基因组最佳线性无偏预测和四种机器学习模型的性能比较,用于估计工作犬的基因组育种值
Joseph A Thorsrud1, Katy M Evans2,3, Kyle C Quigley2
1Department of Animal Sciences, College of Agriculture and Life Sciences, Cornell University, 201 Morrison Hall, 507 Tower Road, Ithaca, NY 14853, USA.
Animals : an open access journal from MDPI
|February 13, 2025
概括
基因组预测模型对导犬特征表现相似. 基因组最佳线性无偏预测 (GBLUP) 是最有效的,低密度SNP数据对基因组繁殖值有效.
科学领域:
- 动物遗传学动物遗传学
- 量化遗传学 量化遗传学
- 狗的基因组学 狗的基因组学
背景情况:
- 导狗的成功受健康和行为特征的影响.
- 精确预测基因组育种值 (gEBVs) 对于选择性育种至关重要.
- 存在各种基因组预测模型,但它们对狗特征的比较效率尚未完全理解.
研究的目的:
- 评估五种基因组预测模型 (GBLUP,RF,SVM,XGB,MLP) 的性能,用于预测导犬中的gEBV.
- 评估特征遗传性,病例数和SNP密度对模型性能的影响.
- 为了确定最有效的模型,用于基因组预测在犬的繁殖计划.
主要方法:
- 利用了来自2050只导狗 (德国牧羊犬,金色回收犬,拉布拉多回收犬和交叉犬) 种群的表型和基因组数据.
- 评估了五种基因组预测模型:GBLUP,随机森林,SVM,XGBoost和MLP.
- 分析了四种特征的模型性能 (牙,牙,口腔乳头瘤,分心),具有不同的遗传性和病例数量,以及不同的SNP标记密度.
主要成果:
- 所有测试的模型在不同的遗传性,病例数和SNP密度上都显示出相似的预测性能.
- 基因组最佳线性无偏预测 (GBLUP) 是最有效的模型,因为它不需要参数优化.
- 排骨症表现出最高的预测准确性,其次是无牙症和分心症,口腔乳头炎表现出最低的准确性,与各自的遗传能力相关.
结论:
- 较低密度的SNP数据集足以构建准确的gEBV,减少对高成本基因型的需求.
- 像GBLUP这样的更简单的模型足以用于犬种育种计划中的基因组预测,提供效率和易用性.
- 标准化的表型评估和精确构建的参考种群对于优化犬种育种中的基因组选择至关重要.
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