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

Abstract

a equal supervisory contribution AbstractBackground: Mendelian 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. Methods:We 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. 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). 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. 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.

Evaluating the potential role of pleiotropy in Mendelian randomization studies

“…In practice there are very few epidemiological examples, including the alcohol example, with a perfect no-relevance point ( 87 , 91 ). MRGxE builds on this approach, by relaxing the requirement that a no-relevance point has to be observed: its value can instead be estimated as long as there is variation in the strength of the SNP-exposure association across subgroups of the environmental covariate ( 91 ).…”

“…In practice there are very few epidemiological examples, including the alcohol example, with a perfect no-relevance point ( 87 , 91 ). MRGxE builds on this approach, by relaxing the requirement that a no-relevance point has to be observed: its value can instead be estimated as long as there is variation in the strength of the SNP-exposure association across subgroups of the environmental covariate ( 91 ). While the dependence on GxE or GxG interactions implies that individual level data are required, MRGxE can be performed using summary data if estimates for the SNP-exposure and SNP-outcome associations at different levels of the environmental variable are available.…”

“…Individual level data are valuable because it can be used to perform some sensitivity analyses that cannot be done with summary data, e.g. the use of interactions ( 87 , 90 , 91 ), and triangulating MR estimates with alternative causal inference strategies ( 16 , 18 , 103 ). Other advantages of using individual level data from the same sample are that causal estimates are robust to misspecification of the SNP-exposure association model, and when LD patterns are needed an external reference panel can be avoided.…”

Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study

“…[5][6][7][8][9][10][11][12] These observational studies are, however, prone to reverse causation, unmeasured confounding and recall bias, which can preclude causal inferences. 13 Additionally, the high frequency of exposure ascertainment by proxy is another source of bias. 14 Finally, the studies performed to date have had a limited scope of enquiry either examining factors that have well-established associations for other cancers or hypothesised risk factors based on limited insight into glioma biology, thereby reducing the prospects of revealing causal relationships.…”

Lack of association between modifiable exposures and glioma risk: A Mendelian randomisation analysis

“…Biases include selection bias in controls, recall bias, reverse causation, or confounding from unmeasured effects. 15 Furthermore, the high frequency of exposure ascertainment by proxy in studies of glioma represents an additional source of bias. 16 Mendelian randomization (MR) is an analytical approach, whereby germline genetic variants are used as proxies, or instrumental variables (IVs), for putative risk factors.…”