基于机器视觉获取的羊生长特征遗传参数的估计
1Inner Mongolia Agricultural University Animal Science Department, Inner Mongolia Agricultural University, Zhaowuda Road, No.8 Teaching and Research Building, 010018 Hohhot City, Inner Mongolia Autonomous Region, China; Key Laboratory Of Mutton Sheep and Goat Genetics And Breeding, Ministry Of Agriculture And Rural Affairs, Zhaowuda Road, No.8 Teaching and Research Building, 010018, Hohhot City, Inner Mongolia Autonomous Region, China.
Animal : an international journal of animal bioscience
|June 25, 2024
概括
机器对绵羊表型的测量显示了高通量育种的潜力. 这项研究将机器生成的数据与手动测量进行了比较,发现机器数据可能会高估遗传性,但不会影响育种模型选择.
科学领域:
- 动物科学动物科学
- 量化遗传学 量化遗传学
- 农业技术 农业技术
背景情况:
- 手动测量是动物表型定型的标准,但需要高通量方法进行遗传评估.
- 机器生成的数据提供了提高效率的潜力,但需要验证.
研究的目的:
- 为了比较Ujumqin绵羊生长特征的机器生成和手动测量.
- 用两种测量类型来调查遗传参数的变化.
- 确定遗传评估的最佳统计模型.
主要方法:
- 使用羊数据记录器和羊体型收集器系统进行数据收集.
- 评估了六种随机回归模型,考虑了母体和永久的环境影响.
- 分析了来自75个母和399个母的552个数据点,并考虑了年龄和性别等固定影响.
- 雇员Akaike信息标准,贝叶斯信息标准,以及模型选择的交叉验证.
主要成果:
- 对手工测量的特征的遗传概率估计在0.03到0.53之间.
- 机器测量特征的遗传概率估计在0.10至0.55之间.
- 手动和机器数据的遗传相关性分别在0.35-0.99和-0.05-0.99之间.
- 现型相关性在0.07-0.90 (人工) 和0.03-0.84 (机器) 之间.
结论:
- 基于机器的测量可能会高估某些绵羊生长特征的遗传性.
- 尽管有可能高估,但机器数据不会改变选择合适的育种模型.
- 验证的机器数据作为在绵羊繁殖计划中对遗传参数估计的可行工具.
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