贝叶斯网络和结构方程模型揭示了生产力,防御力和气候适应性特征之间的遗传因果关系,这些特征都存在于内部的松树
Eduardo P Cappa1,2, Jennifer G Klutsch3,4, Andy Benowicz5
1Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina.
G3 (Bethesda, Md.)
|December 24, 2025
概括
这项研究整合了贝叶斯网络和结构方程模型,以揭示松中的基因组关系. 新的BN-SEM方法通过提高预测能力来改善遗传评估和育种策略.
科学领域:
- 森林遗传学和森林繁殖
- 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:
- 了解特征之间的复杂基因组关系对于有效的树木育种计划至关重要.
- 传统的多特征模型 (MTM) 可能无法完全捕捉复杂的遗传架构.
- 贝叶斯网络 (BN) 和结构方程模型 (SEM) 为剖析特征相互依赖提供了先进的方法.
研究的目的:
- 整合贝叶斯网络 (BN) 和结构方程模型 (SEM) 来探索松的基因组关系.
- 将BN-SEM与传统的多特征模型 (MTM) 在估计遗传参数和预测繁殖值方面的性能进行比较.
- 通过使用集成的BN-SEM,加强森林树木的基因组评估和育种策略.
主要方法:
- 分析了392棵室内松树的基因型数据 (25,099个SNP标记).
- 应用联合贝叶斯网络和结构方程模型 (BN-SEM) 方法.
- 与传统的多特征模型 (MTM) 进行比较,使用基于血统 (ABLUP) 和基于基因组 (GBLUP) 的混合模型.
主要成果:
- 在九个生产力,国防和气候适应性特征中确定了12个重要的因果结构.
- 与MTM相比,BN-SEM产生了更高的遗传性估计和更好的预测能力 (ABLUP的平均收益为6.62%,GBLUP为6.03%).
- 在BN-SEM因果效应和遗传相关性之间观察到强烈的相关性,表明模型一致性.
结论:
- 综合BN-SEM方法有效地阐明了森林树木中复杂的基因组特征网络.
- BN-SEM提高了遗传评估和育种价值预测的准确性.
- 这种方法提供了一种强大的工具,可以优化育种策略,并加速树木改进计划中的遗传收益.
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