Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions

Abstract

BackgroundMendelian randomization has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial as pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in Mendelian randomization analyses.\n\nMethodsWe introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias, drawing upon developments in econometrics and epidemiology. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary Mendelian randomization approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument.\n\nResultsWe investigate the effect of BMI upon systolic blood pressure (SBP) using data from the UK Biobank and the GIANT consortium using a single instrument (a weighted allelic score). We find MRGxE produces findings in agreement with MR Egger regression in a two-sample summary MR setting, however, association estimates obtained across all methods differ considerably when excluding related participants or individuals of non-European ancestry. This could be a consequence of selection bias, though there is also potential for introducing bias by using a mixed ancestry population. Further, we assess the performance of MRGxE with respect to identifying and correcting for horizontal pleiotropy in a simulation setting, highlighting the utility of the approach even when the MRGxE assumptions are violated.\n\nConclusionsBy utilising instrument-covariate interactions within a linear regression framework, it is possible to identify and correct for pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction covariate subgroups.\n\nO_TEXTBOXKey MessagesO_LIInstrument-covariate interactions can be used to identify pleiotropic bias in Mendelian randomization analyses, provided they induce sufficient variation in the association between the genetic instrument and exposure.\nC_LIO_LIBy regressing the gene-outcome association upon the gene-exposure association across interaction covariate subgroups, it is possible to obtain an estimate of the average pleiotropic effect and a causal effect estimate.\nC_LIO_LIThe interpretation of MRGxE is analogous to that of MR-Egger regression.\nC_LIO_LIThe approach serves as a valuable test for directional pleiotropy and can be used to inform instrument selection.\nC_LI\n\nC_TEXTBOX