Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations.

ABSTRACT: In genetic association studies, it is important to distinguish direct and indirect genetic effects in order to build truly functional models. For this purpose, we consider a directed acyclic graph setting with genetic variants, primary and intermediate phenotypes, and confounding factors. In order to make valid statistical inference on direct genetic effects on the primary phenotype, it is necessary to consider all potential effects in the graph, and we propose to use the estimating equations method with robust Huber-White sandwich standard errors. We evaluate the proposed causal inference based on estimating equations (CIEE) method and compare it with traditional multiple regression methods, the structural equation modeling method, and sequential G-estimation methods through a simulation

SUBMITTER: Konigorski S 

PROVIDER: S-EPMC6619348 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature