截断历史数据对乳羊选择候选人的预测能力的影响
1Department of Animal Production, NEIKER - Basque Institute of Agricultural Research and Development, Basque Research and Technology Alliance (BRTA), Agrifood Campus of Arkaute s/n, Arkaute 01192, Spain.
Animal : an international journal of animal bioscience
|August 3, 2024
概括
实施单步基因组最佳线性无偏预测 (ssGBLUP) 改善了Latxa奶羊的遗传评估. 删除历史数据和使用较浅的血统可以提高预测的准确性,并减少育种价值估计的偏差.
科学领域:
- 动物遗传学和动物繁殖
- 量化遗传学 量化遗传学
- 乳制品绵羊生产 乳制品绵羊生产
背景情况:
- 遗传评估方法有所进步,提高了育种价值的估计.
- 传统的育种计划拥有广泛的历史数据,但其实用性可能有限.
- 评估数据管理和方法对预测准确性的影响对于优化育种策略至关重要.
研究的目的:
- 通过使用不同的数据管理策略,评估基因型年轻动物的预测能力.
- 将传统BLUP (最佳线性无偏预测) 与单步基因组BLUP (ssGBLUP) 方法进行比较.
- 为了确定最优的使用历史的表型数据和血统深度为Latxa奶羊种群.
主要方法:
- 利用了来自三个Latxa奶羊群的40年牛奶产量记录.
- 通过同时评估历史数据删除,血统深度 (两级) 和方法 (BLUP vs ssGBLUP) 来评估预测能力.
- 使用线性回归来比较后代测试前后年轻公羊的预测,计算准确性,偏差和分散率.
主要成果:
- 单步基因组BLUP (ssGBLUP) 在所有人群中显示出最高的预测准确性 (0.540.69).
- 删除历史的表型数据在较大的群体中产生了适度的准确度增长 (平均为2.5%),在较小的群体中产生了特定的好处 (删除数据至2004年时增加了2.7%).
- ssGBLUP减少了偏差 (2.113.9%),当删除历史数据时观察到更大的减少.
结论:
- ssGBLUP的评估显著超过了传统的BLUP.
- 删除历史的表型数据是有益的,最佳的截断点因种群大小而异.
- 拉特克萨常规遗传评估将受益于截断记录 (2000年至2004年),使用两代血统,并实施ssGBLUP.
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