Lyu Xuanyu, Garrison S Mason
Department of Psychology, Wake Forest University, Winston Salem, North Carolina, USA.
Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, Colorado, USA.
Twin Res Hum Genet. 2023 Oct 6:1-12. doi: 10.1017/thg.2023.40.
The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher's (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (H) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger H and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.
本研究探讨了遗传相关性差异（ΔH）和样本量对非经典ACE模型性能的影响，重点关注同性和异性双胞胎群体。ACE模型是一种统计模型，它假定加性遗传因素（A）、共同环境因素（C）以及特定（或非共享）环境因素加测量误差（E）可解释表型中的个体差异。通过扩展Visscher（2004）的最小二乘范式并进行模拟，我们阐述了同性双胞胎的遗传相关性（H）如何影响加性遗传估计（A）的统计功效、基于AIC的模型性能以及负估计的频率。我们发现，较大的H和增加的样本量与检测加性遗传成分的能力增强、模型性能改善以及负估计的减少呈正相关。我们还发现，将性别限制效应的共同环境相关性固定为0.95的常见解决方案在大多数情况下会导致模型性能略有下降。此外，负估计是可能出现的，并不总是表明模型失败，相反，它们有时表明功效较低或模型设定错误。使用ΔH小于0.5的亲属对的研究人员应仔细考虑性能影响并进行全面的功效分析。我们的研究结果为那些处理非双胞胎亲属对或无法确定合子性的情况的研究人员提供了有价值的见解和实用指南，以及未来研究的方向。
