Breeding method and device for hybrid rice and storage medium

Abstract

The invention provides a hybrid rice breeding method and device and a storage medium. The breeding method of the hybrid rice comprises the following steps: pairwise inputting a plurality of candidate hybrid rice varieties into a pre-trained hybrid rice breeding model, and respectively predicting various genotype phenotype data of the hybrid rice obtained by breeding according to the input SNP (Single Nucleotide Polymorphism) sites of the two hybrid rice varieties, each type of genotype phenotypic data represents one agronomic character of the hybrid rice; according to the planting time of the hybrid rice obtained by breeding, determining a character scoring rule of various genotype phenotype data so as to calculate the total score of the hybrid rice obtained by breeding about various agronomic characters; and selecting at least one hybrid rice with the highest total score for breeding.

Description

Technical Field

[0001] The present invention relates to the field of rice cultivation, and in particular to a hybrid rice breeding method, a hybrid rice breeding device, and a computer-readable storage medium. Background Art

[0002] Rice is an important food crop and a major source of carbon in the world. In the past few decades, my country has proposed a series of hybrid rice pairing methods, such as the three-line method and the two-line method, which have enabled hybrid breeding to be widely used in China and significantly increased my country's rice yield.

[0003] Hybrid breeding is an effective way to breed new high-yield and high-quality rice varieties. This method relies on a large number of hybrid combination experiments and phenotypic studies in the field, which is time-consuming and laborious. In addition, the important agronomic traits of most food crops are quantitative traits, which are controlled by multiple genes and are greatly affected by the environment.

[0004] In order to overcome the above-mentioned defects of the prior art, the art is in urgent need of a hybrid rice breeding technology to reduce the breeding workload, shorten the breeding cycle, improve the breeding efficiency, adapt to the planting time of early rice, mid-season rice and late rice, and select hybrid rice with suitable agronomic traits for breeding. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or decisive elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a hybrid rice breeding method, a hybrid rice breeding device, and a computer-readable storage medium, which can not only reduce the breeding workload, shorten the breeding cycle, and improve the breeding efficiency, but also adapt to the planting time of early rice, middle rice, and late rice, and select hybrid rice with suitable agronomic traits for breeding.

[0007] Specifically, the hybrid rice breeding method provided according to the first aspect of the present invention comprises the following steps: inputting a plurality of candidate hybrid rice varieties into a pre-trained hybrid rice breeding model in pairs, and predicting a plurality of genotype-phenotype data of the hybrid rice obtained through breeding according to the SNP sites of the two input hybrid rice varieties, wherein each of the genotype-phenotype data represents an agronomic trait of the hybrid rice; determining a trait scoring rule for the various genotype-phenotype data according to the planting time of the hybrid rice obtained through breeding, so as to calculate the total score of the hybrid rice obtained through breeding with respect to the plurality of agronomic traits; and selecting at least one hybrid rice with the highest total score for breeding.

[0008] Furthermore, in some embodiments of the present invention, the step of training the hybrid rice breeding model includes: collecting genotype and phenotype data and SNP site data of hybrid rice samples of multiple varieties obtained from multiple regions to establish a data sample set; constructing a hybrid rice breeding model to be trained based on the Bayesian rule; inputting the SNP site data of each hybrid rice sample in the data sample set into the hybrid rice breeding model in pairs to obtain the genotype and phenotype prediction values ​​output by it; and verifying the accuracy of the output genotype and phenotype prediction values ​​based on the genotype and phenotype data corresponding to the input hybrid rice samples to train the hybrid rice breeding model.

[0009] Furthermore, in some embodiments of the present invention, the step of collecting genotype and phenotype data of hybrid rice samples of multiple varieties obtained from multiple regions includes: collecting hybrid rice samples of multiple varieties from multiple regions, and planting materials for the hybrid rice samples of each variety in a multi-row and multi-column manner; selecting multiple planting samples in the middle rows and middle columns for phenotypic investigation to respectively obtain multiple genotype and phenotype data of each planting sample of each variety; and respectively calculating the average value of each genotype and phenotype data of each planting sample of each variety as the multiple genotype and phenotype data of the hybrid rice samples of the corresponding variety.

[0010] Furthermore, in some embodiments of the present invention, the multiple genotype phenotypic data include at least plant height, heading period, yield, fruit setting rate, average number of grains per ear, grain shape and number of effective ears. The heading period refers to the number of days from sowing to the appearance of inflorescences on the leaf sheaths of a predetermined number of samples. The yield refers to the total weight of all filled grains of a sample. The fruit setting rate refers to the ratio of the number of filled grains to the total number of grains. The grain shape refers to the ratio of the length of a full grain to the width of a grain. The number of effective ears is the ratio of the total number of ears to the number of ineffective ears.

[0011] Furthermore, in some embodiments of the present invention, the step of collecting SNP site data of hybrid rice samples of multiple varieties obtained from multiple regions includes: extracting and sequencing DNA from leaves of multiple selected planting samples, and filtering out joint sequences and low-quality sequences with missing data; comparing the sequence data obtained by filtering with a pre-prepared reference genome and performing SNP identification to determine multiple high-quality variation sites; typing the SNP site data of the parents of each of the planting samples, and based on the typing results, using the variation information of the parents to fill in the variation information of the corresponding planting samples; integrating the variation information of each of the planting samples to determine the intersection of their variation sites; and selecting multiple SNP sites from the intersection, and marking and encoding them to obtain the SNP site data.

[0012] Furthermore, in some embodiments of the present invention, the step of encoding the SNP site includes: using "11" to represent the homozygous genotype that is the same as the reference genome, using "1 2" to represent the heterozygous genotype, using "2 2" to represent the homozygous genotype that is different from the reference genome, and using "00" to represent the missing genotype.

[0013] Further, in some embodiments of the present invention, the plurality of hybrid rice samples include hybrid rice samples and isolated samples of indica-indica hybrid and / or indica-japonica hybrid. The sequencing depth of the hybrid rice samples is 35, and the sequencing depth of the isolated samples of indica-indica hybrid and / or indica-japonica hybrid is 0.1-0.5.

[0014] Further, in some embodiments of the present invention, the hybrid rice is divided into early rice, mid-season rice and late rice according to the planting time. The step of determining the trait scoring rules of various genotype phenotype data according to the planting time of the hybrid rice obtained by breeding to calculate the total score of the hybrid rice obtained by breeding for multiple agronomic traits includes: according to the standard plant height, standard heading period and standard grain type of various hybrid rice, respectively determine multiple level intervals in the corresponding phenotypic data dimension, and the first score corresponding to each level interval, wherein the first score is the highest in the optimal level interval containing the standard plant height, the standard heading period and the standard grain type, and decreases with the deviation from the optimal level interval; according to the absolute values ​​of the yield, fruit setting rate, average number of grains per panicle and number of effective panicles of various hybrid rice, respectively determine the second score in the corresponding phenotypic data dimension; and cumulatively sum the first score or the second score of the hybrid rice obtained by breeding in each phenotypic data dimension to determine its total score for multiple agronomic traits.

[0015] Furthermore, in some embodiments of the present invention, before determining the trait scoring rules of various genotype and phenotype data based on the planting time of hybrid rice obtained through breeding, the breeding method also includes the following steps: statistically analyzing the plant height distribution, heading period distribution and grain type distribution of hybrid rice samples of various hybrid rice in the data sample set to respectively determine the standard plant height, standard heading period and standard grain type of the early rice, the mid-season rice and the late rice.

[0016] In addition, a hybrid rice breeding system provided according to the second aspect of the present invention comprises a memory and a processor. The memory stores computer instructions. The processor is connected to the memory and is configured to execute the computer instructions stored in the memory to implement the hybrid rice breeding method as described in the first aspect of the present invention.

[0017] In addition, according to the third aspect of the present invention, a computer readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the hybrid rice breeding method according to the first aspect of the present invention is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0019] Figure 1 A schematic diagram of a process for training a hybrid rice breeding model provided according to some embodiments of the present invention is shown.

[0020] Figure 2 A schematic diagram showing principal component analysis results of all analyzed materials provided according to some embodiments of the present invention.

[0021] Figure 3 A schematic diagram showing the prediction accuracy and heritability of seven important agronomic traits provided according to some embodiments of the present invention.

[0022] Figure 4 A schematic diagram showing the distribution of heading period, plant height and grain shape of rice materials in a hybrid rice population according to some embodiments of the present invention is shown.

[0023] Figure 5 A schematic diagram of mid-season rice selection coefficients provided according to some embodiments of the present invention is shown.

[0024] Figure 6 A schematic diagram of the score distribution after scoring excellent mid-season rice materials and pseudo F1 materials based on mid-season rice selection coefficients according to some embodiments of the present invention is shown.

[0025] Fig. 7A A schematic diagram showing the distribution of heading period, plant height and grain shape of early rice materials in a hybrid rice population according to some embodiments of the present invention is shown.

[0026] Figure 7B A schematic diagram of early rice selection coefficients provided according to some embodiments of the present invention is shown.

[0027] Fig. 8A A schematic diagram showing the distribution of heading period, plant height and grain type of late rice materials in a hybrid rice population according to some embodiments of the present invention is shown.

[0028] Figure 8B A schematic diagram of late rice selection coefficients provided according to some embodiments of the present invention is shown.

[0029] Fig. 9 A schematic diagram showing a comparison of the actual yields of high-scoring materials and low-scoring materials provided according to some embodiments of the present invention is shown.

[0030] Fig.10 A schematic flow chart of a hybrid rice breeding method provided according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0031] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description.

[0032] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] In addition, the terms "upper", "lower", "left", "right", "top", "bottom", "horizontal" and "vertical" used in the following description should be understood as the directions shown in the paragraph and the related drawings. Such relative terms are only used for the convenience of description and do not mean that the device described therein must be manufactured or operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0034] It is understood that although the terms "first", "second", "third", etc. may be used herein to describe various components, regions, layers and / or parts, these components, regions, layers and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers and / or parts. Therefore, the first component, region, layer and / or part discussed below may be referred to as a second component, region, layer and / or part without departing from some embodiments of the present invention.

[0035] As mentioned above, hybrid breeding is an effective way to breed new high-yield and high-quality rice varieties. This method relies on a large number of hybrid combination experiments and phenotypic investigations in the field, which is time-consuming and labor-intensive. Moreover, the important agronomic traits of most food crops are quantitative traits, which are controlled by multiple genes and greatly affected by the environment. With the development of genome sequencing technology, computer technology and breeding algorithms, it has become possible to predict important agronomic traits of crops through high-density molecular markers. Modern plant whole genome selection breeding technology collects phenotypic data and genotypic data through training populations, uses specific models to estimate molecular marker effect values ​​and individual breeding values, and then predicts phenotypic values ​​based on the genotypic data of the population to be predicted.

[0036] In order to overcome the above-mentioned defects of the prior art, the present invention provides a hybrid rice breeding method, a hybrid rice breeding device, and a computer-readable storage medium, which can not only reduce the breeding workload, shorten the breeding cycle, and improve the breeding efficiency, but also adapt to the planting time of early rice, middle rice, and late rice, and select hybrid rice with suitable agronomic traits for breeding.

[0037] In some non-limiting embodiments, the hybrid rice breeding method provided in the first aspect of the present invention can be implemented based on the hybrid rice breeding system provided in the second aspect of the present invention. Specifically, the hybrid rice breeding system is configured with a memory and a processor. The memory includes but is not limited to the computer-readable storage medium provided in the third aspect of the present invention, on which computer instructions are stored. The processor is connected to the memory and is configured to execute the computer instructions stored on the memory to implement the hybrid rice breeding method provided in the first aspect of the present invention.

[0038] In some non-limiting embodiments, the hybrid rice breeding method provided in the first aspect of the present invention can be implemented in two stages: offline training of the breeding model and online scoring based on the breeding model.

[0039] The following will first describe the working principle of the hybrid rice breeding system in conjunction with some examples of offline training breeding models. Those skilled in the art will understand that these examples of offline training breeding models are only some non-limiting implementation methods provided by the present invention, which are intended to clearly demonstrate the main concept of the present invention and provide some specific solutions that are convenient for the public to implement, rather than to limit all functions or all working methods of the hybrid rice breeding system. Similarly, the hybrid rice breeding system is also only a non-limiting implementation method provided by the present invention, and does not limit the execution subject or execution order of each step in the offline training breeding model stage.

[0040] First, please refer to Figure 1 , Figure 1 A schematic diagram of a process for training a hybrid rice breeding model provided according to some embodiments of the present invention is shown.

[0041] like Figure 1 As shown, in the process of training the hybrid rice breeding model, the present invention can first collect genotype and phenotype data of hybrid rice samples of multiple varieties from multiple regions. Here, the hybrid rice samples of multiple varieties can include excellent hybrid rice F1 samples produced in China, and isolated F2 samples of indica-indica hybrid and / or indica-japonica hybrid.

[0042] Specifically, hybrid rice samples of multiple varieties collected from multiple regions can be selected from the experimental fields planted in Hangzhou China Rice Research Institute in the summer of 2020. The present invention selects 15 rice materials for each variety, and plants them in three rows and five columns with a row spacing of 20 cm and a column spacing of 26.7 cm. Three rices in the middle area of ​​each block are selected for phenotypic investigation, and the average result is used as the final phenotype of the variety. Here, the present invention mainly investigates 18 phenotypic data such as plant height, heading period, yield, fruit setting rate, average number of grains per panicle, grain type and effective panicle number to characterize 18 important agronomic traits of rice, among which plant height and heading period are investigated in the field, while the remaining traits are all tested indoors. Heading period refers to the number of days from sowing to the appearance of inflorescences on the leaf sheaths of a predetermined number of samples. Yield refers to the total weight of all solid grains in a sample. Fruit setting rate refers to the ratio of the number of solid grains to the total number of grains. Grain type refers to the ratio of the length of a full grain to the width of a grain. The effective ear number is the ratio of the total ear number to the ineffective ear number. Afterwards, the present invention can calculate the average values ​​of the genotype and phenotype data of various plant samples of each variety respectively to serve as the multiple genotype and phenotype data of the hybrid rice samples of the corresponding variety.

[0043] In addition, in the process of training the hybrid rice breeding model, the present invention can also collect hybrid rice samples of multiple varieties from multiple regions to extract SNP site data, and combine the multiple genotype and phenotype data of each hybrid rice sample to establish a data sample set.

[0044] Specifically, in the process of extracting SNP site data of hybrid rice samples of each variety, the present invention can first collect fresh leaves of hybrid rice F1 samples of each variety from the field, and use DNeasy Plant Mini Kit (Qiagen) reagent to extract its genomic DNA. Here, the insert size of the sequencing library is 400bp, and the reagent for library construction is Tn5 transposase (Trueprep Tagment 3Enzyme). The present invention can use a PCR-free method to build a library to more accurately judge the heterozygous site. Afterwards, the present invention can use the NovaSeq 6000 sequencer to sequence hybrid rice with a depth of 35, and sequence the segregated population with a depth of 0.1 to 0.5, and then use Trimmomatic (version 0.38) software to perform quality control on the raw data obtained by sequencing, remove the low-quality sequences and adapter sequences in which the data is missing, and then use BWA (version 0.7.1) software to align the filtered reads to the rice Nipponbare reference genome IRGSP version1 to obtain preliminary SNP data. Subsequently, the present invention can use GATK (the genome analysis toolkit v4.1.4.1) software to identify and filter the initially obtained SNP data to finally obtain 5,222,902 high-quality variable sites. The genotyping method of the F2 material refers to the SEG-Map method, which will not be described in detail here.

[0045] Afterwards, the present invention can type the individuals of each material according to the SNP data of the parents, and then fill and merge them with the variation information of the parents to obtain the variation information of all individuals. After that, the present invention can integrate the variation information of all 2839 Chinese excellent hybrid rice F1 materials, 3911 indica-indica F2 individuals, and 5928 indica-japonica F2 individuals, and take out the intersection of their sites, select multiple SNP sites from the intersection and mark and encode them to obtain the SNP site data of each hybrid rice sample. Here, the present invention can use "1 1" to represent the homozygous genotype that is the same as the reference genome, "1 2" to represent the heterozygous genotype, "2 2" to represent the homozygous genotype that is different from the reference genome, and "0 0" to represent the missing genotype to achieve SNP site encoding.

[0046] Please continue to refer to Figure 1After establishing a sample set of genotype and phenotype data and SNP site data, the present invention can construct a hybrid rice breeding model to be trained based on the Bayesian Rule and verify its accuracy to train the constructed hybrid rice breeding model.

[0047] In some embodiments, the model may include a total of 12,678 individuals and involve at least 7 phenotypes, including plant height, heading date, yield, fruit set rate, average number of grains per ear, grain type, and number of effective ears. The present invention may randomly select 80% of the individuals from the above data sample set as a training population, and use the remaining 20% ​​of the individuals as a test population to cross-validate the accuracy of the model.

[0048] Specifically, in the process of training the hybrid rice breeding model, the present invention can first input the SNP site data of each hybrid rice sample in the data sample set into the hybrid rice breeding model in pairs to obtain the genotype-phenotype prediction value output by it, and then verify the accuracy of the output genotype-phenotype prediction value according to the genotype-phenotype data corresponding to the input hybrid rice sample to train the hybrid rice breeding model. Here, the accuracy of the model can be characterized by the Pearson correlation coefficient between the predicted phenotype output by the model and the real phenotype recorded in the data sample set.

[0049] In addition, for rice, a species with high LD decay distance, the present invention randomly selected a total of 88,909 SNP sites distributed throughout the genome as markers and performed downstream analysis, and used 367 restorer line materials and 159 sterile line materials to simulate and generate 58,383 pseudo F1 offspring to represent all possible combinations of these parental materials. In addition, in order to examine the population structure of the current hybrid rice materials and the genetic distribution of all possible combinations, the present invention also used GCTA (version 1.93.2) software to perform principal component analysis on all 71,061 materials, and used the software to estimate the broad heritability of the above 7 phenotypes.

[0050] Please refer to Figure 2 and Figure 3 . Figure 2 A schematic diagram showing principal component analysis results of all analyzed materials provided according to some embodiments of the present invention. Figure 3 A schematic diagram showing the prediction accuracy and heritability of seven important agronomic traits provided according to some embodiments of the present invention.

[0051] like Figure 2As shown, the principal component analysis results show that the F2 segregation population and the hybrid rice population have obvious population differences, especially the indica-japonica population. This may be related to a large number of recombination and segregation events in the segregation population, which leads to genomic differences between the two populations. Therefore, the large number of indica-japonica individuals in the segregation population added to the data sample set by the present invention make up for the shortage of indica-japonica varieties in the hybrid rice population, so that the model can be extended to hybrid breeding between rice subspecies. In addition, the present invention also finds that the existing excellent hybrid rice only accounts for a small part of all possible parent combinations, which shows that the previous hybrid breeding in this field may have only used limited rice germplasm resources, and hybrid breeding still has a large breeding improvement potential.

[0052] like Figure 3 As shown, the accuracy of the present invention in yield, effective ear number, fruiting rate, average number of grains per ear, plant height, grain type and heading period is 0.367, 0.454, 0.648, 0.589, 0.723, 0.919, 0.694, respectively, and its broad heritability is 0.145, 0.273, 0.513, 0.597, 0.750, 0.780, 0.935, respectively. The two have a high positive correlation (Pearson correlation coefficient is 0.850). This shows that the above-mentioned model provided by the present invention has a high prediction accuracy, and the traits with higher heritability tend to have a higher prediction accuracy.

[0053] Furthermore, compared to the traditional scheme of giving each phenotype a weight, then multiplying the predicted value of each phenotype by its weight and then adding them to obtain a final value, and then selecting according to the level of the final value, the three traits of rice heading period, plant height and grain type are not the higher the value, the better, but should be adjusted in accordance with actual agricultural production. To this end, the present invention also proposes a selection method for early rice, mid-season rice and late rice based on the heading period, plant height and grain type characteristics of real early rice, mid-season rice and late rice. The method first grades the phenotype according to the predicted value, and then gives a score according to the level of each individual, wherein the individual at the best level has the highest score, and the score decreases with the amplitude of deviation from the optimal level interval.

[0054] Taking the selection coefficient of mid-season rice as an example, the present invention can perform hybrid breeding selection tests on a validation population containing 58 rice inbred lines and 19 commercial rice sterile lines. These materials are not included in the existing training population. The present invention can obtain 86,683 sites after finding the intersection of the SNP sites of the prediction population and the positions of the SNP sites of the training population, and use these sites as new markers for model training and prediction.

[0055] Specifically, these parent materials are completely random and have a total of 1,102 parent combinations. The present invention can predict the phenotypes of all combinations and score them using the mid-season rice selection coefficient, and determine multiple level intervals in the corresponding phenotypic data dimension and the first score corresponding to each level interval according to the standard plant height, standard heading period and standard grain type of various hybrid rice. Here, the standard plant height, standard heading period and standard grain type of various hybrid rice can be determined by the plant height distribution, heading period distribution and grain type distribution of hybrid rice samples of various hybrid rice in the statistical data sample set.

[0056] In addition, the present invention can also determine the second score of the corresponding phenotypic data dimension according to the absolute values ​​of the yield, fruit setting rate, average number of grains per panicle and number of effective panicles of various hybrid rices. In other words, the second score will increase with the increase of the yield, fruit setting rate, average number of grains per panicle and number of effective panicles of hybrid rice.

[0057] Afterwards, the present invention can cumulatively sum the first scores or second scores of the hybrid rice obtained through breeding in each phenotypic data dimension to determine its total score for multiple agronomic traits, and plant 9 high-scoring combinations (top 20% in scores) and 10 low-scoring combinations (bottom 20% in scores) in Songjiang, Shanghai to examine their actual yield traits.

[0058] Please refer to Figures 4 to 8B , Figure 4 A schematic diagram showing the distribution of heading period, plant height and grain shape of hybrid rice materials in a hybrid rice population according to some embodiments of the present invention is shown. Figure 5 A schematic diagram of mid-season rice selection coefficients provided according to some embodiments of the present invention is shown. Figure 6 A schematic diagram of the score distribution of excellent mid-season rice materials and pseudo F1 materials after scoring based on the mid-season rice selection coefficient provided according to some embodiments of the present invention is shown. Fig. 7A A schematic diagram showing the distribution of heading period, plant height and grain type of early rice materials in a hybrid rice population according to some embodiments of the present invention is shown. Figure 7B A schematic diagram of early rice selection coefficients provided according to some embodiments of the present invention is shown. Fig. 8A A schematic diagram showing the distribution of heading period, plant height and grain type of late rice materials in a hybrid rice population according to some embodiments of the present invention is shown. Figure 8B A schematic diagram of late rice selection coefficients provided according to some embodiments of the present invention is shown.

[0059] like Figure 4 and Figure 5 As shown in the figure, after investigating the distribution intervals of heading period, plant height and grain type of the mid-season rice materials in the validation population among all hybrid rice materials, the present invention selected the mid-season rice materials based on the investigation results. Figure 5The dark area in the figure is the best trait classification range.

[0060] Furthermore, if Figure 6 As shown, the scoring results of using the mid-season rice selection coefficient for 500 excellent mid-season rice materials and all pseudo F1 materials show that the excellent mid-season rice materials tend to have higher selection scores, while the materials obtained by random combination have no obvious distribution trend. This shows that the mid-season rice selection coefficient provided by the present invention can select possible excellent mid-season rice materials.

[0061] In addition, the present invention constructs the selection coefficients of early rice and late rice based on the trait characteristics of early rice and late rice. Similarly, the above-mentioned verification population may also include 58 rice inbred lines and 19 commercial rice sterile lines, and these materials are not included in the existing training population. The present invention can combine these materials completely randomly to obtain a total of 1102 possible parent combinations. Afterwards, the present invention can predict the phenotypes of all combinations and score them using the mid-season rice selection coefficients to ultimately obtain the scores of all combinations, and then planted 9 high-scoring combinations (scoring the top 20%) and 10 low-scoring combinations (scoring the bottom 20%) in Songjiang, Shanghai, and examined the yield traits, as shown in the following figure. Figure 7A to Figure 7B and Figure 8A to Figure 8B As shown, the dark areas in the figure are selected as the best early rice trait classification interval and late rice trait classification interval respectively.

[0062] Please refer to further Fig. 9 , Fig. 9 A schematic diagram showing a comparison of the actual yields of high-scoring materials and low-scoring materials provided according to some embodiments of the present invention is shown.

[0063] like Fig. 9 As shown, the results of the real yield comparison indicate that the yield of the high-scoring combination is significantly higher than that of the low-scoring combination (p-value = 0.005091). Therefore, the hybrid rice breeding model constructed by the present invention can consider multiple traits at the same time, has high prediction accuracy, and can perform effective parent selection in a new population.

[0064] Furthermore, after the hybrid rice breeding model is obtained through training, the present invention can implement the steps of the online scoring stage to select at least one hybrid rice with the highest total score for breeding.

[0065] Please refer to Fig.10 , Fig.10 A schematic flow chart of a hybrid rice breeding method provided according to some embodiments of the present invention is shown.

[0066] like Fig.10As shown, in some embodiments of the present invention, in the process of hybrid rice breeding, the present invention can first input multiple candidate hybrid rice varieties into a pre-trained hybrid rice breeding model in pairs from hybrid rice materials and hybrid rice segregation groups in a data sample concentration, and predict the various genotype and phenotype data of the hybrid rice obtained by breeding according to the SNP sites of the two input hybrid rice varieties. Here, each genotype and phenotype data represents an agronomic trait of hybrid rice. Afterwards, the present invention can determine the trait scoring rules of various genotype and phenotype data according to the planting time of the hybrid rice obtained by breeding, so as to calculate the total score of the hybrid rice obtained by breeding for multiple agronomic traits, and then select at least one hybrid rice with the highest total score for breeding.

[0067] In this way, the hybrid rice breeding method, breeding system and computer-readable storage medium provided by the present invention can not only reduce the breeding workload, shorten the breeding cycle, and improve the breeding efficiency, but also adapt to the planting time of early rice, mid-season rice and late rice, and select hybrid rice with suitable agronomic traits for breeding.

[0068] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0069] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hybrid rice breeding method, characterized in that: The following steps are involved: Inputting multiple candidate hybrid rice varieties into a pre-trained hybrid rice breeding model in pairs, and predicting multiple genotype and phenotype data of hybrid rice obtained through breeding according to the SNP sites of the two input hybrid rice varieties, wherein each genotype and phenotype data represents an agronomic trait of the hybrid rice; Determining the trait scoring rules for the various genotype and phenotype data according to the planting time of the hybrid rice obtained by breeding, so as to calculate the total score of the hybrid rice obtained by breeding for multiple agronomic traits; and At least one hybrid rice with the highest total score is selected for breeding.

2. The breeding method according to claim 1, characterized in that The steps of training the hybrid rice breeding model include: Collect genotype and phenotype data and SNP locus data of hybrid rice samples of multiple varieties obtained from multiple regions to establish a data sample set; Constructing a hybrid rice breeding model based on the Bayesian rule to be trained; Inputting the SNP site data of each hybrid rice sample in the data sample set into the hybrid rice breeding model in pairs to obtain the genotype and phenotype prediction values ​​outputted by the model; and According to the genotype and phenotype data corresponding to the input hybrid rice sample, the output genotype and phenotype prediction values ​​are verified for accuracy to train the hybrid rice breeding model.

3. The breeding method according to claim 2, characterized in that The steps for collecting genotype and phenotype data of hybrid rice samples of multiple varieties obtained from multiple regions include: Collect multiple varieties of hybrid rice samples from multiple regions, and plant the hybrid rice samples of each variety in multiple rows and columns; Selecting a plurality of planting samples in the middle row and the middle column for phenotypic investigation to obtain a plurality of genotypic and phenotypic data of each planting sample of each variety; and The average values ​​of the genotype and phenotype data of the planting samples of each variety are calculated respectively to serve as the multiple genotype and phenotype data of the hybrid rice samples of the corresponding variety.

4. The breeding method according to claim 3, characterized in that The multiple genotype phenotypic data include at least plant height, heading period, yield, fruit setting rate, average number of grains per ear, grain shape and effective number of ears, wherein the heading period refers to the number of days from sowing to the appearance of inflorescence on the leaf sheaths of a predetermined number of samples, the yield refers to the total weight of all filled grains of a sample, the fruit setting rate refers to the ratio of the number of filled grains to the total number of grains, the grain shape refers to the ratio of the length of full grains to the width of full grains, and the effective number of ears is the ratio of the total number of ears to the ineffective number of ears.

5. The breeding method according to claim 3, characterized in that The steps of collecting SNP loci data of hybrid rice samples of multiple varieties obtained from multiple regions include: DNA was extracted and sequenced from leaves of multiple selected planting samples, and the adapter sequences and low-quality sequences with missing data were filtered out; Compare the filtered sequence data with the pre-prepared reference genome and identify SNPs to determine multiple high-quality variant sites; Typing the SNP site data of the parents of each of the planting samples, and using the variation information of the parents to fill in the variation information of the corresponding planting samples according to the typing results; Integrating the variation information of each of the planting samples to determine the intersection of their variation sites; and A plurality of SNP sites are selected from the intersection, and are marked and encoded to obtain the SNP site data.

6. The breeding method according to claim 5, characterized in that The step of encoding the SNP site comprises: "1 1" represents a homozygous genotype that is the same as the reference genome, "1 2" represents a heterozygous genotype, "2 2" represents a homozygous genotype that is different from the reference genome, and "00" represents a missing genotype.

7. The breeding method according to claim 5, characterized in that The plurality of hybrid rice samples include hybrid rice samples, and segregated samples of indica-indica hybrid and / or indica-japonica hybrid, wherein: The sequencing depth of the hybrid rice sample is 35, and the sequencing depth of the separation samples of the indica-indica hybrid and / or the indica-japonica hybrid is 0.1-0.

5.

8. The breeding method according to claim 2, characterized in that The hybrid rice is divided into early rice, mid-season rice and late rice according to the planting time. The step of determining the trait scoring rules of various genotype and phenotype data according to the planting time of the hybrid rice obtained by breeding to calculate the total score of the hybrid rice obtained by breeding for multiple agronomic traits comprises: According to the standard plant height, standard heading period and standard grain type of the various hybrid rices, a plurality of level intervals in the corresponding phenotypic data dimensions and a first score corresponding to each level interval are determined respectively, wherein the first score is the highest in the optimal level interval including the standard plant height, the standard heading period and the standard grain type, and decreases with the amplitude of deviation from the optimal level interval; Determining the second scores of the corresponding phenotypic data dimensions according to the absolute values ​​of the yield, fruit setting rate, average number of grains per panicle and number of effective panicles of the various hybrid rice varieties; and The first scores or the second scores of the hybrid rice obtained through breeding in each of the phenotypic data dimensions are cumulatively summed to determine the total score for the multiple agronomic traits.

9. The breeding method according to claim 8, characterized in that Before determining the trait scoring rules for the various genotype and phenotype data according to the planting time of the hybrid rice obtained through breeding, the breeding method further comprises the following steps: The plant height distribution, heading period distribution and grain type distribution of the hybrid rice samples of various hybrid rice in the data sample set are statistically analyzed to respectively determine the standard plant height, standard heading period and standard grain type of the early rice, the mid-season rice and the late rice.

10. A hybrid rice breeding system, characterized in that: include: a memory having computer instructions stored thereon; as well as A processor is connected to the memory and is configured to execute computer instructions stored in the memory to implement the hybrid rice breeding method according to any one of claims 1 to 9.

11. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the hybrid rice breeding method according to any one of claims 1 to 9 is implemented.

Images