A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait

Chang Xing Ma, Qibin Yu, Arthur Berg, Derek Drost, Evandro Novaes, Guifang Fu, John Stephen Yap, Aixin Tan, Matias Kirst, Yuehua Cui, Rongling Wu

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Abstract

The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by- environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.

Original languageEnglish (US)
Pages (from-to)627-636
Number of pages10
JournalGenetics
Volume179
Issue number1
DOIs
Publication statusPublished - May 1 2008

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All Science Journal Classification (ASJC) codes

  • Genetics

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