Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980033, Richmond, VA, 23298-0033, USA.
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Behav Genet. 2021 Jan;51(1):82-96. doi: 10.1007/s10519-020-10026-8. Epub 2020 Nov 4.
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.
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.
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.
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.
通过估计模型平均参数，利用多模型推断来探索和应用于测试潜在遗传、环境和直接因果因素对来自经典双胞胎设计的数据中两个变量之间的协变的相对贡献。
行为遗传学旨在了解表型变异的原因以及两个或更多表型之间的协变原因。两个变量可能会因为遗传、共同环境或独特环境因素导致两个变量都发生变化而相关。两个变量也可能会因为一个或两个变量直接导致另一个变量的变化而相关。此外，协变可能来自这些来源的任意组合，从而导致 25 种不同的确定结构方程模型。OpenMx 被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。多模型推断和模型平均被用于总结协变的关键来源，并估计这些协变原因的大小。还讨论了这些模型扩展到通过性别检验异质性的方法。
我们通过将一套全面的双变量模型拟合到弗吉尼亚双胞胎精神疾病和物质使用障碍研究中的双胞胎数据，说明了多模型推断的应用。对体重指数和吸烟数据的分析表明，有足够的能力拒绝不同的模型，并估计五个潜在协变来源中的每一个的贡献，而无需选择最佳拟合模型。模型在样本量、变量类型（连续与二项或有序测量）以及方差和协方差源的效应大小方面存在差异。
我们在测试特质之间的协变原因的遗传、环境和因果解释模型的背景下，向行为遗传学界引入了多模型推断和模型平均方法。
