Polynomial Mendelian randomization reveals non-linear causal effects for obesity-related traits.

ABSTRACT: Causal inference is a critical step in improving our understanding of biological processes, and Mendelian randomization (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here, we propose PolyMR, an MR-based method that provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than exist

SUBMITTER: Sulc J 

PROVIDER: S-EPMC9272036 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature