Abstract
Background: Mendelian randomization has developed into an established method for strengthening causal inference and estimating causal effects, largely as a consequence of 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 correcting for pleiotropic bias in Mendelian randomization analyses.
Methods: We introduce linear Slichter regression (LSR) 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 LSR is similar to conventional summary Mendelian randomization approaches. A particular advantage of LSR is the ability to assess pleiotropic effects using individual genetic variants.
Results: We 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), finding evidence of a positive association between BMI and SBP in agreement with two sample summary Mendelian randomization approaches. We assess the performance of LSR with respect to identifying and correcting for horizontal pleiotropy in a simulation setting, highlighting the utility of the approach where the LSR assumptions are violated.
Conclusions: By 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.
Key Messages
Instrument-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.
The interpretation of LSR is similar to that of summary MR methods such as MR-Egger regression.
By 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.
The approach serves as a valuable test for directional pleiotropy, and can be used to inform instrument selection.