The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)—common environmental (C) covariance (σ_AC) identified. We study the statistical power to reject σ_AC = 0 in the ACE model and present the results of simulations.

双胞胎模型中基因型和环境变量不相关的假设主要是为了确保参数识别，而不是因为研究人员必然认为这些变量不相关。尽管这种相关性的偏差效应已被很好地理解，但在孪生模型中估计这些参数的方法将是有用的。在这里，我们通过向（单变量）双胞胎模型添加多基因分数来探索放松这一假设的可能性。我们证明了这种扩展使加性遗传 (A) - 共同环境 (C) 协方差 (σ _AC ) 被识别。我们研究了在 ACE 模型中拒绝 σ _AC  = 0 的统计功效，并展示了模拟结果。