通过大规模传感器数据改善牲畜社会行为:数据模拟和遗传分析
Zhuoshi Wang1, Harmen Doekes2, Piter Bijma2
1Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands. zhuoshi.wang@wur.nl.
Genetics, selection, evolution : GSE
|September 28, 2023
概括
遗传模型现在可以使用大型数据集分析牲畜的社会行为. 通过有限的数据,估计的育种值 (EBV) 获得了有希望的准确性,支持遗传改进策略.
科学领域:
- 动物遗传学动物遗传学
- 伦理学 伦理学 伦理学
- 量化遗传学 量化遗传学
背景情况:
- 畜牧业有害的社会行为降低了福利和生产力.
- 选择性育种受到大型群体和遗传模型缺少表型化方法的阻碍.
- 来自计算机视觉的未来大规模纵向数据需要新的遗传分析模型.
研究的目的:
- 开发模拟和分析大规模纵向社会行为数据的模型.
- 调查样本大小要求,以准确地估计遗传参数和育种价值.
- 评估群体养动物社会特征的遗传改进潜力.
主要方法:
- 基于代理的建模模拟了2000只动物的20万个社交互动.
- 定义了表演者和受体效应的潜在特征,具有不同的遗传性和遗传相关性.
- 使用一般线性混合模型估计的方差成分和繁殖值.
主要成果:
- 模拟的遗传参数与真实值非常接近.
- 当包括所有相互作用时,估计的繁殖值 (EBV) 的高准确性得到了实现.
- 在每个人只有100个相互作用的情况下,获得了有希望的准确性 (0.47-0.71).
结论:
- 开发了新的模型,用于对群体养动物的社会行为进行遗传分析.
- 证明了可以用有限的相互作用数据 (约. 每个人100个).
- 得出结论,动物繁殖是改善牲畜社会行为的可行策略.
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