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

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We analyzed the Genetic Analysis Workshop 13 (GAW13) simulated data to contrast and compare different methods for the genetic linkage analysis of hypertension and change in blood pressure over time. We also examined methods for incorporating covariates into the linkage analysis. We used methods for quantitative trait loci (QTL) linkage analysis with and without covariates and affected sib-pair (ASP) analysis of hypertension followed by ordered subset analysis (OSA), using variables associated with change in blood pressure over time. Four of the five baseline genes and one of the three slope genes were not detected by any method using conventional criteria. OSA detected baseline gene b35 on chromosome 13 when using the slope in blood pressure to adjust for change over time. Slope gene s10 was detected by the ASP analysis and slope gene s11 was detected by QTL linkage analysis as well as by OSA analysis. Analysis of null chromosomes, i.e., chromosomes without genes, did not reveal significant increases in type I error. However, there were a number of genes indirectly related to blood pressure detected by a variety of methods. We noted that there is no obvious first choice of analysis software for analyzing a complicated model, such as the one underlying the GAW13 simulated data. Inclusion of covariates and longitudinal data can improve localization of genes for complex traits but it is not always clear how best to do this. It remains a worthwhile task to apply several different approaches since one method is not always the best.

Original languageEnglish (US)
JournalBMC genetics
Volume4 Suppl 1
StatePublished - 2003

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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