提高多种群的基因组预测准确度,使用多特征的GBLUP模型,这些模型包含全球或本地遗传相关信息
Jun Teng1,2, Tingting Zhai3, Xinyi Zhang1
1Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China.
Briefings in bioinformatics
|June 10, 2024
概括
多特征基因组最好的线性无偏预测 (MTGBLUP) 通过计算遗传相关性来改善多种种群的基因组预测 (GP). 当地遗传相关性 (LGC) 进一步提高了预测准确性,超过了传统方法.
科学领域:
- 定量遗传学 是一种定量遗传学.
- 动物繁殖 动物繁殖
- 统计基因组学 统计基因组学
背景情况:
- 基因组预测 (GP) 对育种计划至关重要.
- 多种群的GP经常结合种群,可能忽视基因型与环境的相互作用.
- 由于人口异质性,现有的方法可能会限制预测的准确性.
研究的目的:
- 评估多特征基因组最佳线性无偏预测 (MTGBLUP) 对于多种群的GP.
- 引入和评估本地遗传相关性 (LGC) 模型以提高准确性.
- 将MTGBLUP和LGC模型与传统的多人群GP方法进行比较.
主要方法:
- 通过将种群视为特征并使用种群间的遗传相关性来应用MTGBLUP.
- 开发并实施了两种LGC模型 (LGC-model-1,LGC-model-2),以根据LGC划分基因组区域.
- 使用真实数据集验证的方法用于基因组预测准确度.
主要成果:
- MTGBLUP显著超过了传统的综合人口模型.
- 与MTGBLUP相比,LGC模型在预测准确度方面表现出了显著的改善.
- 与LGC模型相对改善达到了163.86% (平均25.64%).
结论:
- 与简单的人口聚合相比,MTGBLUP是多种群体基因组预测的优越方法.
- 将遗传相关性划分为局部遗传相关性 (LGC) 提供了预测准确度的显著提升.
- 拟议的LGC模型为多种群基因组预测策略提供了强大的增强.
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