Functional mapping of reaction norms to multiple environmental signals through nonparametric covariance estimation

BMC Plant Biology

Methodology Article

Rongling Wu¹  Jiahan Li²  Kiranmoy Das²  John S Yap³  Yao Li⁴ 

[1] Center for Computational Biology, Beijing Forestry University, 100083, Beijing, PR, China;Center for Statistical Genetics, Pennsylvania State University, 17033, Hershey, PA, USA;Center for Statistical Genetics, Pennsylvania State University, 17033, Hershey, PA, USA;Department of Statistics, University of Florida, 32611, Gainesville, FL, USA;Department of Statistics, West Virginia University, 26506, Morgantown, WV, USA;

关键词: Photosynthetic Rate;    Covariance Model;    Reaction Norm;    Kronecker Product;    Functional Mapping;   

DOI  :  10.1186/1471-2229-11-23

 received in 2009-11-21, accepted in 2011-01-26,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe identification of genes or quantitative trait loci that are expressed in response to different environmental factors such as temperature and light, through functional mapping, critically relies on precise modeling of the covariance structure. Previous work used separable parametric covariance structures, such as a Kronecker product of autoregressive one [AR(1)] matrices, that do not account for interaction effects of different environmental factors.ResultsWe implement a more robust nonparametric covariance estimator to model these interactions within the framework of functional mapping of reaction norms to two signals. Our results from Monte Carlo simulations show that this estimator can be useful in modeling interactions that exist between two environmental signals. The interactions are simulated using nonseparable covariance models with spatio-temporal structural forms that mimic interaction effects.ConclusionsThe nonparametric covariance estimator has an advantage over separable parametric covariance estimators in the detection of QTL location, thus extending the breadth of use of functional mapping in practical settings.

【 授权许可】

Unknown   
© Yap et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.