通过加权多个内核脊回归集成基因表达数据,提高了基因组预测的准确性
Xue Wang1, Jingfang Si1, Yachun Wang1
1State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, China.
权重多核脊回归 (WMKRR) 通过整合基因组和预测的转录基因组数据来改善基因组预测. 这种方法可以提高繁殖价值的预测,而无需额外的奥米克测序成本.
科学领域:
- 动物育种与遗传学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 基因表达特征为预测繁殖值和表型提供了宝贵的见解.
- 实际的育种计划往往缺乏转录基因组数据,仅依赖基因组数据.
- 从遗传标记物预测基因表达,为整合转录组信息提供了一个解决方案.
研究的目的:
- 开发和评估一种新的方法,加权多核脊回归 (WMKRR),用于整合基因组和遗传预测的转录基因组数据.
- 将WMKRR的预测能力与传统的基因组最佳线性无偏预测 (GBLUP) 和组合的基因组和转录组最佳线性无偏预测 (GTBLUP) 进行比较.
主要方法:
- 扩展的内核回归 (KRR) 到加权的多重内核回归 (WMKRR).
- 利用多个内核学习 (MKL) 方法整合基因组数据和从遗传标记中预测的转录基因数据.
- 评估WMKRR使用基于CattleGTEx数据集和真实乳牛数据的模拟数据,使用特征选择和非特征选择场景.
主要成果:
- 与GBLUP和GTBLUP相比,WMKRR在模拟和真实乳牛数据集中表现出优越的预测能力.
- 在模拟数据中,WMKRR在预测能力方面比GBLUP和GTBLUP取得了1.12%-3.23%的平均改善.
- 在真实的乳牛数据中,WMKRR在不同的验证场景中显示了比GBLUP和GTBLUP平均5.56%-8.41%的改善.
结论:
- WMKRR模型有效地整合了基因组和基因预测的转录组数据,超过了传统的基因组预测模型.
- 这项研究强调了通过利用omics数据而提高基因组育种应用的潜力,而不会增加测序费用.
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