Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
BMC Plant Biol. 2011 Jan 26;11:23. doi: 10.1186/1471-2229-11-23.
The 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.
We 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.
The 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.
通过功能映射，鉴定对不同环境因子（如温度和光照）有响应的基因或数量性状基因座，关键依赖于对协方差结构的精确建模。先前的工作使用了可分离的参数协方差结构，如自回归（AR(1)）矩阵的 Kronecker 积，这些结构没有考虑到不同环境因子的相互作用效应。
我们在两个信号的反应规范功能映射框架内，实现了一个更稳健的非参数协方差估计器来对这些相互作用进行建模。我们通过蒙特卡罗模拟得到的结果表明，这个估计器在对两个环境信号之间存在的相互作用进行建模时非常有用。通过具有模仿相互作用效应的时空结构形式的不可分离协方差模型来模拟相互作用。
非参数协方差估计器在 QTL 定位的检测中优于可分离参数协方差估计器，从而扩展了功能映射在实际环境中的应用范围。
