Facultad de Telemática, Universidad de Colima, 28040, México.
Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430 Jalisco, México.
G3 (Bethesda). 2017 May 5;7(5):1595-1606. doi: 10.1534/g3.117.039974.
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.
当植物科学家希望对多个个体在多个环境中测量的多个性状进行基因组预测时，最常用的分析策略是一次使用一个性状，同时考虑基因型×环境互作（G×E），因为缺乏同时考虑相关计数性状和 G×E 的综合模型。出于这个原因，在本研究中，我们提出了一个用于计数数据的多性状和多环境模型。所提出的模型是在贝叶斯范例下开发的，我们为此开发了一个具有非信息先验的马尔可夫链蒙特卡罗（MCMC）。这使得可以获得参数的所有必需的完全条件分布，从而为后验分布生成精确的吉布斯抽样器。我们的模型用模拟数据和真实数据集进行了测试。结果表明，所提出的多性状、多环境模型是对多个环境中测量的多个计数性状进行建模的一种有吸引力的选择。
