深度学习的应用与双变体模型用于基因组预测母猪一生生产力相关的特征
Joon-Ki Hong1, Yong-Min Kim1, Eun-Seok Cho1
1Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea.
Animal bioscience
|January 16, 2024
概括
深度学习 (DL) 模型,特别是CNN,显示出对母猪一生生产力特征的优越基因组预测,如子数量和生产量. 这些先进的模型通过捕捉复杂的遗传相互作用,优于传统方法,提高了猪繁殖选择的准确性.
科学领域:
- 动物遗传学动物遗传学
- 基因组预测 基因组预测
- 机器学习在动物育种中的应用
背景情况:
- 播种寿命生产率 (SLP) 特性对农场利能力至关重要,但缺乏及时的表型数据来进行选择.
- 传统的基因组预测方法往往无法捕捉复杂的遗传效应,如表观和主导.
- 深度学习 (DL) 提供了一种有前途的方法,可以更有效地模拟非线性遗传架构.
研究的目的:
- 评估深度学习 (DL) 模型对母猪一生生产率 (SLP) 的基因组预测的有效性.
- 将DL模型的性能与传统线性模型进行比较,以预测一生子数量 (LNL) 和一生猪产量 (LPP).
主要方法:
- 开发和比较了两种多变的深度学习模型,CNN和LCNN.
- 传统的双变线性模型 (GBLUP,BRR,贝叶斯A,贝叶斯B) 被用作基准.
- 数据包括来自40011头母猪的表型和血统以及来自3652头猪的基因型.
主要成果:
- 对于LNL (0.28) 和LPP (0.29),CNN实现了最高的预测相关性,超过了LCNN和线性模型.
- 此外,LCNN对LNL (0.26) 和LPP (0.27) 的线性模型进行了更好的预测.
- 对于预测的平均平方误差,观察到有利于DL模型的类似趋势.
结论:
- 深度学习,特别是CNN,在基因组预测方面表现出卓越的表现,当像表观症这样的非线性遗传效应显著时.
- 两变DL模型可以通过利用特征之间的遗传相关性来提高预测准确性.
- 这些发现支持DL在养猪计划中用于SLP的基因改进.
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