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通过R方法对基因组信息的遗传性的估计

Mary Kate Hollifield¹, Daniela Lourenco¹, Ignacy Misztal¹

¹Department of Animal and Dairy Science, University of Georgia, Athens, Georgia, USA.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie

|March 25, 2024

概括

方法R在大型基因组模型中提供了一种计算效率高的方法来估计遗传性,与AI-REML等传统方法相比,大大减少了计算时间. 这种方法对基因组选择有前途,当多代数据可用时.

科学领域:

量化遗传学量化遗传学
动物育种动物育种
基因组选择基因组选择

背景情况:

使用大型基因组模型以限制最大概率 (REML) 或贝叶斯基布斯采样等方法估计遗传性是计算密集的.
其他方法,如方法R和最大预测性 (MaxPred),为遗传性估计提供较低的计算成本.

研究的目的:

在大型基因组模型中使用平均信息REML (AI-REML),方法R和MaxPred来比较遗传性估计的准确性和计算效率.
评估方法R和MaxPred的性能,以估计多代基因组信息的遗传性.

主要方法:

使用了一个模拟群体,每个群体有10代5000只动物,每只动物的遗传性为0.3和5000个SNP的特征.
在基因组最佳线性无偏预测 (GBLUP) 框架内,使用AI-REML (带有和没有基因组学) 和方法R (带有基因组学) 估计了遗传性.
方法R通过确保GEBV的回归系数从部分数据 (删除上一代表型) 来获得GEBV的整体数据来估计遗传性,等于1.
MaxPred通过从部分数据中最大化调整的表型和GEBV之间的相关性来估计遗传性.

主要成果:

与基因组学相比,使用基因组学的R方法减少了83%的计算时间 (从9.5小时减少到1.6小时),而与使用基因组学的AI-REML相比,产生了类似的遗传性估计 (0.30±0.04对比0.26±0.01).
马克斯普雷德显示,随着数据的增加,预测性增加,但子集之间的差异很小 (≤0.01),这表明它不是估计遗传性的可靠指标.
没有基因组学的AI-REML估计遗传率为0.30 ± 0.01,与基因组学的方法R相比.

结论:

方法R是一种计算效率高的替代方案,用于估计大型基因组数据集的遗传性,特别是当多代数据可用时.
虽然方法R提供了显著的计算节省,但其标准误差在有限的代中可能很高.
由于其对不同数据子集和遗传性的敏感性较低,因此不建议使用MaxPred作为遗传性估计的主要方法.

关键词:

大数据的大数据大数据线性回归是一种线性回归.预测性预测性预测性差异组件的变异组件是什么

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