在全国历史数据中建模和使用空间效应改善了在日本大米头期的基因组预测
Shoji Taniguchi1, Takeshi Hayashi1, Hiroshi Nakagawa1
1Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan.
概括
将空间效应纳入基因组预测模型显著提高了预测大米标题日期的准确性. 这种方法通过考虑历史试验数据的地理差异来增强作物育种.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学 是一个
- 植物育种 植物育种
背景情况:
- 基因组预测模型通过利用历史数据来提高作物育种效率.
- 地理多样化的历史数据提供了丰富的信息,但需要有效的处理方法.
- 现场站之间的空间结构可以影响预测的准确性.
研究的目的:
- 通过使用历史的育种和栽培数据,提高基因组预测模型的预测准确度.
- 探索有效处理地理范围广泛的历史数据的方法.
- 研究空间效应对大米基因组预测的影响.
主要方法:
- 传统的基因组预测模型是使用基因组和气象元素构建的.
- 一个空间模型被应用到剩余项来计算现场站的空间效应.
- 模型性能在交叉验证和离开一行外表方案下使用根平均平方误差 (RMSE) 进行了评估.
主要成果:
- 结合基因组,气象元素及其相互作用的基因组预测模型表现最好.
- 包括空间效应改善了预测准确性,将RMSE降低了约15% (10倍CV) 和9% (LOLO CV).
- 空间效应异质,检测到区域模式,特别是在东北地区,并确定了有改进预测的线条.
结论:
- 空间效应对于提高基因组预测模型的预测性能至关重要.
- 对空间效应的计算有助于剖析模型并确定有助于改进预测的因素.
- 这种方法为在作物育种计划中利用历史数据提供了一种有价值的方法.
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