将基因组和转录组效应纳入联合线性和非线性结构模型,以预测猪的复杂特征
I-T Vourlaki1, M Piles1, T Jové-Juncà1
1IRTA, Animal Breeding and Genetics Program, Torre Marimón, 08140 Caldes de Montbui, Barcelona, Spain.
Animal : an international journal of animal bioscience
|February 11, 2026
概括
血液转录数据显著改善了对猪免疫特征的预测. 特征选择提高了准确性,并识别了关键基因,优于相关特征的基因组数据.
科学领域:
- 动物基因组学与繁殖
- 转录组学和基因表达分析
- 定量遗传学和特征预测
背景情况:
- 牲畜表型是由遗传变异和调节机制之间的复杂相互作用造成的.
- 预测复杂的特征对于有效的动物育种至关重要,但仍然具有挑战性.
- 转录组数据提供了对监管信号,基因型和表型架构桥梁的见解.
研究的目的:
- 评估血液转录组数据对Duroc猪6种免疫,压力和生产特征的预测价值.
- 为了比较单独的转录基因组数据与基因组信息相结合的性能.
- 评估不同的统计模型和特征选择方法用于特征预测.
主要方法:
- 利用了来自255头Duroc猪的血液转录和基因组 (SNP) 数据.
- 采用贝叶斯回归 (BayesC,RKHS) 和神经网络线性混合模型.
- 应用部分最小平方 (PLS) 对于转录组特征选择.
主要成果:
- 使用转录组数据 (r=0.74,r=0.67) 实现了与免疫相关的特征 (例如,玛三角T细胞,白细胞计数) 的高预测准确度.
- 皮质醇预测的中度改善 (r=0.39);基于SNP的模型在尸体重量方面表现出色 (r=0.45).
- PLS特征选择确定了关键基因 (MAF,SOX13,DDIT4,FOS) 并提高了预测效率.
结论:
- 血液转录学显著提高了对采样组织相关的特征的预测.
- 基于SNP的模型对于与血液没有生物学相关的特征更优越.
- 特性选择对于优化预测性能,计算效率和基因发现至关重要.
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