title: Linkage analysis of longitudinal data and design consideration.
abstract: BACKGROUND
  Statistical methods have been proposed recently to analyze longitudinal data in genetic studies. So far, little attention has been paid to examine the relationship among key factors in genetic longitudinal studies including power, the number of families or sibships, and the number of repeated measures per individual subjects.
  
  
  RESULTS
  We proposed a variance component model that extends classic variance component models for a single quantitative trait to mapping longitudinal traits. Our model includes covariate effects and allows genetic effects to vary over time. Using our proposed model, we examined the power, pedigree structures, and sample size through simulation experiments.
  
  
  CONCLUSIONS
  Our simulation results provide useful insights into the study design for genetic, longitudinal studies. For example, collecting a small number of large sibships is much more powerful than collecting a large number of small sibships or increasing the number of repeated measures, when the total number of measurements is comparable.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Zhong
mesh_terms: D003198:Computer Simulation; D008040:Genetic Linkage; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010375:Pedigree; D040641:Quantitative Trait Loci; D012107:Research Design; D018401:Sample Size