使用视觉分数用于在育种计划中对复杂特征的基因组预测
Camila Ferreira Azevedo1,2, Luis Felipe Ventorim Ferrão2, Juliana Benevenuto2
1Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
概括
植物育种中的视觉评分可能不可靠,但特定的方法可以改善基因组预测和遗传参数估计. 使用中间类别 (1-5) 和贝叶斯顺序回归模型 (BORM) 提高了准确性,即使是主观数据.
科学领域:
- 植物育种和遗传学 植物育种和遗传学
- 定量遗传学 是一个量子遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组预测方法通常假定正常分布的数据.
- 植物/动物育种中的视觉评分经常是分类的,违反了这一假设.
- 这种违规行为可能会影响育种价值预测和遗传参数估计.
研究的目的:
- 评估在基因组预测和遗传参数估计中处理视觉分数的方法.
- 解决主观性和视觉评分中的错误所带来的挑战.
- 通过循环选择方案,改善育种者的决策.
主要方法:
- 比较线性混合模型,贝叶斯线性回归,贝叶斯顺序回归模型 (BORM) 和随机森林分类.
- 使用模拟和真实育种数据集,包括自动四叶复合蓝.
- 评估了数据收集策略 (类别数量) 和表型类型 (连续与分类).
主要成果:
- 采集1-5类别的数据是最优的,即使有得分错误.
- BORM和随机森林分类比线性混合模型和贝叶斯线性回归等强大的方法提供了边际收益.
- BORM提供了对遗传参数的优越估计.
- 投资600-1000个低误差的分类数据点可以改善当连续表型是不可行的预测能力.
结论:
- 贝叶斯顺序回归模型 (BORM) 和随机森林分类对于基因组预测中的视觉得分是有效的.
- 用中间类别 (1-5) 收集数据和高质量的表型是至关重要的.
- 结果适用于真实的育种数据,有助于育种者决策和提高预测能力.
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