Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.

定位特质对之间的因果关系是科学研究的一项基本任务，在实践中具有重大意义，例如在确定复杂疾病的治疗和干预策略的分子靶标和可改变的危险因素的优先级时。此后，在孟德尔随机化（MR）框架中使用单个SNP作为工具变量（IV）的一种称为Steiger方法的方法已得到广泛应用。我们报告以下新的贡献。首先，我们提出了一个基于SNP的替代方案，克服了Steiger方法的严重局限性，因为它仅假设而不是推断因果关系的存在。我们还阐明了存在隐藏混杂因素时方法有效性的必要条件。第二，提高统计能力，我们建议将多个（可能是相关的）SNP的结果组合为多种工具。第三，我们开发了三个拟合优度检验来检查建模假设，包括有效IV所需的那些假设。第四，通过放宽MR中三个IV假设之一，我们提出了几种方法，包括Egger回归样方法及其多变量版本（类似于多变量MR），以解决SNP / IV的水平多向性问题。在实践中不可避免。我们所有的方法都可以同时推断因果关系的存在和方向（如果这样），大大扩展了其适用性，超过了Steiger方法。尽管我们专注于单向因果关系，但我们也简要讨论了双向关系的扩展。通过广泛的模拟和推断低密度脂蛋白（LDL）胆固醇或高密度脂蛋白（HDL）胆固醇与冠状动脉疾病（CAD）之间因果关系的应用，我们证明了我们提出的方法优于Steiger's的性能和优势方法和双向MR。特别是，在考虑了水平多向性之后，我们的方法证实了从LDL到CAD的众所周知的因果关系，而其他方法（包括双向MR）可能会失败。