Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data.

ABSTRACT: Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on

SUBMITTER: DiPrete TA 

PROVIDER: S-EPMC5984483 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature