在猪的基因组评估中,基因型,表型和血统截断的边界
Fernando Bussiman1, Ching-Yi Chen2, Justin Holl2
1Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.
Journal of animal science
|August 16, 2023
概括
通过删除非信息性基因型和表型来减少数据集,可以提高基因组预测 (GEBV) 的计算效率,而不会牺牲准确性. 这一策略优化了动物育种中的遗传评估.
科学领域:
- 动物遗传学和动物繁殖
- 量化遗传学 量化遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 历史数据对于动物种群的遗传评估至关重要,但大型数据集增加了计算需求.
- 将模型与历史数据和最近数据相匹配可能是不适当的,需要数据管理策略.
- 基因型构成了基因分析计算增长的重要组成部分.
研究的目的:
- 评估删除基因型,表型和血统数据对基因组预测繁殖值 (GEBV) 的计算性能,可靠性和通胀的影响.
- 评估不同基因型删除场景和血统深度,用于猪种群的单步基因组最佳线性无偏预测 (ssGBLUP).
- 确定最佳的数据截断策略,以实现高效可靠的遗传评估.
主要方法:
- 分析了来自两个猪系 (终端主L1,母系L2) 的生长,死亡率,断奶前和生殖特征的数据.
- 实施了四种基因型删除场景:noInfo (没有表型/后代的动物),Age (基于出生年份),noInfo + Age和AllGen (所有基因型).
- 测试了三种血统深度 (2,3代和全代) 并使用线性回归来评估最近的父母的GEBV可靠性和分散性.
主要成果:
- 数据截断策略,包括删除非信息型基因型和限制血统深度,显著提高了计算效率.
- 大多数场景显示与使用所有基因型相比,GEBV的可靠性相似,而"noInfo"场景在生长特征方面表现更好.
- 保留过去十年的表型和三代血统,同时删除没有自身或后代表型的基因型动物,提高了效率,而不影响预测能力.
结论:
- 数据减少技术,特别是删除非贡献的基因型动物和限制血统深度,提高遗传评估的计算效率.
- 最佳的数据截断策略取决于诸如特征遗传性,数据量和分析类型 (单特征与多特征) 等因素.
- 通过仔细策划历史数据,平衡计算成本与预测准确度,可以实现高效的遗传评估.
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