通过代表性学习整合omics和功能数据,以优先考虑候选基因在奶羊中产生类效应
Pablo Augusto de Souza Fonseca1,2, Aroa Suárez-Vega2, Laura Casas3
1Instituto de Ganadería de Montaña (CSIC-Univ. de León), Finca Marzanas, Grulleros, 24346 León, Spain.
PNAS nexus
|November 27, 2025
概括
了解基因类型是改善牲畜特征的关键. 基于网络的机器学习模型成功地识别了影响奶牛产量,质量和健康的基因.
科学领域:
- 动物遗传学和繁殖动物遗传学
- 生物信息学和计算生物学
- 基因组学和多基因组学
背景情况:
- 畜牧业生产在提高生产力,可持续性,福利和质量方面面临着挑战.
- 一个基因影响多个特征的类型复杂化复杂化繁殖努力.
- 整合多omics数据和功能注释对于理解复杂的特征相关性至关重要.
研究的目的:
- 通过使用两个不同的GWAS数据集,估计基因水平的P值,以测量类效应.
- 利用基于网络的机器学习来整合多omics数据和功能注释.
- 为了确定具有对乳羊经济重要特征的类效应的候选基因.
主要方法:
- 从牛奶体细胞转录组学中构建加权基因共同表达网络 (WGCNs).
- 使用基因本体学,通路和QTL注释开发基因术语网络.
- 应用一个分层模型,整合基因P值和基因优先级的代表性学习的潜在向量.
主要成果:
- 在特征_GWAS数据集中确定了14个显著的类基因,在EBV_GWAS数据集中确定了111个显著的类基因.
- 在两个数据集中发现了三种共享的类基因 (PHGDH,SLC1A4,CSN3).
- 将优先级基因与生物过程联系在一起,包括氨基酸运输,脂质代谢,乳腺发育和免疫调节.
结论:
- 基于网络的机器学习模型有效地整合了多omics数据,以解开 pleiotropic 效应.
- 这项研究突出了乳羊中确定的基因的潜在类作用.
- 这些发现为畜牧养殖计划中的遗传选择策略提供了宝贵的见解.
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