从全基因组测序中优先选择SNP提高了使用线性和机器学习模型在鱼中基因组预测的准确性
Hailiang Song1,2, Wei Wang1,2, Tian Dong1,2
1Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China.
International journal of molecular sciences
|July 29, 2025
概括
使用全基因组关联研究 (GWAS) 和机器学习模型优化单核酸多态性 (SNP) 选择显著提高了俄罗斯鱼的基因组预测准确性. 这种方法增强了水产养殖养殖计划的特征,如鱼子产量和体重.
科学领域:
- 水产养殖的基因组学
- 动物繁殖 动物繁殖
- 生物信息学是一种生物信息学.
背景情况:
- 基因组预测对于水产养殖繁殖至关重要,但依赖于信息标记和模型.
- 俄罗斯 (Acipenser gueldenstaedtii) 繁殖可以从增强的基因组预测准确性中受益.
- 全基因组测序 (WGS) 为基因组选择提供了密集的标记数据.
研究的目的:
- 为了提高俄罗斯鱼的基因组预测准确度.
- 为了优化单核酸多态性 (SNP) 选择策略.
- 评估用于基因组预测的线性和机器学习模型.
主要方法:
- 从971只俄罗斯鱼获得的WGS数据.
- 应用了三种SNP选择策略:基于GWAS,链接不平衡 (LD) 修剪和随机抽样.
- 使用线性 (GBLUP) 和机器学习 (随机森林) 模型进行了基因组预测,SNP密度各不相同 (1K-50K).
主要成果:
- 基于GWAS的SNP选择提高了高达3.4%的预测准确度,特别是在中等密度 (≥10K) 的情况下.
- 在较高密度 (30K-50K) 的基于LD的选择显示了与完整的WGS数据可比的性能.
- 机器学习模型,特别是随机森林,表现优于线性模型,在GWAS选择的SNP中增加了2.0%的准确性.
结论:
- 将WGS数据与GWAS告知SNP选择相结合,可以有效地改善俄罗斯的基因组预测.
- 在这种情况下,机器学习模型比线性模型提供更高的性能.
- 开发的框架有可能在全球推进水产养殖繁殖计划.
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