Genetic mapping of developmental instability: Design, model and algorithm

Jiasheng Wu, Bo Zhang, Yuehua Cui, Wei Zhao, Li'an Xu, Minren Huang, Yanru Zeng, Jun Zhu, Rongling Wu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1187-1196
Number of pages10
Issue number2
StatePublished - Jun 2007

All Science Journal Classification (ASJC) codes

  • General Medicine


Dive into the research topics of 'Genetic mapping of developmental instability: Design, model and algorithm'. Together they form a unique fingerprint.

Cite this