help button home button Genetics Appl Env Microbiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Genetics, Vol. 168, 1751-1762, November 2004, Copyright © 2004
doi:10.1534/genetics.104.031484

This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Zhao, W.
Right arrow Articles by Wu, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhao, W.
Right arrow Articles by Wu, R.

A Unified Statistical Model for Functional Mapping of Environment-Dependent Genetic Expression and Genotype x Environment Interactions for Ontogenetic Development

Wei Zhao*, Jun Zhu{dagger}, Maria Gallo-Meagher{ddagger} and Rongling Wu*,1

* Department of Statistics, University of Florida, Gainesville, Florida 32611
{ddagger} Agronomy Department, University of Florida, Gainesville, Florida 32611
{dagger} Department of Agronomy, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China

1 Corresponding author: Department of Statistics, 533 McCarty Hall C, University of Florida, Gainesville, FL 32611.
E-mail: rwu{at}stat.ufl.edu

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.




This article has been cited by other articles:


Home page
 GeneticsHome page
R. Yang, H. Gao, X. Wang, J. Zhang, Z.-B. Zeng, and R. Wu
A Semiparametric Approach for Composite Functional Mapping of Dynamic Quantitative Traits
Genetics, November 1, 2007; 177(3): 1859 - 1870.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2004 by the Genetics Society of America.