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Bioinformatics Advance Access published online on March 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti382
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received October 6, 2004
Revised February 23, 2005
Accepted March 7, 2005

Article

A nonstationary model for functional mapping of complex traits

Wei Zhao 1, Ying Q. Chen 1, George Casella 1, James M. Cheverud 2, and Rongling Wu 1*

1 Department of Statistics, University of Florida, Gainesville, FL 32611
2 Department of Anatomy and Neurobiology, Washington University Medical School, St. Louis, MO 631110

* To whom correspondence should be addressed.
Rongling Wu, E-mail: rwu{at}stat.ufl.edu


  Abstract

Summary: Understanding the genetic control of growth is fundamental to agricultural, evolutionary and biomedical genetic research. In this article, we present a statistical model for mapping quantitative trait loci (QTL) that are responsible for genetic differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, implemented with the EM algorithm. We incorporate mathematical aspects of growth processes to model the mean vector and structured antedependence (SAD) models to approximate time-dependent covariance matrices for longitudinal traits. Our model has been employed to map QTL that affect body mass growth trajectories in both male and female mice of an F2 population derived from the Large and Small mouse strains. The results from this model are compared with those from the autoregressive (AR)-based functional mapping approach. Based on results from computer simulation studies, we suggest that these two models are alternative to one another and should be used simultaneously for the same dataset.


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