Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México.
Department of Public Health Sciences, University of California Davis, Davis, CA, 95616, USA.
Theor Appl Genet. 2024 Jan 14;137(1):21. doi: 10.1007/s00122-023-04526-4.
Genomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, that with a Gibbs sampler to explore the corresponding posterior distribution and make the predictions. Two datasets of resistance disease is used in the wheat crop and are then evaluated against the traditional Gaussian model and a lognormal model. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.
基因组预测模型假定数量性状的表型是连续的和正态分布的。在这项研究中，我们提出了一种新的贝叶斯离散对数正态回归模型。基因组选择是现代育种计划中一种强大的工具，它利用基因组信息来预测个体的表现，并选择具有理想性状的个体。它彻底改变了动植物的育种，因为它允许育种者在不需要费力和耗时的表型评估的情况下识别出最佳候选者。虽然已经开发了几种统计模型，但大多数模型都是针对定量连续性状的，只有少数是针对计数响应的。在本文中，我们提出了一个贝叶斯离散对数正态回归模型，该模型使用 Gibbs 抽样器来探索相应的后验分布并进行预测。我们使用了小麦作物中的两个抗性疾病数据集，并将其与传统的高斯模型和对数正态模型进行了比较。结果表明，所提出的模型是一种用于预测计数基因组性状的具有竞争力和自然的模型。
