Medical Research Council Population Health Research Unit, University of Oxford, Roosevelt Drive, Oxford OX3 7LF, UK.
Clinical Trial Service Unit &Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7BN, UK.
Nat Rev Cardiol. 2017 Oct;14(10):577-590. doi: 10.1038/nrcardio.2017.78. Epub 2017 Jun 1.
Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes. MR studies can be straightforward; for example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease. However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret. In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease). Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol). We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations. In doing so, we hope to contribute towards more reliable evaluations of MR findings.
孟德尔随机化(MR)是一个新兴领域,涉及使用遗传变异来评估暴露和结果之间的因果关系。MR 研究可以很简单;例如,与蛋白质浓度相关的编码基因座内或附近的遗传变异可以帮助评估它们在疾病中的因果作用。然而,遗传变异与暴露之间更复杂的关系会使 MR 分析的结果更难解释。在这篇综述中,我们描述了一些解释 MR 分析的挑战,包括使用遗传变异来评估多种特征(如支链氨基酸和糖尿病风险)的因果关系的研究;描述多效性变异(如 C 反应蛋白及其对冠心病的贡献)的研究;以及研究破坏暴露正常功能的变异(如高密度脂蛋白胆固醇或白细胞介素 6 与冠心病)的研究。此外,还讨论了评估负责暴露代谢的变异(如酒精)的 MR 研究,以及评估变异对时间依赖性暴露(细胞外超氧化物歧化酶)、累积暴露(低密度脂蛋白胆固醇)和重叠暴露(甘油三酯和非高密度脂蛋白胆固醇)的影响。我们详细阐述了每种关系的分子特征,并对可能的因果关系进行了解释。通过这样做,我们希望为更可靠地评估 MR 研究结果做出贡献。