对肉牛生长特征的差异成分估计,使用从归算低通序列数据中选择的变异.
Chad A Russell1, Larry A Kuehn2, Warren M Snelling2
1Department of Animal Science, University of Nebraska, Lincoln, NE 68583, USA.
Journal of animal science
|August 16, 2023
概括
肉牛中的功能变异显著影响了对出生体重和断奶后体重增加的遗传参数估计. 特定的变异子集,如未翻译区域,是准确遗传性估计的关键.
科学领域:
- 动物遗传学动物遗传学
- 基因组选择 基因组选择
- 量化遗传学 量化遗传学
背景情况:
- 准确估计遗传参数对于肉牛繁殖计划至关重要.
- 输入低通序 (LPS) 变体提供比传统数组基因型更密集的标记器集.
- 了解LPS变异的功能影响是优化其在基因组评估中的使用的关键.
研究的目的:
- 评估低通序 (LPS) 变异对肉牛出生体重 (BWT) 和断奶后增加 (PWG) 遗传参数估计的影响.
- 为了确定具有高预测能力的特定变异子集,用于遗传性估计.
- 为了比较LPS变体与传统阵列基因型的性能.
主要方法:
- 使用了一个肉牛群 (n=2,343) 具有归算的LPS基因型.
- 根据预测的功能影响 (低,修饰,中等,高) 和突变后果 (G1-G6) 选择和分区变体.
- 构建基因组关系矩阵 (GRMs),使用单变动物模型的不同变异子集,并比较遗传概率估计.
主要成果:
- 根据使用的变异子集,BWT和PWG的遗传概率估计有显著差异 (BWT:0.10-0.42,PWG:0.05-0.38).
- "修饰者"和G1 (未翻译区域) 子集中的变体产生了最高的遗传性估计,与使用所有LPS变体相比.
- 所有LPS变异组合提供了与芯片基因型相似的遗传性,与芯片数据结合时获得的额外信息最小.
结论:
- 特定的功能变异,特别是那些在未翻译地区的变异,对于估计肉牛的遗传参数具有高度信息性.
- 阵列基因型和不那么重要的LPS变异与因果变异处于高度链接不平衡状态,捕获了大量的附加遗传变异.
- 从LPS数据中优化变体选择可以提高基因组评估的准确性,而不一定需要更密集的标记面板.
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