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

doi:10.1186/1471-2156-4-S1-S50

BMC Genetics
Volume 4
Suppl 1

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Rampersaud E
Allen A
Li YJ
Shao Y
Bass M
Haynes C
Ashley-Koch A
Martin ER
Schmidt S
Hauser ER

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Rampersaud E
Allen A
Li YJ
Shao Y
Bass M
Haynes C
Ashley-Koch A
Martin ER
Schmidt S
Hauser ER
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This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors .

Proceedings

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

Evadnie Rampersaud¹ , Andrew Allen² , Yi-Ju Li¹ , Yujun Shao¹ , Meredyth Bass¹ , Carol Haynes¹ , Allison Ashley-Koch¹^,2 , Eden R Martin¹^,2 , Silke Schmidt¹ and Elizabeth R Hauser¹^,2

¹Section of Medical Genetics, Department of Medicine, Center for Human Genetics, Duke University Medical Center, Durham, North Carolina, USA

²Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA

author email corresponding author email

BMC Genetics 2003, 4(Suppl 1):S50doi:10.1186/1471-2156-4-S1-S50


Published:	31 December 2003

Abstract

Background

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.

Results

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.

Conclusions

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.