基因组和现象预测的评估,用于果繁殖中的应用
Michaela Jung1,2, Marius Hodel3, Andrea Knauf3,4
1Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil, 8820, Switzerland. michaela.jung@agroscope.admin.ch.
BMC plant biology
|January 25, 2025
概括
通过基因组选择增强果育种需要更大,相关的培训集,以获得更好的预测能力. RADseq基因型定制是一种成本效益较高的替代方案,但现象预测目前无法改善水果质量.
科学领域:
- 植物育种 植物育种
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组预测模型通过预测材料性能来增强果育种.
- 模型的准确性取决于遗传架构,训练集大小,相关性和验证方法.
- 替代基因型定型 (RADseq) 和现象数据 (NIRS) 提供了成本效益,但需要进一步调查.
研究的目的:
- 评估137个预测场景,用于果育种中的基因组选择.
- 评估培训集大小,生殖质相关性,基因型定型方法和验证策略的影响.
- 为在实际的果育种计划中实施基因组选择提供建议.
主要方法:
- 评估了137个不同训练集大小和生殖质相关性的预测场景.
- 与10倍交叉验证与离开一个家庭的交叉验证进行比较.
- 对SNP数组数据进行基因型鉴定,对RADseq数据进行评估.
- 研究近红外光谱 (NIRS) 用于现象预测.
主要成果:
- 扩展与相关的生殖质相关的训练集提高了预测能力高达0.08.
- 与10倍CV相比,Leave-one-family-out交叉验证降低了0.24的预测能力.
- 导入的RADseq数据显示了类似于SNP数组数据的预测能力.
- 使用NIRS的现象预测降低了0.35的预测能力,使其变得不切实际.
结论:
- 训练组中的相关生殖质对于提高果的基因组预测准确性至关重要.
- RADseq是一种可行的,具有成本效益的基因造型替代SNP阵列.
- 目前使用NIRS的现象预测方法对于果育种是不切实际的.
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