贝叶斯离散逻辑正常回归模型用于基因组预测.
Abelardo Montesinos-López1, Humberto Gutiérrez-Pulido1, Sofía Ramos-Pulido1
1Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, C. P. 44430, Guadalajara, Jalisco, México.
概括
一个新的贝叶斯离散lognormal模型改善了对计数特征的基因组预测. 这种方法为使用基因组选择在育种计划中选择理想的特征提供了有竞争力的替代方案.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 统计建模 统计建模
背景情况:
- 基因组选择 (GS) 对现代育种至关重要,它可以使用基因组数据预测个体的表现.
- 现有的GS模型主要针对连续性特征,对计数数据的选择有限.
- 准确的模型对于在作物和牲畜中有效选择理想特征至关重要.
研究的目的:
- 为基因组预测计数特征引入一个新的贝叶斯离散逻辑正常回归模型.
- 评估拟议模型的性能与传统的高斯式和逻辑正常模型相比.
- 提高基因组选择对非正常分布的特征的准确性和适用性.
主要方法:
- 开发一个贝叶斯离散的逻辑正常回归模型.
- 使用吉布斯采样器进行后部分布探索和预测.
- 在两个小麦疾病耐药性计数数据集上的应用和评估.
主要成果:
- 提出的贝叶斯离散逻辑正常模型展示了竞争性表现.
- 该模型在预测数量基因组特征方面被证明是有效的.
- 结果表明,它适合以计数为基础的基因组数据.
结论:
- 新的贝叶斯离散lognormal模型是一个可行的和有效的工具,用于基因组预测计数特征.
- 这种模型增强了基因组选择在繁殖计划中的能力.
- 它为遗传学中非正常分布的计数数据提供了一个更合适的统计框架.
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