验证统计数据的置信区间与基因组预测中的数据截断.
Matias Bermann1, Andres Legarra2, Alejandra Alvarez Munera3
1Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA. mbermann@uga.edu.
Genetics, selection, evolution : GSE
|March 8, 2024
概括
这项研究为遗传评估验证方法,预测性和线性回归 (LR) 引入了新的分析置信区间. 这些方法提供了准确的抽样变化的估计,而不需要重复计算或启动.
科学领域:
- 动物育种与遗传学
- 量化遗传学 量化遗传学
- 统计遗传学 统计遗传学
背景情况:
- 数据截断在基因评估中很常见,用于预测年轻候选人的优点.
- 预测性和线性回归 (LR) 是使用部分数据集的关键验证方法.
- 当前的置信区间依赖于计算密集的复制或引导.
研究的目的:
- 为预测性和LR方法统计得出分析置信区间.
- 开发大数据集的近似值.
- 为了比较分析和引导的置信区间.
主要方法:
- 为预测性和LR统计 (偏差,分散,准确率,可靠性) 推导标准错误和沃尔德置信区间.
- 开发了使用个别可靠性的大数据集的近似值.
- 用费舍尔变换来计算准确率和预测性置信区间.
主要成果:
- 分析信心区间比启动区间更接近模拟值.
- 引导间隔通常比模拟间隔更窄.
- 估计的分析置信区间与启动区间有相似之处.
结论:
- 分析公式允许估计预测性和LR统计数据的采样变化,而无需复制或引导.
- 衍生的公式适用于任何数据集.
- 这为评估遗传评估验证的可靠性提供了更有效的方法.
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