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An optimal strategy for functional mapping of dynamic trait loci

  • TIANBO JIN (a1), JIAHAN LI (a2), YING GUO (a3), XIAOJING ZHOU (a3), RUNQING YANG (a3) (a4) and RONGLING WU (a2)...

Summary

As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

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Copyright

Corresponding author

*Corresponding author: School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, People's Republic of China. Tel: (8621) 34206146. Fax: (8621) 34206146. e-mail: runqingyang@sjtu.edu.cn
*Corresponding author: Department of Statistics, University of Florida, Gainesville, FL 32611. Tel: (352)392 3806. Fax: (352)392 8555. e-mail: rwu@stat.ufl.edu

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An optimal strategy for functional mapping of dynamic trait loci

  • TIANBO JIN (a1), JIAHAN LI (a2), YING GUO (a3), XIAOJING ZHOU (a3), RUNQING YANG (a3) (a4) and RONGLING WU (a2)...

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