在安格斯牛群中使用从归算全基因组序列预测基因组预测预选标记的效果
Nantapong Kamprasert1, Hassan Aliloo2, Julius H J van der Werf2
1School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia. nkampras@myune.edu.au.
Genetics, selection, evolution : GSE
|September 26, 2025
概括
将选定的显著标记物添加到标准基因型中并没有持续地提高Angus牛的基因组预测准确性. 然而,从全基因组关联研究 (GWAS) 中预选的SNP显示了特定特征的潜力,例如出生体重.
科学领域:
- 动物遗传学动物遗传学
- 基因组预测 基因组预测
- 畜牧养殖 畜牧养殖 畜牧养殖
背景情况:
- 下一代测序为牲畜中的基因组预测 (GP) 提供了更密集的标记.
- 虽然模拟显示全基因组序列 (WGS) 提高了GP的准确性,但实际结果不一致.
- 质量标志系统的好处很可能源于特征特定的显著标记物.
研究的目的:
- 研究添加预选标记的预测能力,以标准的50k基因型为GP在安格斯牛.
- 评估经济重要特征的GP:出生体重 (BW),阴囊周长 (SC),尸体体重 (CWT) 和尸体肌内脂肪 (CIMF).
- 对比不同的标记物选择方法和GP的统计模型.
主要方法:
- 从商业或定制SNP阵列中归因于WGS的基因型.
- 从WGS中提取的信息标记使用基于LD的修剪,GWAS和功能注释.
- 通过将预先选择的标记物添加到5000个基因型控制组中,创建了8个基因型组.
- 使用的统计模型:GBLUP,贝叶斯R,贝叶斯RC和两GRM GBLUP.
主要成果:
- 遗传性估计在所有基因型集和方法中是一致的.
- 两个GRM GBLUP的表现优于单个GRM GBLUP.
- 没有显著的准确性或偏差差异,除了BW,贝叶斯模型略高于GBLUP.
结论:
- 通过在特定人群中从GWAS中预先选择的SNP来实现GP准确度的潜在改进.
- 标记物的表现取决于种群结构,选择方法和特征遗传结构.
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