基因组预测基于在纯杜罗克种群中对低覆盖全基因组序列变异的选择性链接不平衡修剪
Di Zhu1, Yiqiang Zhao1, Ran Zhang1
1State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China.
Genetics, selection, evolution : GSE
|October 18, 2023
概括
选择性链接不平衡修剪 (SLDP) 通过对复杂特征的单核酸多态 (SNP) 集的改进来提高基因组预测的准确性. 这种方法增强了对具有更简单遗传架构的特征的预测,促进基于全基因组测序的基因组选择.
科学领域:
- 动物基因组学 动物基因组学
- 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:
- 全基因组测序 (WGS) 可以加速突变识别,但对基因组预测准确性的影响有限.
- 可靠的基因型定型和预先选择的基因位点可以提高预测准确性.
- 一种低覆盖度测序方法在猪中产生了数百万个高度精确的单核酸多态 (SNP).
研究的目的:
- 引入选择性链接不平衡修剪 (SLDP) 来改进SNP集,以改善基因组预测.
- 评估各种特征和遗传架构的SLDP性能.
- 使用WGS数据提高基因组选择的准确性.
主要方法:
- 开发并应用SLDP方法来选择基于全基因组关联研究 (GWAS) 预先信息的SNP.
- 使用两个预测模型 (GBLUP和BayesR) 对3579只Duroc猪进行SLDP评估.
- 通过交叉验证优化SLDP参数 (GWAS P值值和LD r2值).
主要成果:
- SLDP的表现受特征遗传结构的影响,在具有主要定量特征位点 (QTL) 或少数定量特征核酸 (QTN) 的特征上表现出色.
- 与商业SNP芯片,GBS和未选择的WGS面板相比,SLDP显著提高了预测准确性.
- 准确度的增长范围为真实特征的0.843.22%,模拟特征的1.2311.47%.
结论:
- 对于具有更简单遗传架构的特征,SLDP可以提高主流模型中的预测准确性.
- 对于不是由主要QTL控制的特征,SLDP没有显著的优势.
- 特征遗传架构和GWAS可靠性是影响SLDP性能的关键因素,有助于基于WGS的基因组选择.
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