Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption.

ABSTRACT:

Background

Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.

Methods

Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 'first-order' weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is

SUBMITTER: Bowden J 

PROVIDER: S-EPMC6659376 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature