孟德尔随机化中多效性偏倚校正方法简介

孟德尔随机化以遗传变异作为工具变量，对感兴趣的暴露因素与结局的因果关联进行估计及评价。遗传变异作为有效工具变量需要满足强关联假设及无多效性假设。然而，由于遗传变异与表型性状间存在复杂的生物学效应，其作为工具变量的多效性往往无法避免。基于此，本文分别从工具变量筛选、无效工具变量检验、校正多效性的模型构建以及敏感性分析等方面介绍无效工具变量的多效性偏倚校正方法。在实际应用中，研究者应结合数据类型、样本含量、分析假设等多个方面选择合适的方法进行分析与推断，从而得到一致、稳健的因果效应估计量。     

孟德尔随机化法在因果推断中的应用

孟德尔随机化（Mendelian Randomization,MR）研究设计,遵循“亲代等位基因随机分配给子代”的孟德尔遗传规律,如果基因型决定表型,基因型通过表型而与疾病发生关联,因此可以使用基因型作为工具变量来推断表型与疾病之间的关联。近年来,MR的研究设计随着统计学方法、大样本GWAS数据、表观遗传学以及各种“组学”技术的不断发展,在探讨复杂暴露因素与疾病结局因果关联中应用日益广泛。本文对近年来出现的MR研究设计策略、可靠性评价及局限性进行介绍。     

因果图模型及其在营养流行病学研究中的应用

不良饮食是慢性非传染性疾病最重要的可控危险因素之一,但通过随机对照试验定量阐明具体饮食因素与健康结局的因果关联面临很多困难。近年来,因果推断的迅速发展为充分利用和发掘观察性研究数据,产生高质量的营养流行病学研究证据提供了有力的理论和方法工具。其中,因果图模型通过整合大量先验知识将复杂的因果关系系统可视化,提供了识别混杂和确定因果效应估计策略的基础框架,基于不同的因果图,可选择调整混杂、工具变量或中介分析等不同的分析策略。本文对因果图模型的思想和各种分析策略的特点及其在营养流行病学研究中的应用进行介绍,旨在促进因果图模型在营养领域的应用,为后续研究提供参考和建议。     

身体活动水平与精神类疾病的孟德尔随机化研究

目的 应用孟德尔随机化(Mendelian randomization, MR)探索身体活动水平与十种精神类疾病的因果效应。方法 本研究利用身体活动和精神类疾病的全基因组关联分析汇总数据构建两样本MR模型。选择与暴露显著相关并相互独立的遗传变异作为工具变量,应用MR多效性残差和与异质性检测和Radial MR异质性检测方法剔除多效性工具变量。同时应用F统计量,Q统计量,PRESSO global检验及MR-Egger截距项检验等方法评价工具变量的有效性。应用基于修正权重的逆方差加权法作为主要MR模型,并应用极大似然估计法、加权中位数估计法、MR稳健调整轮廓得分法进行敏感性分析。对于与身体活动水平存在显著关联的精神类疾病,进行反向MR分析,探索精神类疾病对身体活动水平的因果效应。结果 MR分析结果显示身体活动水平升高对注意力缺陷多动障碍(Attention deficit hyperactivity disorder, ADHD;OR:1.956;95%CI:1.605～2.384;P=2.45×10^-11)及精神分裂症(Schizophrenia, SCZ;OR:...     

横断面研究能否进行因果推断

基于变量调查(或测量)的共时性、统计学关联及幸存者偏倚等原因,横断面研究被认为不能进行因果推断,这是当前的"共识"。本文基于因果思维,借助因果图,首先明确定义真实截面和测量截面,并识别截面概念仅存在于理论的特性。实际横断面研究中,测量变量的共时性并不存在,而是无一例外地表现为非共时性时序,其实质上相当于测量变量间互为独立性假设,或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究,横断面研究均为暴露和结局已存在或发生后进行的测量,这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是,测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者,测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。     

东亚人群血压与心力衰竭的因果关联研究:两样本双向孟德尔随机化分析

目的 利用两样本双向孟德尔随机化（MR）研究设计,探究东亚人群中三种血压指标与心力衰竭风险的关联。方法 从发表的全基因组关联研究（GWAS）中提取汇总数据进行分析,收缩压和舒张压的遗传工具变量来自韩国基因组与流行病学研究,脉压的遗传工具变量来自日本生物银行,心力衰竭的遗传工具变量来自一项包含五个日本队列的GWAS研究。采用单变量MR、双向MR和多变量MR方法分析三种血压指标与心力衰竭风险的关联。 结果 逆方差加权法显示收缩压（每升高1 mm Hg,OR=1.52; 95% CI: 1.25~1.84）、舒张压（1.62; 1.34~1.95）和脉压（1.85; 1.27~2.69）升高均可能增加患心力衰竭的风险,而心力衰竭对三种血压指标没有潜在的因果影响（P＞0.05）。多变量分析显示脉压经调整收缩压（1.25; 0.77~2.05）或舒张压（1.46; 0.95~2.23）后与心力衰竭风险没有显著关联。结论 在东亚人群中血压对心力衰竭可能存在单向的因果关联,脉压对心力衰竭风险不存在独立于收缩压和舒张压的直接效应。     

四种因果图模型在观察性研究因果推断中的比较研究

目的比较有向无环图、结构方程模型、贝叶斯网络和TAN贝叶斯网络四种因果图模型在观察性研究因果推断中的原理方法和应用价值,为因果图模型的合理选用提供参考依据。方法以认知障碍为例,基于先验知识构建轻度认知功能障碍的有向无环图。根据有向无环图建立结构方程模型的初始模型,采用极大似然估计进行参数估计和修正指数进行模型优化。运用爬山算法进行贝叶斯网络结构学习、贝叶斯信息准则进行结构优化和贝叶斯估计进行网络参数学习,并进行网络推理。采用专家建模进行TAN贝叶斯网络的构建,似然比进行独立性测试和极大似然估计进行参数学习,并进行诊断推理。结果实例分析显示,有向无环图、结构方程模型和贝叶斯网络均稳定探测到了结局变量的直接原因且各模型探测到的因果路径基本趋同。有向无环图定性推断了变量间因果关系的概念框架;结构方程模型通过标化路径系数定量推断了模型假定的观测变量与结局变量间的因果关系;贝叶斯网络通过条件概率表定量推断了直接原因组合下结局变量的发生概率,正向预测推理了由因到果的路径关系;TAN贝叶斯网络通过变量重要性评分反向诊断推理了由果到因的路径关系。结论有向无环图、结构方程模型、贝叶斯网络和TAN贝叶斯网络因果图模型在观察性研究因果推断中的侧重点和实际意义有所不同,探测到的因果路径亦有所不同,实际应用时应综合四种因果图模型结果进行因果关系的稳健推断。     

两样本孟德尔随机化中工具变量对因果估计的性别特异性影响——人体测量学性状与乳腺癌关系的实证研究

目的 研究两样本孟德尔随机化(mendelian randomization,MR)分析中性别特异工具变量对因果效应估计的影响.方法 利用全基因组关联数据以乳腺癌作为结局,以人体测量学性状(身体质量指数(BMI)、腰臀比(WHR)、腰围(WC)和臀围(HIP))作为暴露,采用两样本MR逆方差加权法估计因果效应;通过差异...     

工具变量模型在卫生政策效果评价中的应用分析

在卫生政策的效果评价中,研究的主要目的就是推断卫生政策与政策干预效果之间的因果关系。但是,卫生政策研究无法根据研究者的意愿将研究对象随机分组,一些具有较高因果关系解释力度的研究设计在实际应用中往往会受到限制,存在一些因素可能诱导我们对解释变量和效果变量之间的因果关系做出错误推断。由此产生的遗漏变量偏倚、选择性偏倚以及信息偏倚通常是通过产生所谓的"内生变量"问题而影响研究结果的。工具变量模型正是一个能够有效解决观察性研究中内生变量问题的统计方法。所以,本研究结合了卫生政策效果评价研究的特点,分析工具变量模型在该研究领域中的应用及前景,为解决卫生领域研究中内生变量问题提供一个新的思路。     

运动改善颈动脉内膜中层厚度的研究进展

采用两样本孟德尔随机化分析的方法,探究循环异亮氨酸、亮氨酸和缬氨酸水平与外周动脉粥样硬化风险的因果关联。

基于大规模的全基因组关联研究（GWAS）数据库,选用与循环异亮氨酸、亮氨酸和缬氨酸水平密切相关的单核苷酸多态性作为工具变量,对于工具变量≥3个单核苷酸多态性的暴露,采用逆方差加权法（IVW）和加权中位数法（WME）进行两样本孟德尔随机化分析,以评估与外周动脉粥样硬化风险的因果关联,并通过MR⁃Egger回归模型和MR⁃PRESSO法检测工具变量的基因多效性,采用留一法进行敏感性分析。

对于异亮氨酸,IVW模型显示工具变量间不存在异质性（P=0.715）,循环异亮氨酸水平升高与外周动脉粥样硬化风险增加之间存在显著的因果关系,且当循环异亮氨酸水平每增加1个标准差,外周动脉粥样硬化风险升高31%（OR=1.31,95%CI：1.07~1.61）。同样,在WME模型中得出OR（95%CI）为1.33（1.04~1.71）。MR⁃Egger回归模型和MR⁃PRESSO结果均显示工具变量不存在基因多效性（P>0.05）。留一法敏感性分析结果稳健;而Wald ratio模型显示循环亮氨酸、缬氨酸水平与外周动脉粥样硬化风险的因果关联无统计学意义,其OR（95%CI）分别为1.13（0.78~1.63）、1.11（0.82~1.50）。

循环异亮氨酸水平升高与外周动脉粥样硬化风险增加之间存在显著的因果关联;对于循环亮氨酸和缬氨酸水平与外周动脉粥样硬化的因果关联仍须后续研究进一步验证。

     

Interpreting findings from Mendelian randomization using the MR-Egger method

Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.     

Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in ‘compliers’, individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.     

Constrained instruments and their application to Mendelian randomization with pleiotropy

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer’s disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55–66) to disentangle causal relationships of several biomarkers with AD progression.     

Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable?

Sometimes instrumental variable methods are used to test whether a causal effect is null rather than to estimate the magnitude of a causal effect. However, when instrumental variable methods are applied to time-varying exposures, as in many Mendelian randomization studies, it is unclear what causal null hypothesis is tested. Here, we consider different versions of causal null hypotheses for time-varying exposures, show that the instrumental variable conditions alone are insufficient to test some of them, and describe additional assumptions that can be made to test a wider range of causal null hypotheses, including both sharp and average causal null hypotheses. Implications for interpretation and reporting of instrumental variable results are discussed.     

Extending the MR‐Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR‐Egger (Mendelian randomization‐Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR‐Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR‐Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high‐density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR‐Egger method will be useful to analyse high‐dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.     

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

ObjectiveMendelian randomization is a popular technique for assessing and estimating the causal effects of risk factors. If genetic variants which are instrumental variables for a risk factor are shown to be additionally associated with a disease outcome, then the risk factor is a cause of the disease. However, in many cases, the instrumental variable assumptions are not plausible, or are in doubt. In this paper, we provide a theoretical classification of scenarios in which a causal conclusion is justified or not justified, and discuss the interpretation of causal effect estimates.ResultsA list of guidelines based on the ‘Bradford Hill criteria’ for judging the plausibility of a causal finding from an applied Mendelian randomization study is provided. We also give a framework for performing and interpreting investigations performed in the style of Mendelian randomization, but where the choice of genetic variants is statistically, rather than biologically motivated. Such analyses should not be assigned the same evidential weight as a Mendelian randomization investigation.ConclusionWe discuss the role of such investigations (in the style of Mendelian randomization), and what they add to our understanding of potential causal mechanisms. If the genetic variants are selected solely according to statistical criteria, and the biological roles of genetic variants are not investigated, this may be little more than what can be learned from a well-designed classical observational study.     

Mendelian randomization in nutritional epidemiology

Nutritional epidemiology aims to identify dietary and lifestyle causes for human diseases. Causality inference in nutritional epidemiology is largely based on evidence from studies of observational design, and may be distorted by unmeasured or residual confounding and reverse causation. Mendelian randomization is a recently developed methodology that combines genetic and classical epidemiological analysis to infer causality for environmental exposures, based on the principle of Mendel's law of independent assortment. Mendelian randomization uses genetic variants as proxies for environmental exposures of interest. Associations derived from Mendelian randomization analysis are less likely to be affected by confounding and reverse causation. During the past 5 years, a body of studies examined the causal effects of diet/lifestyle factors and biomarkers on a variety of diseases. The Mendelian randomization approach also holds considerable promise in the study of intrauterine influences on offspring health outcomes. However, the application of Mendelian randomization in nutritional epidemiology has some limitations.     

Mendelian randomization: using genes as instruments for making causal inferences in epidemiology

Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.     

Genetic instrumental variable analysis: time to call mendelian randomization what it is. The example of alcohol and cardiovascular disease

In recent years, epidemiologists have increasingly sought to employ genetic data to identify ‘causal’ relationships between exposures of interest and various endpoints – an instrumental variable approach sometimes termed Mendelian randomization. However, this approach is subject to all of the limitations of instrumental variable analysis and to several limitations specific to its genetic underpinnings, including confounding, weak instrument bias, pleiotropy, adaptation, and failure of replication. Although the approach enjoys some utility in testing the etiological role of discrete biochemical pathways, like folate metabolism, examples like that of alcohol consumption and cardiovascular disease demonstrate that it must be treated with all of the circumspection that should accompany all forms of observational epidemiology. Going forward, we urge the elimination of randomization or causality in reports of its use and suggest that Mendelian randomization instead be termed exactly what it is – genetic instrumental variable analysis.

     

Alcances de la aleatorización mendeliana para el control de confusores no observables en epidemiología

The Mendelian randomization is an epidemiologic method proposed to control for spurious associations in observational studies. These associations are commonly caused by confusion derived from social, environmental, and behavioral factors, which can be difficult to measure. Mendelian randomization is based on the selection of genetic variants that are used as instrumental variables that influence exposure patterns or are associated with an intermediate phenotype of the disease. The present work aims to discuss how to select the appropriate genetic variants as instrumental variables and to present methodological tools to deal with the limitations of this epidemiological method. The use of instrumental variables for modifiable exposures has the potential to mitigate the effects of common limitations, such as confusion, when robust genetic variants are chosen as instrumental variables.