摘要
由立枯丝核菌引起的水稻纹枯病是世界性的水稻严重病害之一,且在育种和生产上可利用的抗病种质极少,迫切需要发掘抗源。本研究鉴定了来自中国南方11个省（市、自治区）的166个籼型杂交稻组合对纹枯病菌的苗期抗性。未发现免疫或高抗的组合,但不同组合间的抗性差异极显著,平均病情指数符合正态分布N（5.27,0.682）,变化幅度为2.847.64。根据动态聚类分析,参试组合被划分为抗、中抗、中感、感和高感5级,分别占总数的1.20%、13.86%、36.14%、43.37%和5.42%。参试的大多数组合为感病,仅K优88和中优9801抗病,但其抗性还需在大田进行成株期验证。用Bayes法建立了各抗性级的判别函数,判别准确率达96.39%。根据综合病情指数,上述5级的组合分别占总数的1.20%、13.25%、63.25%、21.69%和0.60%。与动态聚类比较,两者极显著相关（r=0.81,P〈0.01）,说明这两种方法均可用于水稻对纹枯病的抗性评价。但动态聚类法较适用于接种环境相对一致的苗期鉴定,而不能比较不同环境条件下的鉴定结果;综合病情指数法则不受时间、地点和批次的限制,更具实用性。通过这两种方法共筛选出26个抗或中抗的组合,它们之间的遗传距离为0.040.71;在遗传距离为0.36处,可将其中的22个组合聚为一簇,表明其遗传基础较窄。系谱追踪表明26个抗或中抗的杂交稻亲本亲缘关系较近。因此,在抗纹枯病育种中,需要拓宽抗病种质的遗传基础,培育聚合有数量抗性的材料或组合。
Rice sheath blight caused by Rhizoctonia solani is one of the most devastative diseases worldwide,which causes serious yield loss under favorable environmental conditions.R.solani has a very wide host range and the rice germplasm with complete resistance to this disease is zezo.Sheath blight resistance is generally controlled by polygene,with a wide variation among rice cultivers.It is necessary to evaluate probably partial resistance to sheath blight from germplasms for rice breeding program.A total of 166 indica hybrid rice combinations collected from 11 provinces in southern China were inoculated by using five isolates with different pathogenicities at seedling stage in the greenhouse.No combination was found to be completely immune or highly resistant,and the resistance to sheath blight varied significantly among different combinations.Average disease index of these combinations were in accordance with normal distribution N（5.27,0.682） and ranged from 2.84 to 7.64.The results showed that this inoculation method at seedling stage was simple,rapid,uniform,and allowed to distinguish minor differences in susceptibility,so it can be used for a large-scale screening or primary resistance identification.According to the disease index of each isolate,166 combinations were classified into five types based on dynamic clustering analysis（DCA）,namely resistant（R）,moderately resistant（MR）,moderately susceptible（MS）,susceptible（S）,and highly susceptible（HS）,accounting for 1.20%,13.86%,36.14%,43.37%,and 5.42%,respectively.The discriminant functions for each type were calculated based on Bayes method,and the accuracy rate for discrimination reached 96.39%.The results indicated that most of the combinations were susceptible.Only two combinations,K you 88 and Zhongyou 9801,were resistant,but should be verified at the adult stage in paddy field.Synthetic disease index（SDI） of all combinations was also calculated by average disease index of the five isolates,the combinations were graded as R,MR,MS,S,and HS based on SDI,accounting for 1.20%,13.25%,63.25%,21.69%,and 0.60%,respectively.There was significant correlation between the results of DCA and SDI（r = 0.81,P 〈 0.01）,showing that both methods can be used for evaluating the disease resistance.But the former method is suitable for seedling screening under the uniform growth conditions;while the latter is independent of time,place and batch of the identification,and more versatile than the former.Only 26 combinations with resistance or moderate resistance to sheath blight were gained by DCA and SDI,the genetic distances ranged from 0.04 to 0.71.Twenty six combinations were divided into two groups by UPGMA cluster analysis,and 22 out of them were grouped into a group with the narrow genetic background at genetic distance of 0.36.There was a close relationship among the parents of the 26 combinations according to the pedigree tracing.Therefore,the practical strategy for rice resistance breeding to sheath blight is further to broaden genetic bases of germplasm,pyramid quantitative resistance genes,construct hybrid combinations with parents having desirable traits and resistance to sheath blight.
出处
《作物学报》
CAS
CSCD
北大核心
2011年第2期263-270,共8页
Acta Agronomica Sinica
基金
国家公益性行业(农业)科研专项经费项目(nyhyzx3-16)
中央级公益性科研院所基本科研业务费专项基金项目(2009RG004-4)
浙江省三农五方科技协作计划项目(SN200711)
国家科技支撑计划项目(2006BAD08A04)资助
关键词
籼型杂交稻
纹枯病
抗性鉴定
动态聚类分析
综合病情指数
Indica hybrid rice； Sheath blight； Resistance evaluation； Dynamic clustering analysis（DCA）； Synthetic disease index（SDI）
