Center for Biostatistics and Health Data Science, Virginia Tech, 1270 Hill Hollow Rd., Faber, Roanoke, VA, 22938, USA.
Behav Genet. 2021 Nov;51(6):654-664. doi: 10.1007/s10519-021-10060-0. Epub 2021 May 12.
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，个体得分是通过对生理传感器或经验采样研究的密集重复测量进行个体水平模型拟合得到的参数估计。对这种表型进行生物计量遗传分析通常采用两步序列：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。本研究表明，两步法估计遗传信号的准确性和统计功效存在缺陷，并提倡使用层次模型来克服这两个问题。模拟用于演示分层建模方法在准确性和统计功效方面的优势。心率波动模型应用于在独立试验中记录的双胞胎对的实验数据。数据分析结果揭示了心率的两个参数：振荡频率和阻尼比的中等但不相关的遗传力。通过将生物计量遗传分析与过程模型相结合，分层混合效应模型有可能帮助从个体内数据中发现和提取新的表型，并验证个体内过程的理论模型。
