State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, People's Republic of China.
Plant Dis. 2021 Oct;105(10):2964-2969. doi: 10.1094/PDIS-08-20-1681-RE. Epub 2021 Nov 9.
Rice sheath blight (SB) disease is a global issue that causes great yield losses each year. To explore whether SB field resistance can be predicted, 273 rice genotypes were inoculated and evaluated for SB field resistance across nine environments from 2012 to 2019 to identify loci associated with SB resistance by association mapping. A total of 80 significant marker-trait associations were detected in nine environments, among which six loci (D130B, D230A, D304B, D309, D427A, and RM409) were repeatedly detected in at least two environments. A linear regression model for predicting SB lesion length was developed using genotypic data of these six loci and SB field resistance data of the 273 rice genotypes: = 34.44 - 0.56, where is the predicted value of lesion length, and is the total genotypic value of the six loci. A recombinant inbred line (RIL) population consisting of 219 lines that was grown in six environments (from 2013 to 2018) for evaluation of SB field resistance was used to check the prediction accuracy of the prediction model. The average absolute error between the predicted lesion length and real lesion length for the RIL population was 6.67 cm. The absolute errors between predicted and real lesion lengths were <6 cm for 51.22% of the lines and <9 cm for 71.22% of the lines. An SB visual rating prediction model was also developed, and the average absolute error between the predicted visual rating and real visual rating for the RIL population was 0.94. These results indicated that the rice SB lesion length can be predicted by the development of a linear regression model using both genotypic and phenotypic data.
水稻纹枯病(SB)是一个全球性问题,每年都会导致巨大的产量损失。为了探索田间抗性是否可以预测,我们在 2012 年至 2019 年的 9 个环境中接种和评估了 273 个水稻基因型的田间抗性,以通过关联作图鉴定与 SB 抗性相关的基因座。在 9 个环境中共检测到 80 个显著的标记-性状关联,其中 6 个基因座(D130B、D230A、D304B、D309、D427A 和 RM409)在至少两个环境中重复检测到。利用这 6 个基因座的基因型数据和 273 个水稻基因型的田间抗性数据,建立了一个预测 SB 病斑长度的线性回归模型: = 34.44 - 0.56,其中 是病斑长度的预测值, 是 6 个基因座的总基因型值。利用在 6 个环境(2013 年至 2018 年)中生长的 219 个系的重组自交系(RIL)群体来检验预测模型的预测准确性。RIL 群体的预测病斑长度与实际病斑长度的平均绝对误差为 6.67cm。对于 51.22%的系,预测病斑长度与实际病斑长度的绝对误差<6cm;对于 71.22%的系,预测病斑长度与实际病斑长度的绝对误差<9cm。还建立了一个 SB 视觉评分预测模型,RIL 群体的预测视觉评分与真实视觉评分的平均绝对误差为 0.94。这些结果表明,可以使用基于基因型和表型数据的线性回归模型来预测水稻 SB 病斑长度。