-link [19] makes use of individual-level data from a GWAS study and summary statistics for eQTL to perform MR analysis.…”