For many phenotypes, individual scores are obtained as the parameter estimates of person-level models fit to intensive repeated measures from physiological sensors or experience sampling studies. Biometrical genetic analysis of such phenotypes is often done in a 2-step sequence: first the phenotypic parameters are estimated for each individual, then classical twin modeling is used to partition their variance. This study demonstrates deficiencies in accuracy and statistical power of the two-step approach to estimate genetic signals and advocates for the use of hierarchical models to overcome both problems. Simulations are used to demonstrate the benefits to accuracy and statistical power from a hierarchical modeling approach. A model of heart rate fluctuations was applied to experimental data from twin pairs recorded in independent trials. Results of the data application reveal moderate but uncorrelated heritabilities for two parameters of heart rate: oscillation frequency and damping ratio. By merging biometrical genetic analysis with process models, hierarchical mixed-effects modeling has potential to assist with discovery and extraction of novel phenotypes from within-person data and to validate theoretical models of within-person processes.

对于许多表型，个人分数是作为个人级别模型的参数估计值来获得的，该估计值适合来自生理传感器或经验抽样研究的密集重复测量。此类表型的生物特征遗传分析通常按两步顺序进行：首先估计每个个体的表型参数，然后使用经典的双胞胎模型来划分它们的方差。这项研究证明了估计遗传信号的两步方法在准确性和统计能力方面的缺陷，并提倡使用分层模型来克服这两个问题。仿真用于展示分层建模方法在准确性和统计能力方面的优势。将心率波动模型应用于独立试验中记录的双胞胎的实验数据。数据应用的结果揭示了心率的两个参数的中等但不相关的遗传性：振荡频率和阻尼比。通过将生物特征遗传分析与过程模型相结合，分层混合效应建模有可能有助于从个人数据中发现和提取新的表型，并验证个人过程的理论模型。