A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

ABSTRACT: Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's reliance on strong parametric assumptions can introduce bias if these are violated. We describe a machine learning MR estimator named quantile instrumental variable (Quantile IV) that achieves a low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes. Employing various MR estimators and colocalization techniques, our analysis reveals th

SUBMITTER: Legault MA 

PROVIDER: S-EPMC12256800 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature