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Hierarchical Biometrical Genetic Analysis of Longitudinal Dynamics

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Abstract

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.

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Notes

  1. ACE refers to a twin model with additive genetic (a), common environmental (c), and unique environmental (e) variance components but is used colloquially to symbolize twin modeling in general, though other specifications such as allelic dominance (d) are common.

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Acknowledgements

Special thanks go to Roxann Roberson-Nay (Virginia Commonwealth University) for providing the heart rate data and Daniel Lee (Columbia University) for technical troubleshooting, guidance with Stan model code. Additional thanks go to Michael Neale (Virginia Commonwealth University) and Steven Boker (University of Virginia) for prompting discussion and work on this topic.

Funding

Research reported in this publication/presentation/work was supported in part by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number KL2TR003016. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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  1. Center for Biostatistics and Health Data Science, Virginia Tech, 1270 Hill Hollow Rd., Faber, Roanoke, VA, 22938, USA

    Kevin L. McKee

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  1. Kevin L. McKee
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Correspondence to Kevin L. McKee.

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Kevin L. McKee declares that he has no conflict of interest.

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This retrospective study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Human Investigation Committee (IRB) of Virginia Commonwealth University approved this study.

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Informed consent was obtained from all individual participants included in the study.

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McKee, K.L. Hierarchical Biometrical Genetic Analysis of Longitudinal Dynamics. Behav Genet 51, 654–664 (2021). https://doi.org/10.1007/s10519-021-10060-0

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