提高基因组预测的准确性,使用从GWAS预选的SNP与猪的计算全基因组序列数据
Yiyi Liu1,2, Yuling Zhang1,2, Fuchen Zhou1,2
1National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
Animals : an open access journal from MDPI
|December 23, 2023
概括
基因组特征BLUP (GFBLUP) 模型通过整合全基因组关联研究 (GWAS) 数据来提高猪特征如腰肌区域的基因组预测准确度. 这种方法显示了改善基因组选择 (GS) 策略的潜力.
科学领域:
- 动物遗传学和动物繁殖
- 基因组选择 基因组选择
- 量化遗传学 量化遗传学
背景情况:
- 基因组选择 (GS) 旨在提高牲畜复杂特征的预测准确性.
- 整合生物信息,例如来自全基因组关联研究 (GWAS),可以增强GS模型.
- 识别显著的单核酸多态 (SNPs) 为特征预测提供了宝贵的基因组见解.
研究的目的:
- 评估基因组特征BLUP (GFBLUP) 模型与基因组最佳线性无偏预测 (GBLUP) 模型相比的有效性.
- 评估将GWAS识别的SNP纳入基因组特征对猪背部脂肪厚度 (BFT) 和腰肌面积 (LMA) 的预测准确性的影响.
- 探索GFBLUP在猪育种计划中改进基因组预测的潜力.
主要方法:
- 在685头Duroc × Landrace × Yorkshire (DLY) 猪身上进行了全基因组关联研究 (GWAS),以确定显著的SNP.
- 比较GBLUP和GFBLUP模型,使用来自651头约克郡猪的归算全基因组测序 (WGS) 数据.
- 利用GWAS衍生SNP作为GFBLUP模型中的预选基因组特征.
主要成果:
- GBLUP实现了0.499的BFT和0.423的LMA的预测准确度.
- 使用基于GWAS的SNP预选的GFBLUP实现了BFT的0.491和LMA的0.440的平均预测准确度.
- 与GBLUP相比,GFBLUP在LMA的预测准确度上显示了4.8%的改善.
结论:
- 在复杂特征的特定场景中,GFBLUP模型可以提供比GBLUP更高的基因组预测准确度.
- 结合来自GWAS的基因组特征,提高了GS模型的精细化.
- 这项研究强调了整合先前生物信息的价值,以便在养猪中更准确地进行基因组预测.
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