A crop germplasm resource evaluation method and system based on prior knowledge

Abstract

The application discloses a crop germplasm resource evaluation method and system based on prior knowledge, and relates to the technical field of crop germplasm resource evaluation. The method comprises the following steps: collecting the genomic data of the to-be-tested germplasm resource and performing variation detection to obtain whole-genome genetic variation information of the to-be-tested germplasm resource; determining the variation scores of various phenotypic traits of the to-be-tested germplasm resource according to the whole-genome genetic variation information and a crop trait variation prior knowledge base, so as to determine the comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of various phenotypic traits of the to-be-tested germplasm resource. The method can quickly evaluate the phenotypic trait scores of the to-be-tested germplasm resource according to the genotype data of the to-be-tested germplasm resource, and calculate the comprehensive score in combination with the various phenotypic trait scores, is applied to the field of crop germplasm resource evaluation, can greatly improve the evaluation efficiency of the phenotypic traits of the germplasm resource, save the evaluation cost, and thus better realize the efficient exploration and utilization of the germplasm resource.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of crop germplasm resource evaluation, in particular to a crop germplasm resource evaluation method and system based on prior knowledge. BACKGROUND

[0002] The breeding industry urgently needs genetic resources with clear genetic background, outstanding breeding traits and reliable comprehensive evaluation results to carry out variety improvement in line with market demand. Therefore, research on germplasm resource evaluation technology, excavation of germplasm resources with important application value and sharing and utilization are the key work of current resource research.

[0003] For germplasm resource research, phenotypic traits are the key to human domestication and utilization of crop germplasm resources, which are determined by genotype and environment; genotype is the direct carrier of resource value, and identifying the contribution of resource genotype to phenotypic traits is the key to evaluating the potential of germplasm resources. For a long time, the evaluation of germplasm resources mainly based on phenotypic identification has excavated a batch of excellent resources for utilization, but the traditional identification and evaluation technology still has the problems of long cycle, great difficulty and low throughput. Therefore, how to efficiently and quickly evaluate the phenotypic trait scores of germplasm resources and improve the efficiency of phenotypic trait identification of germplasm resources is a technical problem to be solved in the field. SUMMARY

[0004] The purpose of the present application is to provide a crop germplasm resource evaluation method and system based on prior knowledge, which can quickly and efficiently realize the efficiency of crop germplasm resource phenotype trait evaluation.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] In a first aspect, the present application provides a crop germplasm resource evaluation method based on prior knowledge, comprising the following steps:

[0007] Collecting the genomic data of the to-be-tested germplasm resource; the genomic data of the to-be-tested germplasm resource includes the gene data of each genetic locus.

[0008] Detecting the variation of the genomic data of the to-be-tested germplasm resource to obtain the whole genome genetic variation information of the to-be-tested germplasm resource.

[0009] Determining the variation score of each phenotypic trait of the to-be-tested germplasm resource according to the whole genome genetic variation information and the crop trait variation prior knowledge base; the crop trait variation prior knowledge base includes the weight of each genetic locus that plays a decisive role in the variation of different phenotypic traits in the genomic data of the same type of germplasm resource.

[0010] Determining the comprehensive variation score of the to-be-tested germplasm resource based on the variation score of each phenotypic trait of the to-be-tested germplasm resource.

[0011] Optionally, the genomic data of the to-be-tested germplasm resource is collected by any one of resequencing, targeted sequencing or gene chip.

[0012] Optionally, the crop trait variation priori knowledge base is constructed by the following steps:

[0013] The genomic data of a plurality of known germplasm resources is obtained; the plurality of known germplasm resources are the same type of germplasm resources as the to-be-tested germplasm resource.

[0014] According to the genomic data of the plurality of known germplasm resources, the gene loci that play a decisive role in the variation of different phenotypic traits are analyzed and determined, and the variation locus set of each phenotypic trait is obtained.

[0015] For any gene locus in the variation locus set of any phenotypic trait, the weight of the gene locus is determined according to the variation effect size of the gene locus on the phenotypic trait.

[0016] According to the weight of each gene locus, the crop trait variation priori knowledge base is constructed.

[0017] Optionally, according to the whole genome genetic variation information and the crop trait variation priori knowledge base, the variation score of each phenotypic trait of the to-be-tested germplasm resource is determined, which specifically includes the following steps:

[0018] For any phenotypic trait, the variation data of the corresponding gene locus is extracted from the whole genome genetic variation information according to the variation locus set of the phenotypic trait.

[0019] For any gene locus in the variation locus set of the phenotypic trait, the variation effect type of the gene locus is determined according to the variation data of the gene locus; the variation effect type includes beneficial allelic variation and other allelic variation.

[0020] According to the variation effect type of each gene locus, each gene locus is scored respectively to obtain the score of each gene locus.

[0021] According to the score of each gene locus of the phenotypic trait and the weight of each gene locus, the variation score of the phenotypic trait is calculated.

[0022] Optionally, the variation score of the phenotypic trait is calculated according to the following formula:

[0023]

[0024] wherein S is the variation score of a single phenotypic trait, m is the number of gene loci in the variation locus set of the phenotypic trait, k is the index of the gene loci in the variation locus set of the phenotypic trait, a k is the score of the kth gene locus, w k is the weight of the kth gene locus.

[0025] Optionally, according to the variation effect type of each genetic locus, each genetic locus is scored respectively to obtain the score of each genetic locus, and the specific steps include the following steps:

[0026] For any genetic locus, if the variation effect type of the genetic locus is a favorable allele variation, the genetic locus is scored as 1, otherwise the genetic locus is scored as 0.

[0027] Optionally, the phenotypic traits include plant type, yield, quality, disease and pest resistance, and stress tolerance.

[0028] Optionally, the crop germplasm resource evaluation method based on prior knowledge further includes the following steps:

[0029] Based on the variation scores of each phenotypic trait of the to-be-tested germplasm resource, a crop germplasm resource evaluation result graph is drawn and visualized.

[0030] Optionally, based on the variation scores of each phenotypic trait of the to-be-tested germplasm resource, a comprehensive variation score of the to-be-tested germplasm resource is determined, specifically including: the variation scores of each phenotypic trait of the to-be-tested germplasm resource are averaged to determine the comprehensive variation score of the to-be-tested germplasm resource.

[0031] In a second aspect, the present application provides a crop germplasm resource evaluation system based on prior knowledge, comprising the following modules:

[0032] A genomic data acquisition module is configured to collect genomic data of a to-be-tested germplasm resource; the genomic data of the to-be-tested germplasm resource includes genetic data of each genetic locus;

[0033] A genomic data variation detection module is configured to detect variations in the genomic data of the to-be-tested germplasm resource to obtain whole-genome genetic variation information of the to-be-tested germplasm resource;

[0034] A phenotypic trait scoring module is configured to determine variation scores of each phenotypic trait according to the whole-genome genetic variation information and a crop trait variation prior knowledge base; the crop trait variation prior knowledge base includes weights of each genetic locus that plays a decisive role in variation of different phenotypic traits in the genomic data of the same type of germplasm resource;

[0035] A comprehensive variation scoring module is configured to determine a comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of each phenotypic trait.

[0036] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0037] The application provides a crop germplasm resource evaluation method and system based on prior knowledge, and the method comprises the following steps: collecting the genomic data of a to-be-tested germplasm resource and performing variation detection to obtain whole-genome genetic variation information of the to-be-tested germplasm resource; determining the variation scores of various phenotypic traits of the to-be-tested germplasm resource according to the whole-genome genetic variation information and a crop trait variation prior knowledge base, so as to determine the comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of various phenotypic traits of the to-be-tested germplasm resource. The method can quickly evaluate the phenotypic trait scores of the to-be-tested germplasm resource according to the genotype data of the to-be-tested germplasm resource, and calculate the comprehensive score by combining the various phenotypic trait scores, and is applied to the field of crop germplasm resource evaluation, can greatly improve the evaluation efficiency of the phenotypic traits of the germplasm resource, save the evaluation cost, and thus better realize the efficient exploration and utilization of the germplasm resource. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0039] Figure 1 A flowchart of a crop germplasm resource evaluation method based on prior knowledge provided by an embodiment of the present application.

[0040] Figure 2 A flowchart of step S3 in a crop germplasm resource evaluation method based on prior knowledge provided by an embodiment of the present application.

[0041] Figure 3 A flowchart of constructing a crop trait variation prior knowledge base in a crop germplasm resource evaluation method based on prior knowledge provided by an embodiment of the present application.

[0042] Figure 4 A schematic diagram of a germplasm resource evaluation result provided by another embodiment of the present application.

[0043] Figure 5 A functional module schematic diagram of a crop germplasm resource evaluation system provided by another embodiment of the present application.

[0044] Figure 6 A structural schematic diagram of a computer device provided by another embodiment of the present application. DETAILED DESCRIPTION

[0045] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0046] The above-mentioned purposes, features and advantages of the present application will be more apparent and understandable, and the present application will be described in further detail below with reference to the drawings and specific embodiments.

[0047] In one exemplary embodiment, as shown in Figure 1 a crop germplasm resource evaluation method based on prior knowledge is provided, including the following steps:

[0048] S1, collecting genomic data of the to-be-tested germplasm resource; the genomic data of the to-be-tested germplasm resource includes gene data of each genetic locus.

[0049] S2, detecting variations of the genomic data of the to-be-tested germplasm resource to obtain whole-genome genetic variation information of the to-be-tested germplasm resource.

[0050] S3, determining variation scores of each phenotypic trait of the to-be-tested germplasm resource according to the whole-genome genetic variation information and a crop trait variation prior knowledge base; the crop trait variation prior knowledge base includes weights of each genetic locus that plays a decisive role in variation of different phenotypic traits in the genomic data of the same type of germplasm resource. The phenotypic traits include plant type, yield, quality, disease and pest resistance, and stress tolerance.

[0051] In the present embodiment, as shown in the flowchart Figure 2 , step S3 includes the following steps:

[0052] S31, for any phenotypic trait, extracting variation data of the corresponding genetic locus from the whole-genome genetic variation information according to a variation locus set of the phenotypic trait.

[0053] S32, for any genetic locus in the variation locus set of the phenotypic trait, determining a variation effect type of the genetic locus according to the variation data of the genetic locus; the variation effect type includes favorable allelic variation and other allelic variation.

[0054] S33, scoring each genetic locus according to the variation effect type of each genetic locus to obtain a score of each genetic locus. Specifically, for any genetic locus, if the variation effect type of the genetic locus is favorable allelic variation, the genetic locus is scored as 1, otherwise the genetic locus is scored as 0.

[0055] S34, calculate the variation score of the phenotypic trait according to the score of each gene locus of the phenotypic trait and the weight of each gene locus. The variation score of the phenotypic trait is calculated according to the following formula:

[0056]

[0057] wherein S is the variation score of a single phenotypic trait, m is the number of gene loci in the variation locus set of the phenotypic trait, k is the label of the gene loci in the variation locus set of the phenotypic trait, a k is the score of the kth gene locus, w k is the weight of the kth gene locus.

[0058] S4, determine the comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of the phenotypic traits of the to-be-tested germplasm resource. Specifically, the comprehensive variation score of the to-be-tested germplasm resource is determined by averaging the variation scores of the phenotypic traits of the to-be-tested germplasm resource.

[0059] In an exemplary embodiment of the present application, any one of resequencing, targeted sequencing or gene chip can be used to collect the genomic data of the to-be-tested germplasm resource.

[0060] In an exemplary embodiment of the present application, the crop germplasm resource evaluation method based on prior knowledge further comprises the following steps:

[0061] S5, based on the variation scores of the phenotypic traits of the to-be-tested germplasm resource, draw a crop germplasm resource evaluation result graph and perform visual display.

[0062] In another exemplary embodiment of the present application, before performing the above steps S1-S4 or steps S1-S5, a crop trait variation prior knowledge base needs to be constructed through the following process, as shown in the flowchart, comprising the following steps: Figure 3

[0063] A1, obtain the genomic data of a plurality of known germplasm resources; the plurality of known germplasm resources and the to-be-tested germplasm resource are the same type of germplasm resources. The above data can be obtained by consulting the functional gene research papers and related databases of the corresponding crops.

[0064] A2, according to the genomic data of the plurality of known germplasm resources, analyze and determine the gene loci that play a decisive role in the variation of different phenotypic traits, and obtain the variation locus set of each phenotypic trait. The gene loci that play a decisive role in the variation include single base nucleotide variation, deletion / insertion variation and other molecular marker variation, and the positions can be inside and upstream and downstream of the gene.

[0065] ​A3, for any gene locus in any variation site set of any phenotypic trait, determining the weight of the gene locus according to the variation effect size of the gene locus on the phenotypic trait.

[0066] A4, constructing a crop trait variation priori knowledge base according to the weight of each gene locus.

[0067] Next, with a specific example, the following process is used to construct a crop trait variation priori knowledge base for rice and evaluate rice germplasm resource "ZG9", which includes the following steps:

[0068] Control genes of agronomic phenotypic traits such as plant type, yield, quality, disease resistance, and stress tolerance of rice are collected from rice functional gene databases and literature databases. The variation site set of each control gene and its upstream and downstream interval is determined by reading the literature knowledge to determine the variation site set of each control gene and its upstream and downstream interval, and further to determine the sequence characteristics of the beneficial allelic variation in the variation site set.

[0069] For all gene loci that determine phenotypic traits of each type of agronomic phenotypic trait, the weight of each gene locus in the phenotypic trait is manually annotated according to the effect size of the gene locus on the variation, with a numerical range of 0-1 and a total weight of 1. Further, the variation information of all phenotypic traits is summarized to construct a rice priori knowledge base.

[0070] Subsequently, the second-generation high-throughput sequencing technology is used to re-sequence the rice germplasm resource to be tested, with a sequencing depth of more than 10x. After obtaining the original sequencing data, the conventional sequencing data analysis method is used for variation detection to obtain the whole genome genetic variation of the tested germplasm resource. In this embodiment, when evaluating the rice germplasm resource "ZG9", the conventional DNA extraction technology is used to obtain the whole genome DNA of the young leaf tissue of the rice germplasm resource "ZG9". After fluorescence quantitative quality inspection, a sequencing library with a fragment size of 400 bp is constructed, and the library is sequenced using a high-throughput sequencer. A total of about 35.01 million raw reads are obtained, with a total data amount of 4.9G. The original data is cleaned and quality controlled, and the quality controlled data is aligned using a variation detection software to detect variations and generate a genotype file. The sequencing depth of ZG9 is 12.3x, and the genome coverage is 90.06%. According to the site information provided by the priori knowledge base, the variation data of the corresponding position of each tested germplasm resource is extracted.

[0071] The scoring rule for a single variation gene locus in the genotype file is that the beneficial allelic variation is scored as 1 and the other allelic variation is scored as 0, and the score of the evaluated germplasm resource on the phenotypic trait is calculated according to the following formula:

[0072]

[0073] wherein S is the score of the to-be-tested germplasm resource on a certain type of phenotypic trait, k is a genetic locus belonging to the type of phenotypic trait, m represents the total number of genetic loci determining the type of phenotypic trait, a is the score of the current genetic locus, and w is the weight of the current genetic locus.

[0074] The score of each phenotypic trait of the to-be-tested germplasm resource is completed according to the above formula, and the evaluation of all phenotypic traits is completed in turn. Meanwhile, a script language or other software can be used to draw a germplasm resource evaluation radar chart or other schematic diagram. In this embodiment, the comprehensive score of the rice germplasm resource "ZG9" is 6.1, and the evaluation result chart is as shown in Figure 4

[0075] Based on the same inventive concept, the embodiments of the present application also provide a priori knowledge-based crop germplasm resource evaluation system for implementing the above-mentioned priori knowledge-based crop germplasm resource evaluation method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more priori knowledge-based crop germplasm resource evaluation system embodiments provided below can be referred to the limitations of the priori knowledge-based crop germplasm resource evaluation method in the above, which will not be repeated here.

[0076] In an exemplary embodiment, as shown in Figure 5 a priori knowledge-based crop germplasm resource evaluation system is provided, comprising the following modules:

[0077] a genomic data acquisition module for collecting genomic data of a to-be-tested germplasm resource; the genomic data of the to-be-tested germplasm resource includes genetic data of each genetic locus;

[0078] a genomic data variation detection module for detecting variations in the genomic data of the to-be-tested germplasm resource to obtain whole-genome genetic variation information of the to-be-tested germplasm resource;

[0079] a phenotypic trait scoring module for determining variation scores of each phenotypic trait according to the whole-genome genetic variation information and a crop trait variation priori knowledge base; the crop trait variation priori knowledge base includes weights of each genetic locus that plays a decisive role in variation of different phenotypic traits in the genomic data of the same type of germplasm resource;

[0080] a comprehensive variation score module for determining a comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of each phenotypic trait.

[0081] Some modules of the above system can also have sub-units for implementing their functions, of course, Figure 5 the architecture shown in the figure is only exemplary, and when different functions are implemented, some modules can be omitted according to actual needs​Figure 5 one or at least two components in the system.

[0082] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided, and an internal structure diagram of the computer device can be as shown. Figure 6 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a crop germplasm resource evaluation method based on prior knowledge.

[0083] Those skilled in the art can understand that Figure 6 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0084] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the relevant data need to comply with relevant regulations.

[0085] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0086] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0087] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0088] The principles and implementation modes of the present application are described by applying specific examples herein. The above description of the embodiments is only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for evaluating crop germplasm resources based on prior knowledge, characterized in that, The method comprises the following steps: Collecting the genomic data of the to-be-tested germplasm resource; The genomic data of the to-be-tested germplasm resource comprises gene data of each genetic locus; Detecting the variation of the genomic data of the to-be-tested germplasm resource to obtain the whole genome genetic variation information of the to-be-tested germplasm resource; According to the whole genome genetic variation information and the crop trait variation priori knowledge base, the variation score of each phenotypic trait of the to-be-tested germplasm resource is determined; the crop trait variation priori knowledge base comprises the weight of each genetic locus which plays a decisive role in the variation of different phenotypic traits in the genomic data of the same type of germplasm resource; Based on the variation score of each phenotypic trait of the to-be-tested germplasm resource, the comprehensive variation score of the to-be-tested germplasm resource is determined; specifically, the average value of the variation score of each phenotypic trait of the to-be-tested germplasm resource is determined to determine the comprehensive variation score of the to-be-tested germplasm resource; The crop trait variation priori knowledge base is constructed by the following steps: Obtaining the genomic data of a plurality of known germplasm resources; the plurality of known germplasm resources and the to-be-tested germplasm resource are the same type of germplasm resource; According to the genomic data of a plurality of known germplasm resources, the genetic loci which play a decisive role in the variation of different phenotypic traits are determined to obtain the variation locus set of each phenotypic trait; For any genetic locus in the variation locus set of any phenotypic trait, the weight of the genetic locus is determined according to the variation effect size of the genetic locus on the phenotypic trait; According to the weight of each genetic locus, the crop trait variation priori knowledge base is constructed; According to the whole genome genetic variation information and the crop trait variation priori knowledge base, the variation score of each phenotypic trait of the to-be-tested germplasm resource is determined, specifically comprising: For any phenotypic trait, the variation data of the corresponding genetic locus is extracted from the whole genome genetic variation information according to the variation locus set of the phenotypic trait; For any genetic locus in the variation locus set of the phenotypic trait, the variation effect type of the genetic locus is determined according to the variation data of the genetic locus; the variation effect type includes beneficial allelic variation and other allelic variation; According to the variation effect type of each genetic locus, each genetic locus is scored to obtain the score of each genetic locus; for any genetic locus, if the variation effect type of the genetic locus is beneficial allelic variation, the genetic locus is scored as 1, otherwise the genetic locus is scored as 0; According to the score of each genetic locus of the phenotypic trait and the weight of each genetic locus, the variation score of the phenotypic trait is calculated; The variation score of the phenotypic trait is calculated according to the following formula: wherein S is a variation score of a single phenotypic trait, m is a number of genetic loci in a variation locus set of the phenotypic trait, k is a label of a genetic locus in the variation locus set of the phenotypic trait, a k is a score of the kth genetic locus, and w k is a weight of the kth genetic locus.

2. The method for evaluating crop germplasm resources based on prior knowledge according to claim 1, characterized in that, The genomic data of the to-be-tested germplasm resource is collected by any one of resequencing, targeted sequencing or gene chip.

3. The method for evaluating crop germplasm resources based on prior knowledge according to claim 1, characterized in that, The phenotypic traits include plant type, yield, quality, disease resistance, stress tolerance.

4. The method of claim 1, wherein the method is characterized by, Further comprising: Based on the variation score of each phenotypic trait of the to-be-tested germplasm resource, a crop germplasm resource evaluation result graph is drawn and visually displayed.

5. A system for evaluating crop germplasm based on prior knowledge, characterized by, The system for implementing the method of any one of claims 1-4 comprises: The genomic data acquisition module is configured to collect genomic data of a to-be-tested germplasm resource; the genomic data of the to-be-tested germplasm resource comprises gene data of each gene locus; The genomic data variation detection module is configured to detect variations in the genomic data of the to-be-tested germplasm resource, to obtain whole-genome genetic variation information of the to-be-tested germplasm resource; The phenotype trait scoring module is configured to determine variation scores of each phenotype trait according to the whole-genome genetic variation information and a crop trait variation prior knowledge base; the crop trait variation prior knowledge base comprises weights of each gene locus that plays a decisive role in variations of different phenotype traits in genomic data of the same type of germplasm resource; The comprehensive variation scoring module is configured to determine a comprehensive variation score of the to-be-tested germplasm resource based on the variation scores of each phenotype trait.

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