Outlier Detection in Mendelian Randomisation

TL;DR

This paper introduces a new method for outlier detection in Mendelian Randomisation that corrects for overdispersion in heterogeneity statistics, improving the identification of pleiotropic genetic variants using summary data.

Contribution

The paper proposes a novel correction technique for heterogeneity statistics in MR, enhancing outlier detection accuracy in the presence of pleiotropy.

Findings

Improves outlier detection in MR analyses.

Reduces false positives caused by overdispersion.

Applicable to summary-level genetic data.

Abstract

Mendelian Randomisation (MR) uses genetic variants as instrumental variables to infer causal effects of exposures on an outcome. One key assumption of MR is that the genetic variants used as instrumental variables are independent of the outcome conditional on the risk factor and unobserved confounders. Violations of this assumption, i.e. the effect of the instrumental variables on the outcome through a path other than the risk factor included in the model (which can be caused by pleiotropy), are common phenomena in human genetics. Genetic variants, which deviate from this assumption, appear as outliers to the MR model fit and can be detected by the general heterogeneity statistics proposed in the literature, which are known to suffer from overdispersion, i.e. too many genetic variants are declared as false outliers. We propose a method that corrects for overdispersion of the heterogeneity statistics in uni- and multivariable MR analysis by making use of the estimated inflation factor to correctly remove outlying instruments and therefore account for pleiotropic effects. Our method is applicable to summary-level data.