UMMS Affiliation

Department of Medicine

Publication Date

6-12-2006

Document Type

Article

Subjects

Computer Simulation; *Genetic Linkage; Humans; Longitudinal Studies; *Models, Statistical; Pedigree; *Quantitative Trait Loci; Research Design; Sample Size

Disciplines

Genetics and Genomics | Life Sciences | Medicine and Health Sciences

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.

CONCLUSION: 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.

Rights and Permissions

Citation: BMC Genet. 2006 Jun 12;7:37. Link to article on publisher's site

DOI of Published Version

10.1186/1471-2156-7-37

Comments

© 2006 Zhang and Zhong; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

BMC genetics

PubMed ID

16768806

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