通过使用最大概率,伪预期-最大化最大概率和关系的增加来估计跨品种和品种内的元发现者的基因组关系
Andres Legarra1, Matias Bermann2, Quanshun Mei3
1CDCB, 4201 Northview Drive, Bowie, MD, 20716, USA. andres.legarra@uscdcb.com.
Genetics, selection, evolution : GSE
|May 2, 2024
概括
新的方法准确地估计了牲畜中的元创始人关系,这对于基因组预测至关重要. 这些方法处理复杂的血统和不平衡的数据,提高精度与最新的基因组信息.
科学领域:
- 动物育种和遗传学动物育种和遗传学
- 定量遗传学 是一种定量遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- "元创始人理论"统一了动物育种中的种群结构,包括品种内部的群体和杂交.
- 估计元创始人关系的现有方法与不平衡的数据和远离基数人群的个人扎.
研究的目的:
- 推导和评估基于概率的方法来估计metafounder关系 ().
- 在复杂场景中解决当前方法的局限性,例如不平衡的数据和有限的基因组信息.
主要方法:
- 开发了最大概率 (ML) 和伪EM算法,用于从血统和基因组数据中估计.
- 扩展的方法来处理多个metafounders,出生年份组,并增加内生繁殖率.
- 通过使用模拟和真实牲畜数据,将伪EM ((+) 与通用最小平方 (GLS) 进行比较.
主要成果:
- 对于单个metafounders的ML估计在Lacaune绵羊数据上是准确的.
- 伪EM ((+) 方法提供了准确和公正的估计,特别是当基因型集中在最近几代人时.
- GLS方法显示了与最近一代基因型的偏差,与拟议的伪EM方法不同.
结论:
- 在各种动物繁殖场景中推导出强大的ML,伪EM和伪EM+方法来估计.
- 这些方法在真实和模拟数据集之间展示了准确性,计算需求低.
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