使用从大规模全基因组序列数据中预先选择的变异来进行猪单步基因组预测
Sungbong Jang1, Roger Ros-Freixedes2, John M Hickey3
1Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA. jsbng8615@gmail.com.
Genetics, selection, evolution : GSE
|July 26, 2023
概括
全基因组序列数据可以改善猪的基因组预测,特别是对母系和生育特征的基因组预测. 然而,益处因猪系和变种选择方法而异,对猪的总体优势有限.
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
- 畜牧养殖 畜牧养殖 畜牧养殖
背景情况:
- 全基因组序列 (WGS) 数据提供了超出标准单核酸多态 (SNP) 芯片数据的致病变异.
- 调查预先选择的WGS变种在养猪中对基因组预测的有用性至关重要.
研究的目的:
- 评估使用WGS预选变异对母猪和终端猪系单步基因组预测的影响.
- 为了比较使用WGS衍生的SNP集与标准SNP芯片的预测准确性.
主要方法:
- 研究了两个母猪和四个终端猪系,分别分析了八个和七个特征.
- 从基于全基因组关联研究 (GWAS) 的WGS数据生成两个预选SNP集 (Top40k和ChipPlusSign).
- 使用预选SNP集与标准猪SNP芯片比较单步基因组预测准确度.
主要成果:
- 母亲线显示精度比标准芯片增加0.6% (ChipPlusSign) 和4.9% (Top40k),Top40k在生育特征方面表现出色.
- 终端线路的平均准确性损失为Top40k的1%,而ChipPlusSign的小收益为0.9%.
- 调整SNP差异略有提高了准确性,但结果在线和特征之间不一致.
结论:
- 使用WGS变异用于猪基因组预测的优势取决于特定的线条,种群大小和变异预选策略.
- 虽然WGS数据在大量种群中提供了好处,但它们在猪中的整体优势仍然有限.
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