Chen Gang, Moraczewski Dustin, Taylor Paul A
Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, United States.
Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, United States.
Front Genet. 2025 Apr 2;16:1522729. doi: 10.3389/fgene.2025.1522729. eCollection 2025.
The conventional approach to estimating heritability in twin studies implicitly assumes either the absence of measurement error or that any measurement error is incorporated into the nonshared environment component. However, this assumption can be problematic when it does not hold or when measurement error cannot be reasonably classified as part of the nonshared environment. In this study, we demonstrate the need for improvement in the conventional structural equation modeling (SEM) used for estimating heritability when applied to trait data with measurement errors. The critical issue revolves around an assumption concerning measurement errors in twin studies. In cases where traits are measured using samples, data is aggregated during preprocessing, with only a centrality measure (e.g., mean) being used for modeling. Additionally, measurement errors resulting from sampling are assumed to be part of the nonshared environment and are thus overlooked in heritability estimation. Consequently, the presence of intra-individual variability remains concealed. Moreover, recommended sample sizes are typically based on the assumption of no measurement errors. We argue that measurement errors in the form of intra-individual variability are an intrinsic limitation of finite sampling and should not be considered as part of the nonshared environment. Previous studies have shown that the intra-individual variability of psychometric effects is significantly larger than the inter-individual counterpart. Here, to demonstrate the appropriateness and advantages of our hierarchical linear modeling approach in heritability estimation, we utilize simulations as well as a real dataset from the ABCD (Adolescent Brain Cognitive Development) study. Moreover, we showcase the following analytical insights for data containing non-negligible measurement errors: i) The conventional SEM may underestimate heritability. ii) A hierarchical model provides a more accurate assessment of heritability. iii) Large samples, exceeding 100 observations or thousands of twins, may be necessary to reduce imprecision. Our study highlights the impact of measurement error on heritability estimation and introduces a hierarchical model as a more accurate alternative. These findings have significant implications for understanding individual differences and improving the design and analysis of twin studies.
在双胞胎研究中，估计遗传力的传统方法隐含地假设要么不存在测量误差，要么任何测量误差都被纳入非共享环境成分中。然而，当这一假设不成立或测量误差不能合理地归类为非共享环境的一部分时，这个假设可能会出现问题。在本研究中，我们证明了在将用于估计遗传力的传统结构方程模型（SEM）应用于存在测量误差的性状数据时，需要进行改进。关键问题围绕着双胞胎研究中关于测量误差的一个假设。在使用样本测量性状的情况下，数据在预处理期间进行汇总，仅使用一个中心性度量（例如均值）进行建模。此外，抽样产生的测量误差被假定为非共享环境的一部分，因此在遗传力估计中被忽略。因此，个体内部变异性的存在仍然被掩盖。此外，推荐的样本量通常基于无测量误差的假设。我们认为，个体内部变异性形式的测量误差是有限抽样的固有局限性，不应被视为非共享环境的一部分。先前的研究表明，心理测量效应的个体内部变异性明显大于个体间的变异性。在此，为了证明我们的分层线性建模方法在遗传力估计中的适用性和优势，我们利用了模拟以及来自ABCD（青少年大脑认知发展）研究的真实数据集。此外，我们展示了对于包含不可忽略测量误差的数据的以下分析见解：i）传统的SEM可能会低估遗传力。ii）分层模型能提供更准确的遗传力评估。iii）可能需要超过100个观测值或数千对双胞胎的大样本，以减少不精确性。我们的研究强调了测量误差对遗传力估计的影响，并引入分层模型作为一种更准确的替代方法。这些发现对于理解个体差异以及改进双胞胎研究的设计和分析具有重要意义。
