改善多品种牛群中的基因组预测:贝叶斯R和GBLUP模型的比较分析
Haoran Ma1, Hongwei Li1,2, Fei Ge1
1Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Genes
|February 24, 2024
概括
与GBLUP相比,贝叶斯R模型显著提高了肉牛的基因组预测准确性,特别是与多种品种和全基因组测序数据相比. 这增强了多品种基因组选择策略.
科学领域:
- 动物遗传学和动物繁殖
- 基因组选择 基因组选择
- 量化遗传学 量化遗传学
背景情况:
- 结合相关品种可以提高基因组预测准确性 (GP).
- 基因组最佳线性无偏预测 (GBLUP) 和贝叶斯模型是为多品种基因组选择 (GS) 建立的.
- 为不同的肉牛种群优化模型对于准确的繁殖价值预测至关重要.
研究的目的:
- 评估贝叶斯R和GBLUP模型与LD加权GRM用于三种肉牛品种的多品种基因组预测.
- 为了比较不同标记密度和遗传关联的预测精度.
- 确定最佳方法,以提高多品种肉牛的基因组选择精度.
主要方法:
- 使用贝叶斯R和GBLUP模型与链接不平衡 (LD) 权重的基因组关系矩阵 (GRMs).
- 使用BovineHD BeadChip (HD) 和全基因组测序 (WGS) 数据评估了预测准确度.
- 评估了中国Wagyu (WG),Huaxi (HX) 和Yunling (YL) 牛品种之间的遗传一致性.
主要成果:
- 贝叶斯R在品种内和多品种基因组预测准确度方面表现优于GBLUP,特别是在WGS数据方面.
- 对于HX牛的预测准确度提高了26.8% (HD) 和9.5% (WGS),贝叶斯R与GBLUP相比.
- 贝叶斯R在多品种预测准确度上实现了高达33.3%的改进,特别是在基因多样化的种群中.
结论:
- 与GBLUP相比,BayesR在多品种基因组预测准确性方面表现出卓越的表现.
- 高单核酸多态度 (SNP) 标记物密度的利用由BayesR提高了预测的准确性.
- 基因相关性和全面的基因组选择方法对于改善肉牛养殖至关重要.
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