Liyanage Janaka S S, Hankins Jane S, Estepp Jeremie H, Srivastava Deokumar, Rashkin Sara R, Takemoto Clifford, Li Yun, Cui Yuehua, Mori Motomi, Weiss Mitchell J, Kang Guolian
Biostatistics Core, Department of Oncology, Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, Michigan, USA.
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Genet Epidemiol. 2025 Jan;49(1):e22602. doi: 10.1002/gepi.22602. Epub 2024 Nov 5.
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.
我们提出了两种新颖的单样本孟德尔随机化（MR）方法，用于从计数型健康结局进行因果推断，适用于等离散和过离散条件。选择有效的单核苷酸多态性（SNP）作为工具变量（IV）对MR方法构成了关键挑战，因为这需要满足必要的IV假设。为了通过解决IV假设的违反情况来支持所提出的方法，我们纳入了一个去除违反假设的无效SNP的过程。在模拟中，我们提出的方法对这些违反情况具有鲁棒性，能给出有效的估计值，以及可解释的I型错误和统计功效。这增加了模型的实际适用性。我们应用所提出的方法来评估胎儿血红蛋白（HbF）对镰状细胞病（SCD）患者血管闭塞性危机和急性胸部综合征（ACS）事件的因果效应，并揭示了这些患者中HbF与ACS事件之间的因果关系。我们还开发了一个用户友好的Shiny网络应用程序，以方便研究人员探索因果关系。
