阅读说明：本技术 一种评价香蕉镰刀菌枯萎病抗病性的方法 (Method for evaluating disease resistance of banana fusarium wilt ) 是由 吴元立 黄秉智 彭新湘 张智胜 杨兴玉 于 2019-11-07 设计创作，主要内容包括：本发明公开了一种对香蕉镰刀菌枯萎病的抗病性进行评价的方法,属于农业—植物保护技术领域。与田间鉴定法和苗期人工接种鉴定法不同,生根试管苗离体接种鉴定法是在无菌条件下将Foc接种到香蕉试管苗的基部,然后按照1-6级的病害评价等级进行抗病性鉴定。鉴于病害级数在病害严重度上是非线性的,利用Logistic回归分析进行模型构建和发病等级概率预测,再根据预测结果进一步将香蕉品种的抗病性划分为高抗、抗病、中抗、感病和高感等5个级别。本发明提供的方法实现了生根试管苗离体接种鉴定法和其它鉴定法的整合,通过对采用不同鉴定方法获得的结果进行比对,并在此基础上调整实验方案和技术路线,推动抗病育种工作的进程。(The invention discloses a method for evaluating disease resistance of fusarium wilt of banana, belonging to the technical field of agriculture and plant protection. Different from a field identification method and a seedling stage artificial inoculation identification method, the rooting test-tube plantlet in-vitro inoculation identification method is characterized in that Foc is inoculated to the base of a banana test-tube plantlet under an aseptic condition, and then disease resistance identification is carried out according to a disease evaluation grade of 1-6 grade. In view of the fact that the disease grade number is nonlinear in disease severity, Logistic regression analysis is utilized to conduct model construction and disease grade probability prediction, and then the disease resistance of the banana variety is further divided into 5 grades such as high resistance, disease resistance, medium resistance, susceptible disease and high sensitivity according to prediction results. The method provided by the invention realizes the integration of the rooting test-tube plantlet in vitro inoculation identification method and other identification methods, compares the results obtained by adopting different identification methods, and adjusts an experimental scheme and a technical route on the basis to promote the progress of disease-resistant breeding work.)

1. A method for evaluating disease resistance of banana fusarium wilt is characterized by comprising the following steps:

1) data collection: observing the disease grade number of the obtained banana rooting test-tube plantlet after being inoculated with Foc;

2) model construction based on cumulative Logistic regression:

wherein y' represents the morbidity of banana rooting test-tube plantlet, α represents the intercept term, β_k(K ═ 1,2,. K) represents regression coefficients; x is the number of_k(K ═ 1, 2.., K) for the kth banana variety; ε is the error term;

3) calculating the cumulative probability:

assigning 6 disease grades set by a rooting test-tube seedling in vitro inoculation identification method to corresponding values of y which is 1, y which is 2, y which is 6, wherein the relation among the y values is (y which is 1) < (y which is 2) < (y which is 6), and 5 dividing lines are used for separating adjacent categories:

if y' is less than or equal to mu₁If y is 1;

if μ₁＜y′≤μ₂If y is 2;

if μ₂＜y′≤μ₃If y is 3;

if μ₃＜y′≤μ₄If y is 4;

if μ₄＜y′≤μ₅If y is 5;

if μ₅If < y', then y is 6;

μ_jis a demarcation point separating classes, and μ₁＜μ₂＜μ₃＜μ₄＜μ₅；

The cumulative probability is calculated as follows:

thus, the probability that the rooting test-tube plantlet of a certain banana variety is in a certain disease grade is obtained, and the sum of the probability values of the grades is 1, namely P (y is 1) + P (y is 2) + … + P (y is 6) ═ 1;

4) and further dividing the disease resistance grade according to the prediction probability of the disease grade.

2. The method according to claim 1, wherein the disease grade of step 1) is 1-6 grade, and the small leaves at the base of the pseudostem of 1 grade are withered, but the color of the pseudostem itself is not changed; grade 2-areas of darkened pseudostem color less than or equal to 1/2 for the entire pseudostem height; grade 3-darkened area of pseudostem color exceeding 1/2 the entire pseudostem height; level 4-yellowing or withered upper leaves less than or equal to 1/2 of total upper leaves of the plantlets; grade 5-the number of yellowing or withered upper leaves exceeds 1/2 for the total number of upper leaves of the plantlets; grade 6-withering and death of the whole test-tube plantlet.

3. The method of claim 1, wherein the disease resistance rating of step 4) is: when P (y is 2) is not less than 50%, P (y is 1) is not more than 50%, it is high resistance.

4. The method of claim 1, wherein the disease resistance rating of step 4) is: when P (y is 2) is more than or equal to 50 percent and P (y is 3) is less than or equal to 50 percent, the disease resistance is realized.

5. The method of claim 1, wherein the disease resistance rating of step 4) is: when P (y is 3) is more than or equal to 50 percent, P (y is 2) is less than or equal to 50 percent or P (y is 4) is less than or equal to 50 percent, the resistance is obtained.

6. The method of claim 1, wherein the disease resistance rating of step 4) is: when P (y is 4) is more than or equal to 50%, P (y is 3) is less than or equal to 50% or P (y is 5) is less than or equal to 50%, the disease is infected.

7. The method of claim 1, wherein the disease resistance rating of step 4) is: when P (y is 5) is more than or equal to 50%, P (y is 4) is less than or equal to 50% or P (y is 6) is less than or equal to 50%, the feeling is high.

Technical Field

The invention belongs to the technical field of agriculture and plant protection, and particularly relates to a method for evaluating disease resistance of fusarium wilt of banana.

Background

The identification of the disease resistance of fusarium wilt of banana is an important link of disease-resistant breeding work and mainly comprises a field identification method, a seedling stage artificial inoculation identification method, a rooting test tube seedling in-vitro inoculation identification method and the like.

Although the observation result of the disease in the field is still the final basis for evaluating the resistance level, the resistance identification of fusarium oxysporum f.sp.cubense in the field needs a large area of land with uniform disease incidence and has higher cost. Introduction evaluation (Liuwenqing and the like) of new varieties of bananas with wilt resistance and comparison of resistance and main characters (flavin plum and the like) of a plurality of banana varieties to wilt resistance are reported, and researchers divide the disease resistance of banana germplasm into five levels of high resistance, disease resistance, infection and high sensitivity on the basis of field morbidity.

And (3) identifying the seedling stage by artificial inoculation: a potting system or a hydroponic system is established in a greenhouse, then pathogenic bacteria (Fusarium oxysporum f.sp.cubense, Foc) are inoculated to the roots of banana seedlings and the disease is recorded. According to the investigation result, calculating the disease index of each variety according to the following formula:

finally, dividing the disease resistance of the tested banana variety into 5 grades of high resistance, disease resistance, medium resistance, susceptibility, high susceptibility and the like according to the disease index of the seedling stage. The relevant documents are reported as follows: evaluation of resistance of 18 Guangdong banana germplasm to wilt disease (Song Xiao Bing et al), and establishment of evaluation method of resistance of banana to Fusarium oxysporum tropical # 4 race (left Save et al).

The rooting test-tube plantlet in vitro inoculation identification method is characterized in that Foc is inoculated to the base of a banana rooting test-tube plantlet under aseptic condition, a culture container is placed in a tissue culture room after inoculation to observe the disease condition, and then the disease grade identification is carried out on a single rooting test-tube plantlet according to the disease evaluation grade of 1-6 grades. Because the disease grade of the rooting test-tube plantlet in-vitro inoculation identification method is nonlinear in disease severity, the numerical value obtained by substituting the numerical value into the disease index calculation formula cannot reflect the true disease degree. That is, the disease index calculation formula is not applicable to the case where there is no quantitative limit in disease classification.

In summary, a reasonable mathematical model is constructed according to the characteristics of disease grade data, and the disease resistance grade is further scientifically divided on the basis, so that the problem to be solved is urgently needed.

Disclosure of Invention

The invention aims to provide a method for evaluating disease resistance of fusarium oxysporum f.sp.cubense.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

a method for evaluating disease resistance of fusarium oxysporum f.sp.cubense comprises the following steps:

1) data collection: observing the disease grade number of the obtained banana rooting test-tube plantlet after being inoculated with Foc;

2) model construction based on cumulative Logistic regression:

the Logistic regression model correspondingly comprises 5 logit functions:

wherein: p is a radical of₁，p₂，p₃，p₄，p₅，p₆The event probabilities respectively represent 1-6 grade disease evaluation grades, and the basic level for comparison is grade 6; x is the number of_k(K1, 2.., K) represents the kth banana variety β_0j(j＝1,2, …,5) represents the regression intercept term β_k(K ═ 1, 2.., K) represents the regression coefficient. Each logic function has the same coefficient term and different intercept terms, and the regression lines of the accumulated logics are parallel to each other;

the estimation method used by the Logistic regression model is a maximum likelihood method, and the obtained cumulative Logistic regression model is described as follows according to the Logistic model function designed for predicting the morbidity degree of the banana rooting test-tube plantlet:

wherein y' represents the morbidity of banana rooting test-tube plantlet, α represents the intercept term, β_k(K ═ 1,2,. K) represents regression coefficients; x is the number of_k(K ═ 1, 2.., K) for the kth banana variety; ε is the error term;

3) calculating the cumulative probability:

assigning 6 disease grades set by the rooting test-tube plantlet in vitro inoculation identification method to corresponding values of y which is 1, y which is 2, y which is 6,

the relationship between the values of y is (y-1) < (y-2) < (y-6), with a total of 5 dividing lines separating adjacent classes:

if y' is less than or equal to mu₁If y is 1;

if μ₁＜y′≤μ₂If y is 2;

if μ₂＜y′≤μ₃If y is 3;

if μ₃＜y′≤μ₄If y is 4;

if μ₄＜y′≤μ₅If y is 5;

if μ₅If < y', then y is 6;

μ_jis a demarcation point separating classes, and μ₁＜μ₂＜μ₃＜μ₄＜μ₅；

The cumulative probability is calculated as follows:

therefore, the probability that the rooting test-tube plantlet of a certain banana variety is in a certain disease grade can be obtained, and the calculation method comprises the following steps:

P(y＝1)＝P(y≤1)

P(y＝2)＝P(y≤2)-P(y≤1)

P(y＝3)＝P(y≤3)-P(y≤2)

P(y＝4)＝P(y≤4)-P(y≤3)

P(y＝5)＝P(y≤5)-P(y≤4)

P(y＝6)＝1-P(y≤5)

and the sum of the probability values of the respective levels is 1, i.e., P (y ═ 1) + P (y ═ 2) + … + P (y ═ 6) ═ 1;

4) and further dividing the disease resistance grade according to the prediction probability of the disease grade.

According to the embodiment of the invention, the disease grade of step 1) is 1-6 grade, 1 grade-the small leaves at the base of the pseudostem wither, but the color of the pseudostem itself is not changed; grade 2-areas of darkened pseudostem color less than or equal to 1/2 for the entire pseudostem height; grade 3-darkened area of pseudostem color exceeding 1/2 the entire pseudostem height; level 4-yellowing or withered upper leaves less than or equal to 1/2 of total upper leaves of the plantlets; grade 5-the number of yellowing or withered upper leaves exceeds 1/2 for the total number of upper leaves of the plantlets; grade 6-withering and death of the whole test-tube plantlet.

According to an embodiment of the present invention, the disease resistance level of step 4) is: when P (y is 2) is not less than 50%, P (y is 1) is not more than 50%, it is high resistance.

According to an embodiment of the present invention, the disease resistance level of step 4) is: when P (y is 2) is more than or equal to 50 percent and P (y is 3) is less than or equal to 50 percent, the disease resistance is realized.

According to an embodiment of the present invention, the disease resistance level of step 4) is: when P (y is 3) is more than or equal to 50 percent, P (y is 2) is less than or equal to 50 percent or P (y is 4) is less than or equal to 50 percent, the resistance is obtained.

According to an embodiment of the present invention, the disease resistance level of step 4) is: when P (y is 4) is more than or equal to 50%, P (y is 3) is less than or equal to 50% or P (y is 5) is less than or equal to 50%, the disease is infected.

According to an embodiment of the present invention, the disease resistance level of step 4) is: when P (y is 5) is more than or equal to 50%, P (y is 4) is less than or equal to 50% or P (y is 6) is less than or equal to 50%, the feeling is high.

The invention has the beneficial effects that:

the Logistic regression model belongs to a probability type nonlinear regression model, does not require normality, variance homogeneity, independent variable type and the like of data, has the advantages of interpretability of coefficients and the like, and greatly improves the accuracy of the in vitro inoculation identification method of the rooting test-tube plantlet by adopting Logistic regression to analyze disease grade data; according to the prediction result of the disease grade probability, the disease resistance of the banana variety to be tested is further divided into 5 grades of high resistance, disease resistance, medium resistance, infection, high sensitivity and the like, the integration of the rooting test-tube plantlet in vitro inoculation identification method and other identification methods is realized, the results obtained by adopting different identification methods are compared, and the experimental scheme and the technical route are adjusted on the basis, so that the progress of banana blight-resistant breeding can be effectively promoted.

Drawings

FIG. 1 shows the results of analyzing disease grade data of banana rooting test-tube plantlets by using the method of the present invention.

Detailed Description

The technical solution of the present invention is clearly and completely illustrated below with reference to the following examples, but is not limited thereto.