一个单步随机回归模型的标记效应对德国Holsteins的4个测试日特征的单步随机回归模型
H Alkhoder1, Z Liu1, R Reents1
1IT-Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany.
Journal of dairy science
|September 14, 2023
概括
单步基因组模型准确地估计了畜牧养殖的标记效应,揭示了14号染色体.
科学领域:
- 动物育种与遗传学
- 基因组预测 基因组预测
- 量化遗传学 量化遗传学
背景情况:
- 单步基因组模型是牲畜遗传评估的标准,特别是在荷尔斯坦乳牛中.
- 从单步评估中准确估计标记效应对于频繁的基因组预测更新至关重要.
- 整合国际育种价值观提高了国家基因组评估的可靠性.
研究的目的:
- 探索和评估来自单步基因组模型的标记效应估计的稳定性和偏差.
- 调查哺乳曲线的遗传结构及其对附加遗传变异的染色体贡献.
- 评估单步随机回归测试日模型在捕获产量特征和体细胞得分的遗传变异方面的性能.
主要方法:
- 使用了来自德国乳品品种的表型,基因型和血统数据 (2021年4月).
- 应用了一种多乳酸随机回归测试日模型,对超过2.42亿个测试日记录的牛奶,脂肪,蛋白质产量和体细胞分数 (SCS) 进行了应用.
- 通过删除最近的数据和从数据集中截断最近的牛来评估标记效应的稳定性.
主要成果:
- 标记效应估计显示,在完整和截断的评估之间存在很高的相关性 (∼0.9) 和接近1.03的回归斜率,表明稳定性.
- 鉴定出与DGAT1基因相关的14号染色体上特定标记物的独特遗传哺乳曲线形状,特别是乳和脂肪产量.
- 观察到蛋白质产量和SCS的多基因遗传,但对于牛奶和脂肪产量而言,染色体14的显著染色体贡献.
结论:
- 单步基因组模型提供了稳定可靠的标记物效应估计.
- 随机回归测试日模型有效地捕捉了标记物特定的遗传变异和哺乳期曲线形状.
- 这种方法提高了对产量特征和SCS的遗传基础的理解,包括染色体贡献和互乳相关性.
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