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:...     

儿童肥胖与冠状动脉疾病的孟德尔随机化研究

目的 利用孟德尔随机化研究（MR）方法评估儿童肥胖与冠状动脉疾病（CAD）是否具有因果关联。方法 利用早期生长遗传学数据库和人体测量学特征遗传学研究数据库中2015年2～10岁儿童BMI的汇总数据,选取27个与儿童BMI相关的遗传变异作为工具变量。从对欧洲最大队列UK Biobank 2015年发布的CAD病例全基因组关联研究的Meta分析中,提取工具变量与CAD汇总水平的关联结果。采用MR-Egger方法进行工具变量的多效性检验,使用基于众数的方法进行MR分析检验儿童肥胖与CAD是否具有因果关联。结果 MR-Egger回归得到的截距项95% CI包含0（-0.008～0.018）,提示所选取的工具变量不具有多效性。儿童BMI每增加1个标准差,成年期患CAD的风险增加37%（OR=1.37,95% CI：1.09～1.72）。结论 儿童肥胖可能与其成年期患CAD的风险具有因果关联。     

两样本孟德尔随机化法分析抑郁症与间质性肺病关系的研究

目的 采用孟德尔随机化方法分析抑郁症与间质性肺病(ILD)之间的因果关系，为ILD的预防、治疗及预后评估提供新的思路。方法 利用大样本全基因关联研究汇总数据，选择与抑郁症密切相关的遗传位点作为工具变量，逆方差加权法为主，加权中位数法和MR-Egger回归为辅对数据进行两样本孟德尔随机化分析。以OR值评价抑郁症与ILD之间的因果关系，异质性检验、基因多效性检验和敏感性分析3种方法评估结果的稳定性和可靠性。结果 共纳入37个单核苷酸多态性(SNP)位点作为工具变量，逆方差加权法估计抑郁症患者患ILD的风险(OR)是健康人群的1.21倍(95%CI:1.075～1.361,P=0.002)。加权中位数法同样支持抑郁症和ILD之间存在因果效应(95%CI:1.118～1.564,P=0.001)。逆方差加权法和MR-Egger回归的异质性检验结果表明不存在异质性，MR-Egger回归截距项和MR-PRESSO方法检验表明结果受基因多效性影响的可能性较小，leave-one-out分析未发现非特异性SNP。结论 抑郁症与ILD之间可能存在正向因果关联。     

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;通过差异检验比较使用女性特异工具变量与性别合并工具变量的因果效应(OR值);进一步通过敏感性分析验证结果的稳健性。结果使用性别合并工具变量的MR结果表明BMI/WC与乳腺癌风险存在因果关联(OR值分别为0.85(P=0.003)和0.87(P=0.020));剔除性别差异的工具变量后,每组OR值与未剔除前基本一致,但WC与乳腺癌的因果关联不再显著(P=0.069);使用女性特异工具变量的MR结果与使用性别合并工具变量结果相比,每组OR值均呈下降趋势;其中BMI/HIP与乳腺癌的因果关联效应大小发生了明显改变(P0.05);例如BMI与乳腺癌因果关联的OR值由0.85下降至0.76。结论工具变量的性别异质会对MR的因果效应估计产生实质影响,使用性别合并的工具变量可能导致有偏的因果关联。     

体力活动与卵巢癌因果关联的孟德尔随机化研究

目的 采用两样本孟德尔随机化研究方法分析体力活动与卵巢癌发生风险的因果关联。方法 体力活动和卵巢癌的数据分别来自两项公开发表的最新最大的欧洲人群全基因组关联研究汇总数据。从体力活动汇总数据中筛选出16个高度相关的单核苷酸多态性位点作为工具变量。利用逆方差加权法、MR-Egger回归和加权中位数估计法评估体力活动与卵巢癌之间的因果效应。另外采用排除回文结构工具变量、排除多效性工具变量和“留一法”进行敏感性分析。结果 逆方差加权法发现遗传预测的体力活动与卵巢癌发生风险无关（OR=0.91, 95%CI:0.66～1.24）,MR-Egger回归（OR=0.61, 95%CI:0.13～2.87）和加权中位数估计法（OR=1.16, 95%CI:0.79～1.69）与逆方差加权法得到的结果一致。敏感性分析表明结果具有稳健性。结论 未有充分的证据支持体力活动与卵巢癌发生风险之间存在因果关联。     

孟德尔随机化研究应用于因果推断的影响因素及其结果解读面临的挑战

孟德尔随机化（Mendelian randomization,MR）研究使用遗传变异作为工具变量,推断暴露因素与结局之间的因果关系,能够有效克服混杂和反向因果问题所导致的偏倚。然而,MR研究中的工具变量须满足关联性、独立性和排他性3个核心假设。即使核心假设成立,MR研究在因果推断中的应用还受到其他局限性的影响。此外,MR研究结果的解读需要基于综合证据。本文将围绕MR研究应用于因果推断的影响因素和研究结果的解读进行综述,以期为MR研究结果应用提供指导。     

血清生长分化因子15水平与慢性淋巴细胞白血病发生风险的关联研究

目的 采用两样本孟德尔随机化研究方法探讨血清生长分化因子15(GDF15)水平与慢性淋巴细胞白血病(CLL)发生之间的关联。方法 基于欧洲人群血清GDF15和CLL的全基因组关联研究公开数据库,筛选与血清GDF15水平相关的遗传变异位点作为工具变量,采用逆方差加权法评估遗传学预测的血清GDF15浓度与CLL发生的关联,采用最大似然比法进行敏感性分析,采用MR-Egger回归探讨工具变量潜在多效性。结果 研究共纳入3个单核苷酸多态位点作为工具变量,逆方差加权法结果显示,血清GDF15水平与CLL发生风险之间存在负相关,GDF15浓度每升高一个标准差(SD),CLL发生风险降低33%(95%置信区间:2%～54%)(P=0.039)。敏感性分析得到了一致的结果。此外,MR-Egger回归未发现存在多效性。结论 本研究结果提示,在欧洲人群中,血清GDF15水平与CLL发生之间可能存在负相关,仍需大样本人群研究及体内外实验进一步阐明GDF15在CLL发生发展中的作用及其潜在生物学机制。     

睡眠与冠心病的孟德尔随机化因果关联研究

目的 对睡眠与冠心病之间的因果关联进行研究与探讨。方法 研究包含5个睡眠相关性状[睡眠时间(连续变量)、长睡眠时间(二分类变量)、短睡眠时间(二分类变量)、早睡早起睡眠习惯、经常性失眠]的全基因组关联分析(GWAS)数据集与1个冠心病GWAS数据集。应用非独立工具变量异质性检测方法评估并排除无效工具变量,利用基于广义的汇总数据孟德尔随机化方法估计睡眠与冠心病之间的因果效应, 应用Bonferroni法校正检验的显著性水平。结果 睡眠时间与冠心病具有显著的因果关联 (OR=0.755,95%CI:0.658~0.867,P=6.68E-05)。短睡眠时间(OR=4.251,95%CI:2.396~7.541,P=7.51E-07)以及经常性失眠 (OR=1.814,95%CI:1.346~2.446,P=9.25E-05)均会显著增加冠心病患病风险。而长睡眠时间以及早睡早起睡眠习惯与冠心病的因果关联不显著。结论 睡眠时间以及经常性失眠与冠心病存在因果关联。     

lncRNAs与肺腺癌因果关联的两样本孟德尔随机化研究

目的 利用两样本孟德尔随机化方法探究长链非编码RNAs(long-chain non-coding RNAs, lncRNAs)与肺腺癌发病风险之间的因果关联。方法 采用肺腺癌全基因组关联分析数据和eQTL Gene联盟的cis-eQTL数据集,将与肺腺癌密切相关的单核苷酸多态性(single nucleotide polymorphisms, SNPs)作为工具变量,运用逆方差加权法、MR-Egger回归模型、加权中位数分析法、简单模式法和加权模式法5种两样本孟德尔随机化(Mendelian randomization, MR)模型来评估lncRNAs与肺腺癌之间的因果效应,并进行异质性检验、基因多效性检验和敏感性分析来评估结果的可靠性和稳定性。结果 PVT1、LINC00824、Z94721.1与肺腺癌的关联效应值差异有统计学意义(均P<0.05),PVT1(与肺腺癌的效应值OR=0.79,95%CI:0.71～0.89)、LINC00824(与肺腺癌的效应值OR=0.59,95%CI:0.42～0.83)降低了原发性支气管肺癌(肺癌)的发病风险;而Z94721.1(与肺腺癌...     

因果思维在效应估计若干问题中的应用

流行病学是研究同质群体中“异乎寻常”的现象及其发生原因的一门科学。本文以因果思维结合其图形工具——有向无环图,围绕效应估计的若干问题——效应与关联的关系、变量及其测量版间的时序关系、动态人群自然图景、易感人群的形成、研究人群的选择、协变量和病例类型对效应估计的影响等方面,考察这种思维如何帮助我们重新认识流行病学理论、方法及应用。应加强对因果思维的认识。     

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.     

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.     

Assessing the robustness of sisVIVE in a Mendelian randomization study to estimate the causal effect of body mass index on income using multiple SNPs from understanding society

The “some invalid, some valid instrumental variable estimator” (sisVIVE) is a lasso-based method for instrumental variables (IVs) regression of outcome on an exposure. In principle, sisVIVE is robust to some of the IVs in the analysis being invalid, in the sense of being related to the outcome variable through pathways not mediated by the exposure. In this paper, we consider the application of sisVIVE to a Mendelian randomization study in which multiple genetic variants are used as IVs to estimate the causal effect of body mass index on personal income in the presence of unobserved confounding. In addition to analyzing data from the large-scale longitudinal household survey Understanding Society, we conduct a simulation study to (a) assess the performance of sisVIVE in relation to that of competing robust methods like “MR-Egger” and “MR-Median” and (b) identify scenarios under which its absolute performance is poor. We find that sisVIVE outperforms alternative robust methods, in terms of mean-square error, across a wide range of scenarios, but that its performance is poor in absolute terms when the presence of indirect pleiotropy leads to failure of the “InSIDE” condition, which is not explicitly required for identification. We argue that this is because the consistency criterion for sisVIVE does not identify the true causal effect when InSIDE fails.     

Causal Genetic Inference Using Haplotypes as Instrumental Variables

In genomic studies with both genotypes and gene or protein expression profile available, causal effects of gene or protein on clinical outcomes can be inferred through using genetic variants as instrumental variables (IVs). The goal of introducing IV is to remove the effects of unobserved factors that may confound the relationship between the biomarkers and the outcome. A valid inference under the IV framework requires pairwise associations and pathway exclusivity. Among these assumptions, the IV expression association needs to be strong for the casual effect estimates to be unbiased. However, a small number of single nucleotide polymorphisms (SNPs) often provide limited explanation of the variability in the gene or protein expression and can only serve as weak IVs. In this study, we propose to replace SNPs with haplotypes as IVs to increase the variant‐expression association and thus improve the casual effect inference of the expression. In the classical two‐stage procedure, we developed a haplotype regression model combined with a model selection procedure to identify optimal instruments. The performance of the new method was evaluated through simulations and compared with the IV approaches using observed multiple SNPs. Our results showed the gain of power to detect a causal effect of gene or protein on the outcome using haplotypes compared with using only observed SNPs, under either complete or missing genotype scenarios. We applied our proposed method to a study of the effect of interleukin‐1 beta (IL‐1β) protein expression on the 90‐day survival following sepsis and found that overly expressed IL‐1β is likely to increase mortality.     

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerable work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent because of the log‐linear approximation of the logistic function. Optimality of such estimators relative to the well‐known two‐stage least squares estimator and the double‐logistic structural mean model is further discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

A comparison of robust Mendelian randomization methods using summary data

The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.     

Genetic markers as instrumental variables

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social scientists. Although IV is commonly used in economics, the appropriate conditions for the use of genetic variants as instruments have not been well defined. The increasing availability of biomedical data, however, makes understanding of these conditions crucial to the successful use of genotypes as instruments. We combine the econometric IV literature with that from genetic epidemiology, and discuss the biological conditions and IV assumptions within the statistical potential outcomes framework. We review this in the context of two illustrative applications.     

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.     

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.