A loquat hybrid population analysis method for localizing key genes for loquat young fruit frost resistance

Abstract

The present invention provides a loquat hybrid population analysis method for locating a key gene for frost resistance in young loquat fruitlets. The method comprises the following steps: first, observing the state of young fruit embryos by cross-section, then judging the state of the young embryos by the size of the young fruitlets, recording the number of surviving young fruitlets and the number of dead young fruitlets of each loquat plant in inflorescences as a unit, and obtaining the average number of young fruitlets per inflorescence per loquat plant; defining the number of surviving young fruitlets per inflorescence as a parameter a, and using this parameter to determine whether each inflorescence on the plant has achieved the expected yield; if the expected yield has been achieved, the inflorescence is considered to have survived frost damage, and the percentage Z of surviving inflorescences per loquat plant is obtained. The greater the percentage Z of surviving inflorescences per loquat plant, the better the frost resistance of the loquat plant, and the presence of the key gene for frost resistance; then calculating the skewness and kurtosis to observe the degree to which the sample deviates from a normal distribution. The present invention realizes an analysis method for rapidly screening individual plants in a loquat hybrid population that meet the requirements for locating the young fruit frost resistance gene.

Description

Technical Field

[0001] The invention belongs to the technical field of gene screening, and particularly relates to a loquat hybrid population analysis method for locating a key gene for antifreeze of young loquat fruits. Background Art

[0002] Cold snaps are a key factor impacting loquat yields in the northern loquat belt. The widespread, dramatic drop in temperature causes embryo death in young fruits, preventing them from developing into mature fruit. This significantly impacts yields, with severe reductions of up to 80%. Therefore, breeding cold-resistant loquat varieties is a crucial measure to ensure the economic benefits of producers in the northern loquat belt.

[0003] Identifying key genes for frost resistance in young loquat fruit is crucial for improving breeding efficiency. Currently, commonly used gene mapping methods are QTL and GWAS, both of which typically require populations containing more than 100 individuals with normally distributed phenotypic data to ensure precise and accurate mapping. Therefore, faced with large numbers of hybrid offspring or genetic material, there is an urgent need for efficient data acquisition and screening methods to quickly determine which populations are most suitable for key functional gene mining studies.

[0004] Conventional frost resistance evaluation uses certain physiological indicators or hormone levels of plants as indicators. The measurement process needs to be carried out in the laboratory with the help of specific instruments, such as electrical conductivity, chlorophyll fluorescence, endogenous hormone content, etc., which cannot be directly obtained during field surveys. The data acquisition time is long and the cost is high, which cannot meet the needs of large-scale evaluation; at the same time, the measurement of the above indicators is greatly affected by the tissue location of the sample. The young embryo of loquat fruit is less frost-resistant than the flesh. Mixed sampling cannot accurately reflect frost resistance. Separating the young embryo and flesh is not only difficult but also greatly increases the time investment; in addition, only loquat varieties that can guarantee a certain number of commercial fruits after suffering multiple cold waves in winter have application and promotion value. The frost resistance defined in this way is the result of the joint action of variables such as the physiological state of the young fruit and meteorological conditions over a longer time span. If the data of a specific organ at a certain point in time is used to characterize the frost resistance of young fruits, it will be a generalization and unable to accurately describe the complete development process of the young fruits, and it will be difficult to apply it to actual production guidance; finally, conventional methods do not consider the statistical characteristics of the group frost resistance data samples. Outliers cause the actual data samples to deviate from the normal distribution, greatly reducing the accuracy of locating key phenotypic genes.

[0005] In summary, the current methods for evaluating young fruit frost resistance in loquat breeding have the following shortcomings: First, specialized instruments and methods are required, and data cannot be directly obtained in the field. Second, young fruit frost resistance is tissue-specific, requiring identification of the weakest areas with the lowest frost resistance. Furthermore, winter cold waves occur with varying frequency, and high frost resistance in young fruit at a single point in time does not guarantee ultimate survival. Population frost resistance data often deviate from a normal distribution. Therefore, developing methods for analyzing loquat hybrid populations to map key genes for frost resistance in young fruit is crucial. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned existing technologies and provide a loquat hybrid population analysis method for locating key genes for frost resistance in loquat young fruits. The present invention is based on the development laws of young fruits, utilizes the indispensability of young embryos for young fruit expansion, and determines the survival status of young fruit embryos by fruit size, thereby greatly improving the efficiency of data acquisition and reducing acquisition costs.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: a loquat hybrid population analysis method for locating key genes for loquat young fruit frost resistance, the method is:

[0008] After the cold wave no longer occurs, select 3 to 6 normal-sized and significantly smaller young fruits from each loquat plant, and observe the state of the young embryos by cross-section. After confirming that the significantly smaller young fruit embryos have been frozen and the normal-sized young fruit embryos have developed normally, the state of the young embryos can be judged by the size of the young fruit.

[0009] The method for judging the state of young embryos by the size of young fruits is as follows: taking the inflorescence on a single loquat plant as a unit, recording the number of surviving young fruits and the number of dead young fruits on each inflorescence; taking a loquat population with n loquat plants as sample set 1, a single loquat plant in the loquat population of sample set 1 has k inflorescences, each inflorescence has x surviving young fruits, and x i represents the number of surviving young fruits of the i-th inflorescence on a single loquat plant, i ranges from 1 to k, and b is used as a parameter to exclude plants with poor flower bud differentiation;

[0010] When k < b, no subsequent analysis was performed on the loquat plant;

[0011] When k≥b, the average number of young fruits per inflorescence on a loquat plant, Y, is obtained:

[0012]

[0013] Where: b represents the minimum value of the total number of inflorescences on a single loquat plant, which is a constant;

[0014] When the average number of young fruits in the inflorescence of a single loquat plant is greater than 5%, the percentage of surviving inflorescences of the single loquat plant, Z, is obtained:

[0015] in

[0016] Where: a represents the minimum number of surviving young fruits on each inflorescence when a loquat tree reaches the expected yield, which is a constant;

[0017] Based on the average number Y of young fruits in the inflorescences of a single loquat plant and the percentage Z of surviving inflorescences of a single loquat plant, we analyzed whether there were key genes for frost resistance in the young fruits of a single loquat plant, and then performed genetic analysis.

[0018] Preferably, the value of b is 3-15.

[0019] Optimally, the value of b is 12.

[0020] Preferably, the value of a is 4, 6 or 7.

[0021] Optimally, the value of a is 4.

[0022] Preferably, 5 young fruits of normal size and 5 young fruits of significantly smaller size are selected from each loquat plant.

[0023] Preferably, the cross-cutting method is specifically: using pruning shears to cross-cut the young fruit on the equatorial plane.

[0024] The method for determining the values ​​of parameter a and parameter b in the above method is:

[0025] The percentage of surviving inflorescences of each loquat in a population of n loquats was used as sample set 2. In order to control the effect of early or late flowering on the survival rate of young fruits, the logarithm of the percentage of surviving inflorescences of each loquat in a population of n loquats was taken, and the loquats with Z=0 were excluded to form sample set 3 to avoid the influence of too many outliers. The skewness and kurtosis of sample set 3 after logarithmic processing were calculated to observe the degree to which sample set 3 deviated from the normal distribution. The calculation formula was:

[0026]

[0027]

[0028] Among them, m2 is the second-order center distance of sample set 3, m3 is the third-order center distance of sample set 3, and m4 is the fourth-order center distance of sample set 3. The m2, m3, and m4 are given by the formula Definition, D i represents the i-th sample in sample set 3, represents the average value of sample set 3;

[0029] The Shapiro-Wilk test is used to further examine whether sample set 3 conforms to the normal distribution. The statistic of the Shapiro-Wilk test is:

[0030]

[0031] D (i) Represents the i-th order statistic from small to large in sample set 3, constant a i By the formula Definition, where C is the vector norm, Vector r=(r1,…,r n ) T ;

[0032] The P value is obtained after the significance test of the Shapiro Wilk test statistic. P>0.05 indicates that the sample conforms to the normal distribution. The larger the P value, the higher the confidence level.

[0033] As the values ​​of parameters a and b increase, more loquat plants are eliminated, the number of loquat plants n in sample set 1 decreases, and the W and P values ​​also change. If sample set 3 is to conform to the normal distribution while ensuring a sufficient number of loquat plants n, the larger the values ​​of W, P, and n, the better. Based on this, the variable S used to evaluate the suitability of the sample for subsequent genetic analysis is defined as:

[0034] S=W×P×n

[0035] On the premise that n is greater than 100, the parameter value when S takes the maximum value is taken as the optimal value of a and b, and the other values ​​with P greater than 0.05 are taken as alternative values ​​of a and b.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. This invention is based on the developmental laws of young fruits, takes advantage of the indispensability of young embryos for young fruit expansion, and determines the survival status of young fruit embryos by fruit size, thereby significantly improving the efficiency of data acquisition and reducing acquisition costs;

[0038] Data samples composed of conventional frost resistance indicators often deviate from a normal distribution due to excessive outliers. This method uses a custom variable Z, "percentage of surviving inflorescences," to control the influence of flowering period on variable Z while eliminating outliers (Z = 0) in the sample. The number of outlier plants is flexibly adjusted using parameter a, representing expected yield, achieving controllable screening of sample outliers. A variable S, representing the degree to which a sample satisfies genetic mapping analysis, is also defined. The optimal value of parameter a is determined by comprehensively considering sample size and conformity to a normal distribution, avoiding the occurrence of low sample capacity due to excessive screening intensity. Furthermore, the method does not require specific instrumentation for data acquisition, is applicable to a wide range of scenarios, and offers high data acquisition efficiency and low cost.

[0039] 2. In the present invention, b is used as a parameter to exclude individual plants with poor flower bud differentiation. The b value can exclude individual loquat plants that are under the stress of biological or abiotic factors. This can reduce the number of loquats to be analyzed and avoid analyzing every individual loquat plant.

[0040] 3. The present invention conducts evaluation after the cold wave weather ends. At this time, the surviving young fruits will not die due to temperature reasons later, which improves the effectiveness of the evaluation. The present invention uses the physiological state of the most frost-resistant part (embryo) of the young fruit as the judgment basis, avoiding the interference of different frost resistance differences in different parts on the results.

[0041] 4. The present invention uses a new young fruit frost resistance index and introduces an "expected yield" parameter to achieve controllable screening of sample outliers, thereby avoiding the common situation in practice where sample data do not conform to a normal distribution.

[0042] 5. The present invention can adjust the screening intensity by changing parameters and flexibly adapt to the breeding needs of different regions.

[0043] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 These are pictures of surviving young fruits (large) and dead young fruits (small) on loquat inflorescences.

[0045] Figure 2 It is the normal distribution fitting of the sample data of the percentage of surviving inflorescences per plant (Z) when a=1 and b=1.

[0046] Figure 3(a) is an analysis of the influence of parameter a on the S value, Figure 3(b) is an analysis of the influence of parameter b on the S value, and Figure 3(c) is an analysis of the influence of parameter b on the S value when parameter a=4.

[0047] Figure 4 This is the normal distribution fitting of the logarithm of the data of the percentage of surviving inflorescences per plant (Z) when a=4 and b=12. DETAILED DESCRIPTION

[0048] Example 1

[0049] The loquat hybrid population analysis method for localizing the key gene for antifreeze of loquat young fruit in this embodiment is as follows:

[0050] After the cold wave weather no longer occurs, 5 normal-sized and 5 significantly smaller young fruits are selected from each loquat tree. The state of the young fruit embryos is observed by cutting the young fruit crosswise at the equator with pruning shears. After confirming that the significantly smaller young fruit embryos have been frozen and the normal-sized young fruit embryos have developed normally, the state of the young embryos is judged by the size of the young fruit. The recurrence of cold wave weather is determined by information such as the annual weather forecast.

[0051] The method for judging the state of young embryos by the size of young fruits is as follows: taking the inflorescence of a single loquat plant as a unit, recording the number of surviving young fruits and the number of dead young fruits on each inflorescence; taking a loquat population of n loquat plants as sample set 1, a single loquat plant in the loquat population of sample set 1 has k inflorescences, each inflorescence has x surviving young fruits, i ranges from 1 to k, and b is used as a parameter to exclude plants with poor flower bud differentiation;

[0052] When k < b, no subsequent analysis was performed on the loquat plant;

[0053] When k≥b, the average number of young fruits per inflorescence on a loquat plant, Y, is obtained:

[0054]

[0055] Where: b represents the minimum number of inflorescences on a single loquat plant, which is a constant ranging from 3 to 15;

[0056] When the average number of young fruits in the inflorescence of a single loquat plant is greater than 5%, the percentage of surviving inflorescences of the single loquat plant, Z, is obtained:

[0057] in

[0058] Where: a represents the minimum number of surviving young fruits on each inflorescence when a loquat plant reaches the expected yield, which is a constant and takes values ​​of 4, 6 or 7;

[0059] The average number of young fruits on a loquat plant (Y) and the percentage of surviving inflorescences (Z) on a loquat plant were used to determine whether the young fruits of the loquat plant had key genes for frost resistance, and then genetic analysis was performed.

[0060] The method for determining the values ​​of parameter a and parameter b in the above method is:

[0061] The percentage of surviving inflorescences of each loquat in a population of n loquats was used as sample set 2. In order to control the effect of early or late flowering on the survival rate of young fruits, the logarithm of the percentage of surviving inflorescences of each loquat in a population of n loquats was taken, and the loquats with Z=0 were excluded to form sample set 3 to avoid the influence of too many outliers. The skewness and kurtosis of sample set 3 after logarithmic processing were calculated to observe the degree to which sample set 3 deviated from the normal distribution. The calculation formula was:

[0062]

[0063]

[0064] Among them, m2 is the second-order center distance of sample set 3, m3 is the third-order center distance of sample set 3, and m4 is the fourth-order center distance of sample set 3. The m2, m3, and m4 are given by the formula Definition, D i represents the i-th sample in sample set 3, represents the average value of sample set 3;

[0065] The Shapiro-Wilk test is used to further examine whether sample set 3 conforms to the normal distribution. The statistic of the Shapiro-Wilk test is:

[0066]

[0067] D (i) Represents the i-th order statistic from small to large in sample set 3, constant a i By the formula Definition, where C is the vector norm, Vector r=(r1,…,r n ) T ;

[0068] The P value is obtained after the significance test of the Shapiro Wilk test statistic. P>0.05 indicates that the sample conforms to the normal distribution. The larger the P value, the higher the confidence level.

[0069] As the values ​​of parameters a and b increase, more loquat plants are eliminated, the number of loquat plants n in sample set 1 decreases, and the W and P values ​​also change. If sample set 3 is to conform to the normal distribution while ensuring a sufficient number of loquat plants n, the larger the values ​​of W, P, and n, the better. Based on this, the variable S used to evaluate the suitability of the sample for subsequent genetic analysis is defined as:

[0070] S=W×P×n

[0071] The optimal values ​​for parameters a and b were determined when S reached its maximum value, resulting in parameter b equal to 12 and parameter a equal to 4. Furthermore, the values ​​for parameters a and b that satisfied the conditions of n being greater than 100 and P greater than 0.05 were selected as alternative values, which could be flexibly adjusted based on actual breeding needs. This allowed accurate determination of the frost resistance index of young fruit in loquat hybrid offspring, as well as the selection of individual plants for gene mapping analysis, to precisely locate the key loquat frost resistance genes.

[0072] Using the above method, in early April 2023, we screened the hybrid offspring of 'Ninghai Bai' and 'Jiefang Zhong' for individual plants that could be used to locate the antifreeze gene in young fruits. At this time, the difference in fruit shape between young fruits that died of freezing in winter and those that developed normally was already very obvious ( Figure 1 ). Using this feature, we obtained the total number of inflorescences of each tree (k), the number of surviving young fruits on each inflorescence (x i ); Then, according to the definition, the Z variable value of each tree is calculated to form a data sample D. The statistical variable values ​​of this sample are shown in Table 1. The initial values ​​of parameters a and b are set to 1.

[0073] Table 1 Statistical characteristics analysis of sample D

[0074]

[0075] Plant frost resistance involves multiple physiological processes and is a typical quantitative trait controlled by multiple genes. The probability density function of the Z variable should conform to the normal distribution characteristics. Therefore, the accuracy of the Z variable as a loquat fruit frost resistance indicator can be evaluated by whether the sample S conforms to the normal distribution. After fitting calculations, when the parameters a and b are 1, the similarity of the Z variable to the normal distribution is as follows: Figure 2 As shown in the figure, it can be seen that the Z variable is still different from the normal distribution, and the p-value of the Shapiro Wilk test result is much less than 0.05, indicating that the Z variable is likely not in line with the normal distribution, and it is necessary to adjust the parameter value.

[0076] Parameter a represents expected yield. A value that is too low reduces the intensity of sample screening, and the resulting "superior frost-resistant strain" will lack application and promotion value due to its low actual yield. However, blindly increasing the value of a will also reduce the sample size, which in turn affects the accuracy of key gene positioning. To simultaneously meet the three requirements of parameter a and b values ​​being consistent with production reality, Z samples conforming to a normal distribution, and sample size being as large as possible, the Z variable is logarithmically processed and then subjected to a Shapiro-Wilk test. The W and P values ​​in the results are used to define a new variable S. The value of S is calculated for different combinations of a and b values, and the values ​​of parameters a and b when the S variable reaches its maximum value are taken as the optimal values. When parameter b ranges from 1 to 15, the median of S remains constant around 10, regardless of the value of a, with a small fluctuation (Figure 3(a)-(b)). The median variance between different values ​​of a is 3.038. Parameter a significantly influences the value of S. For each specific value of a, the median of S ranges from 0.028 to 40, with a significant variance of 13.37 (Figure 3(a)). When parameter a ranges from 1 to 4, the median of S continues to rise, and when a is 4, the median of S rapidly increases from 8.35 to 34 (Figure 3(a)). Because increasing the value of a causes a decrease in sample size, this result indicates a significant improvement in the symmetry of the Z variable sample data distribution, as evidenced by a significant increase in the W and p-values. However, as the value of a further increases, the median of S shows a trend of first decreasing and then increasing, reaching its maximum value when a is 10. At this time, the median of S is 40.23, but the median of the sample size is 50. Considering the sample size, 4 is selected as the optimal value of parameter a. After determining the value of a, the effect of different b values ​​on the S value is observed. It can be seen that the S value is the largest when b is equal to 12 (Figure 3(c)). Under this condition, the probability density function obtained by fitting is very close to the normal distribution, and the theoretical values ​​of quantiles, cumulative probability density functions, and probabilities are highly consistent with the actual values ​​( Figure 4 ), indicating that the Z variable can be considered to conform to a normal distribution. Furthermore, the values ​​of parameters a and b that satisfy the conditions of sample size greater than 100 and P value greater than 0.05 are selected as alternative values. The screening intensity of the Z variable can be flexibly adjusted according to actual breeding needs to find a balance between frost resistance and other traits (Table 2).

[0077] After the optimal parameter values ​​were determined, the sample size remained unchanged, and the sample composition for subsequent gene mapping experiments was determined accordingly.

[0078] Table 2 Optimal and alternative values ​​of parameters a and b

[0079]

[0080]

[0081] Note: The W value indicates the similarity between the sample and the normal distribution. The larger the value, the closer the sample tends to the normal distribution. The p-value is the threshold for rejecting the null hypothesis. The larger the p-value, the higher the confidence that the sample conforms to the normal distribution.

[0082] The above case shows that the method of this embodiment is used to evaluate the frost resistance of loquat young fruits with the "inflorescence survival percentage" (Z) variable. By adjusting the expected yield parameters, it is possible to quickly and efficiently determine which individual plants constitute the group. The method is simple and does not require measuring the physiological indicators of the plant. The value of the young fruit frost resistance index (Z) conforms to the normal distribution and is suitable as an indicator for breeding frost-resistant loquats.

[0083] Each combination of parameters a and b corresponds to a sample set of Y and Z. Table 3 shows the sample sets of Y and Z when a = 4, b = 12, and after removing the 5% minimum value. Taking the logarithm will exclude individual trees with Z = 0, so there will be no individual loquats with Z = 0 in the determined loquat population, such as trees numbered 1, 3, and 4.

[0084] Table 3 Y and Z values ​​of loquats when a=4, b=12, and Y>5%

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent variation made to the above embodiment based on the essence of the invention technology shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A loquat hybrid population analysis method for localizing key genes for loquat fruit antifreeze, characterized in that: The method is: After the cold wave no longer occurs, select 3 to 6 normal-sized and significantly smaller young fruits from each loquat plant, and observe the state of the young embryos by cross-section. After confirming that the significantly smaller young fruit embryos have been frozen and the normal-sized young fruit embryos have developed normally, the state of the young embryos can be judged by the size of the young fruit. The method for judging the state of young embryos by the size of young fruits is as follows: taking the inflorescence on a single loquat plant as a unit, recording the number of surviving young fruits and the number of dead young fruits on each inflorescence; taking a loquat population with n loquat plants as sample set 1, a single loquat plant in the loquat population of sample set 1 has k inflorescences, each inflorescence has x surviving young fruits, and x i represents the number of surviving young fruits of the i-th inflorescence on a single loquat plant, i ranges from 1 to k, and b is used as a parameter to exclude plants with poor flower bud differentiation; When k < b, no subsequent analysis was performed on the loquat plant; When k≥b, the average number of young fruits per inflorescence on a loquat plant, Y, is obtained: Where: b represents the minimum value of the total number of inflorescences on a single loquat plant, which is a constant; When the average number of young fruits in the inflorescence of a single loquat plant is greater than 5%, the percentage of surviving inflorescences of the single loquat plant, Z, is obtained: in Where: a represents the minimum number of surviving young fruits on each inflorescence when a loquat tree reaches the expected yield, which is a constant; Based on the average number Y of young fruits in the inflorescences of a single loquat plant and the percentage Z of surviving inflorescences of a single loquat plant, we analyzed whether there were key genes for frost resistance in the young fruits of a single loquat plant, and then performed genetic analysis.

2. The loquat hybrid population analysis method for positioning a key gene for loquat fruit antifreeze according to claim 1, characterized in that: The value of b is 3 to 15.

3. The loquat hybrid population analysis method for positioning a loquat young fruit antifreeze key gene according to claim 2, wherein: The value of b is 12.

4. The loquat hybrid population analysis method for positioning a key gene for loquat young fruit antifreeze according to claim 1, wherein The value of a is 4, 6 or 7.

5. The loquat hybrid population analysis method for positioning a key gene for loquat young fruit antifreeze according to claim 4, characterized in that, The value of a is 4.

6. The loquat hybrid population analysis method for positioning a key gene for loquat young fruit antifreeze according to claim 1, characterized in that, From each loquat plant, 5 young fruits of normal size and 5 young fruits of significantly smaller size were selected.

7. The loquat hybrid population analysis method for positioning a key gene for loquat young fruit antifreeze according to claim 1, characterized in that, The cross-cutting method is specifically: using pruning shears to cross-cut the young fruit on the equatorial plane.

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