Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
PLoS Genet. 2023 Feb 21;19(2):e1010638. doi: 10.1371/journal.pgen.1010638. eCollection 2023 Feb.
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.
中介分析通常用于确定因果关系和结果之间的机制和中间因素。利用多基因分数（PGS）的研究可以很容易地采用传统的基于回归的程序来评估特质 M 是否在遗传成分 Y 的结果和 Y 本身之间的关系中起中介作用。然而，这种方法存在衰减偏差，因为 PGS 仅捕获给定特质遗传方差的一小部分。为了克服这一局限性，我们开发了 MA-GREML：一种基于基于基因组的限制极大似然估计（GREML）的中介分析方法。使用 MA-GREML 评估遗传因素与特质之间的中介作用有两个主要优点。首先，我们规避了基于回归的中介分析方法所面临的 PGS 预测精度有限的问题。其次，与使用全基因组关联研究汇总统计数据的方法相比，GREML 的个体水平数据方法可以直接控制 M 和 Y 之间关联的混杂因素。除了典型的 GREML 参数（例如遗传相关性）之外，MA-GREML 还估计了（i）M 对 Y 的影响，（ii）直接效应（即，M 不介导的 Y 的遗传方差），以及（iii）间接效应（即，M 介导的 Y 的遗传方差）。MA-GREML 还为这些估计值提供标准误差，并评估间接效应的显著性。我们使用分析推导和模拟来证明在两个主要假设下我们方法的有效性，即 M 先于 Y，并且控制了 M 和 Y 之间关联的环境混杂因素。我们得出结论，MA-GREML 是评估特质 M 在 Y 的遗传成分与结果 Y 之间关系中的中介作用的合适工具。我们使用来自美国健康与退休研究的数据，提供了遗传对身体质量指数（BMI）、认知功能和晚年自我报告健康的影响部分通过教育程度的证据。对于心理健康，我们没有发现通过教育程度的间接效应的显著证据。进一步的分析表明，这四个结果的加性遗传因素部分（认知和心理健康）和完全（BMI 和自我报告的健康）通过这些特质的早期实现而实现。
