Objective: To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. Methods: Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. Results: We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. Conclusions: We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.

目的：探索并应用多模型推理，通过估计模型平均参数，用经典双胞胎设计的数据来测试潜在遗传、环境和直接因果因素对两个变量之间协变的相对贡献。方法：行为遗传学涉及了解表型变异的原因以及两个或多个表型之间共变的原因。由于遗传、共同的环境或独特的环境因素导致两个变量的变化，两个变量可能相关。两个变量也可能相关，因为一个或两个变量直接导致另一个变量的变化。此外，协变可能由这些来源的任意组合产生，从而产生 25 个不同的已识别结构方程模型。 OpenMx 用于拟合所有这些模型，以解释双胞胎中收集的两个变量之间的协变。使用多模型推理和模型平均来总结协方差的关键来源，并估计这些协方差原因的大小。讨论了这些模型的扩展以测试性别异质性。结果：我们通过将一组全面的双变量模型拟合到来自弗吉尼亚双胞胎精神和药物使用障碍研究的双胞胎数据来说明多模型推理的应用。对体重指数和烟草消费数据的分析显示出足够的能力来拒绝不同的模型，并估计五个潜在协变来源中每一个的贡献，而不管选择最佳拟合模型。模型之间关于样本大小、变量类型（连续与二元或序数测量）以及方差和协方差来源的效应大小的区分。 结论：我们在遗传、环境和因果解释方面测试性状之间共变原因的模型的背景下，向行为遗传学界引入了多模型推理和模型平均方法。