Adjusting for covariates on a slippery slope: linkage analysis of change over time
2003

Linkage Analysis of Blood Pressure Changes Over Time

Sample size: 2860 publication Evidence: moderate

Author Information

Author(s): Evadnie Rampersaud, Andrew Allen, Yi-Ju Li, Yujun Shao, Meredyth Bass, Carol Haynes, Allison Ashley-Koch, Eden R Martin, Silke Schmidt, Elizabeth R Hauser

Primary Institution: Duke University Medical Center

Hypothesis

Can different methods for genetic linkage analysis improve the detection of genes related to hypertension and blood pressure changes over time?

Conclusion

Incorporating covariates and longitudinal data can enhance gene localization for complex traits, but the best analysis method is not always clear.

Supporting Evidence

  • Four of the five baseline genes were not detected by any method using conventional criteria.
  • Slope gene s10 was detected by the ASP analysis.
  • OSA detected baseline gene b35 on chromosome 13 when using the slope in blood pressure.
  • The analysis of null chromosomes did not reveal significant increases in type I error.

Takeaway

The study looked at how to find genes that affect blood pressure over time and found that using different methods can help, but there's no one best way to do it.

Methodology

The study used genetic analysis programs to analyze simulated data from families, focusing on blood pressure changes and covariate adjustments.

Potential Biases

The study acknowledges potential biases in gene detection due to the complexity of the model and the methods used.

Limitations

The analysis was limited to one replicate of simulated data, and the optimal methods for covariate adjustment remain unclear.

Participant Demographics

2860 genotyped individuals from 330 families.

Statistical Information

P-Value

p<0.05

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1186/1471-2156-4-S1-S50

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