- THIS ARTICLE
- Full Text (Rapid PDF)
-
All Versions of this Article:
genetics.107.072843v1
176/2/1187 most recent - Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Wu, J.
- Articles by Wu, R.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Wu, J.
- Articles by Wu, R.
doi:10.1534/genetics.107.072843
A more recent version of this article appeared on June 1, 2007.
REGULAR RESEARCH PAPERS |
Genetic Determination of Developmental Instability: Design, Model and Algorithm
Jiasheng Wu 1, Bo Zhang 2, Yuehua Cui 1, Wei Zhao 1, Minren Huang 2, Yanru Zeng 3 and Rongling Wu 1*
1 University of Florida
2 Nanjing Forestry University
3 Zhejiang Forestry University
* To whom correspondence should be addressed. E-mail: rwu{at}stat.ufl.edu.
Submitted on March 2, 2007
Revised on March 27, 2007
Accepted on 27 March 2007
Developmental instability or noise, defined as the phenotypic imprecision of an organism in the face of internal or external stochastic disturbances, has been thought to play an important role in shaping evolutionary processes and patterns. The genetic studies of developmental instability have been based on fluctuating asymmetry (FA) that measures random differences between the left and right sides of bilateral traits. In this article, we frame an experimental design characterized by a spatial autocorrelation structure for determining the genetic control of developmental instability for those traits that cannot be bilaterally measured. This design allows the residual environmental variance of a quantitative trait to be dissolved into two components due to permanent and random environmental factors. The degree of developmental instability is quantified by the relative proportion of the random residual variance to the total residual variance. We formulate a mixture model to estimate and test the genetic effects of quantitative trait loci (QTL) on the developmental instability of the trait. The genetic parameters including the QTL position, the QTL effects and spatial autocorrelations are estimated by implementing the EM algorithm within the mixture model framework. Simulation studies were performed to investigate the statistical behavior of the model. A live example for poplar trees was used to map the QTL that control root length growth and its developmental instability from cuttings in water culture.
Key Words: Complex Traits, Developmental Instability, QTL Mapping, Spatial autocorrelation
