The identification of mediating effects using genome-based restricted maximum likelihood estimation

Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a (small) part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation.Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between M and Y. In addition to typical GREML parameters (e.g., the genetic correlation), MA-GREML estimates (i) the effect of M on Y, (ii) the direct effect (i.e., the genetic variance of Y that is not mediated by M), and (iii) the indirect effect (i.e., the genetic variance of Y that is mediated by M). MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect.We use analytical derivations and simulations to show the validity of our approach under two main assumptions, viz., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y. Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially (cognition and mental health) and fully (BMI and self-reported health) run through an earlier realization of these traits.Author summary: Mediation analysis is instrumental to identify intermediate factors between causes and outcomes. We developed Mediation Analysis using Genome-based Restricted Maximum Likelihood (MA-GREML) estimation to quantify to which degree the genetic component (cause) of a trait (outcome) is mediated by another trait (intermediate factor). This method has two main advantages. First, it captures the total additive genetic variance as estimated using GREML rather than the smaller part of the genetic variance typically captured by polygenic scores. Second, the individual-level data approach GREML takes allows users to directly control for confounders in the model. We use analytical derivations, simulations, and empirical data to validate the underlying model that is used to quantify to which degree an intermediate trait mediates the relationship between the genetic component of the outcome and the outcome itself. We conclude that MA-GREML is an appropriate method to estimate genetic mediation and to test the significance of the indirect effect. We implemented the estimation procedure in the freely available Python-based software package MGREML, available at https://github.com/devlaming/mgreml/.