Abstract

Mendelian randomization (MR) considers using genetic variants as instrumental

variables (IVs) to infer causal effects in observational studies. However, the validity of causal

inference in MR can be compromised when the IVs are potentially invalid. In this work,

we propose a new method, MR-Local, to infer the causal effect in the existence of possibly

invalid IVs. By leveraging the distribution of ratio estimates around the true causal effect,

MR-Local selects the cluster of ratio estimates with the least uncertainty and performs

causal inference within it. We establish the asymptotic normality of our estimator in the

two-sample summary-data setting under either the plurality rule or the balanced pleiotropy

assumption. Extensive simulations and analyses of real datasets demonstrate the reliability

of our approach.

Information

Preprint No.SS-2023-0344
Manuscript IDSS-2023-0344
Complete AuthorsZiya Xu, Sai Li
Corresponding AuthorsSai Li
Emailssaili@ruc.edu.cn

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant

no. 12201630).

Supplementary Materials

The supplementary material includes the following sections: S1, technical lemmas;

S2, proofs for the theorems; S3, supplementary propositions; S4, further information

on simulations; S5, further results on real studies.

Supplementary materials are available for download.