基因调节的奥米克集成改善了对猪复杂特征的基因组预测
1Frontiers Science Center for Molecular Design Breeding (MOE), State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Animal : an international journal of animal bioscience
|December 10, 2025
概括
我们开发了基因调节添加剂和主导 (GRAD) 模型,通过分析omics数据中的遗传信号来改善特征预测. 通过分离遗传和非遗传影响,GRAD提高了准确性,优于现有的方法.
科学领域:
- 遗传学 遗传学 是一个
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 复杂的特征受到DNA变异的影响,但中间的奥米克数据 (例如,转录组) 是杂的,并混合遗传/非遗传信号.
- 精确预测复杂的特征需要解开遗传架构,包括添加和主导效应.
研究的目的:
- 开发和评估一种新的框架,即基因调节添加剂和主导 (GRAD) 模型,用于预测复杂的特征.
- 通过从omics数据明确建模基因调节的添加剂和主导效应来提高预测准确度.
主要方法:
- 开发了GRAD模型,一个单步框架,将omics特征分解为基因调控的添加剂和主导成分.
- 在基因组和中间体层面上共同建模的添加和支配效应.
- 使用模拟和Landrace猪数据集验证了GRAD.
主要成果:
- 与标准基因组预测相比,GRAD在模拟中获得了高达21.98%的平均准确度.
- 使用原始奥米克或仅添加式奥米克信号的GRAD超越了性能模型.
- 在猪中,GRAD提高了7.4%和14.9%的生长和生殖特征的预测准确度.
结论:
- 从omics数据中明确提取受遗传调节的添加物和主导信号可以提高预测的准确性.
- 格拉德的实用性取决于所分析的动物的比例以及所采样的组织与目标特征的相关性.
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