Linkage Analysis of Longitudinal Data
Author Information
Author(s): Suh Young Ju, Park Taesung, Cheong Soo Yeon
Primary Institution: Seoul National University
Hypothesis
Can a mixed model improve the detection of linkage in longitudinal data for systolic blood pressure?
Conclusion
The proposed mixed models are effective in detecting linkage for longitudinal data related to systolic blood pressure.
Supporting Evidence
- The proposed model allows for several random effects.
- Random effects models performed slightly better than the independence model.
- The study analyzed data from two cohorts collected over 30 years.
Takeaway
The researchers created a new way to analyze family data over time to find out how genes affect blood pressure.
Methodology
The study used mixed models to analyze longitudinal data from two cohorts of sib pairs.
Potential Biases
Potential bias due to the use of simulated data and the assumptions of the models.
Limitations
The study used simulated data, which may not fully represent real-world scenarios.
Participant Demographics
Data from 330 pedigrees containing 4692 individuals.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website