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在比利时蓝牛中使用全基因组序列数据和功能注释对基因组选择模型的评估
Can Yuan1, Alain Gillon2, José Luis Gualdrón Duarte2
1Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, 4000, Liège, Belgium. can.yuan@uliege.be.
Genetics, selection, evolution : GSE
|March 4, 2025
概括
在比利时蓝牛 (BBC) 中使用全基因组序列 (WGS) 数据和功能注释的基因组选择 (GS) 提高了特征预测的准确性. 选择功能变体提高了可靠性,尽管需要进一步细化注释以获得更大的好处.
科学领域:
- 动物基因组学 动物基因组学
- 量化遗传学 量化遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 旨在使用大型全基因组序列 (WGS) 数据集和功能注释来提高准确性.
- 之前使用GS与WGS数据的GS应用已经显示出有限的好处,尽管参考人口不断增长.
- 比利时蓝牛 (BBC) 提供了一个独特的机会,用WGS数据和功能信息来评估GS模型.
研究的目的:
- 用WGS数据和功能注释在比利时蓝牛中评估GS模型的有效性.
- 为了比较不同的GS方法,包括GBLUP,贝叶斯混合模型和标记物选择策略.
- 评估功能注释对肌肉发育特征预测可靠性的影响.
主要方法:
- 利用了来自16508头比利时蓝牛牛的WGS数据,这些牛具有五种肌肉发育特征的表型.
- 计算的WGS数据,并纳入了silico功能注释和实验性监管变体目录.
- 评估了各种GS模型 (GBLUP,BayesRR-RC,BayesCπ,BSLMM) 使用整个WGS数据和选定的变体面板.
主要成果:
- 使用WGS数据的GS模型显示,与中密度标记模型相比,GS模型的可靠性增加 (平均增加0.016-0.018).
- 功能注释略有改善了GBLUP,但降低了BayesRR-RC的可靠性.
- 根据功能信息或LD修剪选择的变量面板优于WGS数据,BSLMM实现了最高的准确性.
结论:
- 与官方基因组评估相比,将从WGS数据中选择的功能变异纳入GS的可靠性显著增加.
- 在BBC中WGS和功能数据的好处仍然很小,这表明有改进的潜力.
- 改进特定于BBC品种的功能注释可以进一步提高GS准确性.
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