A method for molecular identification of crop germplasm resources and its application

Abstract

The present invention discloses a method for molecular identification of crop germplasm resources and its application, which solves the technical problem that the existing genetic markers are used to classify and identify rice germplasm resources. Due to the relatively few polymorphic markers, the identification results are often not ideal, and there is a lack of a method for quickly and accurately identifying germplasm resources at the molecular level. The present invention includes the following steps: Step 1: Using the differences in agronomic traits, screening gradient classification markers; Step 2: Using the screened gradient markers to detect germplasm resources; Step 3: According to the detection results of the gradient markers on crop germplasm resources, evaluating the genetic relationship of the resources. The present invention has the advantages of making up for the deficiencies of traditional phenotypic identification techniques and increasing the accuracy and reliability of resource identification.

Description

Technical Field

[0001] The present invention relates to the technical field of germplasm resource identification, and particularly relates to a molecular identification method for crop germplasm resources and its application. Background Art

[0002] Crop germplasm resources are the sources of food and clothing for human society, the basic material for modern scientific and technological innovation, and an important tool for humans to understand themselves and nature. Rice is the main food crop in China. Increasing yield and improving quality are crucial for ensuring the sustainable and stable development of China. Abundant rice germplasm resources are the key to rice variety improvement. China has a vast territory, complex terrain, and a long agricultural history. Under the long-term natural selection and artificial selection, extremely rich rice germplasm resources have been formed. Under different ecological conditions, various rice varieties and types, as well as wild species, are distributed, many of which are precious, rare, famous, and excellent genetic resources in China. According to statistics, there are more than 40,000 local rice variety resources in China, including various types such as japonica rice, indica rice, paddy rice, upland rice, early, middle, and late rice, and glutinous and non-glutinous rice.

[0003] China has rich rice germplasm resources, but their utilization rate is not high. One of the reasons is that the traditional classification, research, and evaluation of rice germplasm resources are only limited to the simple description and identification of morphological characteristics. Later, the developed enzyme markers, due to the influence of quantity, environment, and the growth and development stage of organisms, cannot accurately identify and evaluate rice germplasm resources, which limits their application scope to a certain extent. Molecular markers can reveal the differences in rice germplasm resource materials at the DNA molecular level and become a reliable and efficient tool for germplasm resource identification and analysis. At present, molecular markers have been widely used in aspects such as indica-japonica subspecies, main cultivated varieties, local varieties, backbone parents of hybrid rice, and genetic diversity of rice germplasm resources.

[0004] There have been many research results on classifying and identifying rice germplasm resources using genetic markers. Due to the relatively few polymorphic markers, the identification results are often not ideal, especially for resources with relatively close genetic relationships. So far, there is still a lack of a method for quickly and accurately identifying germplasm resources at the molecular level. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that when classifying and identifying rice germplasm resources using existing genetic markers, due to the relatively few polymorphic markers, the identification results are often not ideal, especially for resources with relatively close genetic relationships. So far, there is still a lack of a method for quickly and accurately identifying germplasm resources at the molecular level.

[0006] The present invention is achieved through the following technical solutions:

[0007] A molecular identification method for crop germplasm resources includes the following steps:

[0008] Step 1: Screen gradient classification markers using phenotypic trait differences;

[0009] Step 2: Detect germplasm resources using the screened gradient markers;

[0010] Step 3: Evaluate the genetic relatedness of resources based on the detection results of the gradient markers for crop germplasm resources.

[0011] The present invention preferably provides a method for molecular identification of crop germplasm resources. The method for screening gradient classification markers in Step 1 is as follows: From the germplasm resources to be identified, select N different agronomic traits. For each agronomic trait, select multiple extreme resources respectively to establish gene pools, screen differential molecular markers, and determine gradient classification markers at different levels by comparing the differential classification markers screened from the N agronomic traits. The molecular markers commonly present in all N agronomic traits are designated as first-level markers, the molecular markers commonly present in N - 1 agronomic traits are designated as second-level markers, the molecular markers commonly present in N - 2 agronomic traits are designated as third-level markers, the molecular markers commonly present in N - 3 agronomic traits are designated as fourth-level markers, the molecular markers showing differences in N - 4 agronomic traits are designated as fifth-level markers, and so on. The molecular markers showing differences in 1 agronomic trait are designated as N-level markers.

[0012] The present invention preferably provides a method for molecular identification of crop germplasm resources. In Step 2, the method for detecting germplasm resources using the screened gradient markers is as follows: Starting from the first-level markers to the N-level markers, increase step by step and screen level by level to obtain the detection results of each level of gradient markers for the germplasm resources.

[0013] The present invention preferably provides a method for molecular identification of crop germplasm resources. In Step 3, based on the detection results of the N-level gradient markers for the germplasm resources, evaluate the genetic relatedness of the resources. As the screening level of the first to N-level gradient markers increases, the number of resources showing differences becomes fewer and fewer. The resources with the N-level markers show differences in all the first to N-level gradient markers and are the first dominant group with relatively distant genetic relatedness. The resources with the N - 1-level markers show differences in the first to (N - 1)-level gradient markers and are the second dominant group. The resources with the N - 2-level markers show differences in the first to (N - 2)-level gradient markers and are the third dominant group, and so on. The resources with the first-level markers show differences in the 1-level gradient marker and are the Nth dominant group. The remaining resources show no differences in each gradient marker and are the approximate group, and these resources have very close genetic relatedness. From the first dominant group to the Nth dominant group, the genetic relatedness of the resources corresponds to being from far to near.

[0014] An application of a method for molecular identification of crop germplasm resources, applied to the evaluation of the genetic relationship among rice germplasm resources.

[0015] The present invention preferably relates to the application of a molecular identification method for crop germplasm resources, comprising the following steps:

[0016] Step S1: Screening gradient classification markers by using the agronomic trait differences of rice;

[0017] Step S2: Detecting rice germplasm resources by using the screened gradient markers;

[0018] Step S3: Evaluating the genetic relationship of rice resources based on the detection results of rice germplasm resources by the gradient markers.

[0019] The present invention preferably relates to the application of a molecular identification method for crop germplasm resources. The specific method for screening gradient classification markers in step S1 is as follows: From the rice seed resources to be identified, select 5 different agronomic traits, plant height, panicle length, 1000-grain weight, spikelet number per panicle, and flag leaf length. For each agronomic trait, select multiple extreme resource varieties to establish gene pools, screen for differential molecular markers, and determine different levels of gradient classification markers by comparing the differential classification markers screened from the 5 agronomic traits. The molecular markers commonly present in all 5 agronomic traits are designated as first-level markers, those commonly present in 4 agronomic traits are designated as second-level markers, those commonly present in 3 agronomic traits are designated as third-level markers, those commonly present in 2 agronomic traits are designated as fourth-level markers, and the molecular markers showing differences in only 1 agronomic trait are designated as fifth-level markers.

[0020] The present invention preferably relates to the application of a molecular identification method for crop germplasm resources. In step S2, the method for detecting germplasm resources by using the screened gradient markers is as follows: Starting from the first-level markers to the fifth-level markers, incrementally screen level by level to obtain the detection results of each level of gradient markers for rice germplasm resources.

[0021] The present invention preferably relates to the application of a molecular identification method for crop germplasm resources. In step S3, based on the detection results of local rice seed resources by the five-level gradient markers, evaluate the genetic relationship of the resources. As the screening levels of the first to fifth-level gradient markers increase, the number of resources showing differences becomes fewer and fewer. The resources with the fifth-level markers show differences in all the first to fifth-level gradient markers and are the first dominant group with relatively distant genetic relationships. The resources with the fourth-level markers show differences in the first to fourth-level gradient markers and are the second dominant group. The resources with the third-level markers show differences in the first to third-level gradient markers and are the third dominant group. The resources with the second-level markers show differences in the first to second-level gradient markers and are the fourth dominant group. The resources with the first-level markers show differences in the first-level gradient markers and are the fifth dominant group. The remaining resources show no differences in each gradient marker and are the approximate group. From the first dominant group to the fifth dominant group, the genetic relationship of the resources corresponds to being from far to near.

[0022] The present invention preferably relates to the application of a molecular identification method for crop germplasm resources. The specific method of step S1 is as follows:

[0023] Using 136 local rice germplasm resources collected in Chongqing as molecular marker identification materials, through the identification and evaluation of field agronomic traits, five agronomic traits with large differences in plant height, panicle length, 1000-grain weight, spikelet number per panicle, and flag leaf length were selected as the screening gradient classification markers. Ten resource varieties with the highest plant height and ten resource varieties with the lowest plant height were selected to establish gene pools. Using 362 pairs of SSR primers, 62 pairs of well-polymorphic classification markers were screened out. Ten resource varieties with the longest panicle length and ten resource varieties with the shortest panicle length were selected to establish gene pools. Using 362 pairs of SSR primers, 57 pairs of well-polymorphic classification markers were screened out. Ten resource varieties with the highest 1000-grain weight and ten resource varieties with the lowest 1000-grain weight were selected to establish gene pools. Using 362 pairs of SSR primers, 44 pairs of well-polymorphic classification markers were screened out. Ten resource varieties with the most spikelet number per panicle and ten resource varieties with the least spikelet number per panicle were selected to establish gene pools. Using 362 pairs of SSR primers, 41 pairs of well-polymorphic classification markers were screened out. Ten resource varieties with the longest flag leaf length and ten resource varieties with the shortest flag leaf length were selected to establish gene pools. Using 362 pairs of SSR primers, 61 pairs of well-polymorphic classification markers were screened out. By comparison, 8 first-level markers, 13 second-level markers, 21 third-level markers, 32 fourth-level markers, and 46 fifth-level markers were found.

[0024] The specific method of step S2 is as follows:

[0025] According to the resource detection and screening principle, starting from the first-level markers to the fifth-level markers, they are gradually increased and screened level by level. Finally, the resources screened out show differences in all five gradient markers. Using 8 first-level markers to detect the polymorphism of 136 local rice germplasm resources in Chongqing, 6 markers showed polymorphism. By comparing the molecular markers of each resource, 52 resources with molecular differences were found. Using 13 second-level markers to detect 52 resources, 5 markers showed polymorphism. By comparing the molecular markers of each resource, 37 resources with molecular differences were found. Using 21 third-level markers to detect 37 resources, 9 markers showed polymorphism. By comparing the molecular markers of each resource, 18 resources with molecular differences were found. Using 32 fourth-level markers to detect 18 resources, 12 markers showed polymorphism. By comparing the molecular markers of each resource, 11 resources with molecular differences were found. Using 46 fifth-level markers to detect 11 resources, 15 markers showed polymorphism. By comparing the molecular markers of each resource, 6 resources with molecular differences were found.

[0026] The specific method of step S3 is as follows:

[0027] According to the test results of local rice germplasm resources in Chongqing based on five - level gradient markers, the genetic relationship of the resources was evaluated. As the screening level of the first - to - fifth - level gradient markers increased, the number of resources showing differences became fewer and fewer. By the fifth - level marker, only 6 resources showed differences, and they showed differences in the 1 - 5 - level gradient markers. They were the first dominant group with relatively distant genetic relationships. At the fourth - level marker, 5 resources showed differences, and they showed differences in the 1 - 4 - level gradient markers, being the second dominant group. At the third - level marker, 7 resources showed differences, and they showed differences in the 1 - 3 - level gradient markers, being the third dominant group. At the second - level marker, 19 resources showed differences, and they showed differences in the 1 - 2 - level gradient markers, being the fourth dominant group. At the first - level marker, 15 resources showed differences, and they only showed differences in the 1 - level gradient marker, being the fifth dominant group. The remaining 84 resources showed no differences in each gradient marker and were the approximate group, indicating that these resources had very close genetic relationships.

[0028] The present invention has the following advantages and beneficial effects:

[0029] 1. By selecting and identifying the differential phenotypic traits of resources, screening gradient classification markers, and using gradient molecular markers to classify and detect the identified resources, the genetic difference levels of resources are determined according to the number of differential resources detected by different - level gradient markers. The genetic relationship of resources is evaluated at the molecular level, making up for the deficiencies of traditional phenotypic identification techniques, providing a method to improve the accuracy and reliability of crop germplasm resource identification, playing an important role in the rational utilization of resources, and promoting new breakthroughs in crop breeding.

[0030] 2. The molecular identification method of crop germplasm resources described in the present invention is mainly applicable to the molecular identification of crop germplasm resources, especially suitable for crop resources that have completed whole - genome sequencing and have developed many molecular markers. Rice is the first crop to complete whole - genome sequencing and has developed many markers. Therefore, this identification method is particularly suitable for the molecular identification of rice resources.

[0031] 3. The present invention conducts identification at the molecular level on the basis of phenotypic identification. If it is necessary to select and utilize the identified resources for crop breeding, resources that show differences in both molecular identification and phenotypic identification and have good comprehensive agronomic traits should be selected as parental materials. The dual selection of molecular identification and phenotypic identification increases the accuracy and reliability of selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0033] Figure 1 It is a flow chart of the method of the present invention. Detailed implementation manners

[0034] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with embodiments and the accompanying drawings. The illustrative implementation manners of the present invention and their descriptions are only used to explain the present invention and do not limit the present invention.

[0035] Embodiment 1

[0036] As Figure 1 shown, a method for molecular identification of crop germplasm resources includes the following steps:

[0037] Step 1: Using phenotypic trait differences, screening gradient classification markers;

[0038] From the germplasm resources to be identified, select 5 different agronomic traits. For each agronomic trait, select multiple extreme resource varieties respectively to establish a gene pool, screen for differential molecular markers, and determine different levels of gradient classification markers by comparing the differential classification markers screened from the 5 agronomic traits. The molecular markers commonly present in the 5 agronomic traits are designated as first-level markers, the molecular markers commonly present in 4 agronomic traits are designated as second-level markers, the molecular markers commonly present in 3 agronomic traits are designated as third-level markers, the molecular markers commonly present in 2 agronomic traits are designated as fourth-level markers, and the molecular markers showing differences in only 1 agronomic trait are designated as fifth-level markers.

[0039] Step 2: Using the screened gradient markers to detect germplasm resources

[0040] Starting from the first-level markers to the fifth-level markers, incrementally screen level by level to obtain the detection results of the five-level gradient markers for the germplasm resources.

[0041] Step 3: Evaluating the genetic relatedness of resources based on the detection results of the gradient markers for crop germplasm resources

[0042] Based on the detection results of the five-level gradient markers for the germplasm resources, evaluate the genetic relatedness of the resources. As the screening levels of the first to fifth-level gradient markers increase, the number of resources showing differences becomes fewer and fewer. The resources with the fifth-level markers show differences in all the first to fifth-level gradient markers and are the first dominant group with relatively distant genetic relatedness. The resources with the fourth-level markers show differences in the first to fourth-level gradient markers and are the second dominant group. The resources with the third-level markers show differences in the first to third-level gradient markers and are the third dominant group. The resources with the second-level markers show differences in the first to second-level gradient markers and are the fourth dominant group. The resources with the first-level markers show differences in the first-level gradient marker and are the fifth dominant group. The remaining resources show no differences in each gradient marker and are the approximate group. From the first dominant group to the fifth dominant group, the genetic relatedness of the resources corresponds to being from far to near.

[0043] The molecular identification method for crop germplasm resources described in the present invention is mainly applicable to the molecular identification of crop germplasm resources, and is particularly applicable to crop resources for which whole-genome sequencing has been completed and a large number of molecular markers have been developed. Rice is the first crop to complete whole-genome sequencing and a large number of markers have been developed. Therefore, it is particularly suitable for the molecular identification of rice resources.

[0044] Example 2

[0045] An application of a molecular identification method for crop germplasm resources, which is applied to the evaluation of the genetic relationship among rice germplasm resources, includes the following steps:

[0046] Step S1: Using the phenotypic trait differences of rice, screen gradient classification markers.

[0047] From the rice seed resources to be identified, select 5 agronomic traits with large differences, including plant height, panicle length, 1000-grain weight, spikelet number per panicle, and flag leaf length. For each agronomic trait, select 10 extreme resource varieties. In this example, select 10 resource varieties with the highest plant height and 10 resource varieties with the lowest plant height to establish gene pools and screen for differential molecular markers. By comparing the differential classification markers screened from the 5 agronomic traits, determine the gradient classification markers at different levels. The molecular markers that are common to all 5 agronomic traits are designated as first-level markers, those common to 4 agronomic traits are designated as second-level markers, those common to 3 agronomic traits are designated as third-level markers, those common to 2 agronomic traits are designated as fourth-level markers, and the molecular markers that show differences in only 1 agronomic trait are designated as fifth-level markers.

[0048] Using 136 local rice germplasm resources collected in Chongqing as materials for molecular marker identification, through the identification and evaluation of field agronomic traits, five agronomic traits with large differences in plant height, panicle length, 1000-grain weight, spikelet number per panicle, and flag leaf length were selected as the screening gradients for hierarchical markers. Ten resource varieties with the highest plant height and ten resource varieties with the lowest plant height were selected to establish gene pools. Using 362 pairs of SSR primers, 62 pairs of hierarchical markers with good polymorphism were screened out. Ten resource varieties with the longest panicle length and ten resource varieties with the shortest panicle length were selected to establish gene pools. Using 362 pairs of SSR primers, 57 pairs of hierarchical markers with good polymorphism were screened out. Ten resource varieties with the highest 1000-grain weight and ten resource varieties with the lowest 1000-grain weight were selected to establish gene pools. Using 362 pairs of SSR primers, 44 pairs of hierarchical markers with good polymorphism were screened out. Ten resource varieties with the most spikelet number per panicle and ten resource varieties with the least spikelet number per panicle were selected to establish gene pools. Using 362 pairs of SSR primers, 41 pairs of hierarchical markers with good polymorphism were screened out. Ten resource varieties with the longest flag leaf length and ten resource varieties with the shortest flag leaf length were selected to establish gene pools. Using 362 pairs of SSR primers, 61 pairs of hierarchical markers with good polymorphism were screened out. By comparison, 8 primary markers were found, namely: RM17, RM85, RM274, RM297, RM1195, RM5414, RM6427, RM25232; 13 secondary markers, namely: RM19, RM71, RM162, RM190, RM209, RM304, RM311, RM231, RM337, RM267, RM1163, RM7102, RM21598; 21 tertiary markers, namely: RM18, RM72, RM103, RM154, RM202, RM208, RM213, RM219, RM223, RM232, RM253, RM259, RM273, RM331, RM327, RM341, RM336, RM306, RM1385, RM2615, RM8277; 32 quaternary markers, namely: RM1, RM21, RM109, RM113, RM131, RM140, RM161, RM176, RM210, RM212, RM214, RM224, RM258, RM278, RM302, RM307, RM315, RM345, RM423, RM438, RM443, RM493, RM551, RM567, RM1282, RM3148, RM3763, RM6172, RM7081, RM15447, RM21605, RM21587;There are 46 five - level markers, namely: RM7, RM10, RM29, RM48, RM102, RM129, RM207, RM217, RM229, RM234, RM248, RM235, RM236, RM251, RM252, RM257, RM270, RM276, RM284, RM287, RM288, RM289, RM296, RM316, RM332, RM339, RM348, RM401, RM406, RM424, RM432, RM440, RM480, RM490, RM526, RM542, RM561, RM571, RM589, RM590, RM1942, RM3331, RM3515, RM7245, RM27390, RM27513.;

[0049] It can be seen that there are a total of 120 molecular markers with differences in 5 different agronomic traits, as shown in Table 1 below.

[0050] Table 1 Molecular markers with differences in 5 different agronomic traits

[0051]

[0052]

[0053] Note: In the table, the superscript a represents the first - level marker, b represents the second - level marker, c represents the third - level marker, d represents the fourth - level marker, and e represents the fifth - level marker.

[0054] Among the 362 pairs of SSR primers, 242 markers have no polymorphism, which are respectively:

[0055] RM22, RM27, RM35, RM39, RM42, RM49, RM50, RM51, RM53, RM55, RM60, RM70, RM80, RM81, RM82, RM83, RM84, RM86, RM87, RM88, RM100, RM105, RM108, RM120, RM127, RM130, RM131, RM132, RM134, RM136, RM139, RM145, RM146, RM147, RM151, RM156, RM157, RM158, RM159, RM160, RM168, RM170, RM171, RM172, RM173, RM174, RM175, RM177, RM178, RM179, RM180, RM181, RM182, RM183, RM184, RM185, RM186, RM187, RM188, RM189, RM191, RM192, RM195, RM200, RM201, RM205, RM215, RM220, RM221, RM222, RM225, RM226, RM227, RM228, RM230, RM233, RM237, RM238, RM239, RM240, RM241, RM245, RM246, RM247, RM249, RM250, RM254, RM255, RM256, RM260, RM261, RM262, RM263, RM264, RM265, RM266, RM269, RM271, RM272, RM275, RM277, RM279, RM280, RM281, RM282, RM283, RM285, RM286, RM290, RM295, RM298, RM300, RM301, RM303, RM305, RM308, RM309, RM319, RM320, RM321, RM322, RM323, RM324, RM325, RM335, RM340, RM342, RM343, RM344, RM346, RM347, RM349, RM350, RM351, RM400, RM404, RM405, RM409, RM412, RM413, RM418, RM419, RM420, RM421, RM422, RM425, RM426, RM427, RM428, RM429, RM430, RM431, RM450, RM455, RM460, RM461, RM466, RM467, RM468, RM469, RM470, RM471, RM472, RM473, RM474, RM475, RM477, RM495, RM501, RM509RM520, RM525, RM534, RM566, RM568, RM1247, RM1843, RM3092, RM3252, RM3431, RM3438, RM3740, RM6712, RM6875, RM10025, RM10047, RM10076, RM10217, RM10549, RM10615, RM11126, RM12254, RM12336, RM12554, RM12631, RM12781, RM12864, RM14017, RM14445, RM14471, RM14535, RM14616, RM14850, RM14949, RM15472, RM16611, RM16972, RM17055, RM17190, RM17860, RM18005, RM18106, RM18332, RM18352, RM18586, RM18858, RM19163, RM19261, RM19323, RM19387, RM19420, RM19460, RM19481, RM19520, RM19569, RM20820, RM21173, RM21542, RM21545, RM21576, RM21966, RM22417, RM22588, RM22714, RM22917, RM23582, RM23743, RM23865, RM23951, RM25272, RM27361, RM27845。

[0056] The sequence numbers of the above SSR primers were obtained from the public database at https: / / archive.gramene.org / markers / .

[0057] Step S2: Detecting rice resources using five - level gradient markers

[0058] Resource detection and screening principle: Starting from the first-level markers to the fifth-level markers, it increases gradually level by level for screening. The finally screened resources show differences in all five gradient markers. Using 8 first-level markers to detect the polymorphisms of 136 local rice germplasm resources in Chongqing, 6 markers showed polymorphisms. By comparing the molecular markers of each resource, it was found that 52 resources had molecular differences; using 13 second-level markers to detect 52 resources, 5 markers showed polymorphisms. By comparing the molecular markers of each resource, it was found that 37 resources had molecular differences; using 21 third-level markers to detect 37 resources, 9 markers showed polymorphisms. By comparing the molecular markers of each resource, it was found that 18 resources had molecular differences; using 32 fourth-level markers to detect 18 resources, 12 markers showed polymorphisms. By comparing the molecular markers of each resource, it was found that 11 resources had molecular differences; using 46 fifth-level markers to detect 11 resources, 15 markers showed polymorphisms. By comparing the molecular markers of each resource, it was found that 6 resources had molecular differences. Step S3: Evaluation of the genetic relationship of rice

[0059] According to the detection results of the local rice germplasm resources in Chongqing by the five-level gradient markers (see Table 2), evaluate the genetic relationship of the resources. As the number of screening levels of the first to fifth-level gradient markers increases, the number of resources showing differences becomes fewer and fewer. At the fifth-level markers, only 6 resources showed differences, and they showed differences in the 1st to 5th level gradient markers. They are the first dominant group with relatively distant genetic relationships. At the fourth-level markers, 5 resources showed differences, and they showed differences in the 1st to 4th level gradient markers, being the second dominant group. At the third-level markers, 7 resources showed differences, and they showed differences in the 1st to 3rd level gradient markers, being the third dominant group. At the second-level markers, 19 resources showed differences, and they showed differences in the 1st to 2nd level gradient markers, being the fourth dominant group. At the first-level markers, 15 resources showed differences, and they only showed differences in the 1st level gradient marker, being the fifth dominant group. The remaining 84 resources showed no differences in each gradient marker and are the approximate group, and these resources have very close genetic relationships.

[0060] Table 2 Molecular detection results of local rice germplasm resources in Chongqing by five-level gradient markers

[0061]

[0062] A molecular identification method for crop germplasm resources of the present invention is applied to evaluate the genetic relationship of rice germplasm resources. Taking crop resources (such as rice) in a certain area as the identification materials, 5 agronomic traits with large differences are selected, and 10 extreme resource varieties are selected for each agronomic trait to establish a gene pool and screen differential classification markers. By comparing the differential classification markers screened from the 5 agronomic traits, 1-5 level gradient molecular markers are determined. Through the gradient molecular markers for hierarchical detection of crop resources, the genetic difference level of resources is determined according to the number of differential resources detected by different level gradient markers, and the genetic relationship of resources is evaluated at the molecular level, making up for the deficiencies of traditional phenotypic identification techniques, increasing the accuracy and reliability of resource identification, and playing an important role in the rational utilization of resources, especially in the utilization of heterosis in crop breeding.

[0063] 1. The molecular identification method for crop germplasm resources of the present invention is mainly applicable to the molecular identification of crop germplasm resources. At present, the whole genome sequencing has been completed, and it is particularly applicable to crop resources with more developed molecular markers. Rice is the first crop to complete the whole genome sequencing and has more developed markers. Therefore, it is particularly suitable for the molecular identification of rice resources.

[0064] 2. Gradient classification markers. For different regions, different genetic relationships and different crop resources, there are large differences in phenotypic traits. According to the phenotypic agronomic traits of the resources to be identified, 5 agronomic traits with large differences are selected, and 10 extreme resource varieties are selected for each agronomic trait. For example, 10 resource varieties with the highest plant height and 10 resource varieties with the lowest plant height are selected to establish a gene pool and screen gradient classification markers. By comparing the differential classification markers screened from each agronomic trait, different level gradient classification markers are determined.

[0065] 3. Germplasm resource detection. First, all resources are molecularly detected and identified with the first-level markers that are polymorphic in the selected differential agronomic traits. The variety resources with molecular differences in the identification population are retained, and then molecular detection and identification are carried out with the second-level markers, increasing step by step and screening level by level. Finally, the resources screened out show differences in all five gradient markers, and these resources are the resources with the largest molecular genetic differences.

[0066] 4. Genetic kinship evaluation. Crop resources in the same region often have very close genetic relationships, and it is difficult to detect differences with a small number of markers. We used 362 pairs of SSR primers to detect 136 local rice germplasm resources collected in Chongqing. Using the screened five-level gradient markers, we screened and identified them step by step. Finally, 84 resources still showed no differences in each gradient marker, accounting for 61.76% of the identified resources, indicating that the genetic basis of the resources is relatively narrow. There are only 6 resources in the first dominant group, 5 resources in the second dominant group, 7 resources in the third dominant group, 19 resources in the fourth dominant group, and 15 resources in the fifth dominant group. These five dominant groups divided the genetic kinship of 136 local rice germplasm resources at the molecular level.

[0067] 5. Resource utilization. Excellent rice resource materials are the basis of breeding. From the remarkable achievements of modern breeding, the breeding of breakthrough varieties depends on the discovery and utilization of excellent genetic resources. Selecting and using resources well is the key to variety breeding. The crop breeding cycle is long. If the resources are not selected well in the early stage, it will lead to failure in the later stage, resulting in waste of time, money and effort. In this embodiment, molecular-level identification is carried out on the basis of phenotypic identification. Therefore, if it is necessary to select and utilize the identified resources for crop breeding, resources that show differences in both molecular identification and phenotypic identification and have good other comprehensive agronomic traits should be selected as parental materials. The dual selection of molecular identification and phenotypic identification increases the accuracy and reliability of the selection.

[0068] The specific implementation manners described above further elaborate on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for molecular identification of crop germplasm resources, characterized in that, It includes the following steps: Step 1: Using the differences in phenotypic traits, screen gradient classification markers; Step 2: Using the screened gradient markers to detect germplasm resources; Step 3: According to the detection results of the gradient markers on crop germplasm resources, evaluate the genetic relatedness of the resources; The method for screening gradient classification markers in Step 1 is as follows: From the germplasm resources to be identified, select N different agronomic traits. For each agronomic trait, select multiple extreme resource varieties respectively to establish gene pools, screen differential molecular markers. By comparing the differential classification markers screened from the N agronomic traits, determine the gradient classification markers at different levels. The molecular markers commonly present in all N agronomic traits are designated as first-level markers, the molecular markers commonly present in N - 1 agronomic traits are designated as second-level markers, the molecular markers commonly present in N - 2 agronomic traits are designated as third-level markers, the molecular markers commonly present in N - 3 agronomic traits are designated as fourth-level markers, the molecular markers showing differences in N - 4 agronomic traits are designated as fifth-level markers, and so on. The molecular markers showing differences in 1 agronomic trait are designated as N-level markers.

2. The molecular identification method of a crop germplasm resource according to claim 1, characterized in that, In Step 2, the method for using the screened gradient markers to detect germplasm resources is: Starting from the first-level markers to the N-level markers, increasing step by step and screening level by level to obtain the detection results of each level of gradient markers on germplasm resources.

3. A method for molecular identification of crop germplasm resources according to claim 1 or 2, characterized in that, In Step 3, according to the detection results of the N-level gradient markers on germplasm resources, evaluate the genetic relatedness of the resources. As the screening level of the first to N-level gradient markers increases, the number of resources showing differences becomes fewer and fewer. The resources with the N-level markers show differences in all the first to N-level gradient markers and are the first dominant group with relatively distant genetic relatedness. The resources with the N - 1-level markers show differences in the first to (N - 1)-level gradient markers and are the second dominant group. The resources with the N - 2-level markers show differences in the first to (N - 2)-level gradient markers and are the third dominant group, and so on. The resources with the first-level markers only show differences in the first-level gradient markers and are the Nth dominant group. The remaining resources show no differences in each gradient marker and are the approximate group. These resources have very close genetic relatedness. From the first dominant group to the Nth dominant group, the genetic relatedness of the resources corresponds to being from far to near.

4. Use of a method for molecular identification of crop germplasm resources according to any one of claims 1-3, characterized in that, Applied to the evaluation of the genetic relationship among rice germplasm resources, it includes the following steps: Step S1: Using the differences in phenotypic traits of rice, screen gradient classification markers; Step S2: Using the screened gradient markers to detect rice germplasm resources; Step S3: According to the detection results of the gradient markers on rice germplasm resources, evaluate the genetic relatedness of rice resources; The specific method for screening gradient classification markers in step S1 is as follows: From the rice germplasm resources to be identified, select 5 different agronomic traits: plant height, panicle length, 1000-grain weight, spikelet number per panicle, and flag leaf length. For each agronomic trait, select multiple extreme resource varieties to establish gene pools, screen for differential molecular markers, and determine different levels of gradient classification markers by comparing the differential classification markers screened from the 5 agronomic traits. Molecular markers that are common to all 5 agronomic traits are designated as first-level markers, molecular markers that are common to 4 agronomic traits are designated as second-level markers, molecular markers that are common to 3 agronomic traits are designated as third-level markers, molecular markers that are common to 2 agronomic traits are designated as fourth-level markers, and molecular markers that show differences in only 1 agronomic trait are designated as fifth-level markers.

5. Use of a method for molecular identification of crop germplasm resources according to claim 4, characterized in that, In step S2, the method for using the screened gradient markers to detect germplasm resources is as follows: Starting from the first-level markers to the fifth-level markers, increase gradually and screen level by level to obtain the detection results of each level of gradient markers for rice germplasm resources.

6. Use of a method for molecular identification of crop germplasm resources according to claim 4 or 5, characterized in that, In step S3, based on the detection results of the fifth-level gradient markers for local rice germplasm resources, evaluate the genetic relatedness of the resources. As the screening levels of the first to fifth-level gradient markers increase, the number of resources showing differences becomes fewer and fewer. Resources with fifth-level markers show differences in all 1 to 5-level gradient markers and are the first dominant group with relatively distant genetic relatedness. Resources with fourth-level markers show differences in 1 to 4-level gradient markers and are the second dominant group. Resources with third-level markers show differences in 1 to 3-level gradient markers and are the third dominant group. Resources with second-level markers show differences in 1 to 2-level gradient markers and are the fourth dominant group. Resources with first-level markers show differences in 1-level gradient markers and are the fifth dominant group. The remaining resources show no differences in each gradient marker and are the approximate group. These resources have very close genetic relatedness. From the first dominant group to the fifth dominant group, the genetic relatedness of the resources corresponds to being from far to near.

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