Comparison of Regression Analyses for Blood Pressure Linkage Studies
Author Information
Author(s): Mirea Lucia, Shelley B Bull, James Stafford
Primary Institution: Samuel Lunenfeld Research Institute, Mount Sinai Hospital
Hypothesis
To compare different strategies for linkage analyses of longitudinal quantitative trait measures.
Conclusion
The study found that the 'revisited' Haseman-Elston regression model is effective for detecting genetic linkage related to systolic blood pressure.
Supporting Evidence
- Linkage evidence was suggestive at markers neighboring SBP genes Gb35, Gs10, and Gs12.
- Stronger evidence for linkage was observed using the LastSBP and MeanSBP measurements.
- Linkage to baseline genes was best detected using the first SBP measurement.
Takeaway
Researchers looked at different ways to analyze blood pressure data over time to find out if genes affect it. They found some genes that seem to influence blood pressure.
Methodology
The study applied the 'revisited' Haseman-Elston regression model to analyze systolic blood pressure data from sibling pairs.
Limitations
The analysis excluded individuals treated for hypertension, which may have reduced the power to detect linkage.
Participant Demographics
Sibling pairs from the Genetic Analysis Workshop 13 simulated data.
Statistical Information
P-Value
p < 0.0001
Statistical Significance
p < 0.001
Digital Object Identifier (DOI)
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