DataSheet1_Mendelian Randomization: A Review of Methods for the Prevention, Assessment, and Discussion of Pleiotropy in Studies Using the Fat Mass and Obesity-Associated Gene as an Instrument for Adiposity.docx

Pleiotropy assessment is critical for the validity of Mendelian randomization (MR) analyses, and its management remains a challenging task for researchers. This review examines how the authors of MR studies address bias due to pleiotropy in practice. We reviewed Pubmed, Medline, Embase and Web of Science for MR studies published before 21 May 2020 that used at least one single-nucleotide polymorphism (SNP) in the fat mass and obesity-associated (FTO) gene as instrumental variable (IV) for body mass index, irrespective of the outcome. We reviewed: 1) the approaches used to prevent pleiotropy, 2) the methods cited to detect or control the independence or the exclusion restriction assumption highlighting whether pleiotropy assessment was explicitly stated to justify the use of these methods, and 3) the discussion of findings related to pleiotropy. We included 128 studies, of which thirty-three reported one approach to prevent pleiotropy, such as the use of multiple (independent) SNPs combined in a genetic risk score as IVs. One hundred and twenty studies cited at least one method to detect or account for pleiotropy, including robust and other IV estimation methods (n = 70), methods for detection of heterogeneity between estimated causal effects across IVs (n = 72), methods to detect or account associations between IV and outcome outside thought the exposure (n = 85), and other methods (n = 5). Twenty-one studies suspected IV invalidity, of which 16 explicitly referred to pleiotropy, and six incriminating FTO SNPs. Most reviewed MR studies have cited methods to prevent or to detect or control bias due to pleiotropy. These methods are heterogeneous, their triangulation should increase the reliability of causal inference.

多效性评估对于孟德尔随机化（Mendelian randomization, MR）分析的有效性至关重要，但其防控仍是研究者面临的一项挑战性任务。本综述系统考察了孟德尔随机化研究的作者在实际工作中如何应对多效性导致的偏倚。我们检索了PubMed、Medline、Embase及Web of Science数据库中2020年5月21日前发表的孟德尔随机化研究，这些研究均以脂肪量与肥胖相关（FTO）基因中至少1个单核苷酸多态性（SNP）作为体质量指数的工具变量（IV），且不限制研究结局类型。我们对以下内容进行了综述：1）用于预防多效性的方法；2）用于检测或控制独立性假设与排他性约束假设的引用方法，重点明确是否明确提及多效性评估以佐证这些方法的使用合理性；3）与多效性相关的研究结果讨论。本次纳入分析的研究共128项，其中33项研究报道了一种预防多效性的方法，例如将多个（独立）单核苷酸多态性组合为遗传风险评分作为工具变量。120项研究引用了至少1种用于检测或校正多效性的方法，包括稳健工具变量估计及其他工具变量估计方法（n=70）、用于检测工具变量间因果效应估计异质性的方法（n=72）、用于检测或校正工具变量与暴露以外结局间关联的方法（n=85），以及其他方法（n=5）。21项研究怀疑工具变量存在无效性，其中16项明确提及多效性，6项将多效性归咎于FTO单核苷酸多态性。绝大多数纳入综述的孟德尔随机化研究均引用了用于预防、检测或控制多效性相关偏倚的方法。此类方法种类各异，对其开展三角验证可提升因果推断的可靠性。