Taniguchi Shoji, Hayashi Takeshi, Nakagawa Hiroshi, Matsushita Kei, Kajiya-Kanegae Hiromi, Yonemaru Jun-Ichi, Goto Akitoshi
Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan.
Institute of Crop Science, NARO, Tsukuba, Japan.
Rice (N Y). 2025 Apr 11;18(1):27. doi: 10.1186/s12284-025-00778-4.
Genomic prediction is a promising strategy for enhancing crop breeding efficiency. Historical data of breeding and cultivation tests from geographically wide regions presumably contain rich information for training genomic prediction models. Therefore, it is essential to explore methodologies to effectively handle such data. To improve the prediction accuracy of models using historical data, we incorporated a spatial model to account for spatial structures among field stations, in addition to conventional genomic prediction models. Targeting the rice heading date from historical data across Japan, we first constructed conventional genomic prediction models using genomic and/or meteorological elements as predictors. Next, we obtain the residual terms. Assuming that the residual terms were partly explained by the spatial effects assigned to each field station, a spatial model was applied to the residual terms and the spatial effects were calculated. Our genomic prediction models performed best when the genome, meteorological elements, and genome-meteorology interactions were included (model 3), and they performed second best when the genome and meteorological elements were included (model 2). For these genomic prediction models, residual terms were spatially biased and corrected for spatial effects. For the best model (model 3), the root mean squared errors (RMSE) of genomic prediction combined with spatial effects were approximately 3.6 days under tenfold cross-validation and approximately 5.1 days under leave-one-line-out cross-validation. The inclusion of the spatial effects improved the RMSEs by approximately 15% and 9% for the former and latter, respectively. Lines with highly improved predictions of the spatial effects were developed, mainly in the northern Tohoku region. The spatial effects were heterogeneous and regional patterns were detected. These findings imply that spatial effects are important not only for improving prediction performance but also for dissecting the model itself to identify the factors contributing to model improvement.
基因组预测是提高作物育种效率的一种有前景的策略。来自地理范围广泛地区的育种和栽培试验历史数据可能包含丰富的信息，可用于训练基因组预测模型。因此，探索有效处理此类数据的方法至关重要。为了提高使用历史数据的模型的预测准确性，除了传统的基因组预测模型外，我们还纳入了一个空间模型来考虑田间试验站之间的空间结构。针对日本各地的水稻抽穗期历史数据，我们首先构建了以基因组和/或气象要素作为预测因子的传统基因组预测模型。接下来，我们得到残差项。假设残差项部分由分配给每个田间试验站的空间效应解释，将空间模型应用于残差项并计算空间效应。当纳入基因组、气象要素以及基因组 - 气象相互作用时（模型3），我们的基因组预测模型表现最佳，当纳入基因组和气象要素时（模型2），表现次之。对于这些基因组预测模型，残差项存在空间偏差，并针对空间效应进行了校正。对于最佳模型（模型3），在十折交叉验证下，结合空间效应的基因组预测的均方根误差（RMSE）约为3.6天，在留一法交叉验证下约为5.1天。纳入空间效应后，前者和后者的RMSE分别提高了约15%和9%。开发出了对空间效应预测有显著改善的品系，主要集中在东北北部地区。空间效应是异质的，并检测到了区域模式。这些发现表明，空间效应不仅对于提高预测性能很重要，而且对于剖析模型本身以识别有助于模型改进的因素也很重要。
