Linkage analysis of longitudinal data and design consideration
2006
Analyzing Genetic Longitudinal Data
Sample size: 100
publication
Evidence: moderate
Author Information
Author(s): Heping Zhang, Xiaoyun Zhong
Primary Institution: Yale University School of Medicine
Hypothesis
How do key factors like power, family size, and repeated measures affect genetic longitudinal studies?
Conclusion
Collecting a small number of large families is more effective than collecting many small families or increasing repeated measures.
Supporting Evidence
- The proposed model allows genetic effects to vary over time.
- Simulation results provide insights into study design for genetic longitudinal studies.
- Power increases with larger sibships and more repeated measures.
Takeaway
It's better to study a few big families than many small ones when looking at genetics over time.
Methodology
A variance component model was proposed and tested through simulation experiments.
Limitations
The power is low with the sample sizes considered when the significance level is set at 0.0001.
Participant Demographics
Nuclear families with 5 measurements taken at different times.
Digital Object Identifier (DOI)
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