Genetic mapping of developmental instability: design, model and algorithm.
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 the 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.
Wu
Jiasheng
J
College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China.
Zhang
Bo
B
Cui
Yuehua
Y
Zhao
Wei
W
Xu
Li'an
L
Huang
Minren
M
Zeng
Yanru
Y
Zhu
Jun
J
Wu
Rongling
R
D000465:Algorithms; D002874:Chromosome Mapping; D003433:Crosses, Genetic; D042822:Genomic Instability; D008957:Models, Genetic; D010944:Plants / Q000235:genetics; D040641:Quantitative Trait Loci; D013269:Stochastic Processes