Integrating summarized data from multiple genetic variants in Mendelian randomization: bias and coverage properties of inverse-variance weighted methods

TL;DR

This paper examines the bias and coverage properties of inverse-variance weighted methods in Mendelian randomization using summarized genetic data, highlighting issues with heterogeneity and overlapping samples and recommending adjustments.

Contribution

It demonstrates that the original inverse-variance weighted method can over-reject the null hypothesis and advocates for the use of random-effects models and second-order weights to improve accuracy.

Findings

Original IVW method can lead to over-rejection of the null hypothesis.

Heterogeneity among genetic variants affects the validity of causal estimates.

Overlapping samples can cause bias, mitigated by second-order weights.

Abstract

Mendelian randomization is the use of genetic variants as instrumental variables to assess whether a risk factor is a cause of a disease outcome. Increasingly, Mendelian randomization investigations are conducted on the basis of summarized data, rather than individual-level data. These summarized data comprise the coefficients and standard errors from univariate regression models of the risk factor on each genetic variant, and of the outcome on each genetic variant. A causal estimate can be derived from these associations for each individual genetic variant, and a combined estimate can be obtained by inverse-variance weighted meta-analysis of these causal estimates. Various proposals have been made for how to calculate this inverse-variance weighted estimate. In this paper, we show that the inverse-variance weighted method as originally proposed (equivalent to a two-stage least squares or allele score analysis using individual-level data) can lead to over-rejection of the null, particularly when there is heterogeneity between the causal estimates from different genetic variants. Random-effects models should be routinely employed to allow for this possible heterogeneity. Additionally, over-rejection of the null is observed when associations with the risk factor and the outcome are obtained in overlapping participants. The use of weights including second-order terms from the delta method is recommended in this case.