Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.

许多行为遗传学模型遵循相同的一般结构。我们描述了这种一般结构，并分析得出了识别它的简单标准。特别是，我们发现只要定义分量的相关矩阵是线性独立的（即，没有混淆），就可以唯一地估计方差分量。因此，我们强调确定在给定一组遗传和环境关系的情况下可以识别哪些方差分量，而不是估计程序。我们用几个知名模型验证了识别标准，并进一步将它们应用于几个不太常见的模型。第一个模型将育儿环境与大家庭环境区分开来。第二个模型在分开和一起饲养的双胞胎中添加了一个基因与共同环境的相互作用项。第三个模型在假设的功能磁共振成像研究中将测量的基因组相关性与扫描仪位置变化分开。计算简单的分析识别标准使研究人员能够快速解决模型识别问题并定义新的方差分量，从而促进新研究问题的发展。