A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects

Abstract

We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.