Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways.
Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways.
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DOI：
10.1093/ije/dyu176
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发表时间：
2015-04
影响因子：
7.7
通讯作者：
EPIC-InterAct Consortium
中科院分区：
文献类型：
--
作者：
Burgess S;Daniel RM;Butterworth AS;Thompson SG;EPIC-InterAct Consortium
背景资料：孟德尔随机化使用遗传变异，假设是特定暴露的工具变量，以估计该暴露对结果的因果影响。如果工具变量的标准得到满足，得到的估计是一致的，即使在存在不可测量的混杂和反向因果关系。研究方法：我们扩展了孟德尔随机化范式，以研究更复杂的网络变量之间的关系，特别是在一些接触的结果可能会通过一个中间变量（调解人）的影响。如果暴露和中介的工具变量可用，则可以估计暴露对结果的直接和间接影响，例如使用基于回归的方法或结构方程模型。也可以评估暴露和可能的介质之间的作用方向。方法说明了在一个应用的例子，考虑到身体质量指数，C-反应蛋白和尿酸之间的因果关系。结果如下：这些估计是一致的，在存在不可测量的混杂，如果除了工具变量的假设，无论是暴露的中介和中介的结果是均匀的个人和线性没有相互作用的影响。然而，一项模拟研究表明，即使这些效应存在相当大的异质性，也不会导致估计值出现偏差。结论：这些方法可用于估计中介环境中的直接和间接因果效应，并有可能用于调查多种相互关联的暴露和疾病结果之间更复杂的网络。
Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes.
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影响因子：
8.2
作者：
Langenberg, C.;Sharp, S.;Forouhi, N. G.;Franks, P. W.;Schulze, M. B.;Kerrison, N.;Ekelund, U.;Barroso, I.;Panico, S.;Tormo, M. J.;Spranger, J.;Griffin, S.;van der Schouw, Y. T.;Amiano, P.;Ardanaz, E.;Arriola, L.;Balkau, B.;Barricarte, A.;Beulens, J. W. J.;Boeing, H.;Bueno-de-Mesquita, H. B.;Buijsse, B.;Chirlaque Lopez, M. D.;Clavel-Chapelon, F.;Crowe, F. L.;de Lauzon-Guillan, B.;Deloukas, P.;Dorronsoro, M.;Drogan, D.;Froguel, P.;Gonzalez, C.;Grioni, S.;Groop, L.;Groves, C.;Hainaut, P.;Halkjaer, J.;Hallmans, G.;Hansen, T.;Huerta Castano, J. M.;Kaaks, R.;Key, T. J.;Khaw, K. T.;Koulman, A.;Mattiello, A.;Navarro, C.;Nilsson, P.;Norat, T.;Overvad, K.;Palla, L.;Palli, D.;Pedersen, O.;Peeters, P. H.;Quiros, J. R.;Ramachandran, A.;Rodriguez-Suarez, L.;Rolandsson, O.;Romaguera, D.;Romieu, I.;Sacerdote, C.;Sanchez, M. J.;Sandbaek, A.;Slimani, N.;Sluijs, I.;Spijkerman, A. M. W.;Teucher, B.;Tjonneland, A.;Tumino, R.;van der A, D. L.;Verschuren, W. M. M.;Tuomilehto, J.;Feskens, E.;McCarthy, M.;Riboli, E.;Wareham, N. J.
通讯作者：
Wareham, N. J.
影响因子：
3.7
作者：
Angrist, JD;Imbens, GW;Rubin, DB
通讯作者：
Rubin, DB
影响因子：
7.7
作者：
Burgess, Stephen;Thompson, Simon G.
通讯作者：
Thompson, Simon G.
影响因子：
2.3
作者：
Emsley, Richard;Dunn, Graham;White, Ian R.
通讯作者：
White, Ian R.
DOI：
10.1111/j.1467-985x.2012.01032.x
发表时间：
2013-01-01
影响因子：
2
作者：
Imai, Kosuke;Tingley, Dustin;Yamamoto, Teppei
通讯作者：
Yamamoto, Teppei