整合纵向代谢物概况以特征和时间点为依赖的方式改善了猪的特征预测
Quazi Abir Hassan Roddur1, Jiyai Qu1, Dingzhen Liang1
1University of California, Davis, 1 Shields Ave, Davis, CA 95616.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
将猪血血清代谢物概况集成到基因组预测模型中,显著提高了对经济重要特征的准确性. 这种方法通过利用中间的奥米克数据来提高基因改进,以获得更好的育种决策.
科学领域:
- 动物遗传学动物遗传学
- 基因组学就是基因组学.
- 代谢学 代谢学 代谢学
背景情况:
- 准确的遗传优点预测对于加速猪的遗传改进至关重要.
- 像料摄入量,料转化,背部脂肪,每日增量和腰部深度等特征在经济上很重要,但直接测量是具有挑战性的.
- 这项研究探索了将来自两个发育阶段的血清代谢物概况整合到基因组预测模型中.
研究的目的:
- 评估整合代谢物概况对猪五个关键经济特征的基因组预测准确性的影响.
- 为了比较不同的整合策略,包括单个和多个时间点的代谢物数据与基因组数据相结合.
- 确定代谢物概况的价值,作为增强育种计划的中间奥米克层.
主要方法:
- 利用贝叶斯C建模框架对1637头猪进行了测试,并提供了完整的表型,基因型和代谢物数据.
- 评估了七种模型:仅基因型,仅代谢物 (10周,20周或两者兼用) 和组合基因型-代谢物模型.
- 分析了平均每日料摄入量 (DFI),料转换率 (FCR),背部脂肪厚度 (BF),测试每日增益 (TDG) 和腰部深度 (LD).
主要成果:
- 基因组预测准确性随着代谢物概况的整合而持续改善.
- 结合基因型和代谢物数据 (G+M2,G+M1+M2) 的模型显示,DFI (0.31至0.41) 和FCR (0.27至0.33) 的精度显著增加.
- 在G+M2和G+M1+M2模型中,BF和TDG的最大收益被观察到,而LD从结合两个时间点 (G+M1+M2) 中获益最多.
结论:
- 整合代谢物概况,特别是从20周阶段或组合的时间点,提高基因组预测在各种特征的准确性.
- 代谢物概况作为一个有价值的中间奥米克层,提供补充的生物学见解.
- 根据特定的特征和采样时间量身定制整合策略,最大限度地提高了代谢物数据在猪遗传改进方面的潜力.
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