On the use of kernel machines for Mendelian randomization

Abstract

Background: Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure’s causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage.

Results: In this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting.

Conclusions: Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.

Graphical abstract

Keywords

Mendelian randomization / kernel machine / instrumental variable / unmeasured confounder / casual inference

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ACKNOWLEDGEMENTS

This research was supported by the National Science Foundation under Grant (No. NSF ABI 1457935) and the National Institutes of Health under Grant (No.R01 GM117946).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Weiming Zhang and Debashis Ghosh declare they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag GmbH Germany