全基因组协会研究和现型预测大白猪的繁殖特征
Hao Zhang1, Shiqian Bao1, Xiaona Zhao1
1School of Software Technology, Shanxi Agricultural University, Jinzhong 030801, China.
Animals : an open access journal from MDPI
|December 17, 2024
概括
这项研究使用全基因组关联研究确定了猪特征的遗传标记. 机器学习模型提高了健康和断奶幼数量的预测准确度.
科学领域:
- 动物遗传学动物遗传学
- 生殖生物学 生殖生物学
- 生物信息学是一种生物信息学.
背景情况:
- 生殖性能对于猪业的利能力至关重要.
- 识别影响子特征的遗传因素可以改善育种计划.
- 全基因组关联研究 (GWAS) 是解剖复杂特征的强大工具.
研究的目的:
- 进行全基因组关联研究 (GWAS),以确定与大白猪生殖特征相关的遗传位置.
- 确定参与调节 litter 尺寸和生存的生物途径的候选基因.
- 用SNP数据评估机器学习和传统模型预测生殖特征的性能.
主要方法:
- 对385头大白猪进行了GWAS测试,以确定健康子数量 (NHs) 和断奶子数量 (NWs).
- 候选基因鉴定涉及SNP关联分析,基因本体学 (GO) 和KEGG通路丰富.
- 包括机器学习 (GBDT,RF,LightGBM,Adaboost.R2) 和传统 (GBLUP,BRR,BL) 在内的表型预测模型,与主要组件分析 (PCA) 并没有进行评估.
主要成果:
- 显著的SNP位点 (例如,ALGA0098819,ALGA0037969) 与NH和NW相关.
- 像BLVRA,STK17A,PSMA2,C7orf25,PLCXD3,RNASEH1,PYM1和SEPTIN9这样的候选基因都与生殖过程有关.
- 在GBDT和LightGBM模型中,PCA之后,NH和NW的预测精度 (PCC) 分别是最高的.
结论:
- 这项研究确定了新的遗传标记物和候选基因,用于改善大白猪的生殖特征.
- 机器学习模型,特别是GBDT和LightGBM,显示出提高垃圾特征预测的潜力.
- 进一步的研究可以利用这些发现进行标记器辅助的选择,以提高猪的生产力.
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