Method and apparatus for diploid crop backcross improvement and polygenic pyramid breeding optimization

Abstract

The application discloses a diploid crop backcross improvement and multi-gene aggregation breeding optimization method and device, selects a crop RIL reference map, introduces a crop calculation genetic map, determines a backcross super parameter, formulates a backcross breeding scheme, and formulates a multi-gene aggregation breeding scheme based on the last generation of crops obtained according to the backcross breeding scheme and the crop calculation genetic map. The application can reduce the blindness of breeding experiments, improve the utilization rate of the budget and the breeding efficiency, and can be extended to the breeding scheme formulation for diploid plants.

Description

Technical Field

[0001] This invention belongs to the field of genetic breeding, specifically relating to methods and equipment for backcrossing improvement of diploid crops and optimization of multi-gene aggregation breeding. Background Technology

[0002] Currently, under the multiple pressures of climate change and arable land reduction, maize genetic improvement is facing unprecedented challenges. Maize faces problems such as insufficient innovation in basic germplasm, long population improvement cycles, and low efficiency of traditional methods. Although the rapidly developing genomic breeding technology has shown great potential in recent years, its high investment, high technical threshold, and interdisciplinary nature deter many breeding units. Whether it is backcross breeding or hybridization breeding, current breeding models mainly rely on the breeder's subjective judgment, lacking precise decision-making guidance, which leads to a waste of resources and time. To address these issues, this study uses maize as an example of a diploid plant to develop experimental protocols for backcrossing and multi-gene aggregation in diploid plants. Summary of the Invention

[0003] The purpose of this invention is to address the problem of poor decision-making guidance in backcrossing and multi-gene aggregation breeding of diploid plants (taking maize as an example) in existing technologies. It aims to help maize breeding experts accurately calculate and plan breeding costs, thereby improving the efficiency of maize breeding. This will promote the development of intelligent and precise maize breeding, providing strong technical support for cultivating superior maize varieties that better meet the needs of modern agriculture, and ultimately driving the sustainable development of the maize industry. The invention provides methods and equipment for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: Methods for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops include the following steps: Step 1: Select a crop RIL reference map; Step 2: Import the crop computational genetic map. The crop computational genetic map contains gene loci selected or interpolated from the crop RIL reference map. Step 3: Determine the backcross hyperparameters, including the minimum number of selected materials in each generation. Fault tolerance Backcrossing maximum algebra ; Step 4: Develop a backcross breeding program: Calculate the left genetic distance of the gene loci of interest by referring to the genetic map of the crop. And right genetic distance Further calculate the number of child algebras required to break the left chain. Number of child algebras required to break the chain to the right Based on the number of child generations required to break the left chain Number of child algebras required to break the chain to the right Computing subalgebras ; Calculate the mean background response rate of the next generation Variance of background response rate And the distribution of the next generation of background reactivity, calculating the background reactivity greater than or equal to the target background reactivity threshold. Minimum number of sub-algebras required as a subalgebra Selecting subalgebras sum subalgebra The larger one is the offspring number of the next generation. ; Step 5: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, formulate a multi-gene aggregation breeding scheme by referring to the crop's calculated genetic map.

[0005] As mentioned above, step 4 of developing a backcross breeding program includes: Step 4.1: Calculate the background response rate of the F1 generation. , replacing serial number ; Step 4.2, currently... substitute or Generation: Calculate the genetic distance between each gene locus of interest and pre- and post-defined distance gene loci from the crop computational genetic map file. and Proceed to step 4.3; if the current is The maximum generation of the backcross At this time: proceed to step 4.6; Step 4.3: Calculate the left genetic distance of the gene loci of interest. And right genetic distance Substitute each value into the formula for breaking the chain to calculate the probability of breaking the chain on the left. Probability of right-side disconnection ; Step 4.4: Calculate the number of subalgebras required for left-breaking chains based on the subalgebra calculation formula. Number of child algebras required to break the chain to the right ; Step 4.5, currently at number [number missing] When choosing the leftmost child algebra, select the number of child algebras required to break the chain. Number of child algebras required to break the chain to the right The larger value is used as the subalgebra ; Currently When choosing the leftmost child algebra, select the number of child algebras required to break the chain. Number of child algebras required to break the chain to the right The smaller value is used as the subalgebra The current number is generation, and Subalgebra Set to 0; Step 4.6: Use the average background reversion rate of the current generation of experimental materials as the background reversion rate of the next generation of parents. and use Further calculate the mean of the next generation background response rate ,use Calculate the standard deviation of the next generation background response rate , Using the weighted parameters, the distribution of background reversion rate of the next generation of experimental materials is fitted with a normal distribution. ; Step 4.7: Calculate the distribution of background recurrence rate of the next generation of experimental materials. The median value is greater than the target background recovery rate threshold. probability ,in It is the cumulative distribution function of the standard normal distribution. This is the square root of the variance; Step 4.8: Calculate the probability Minimum quantity of materials selected in each generation Fault tolerance Substitution The calculation satisfies Algebra The minimum value is used as the subalgebra ; Indicates from In the offspring The individual background response rate is greater than the target background response rate threshold. The number of combinations; Step 4.9: Selecting Subalgebras sum subalgebra The larger one is the offspring number of the next generation. ; Step 4.10: Record the target background response rate threshold for the next generation. algebra Add 1, return to step 4.2, until... .

[0006] The formula for calculating the disconnection of the chain, as described above, is: ; ; and The operation number, and All are natural numbers including 0. Left genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of breaking the chain on the left Right genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of right-side disconnection .

[0007] As described above, the probability of breaking the chain The probability of breaking the chain on the left When, the probability of breaking the chain will be Minimum quantity of materials selected in each generation Fault tolerance Substituting into the subalgebra calculation formula, the calculation is based on a value not less than... The probability of producing at least The number of sub-generations required for a disconnected experimental material The minimum value is used as the number of child algebras required to break the left chain. ; Probability of Disconnection The probability of right-side disconnection When, the probability of breaking the chain will be Minimum quantity of materials selected in each generation Fault tolerance Substituting into the subalgebra calculation formula, the calculation is based on a value not less than... The probability of producing at least The number of sub-generations required for a disconnected experimental material The minimum value is used as the number of child algebras required to break the right chain. ; The formula for calculating subalgebras is: , For the sequence number parameter, Indicates from In the offspring The number of combinations of disconnected offspring.

[0008] As mentioned above, the target background recovery rate threshold .

[0009] As mentioned above, step 5 of developing a multi-gene aggregation breeding program includes: Step 5.1: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, arrange the gene loci of interest in ascending order according to chromosome number. For gene loci of interest with the same chromosome number, arrange them in ascending order according to physical location to obtain the gene loci of interest sequence. Step 5.2: In the sequence of gene loci of interest, if adjacent gene loci of interest are on the same chromosome, calculate the genetic distance between adjacent gene loci of interest based on the crop genetic map; if adjacent gene loci of interest are on different chromosomes, calculate the genetic distance between adjacent gene loci of interest as infinite. ; Step 5.3: Select the gene loci of interest from the adjacent loci of interest in Step 5.2, from smallest to largest, and perform aggregation operations. Determine each aggregation generation, the number of aggregation operations for each generation, and the gene loci of interest aggregated in each aggregation operation. Each aggregation operation selects two adjacent gene loci of interest with the smallest genetic distance, or adjacent gene loci and clusters of interest with the smallest genetic distance, or two adjacent clusters of interest with the smallest genetic distance, to aggregate and obtain a new cluster. Calculate the new cluster and other gene loci of interest. The distance between them Step 5.4: Calculate the probability of generating selected materials carrying the target superior genes based on each polymerization operation of each polymerization generation determined in Step 5.3. probability The initial value is 1, and the probability is updated with each aggregation operation. Update probability Based on the following circumstances: Scenario 1: In this aggregation operation, the target superior genes at the gene loci of interest will be located on two homologous chromosomes of the same chromosome after aggregation, with a probability of... Updated to ; Scenario 2: In this aggregation operation, it is necessary to recombine the target superior genes located at different gene loci of interest on two homologous chromosomes in the previous generation into the same homologous chromosome. The probability is... Updated to ,in, This refers to the genetic distance between two loci of interest involved in this aggregation operation, or the genetic distance between a locus of interest and an aggregation cluster, or the genetic distance between aggregation clusters. It is a natural constant. and The operation number, and All are natural numbers including 0; ! is the factorial operator. Scenario 3: This aggregation operation involves different chromosomes, and there are target superior genes located on a pair of homologous chromosomes that have not aggregated to the same homologous chromosome. The probability is... Updated to , It represents the genetic distance between two gene loci of interest located on the same homologous chromosome; Step 5.5: The probability of each aggregation operation calculated in Step 5.4. Further calculation of subalgebras : Calculate the probability that a single experimental material carries the target superior gene at the gene locus of interest. , The probability is calculated based on the number of gene loci located on different chromosomes involved in this aggregation operation. Minimum quantity of materials selected in each generation Fault tolerance Substitution The calculation satisfies The smallest subalgebra , Indicates from In the offspring The number of combinations of disconnected offspring.

[0010] As mentioned above, new clusters and other gene loci of interest The distance between them is the distance between each gene locus of interest in the new cluster. The minimum distance between two clusters is the genetic distance between the gene loci of one cluster and the gene loci of the other cluster.

[0011] The method for backcrossing improvement and multigene aggregation breeding optimization of diploid crops also includes visualizing backcross breeding schemes and multigene aggregation breeding schemes to a display module. In the display module, the backcross breeding schemes and multigene aggregation breeding schemes are displayed in the form of images. The horizontal axis is marked with the gene loci of interest, and BC on the vertical axis represents the backcross generation, with the subscript of BC indicating the backcross generation number. AG represents the aggregation generation, with the subscript of AG indicating the aggregation generation number. The numbers in the figure represent the number of offspring corresponding to the backcross generation or aggregation generation.

[0012] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described diploid crop backcrossing improvement and multi-gene aggregation breeding optimization method.

[0013] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described methods for backcrossing improvement of diploid crops and optimization of multi-gene aggregation breeding.

[0014] Compared with the prior art, the present invention has the following advantages: 1. Reduces the blind spots in breeding experiments. By specifying the gene loci that need attention, this method can develop a suitable full-process breeding experiment plan from backcrossing to multi-gene aggregation, including the number of offspring in each generation and the path of multi-gene aggregation, accurately depicting the process of backcrossing and multi-gene aggregation breeding experiments and the distribution of each generation; 2. Improved the efficiency of breeding experiments. Adjusted the minimum number of selected materials per generation. The fault tolerance of the solution Backcrossing maximum algebra They can choose to adopt breeding experiment schemes that are more in line with the budget, improve the utilization rate of the budget, and thus improve breeding efficiency; 3. Use computers to design and visualize multi-gene aggregation schemes. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention.

[0016] Figure 2 A diagram illustrating the experimental protocol for randomly selecting 6 gene loci from all chromosomes; Figure 3 An experimental protocol for randomly selecting 7 gene loci on the same chromosome is illustrated in the diagram. Figure 4 This diagram illustrates an experimental protocol for randomly selecting nine gene loci distributed on different chromosomes. Detailed Implementation

[0017] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] Example 1:

[0019] In this embodiment, the diploid crop backcross improvement and multi-gene aggregation breeding optimization method is used to aggregate several superior genes located in different materials (each individual parent material contains at most one superior gene corresponding to a gene locus) into the same material while ensuring the genetic stability of the material with superior genes (background reversion rate not less than 0.985). Specifically, as... Figure 1 As shown, proceed as follows: Step 1: Select the crop RIL (recombinant inbred line) reference map as the standard maize reference genetic map 10RIL_genetic_map_V3. This crop RIL reference map contains 7010 gene loci distributed across the 10 maize chromosomes. During the breeding process, the following gene loci should be considered: 1_52537654, 1_203127776, 1_293704709, 2_149143204, 6_93373988, and 8_122430511. Ultimately, these six superior genes need to be aggregated into the same material.

[0020] Step 2: Import the Crop Computational Genetic Map (Corn_Genetic_Map). The Crop Computational Genetic Map contains 1126 gene loci selected or interpolated from the Crop RIL Reference Map.

[0021] Step 3: Determine the backcross hyperparameter: the minimum number of selected materials per generation. The fault tolerance of the solution Backcrossing maximum algebra .

[0022] Step 3.1: Prepare the VCF file parent_gene.vcf to record the genotypes of the experimental materials, including the genotypes of the recurrent and non-recurrent parents.

[0023] Step 3.2: Record the genotypes of the experimental materials using homologous chromosomes to obtain genotype records. Since maize is a diploid species, there are two genotype records for each gene locus in each experimental material. For each gene locus on the homologous chromosomes of the experimental materials, gene loci originating from non-recurrent parents were marked as 0, gene loci originating from recurrent parents were marked as 2, and gene loci of interest originating from non-recurrent parents were marked as 3. If a gene locus is identical between recurrent and non-recurrent parents, the gene locus on the non-recurrent parent is marked as 2. Figure 2 The homologous chromosomes P1_1 and P1_2 (P1_1 and P1_2 are a pair of homologous chromosomes) recorded the genotypes of six gene loci of interest on the two homologous chromosomes of the experimental material P1 (from the F1 generation).

[0024] Step 3.3: Summarize the genotype records from Step 3.2 into a CSV file named parent_gene.csv, where the row index represents the gene locus and the column index represents the chromosome number, such as... Figure 3 P1_1 and P1_2 are a pair of homologous chromosomes; P2_1 and P2_2 are another pair of homologous chromosomes.

[0025] Step 4: For each gene locus of interest, there is an experimental material recorded as 3 at the gene locus of interest (for example, if 6 gene loci of interest were selected in step 1: 1_52537654, 1_203127776, 1_293704709, 2_149143204, 6_93373988, 8_122430511, 6 experimental materials carrying these 6 genes of interest will be generated. These experimental materials are all from the F1 generation). Develop backcross breeding programs by referring to the crop genetic map.

[0026] Step 4.1: Calculate the background response rate of the F1 generation. , replacing serial number .

[0027] Step 4.2, currently... substitute or (Code number) Or serial number (Time): Calculate the genetic distance between each gene locus of interest (6 gene loci in this example) and its left-right genetic distance (5 Mbit in this example) from the crop computational genetic map file. and Proceed to step 4.3; if the current is ( and (Time) In this case, proceed to step 4.6.

[0028] Step 4.3: Calculate the left genetic distance of the gene loci of interest. And right genetic distance Substitute them into the genetic distance parameter respectively The probability of disconnection of the gene loci of interest is calculated based on the disconnection calculation formula. Left genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of breaking the chain on the left Right genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of right-side disconnection The formula for breaking the chain is: ; ; and The operation number, and All are natural numbers including 0.

[0029] Step 4.4, using binomial distribution Estimate the distribution of experimental materials that break the linkage in offspring.

[0030] Probability of Disconnection The probability of breaking the chain on the left At that time, , , Substituting into the following subalgebraic calculation formula, calculate with a value not less than The probability of producing at least (According to step 3, here) , The number of sub-generations required for 1 / 3 disconnected experimental material The minimum value is used as the number of child algebras required to break the left chain. .

[0031] Probability of Disconnection The probability of right-side disconnection At that time, , , Substituting into the following subalgebraic calculation formula, calculate with a value not less than The probability of producing at least (According to step 3, here) , The number of sub-generations required for 1 / 3 disconnected experimental material The minimum value is used as the number of child algebras required to break the right chain. .

[0032] The formula for calculating subalgebras is: , The minimum number of materials selected for each generation. For the sequence number parameter, Indicates from In the offspring The number of combinations of disconnected offspring.

[0033] Step 4.5, Combine with the current algebra: the current one is the th ( (growth) Compare the number of child generations required to break the left chain. Number of child algebras required to break the chain to the right ,choose and The larger value is used as the subalgebra , ; Currently ( (growth) Compare the number of child generations required to break the left chain. Number of child algebras required to break the chain to the right ,choose and The smaller value is used as the subalgebra ( ), currently the number generation, and Subalgebra Take 0.

[0034] Step 4.6, using the current generation ( The mean background response rate of the experimental materials of the next generation (1st generation) was used as the next generation (2nd generation). Parental background response rate (generation) and use Further calculate the mean of the next generation background response rate ,use Calculate the standard deviation of the next generation background response rate (Taking weight parameters) ), fitting the next generation with a normal distribution ( Distribution of background response rate of experimental materials (generation) .

[0035] Step 4.7: Calculate the next generation (the...) The target background reversion rate threshold that the experimental materials in the (generation) need to achieve , Further calculation of the next generation (the Distribution of background response rate of experimental materials (generation) The median value is greater than the target background recovery rate threshold. probability ,in It is a standard normal distribution The cumulative distribution function, is the square root of the variance.

[0036] Step 4.8, for the next generation (the first... A single experimental material (in a generation) has a probability The possibility that its background response rate is greater than or equal to the target background response rate threshold. It is assumed that the event-individual background response rate is greater than the target background response rate threshold. Follows binomial distribution Calculations are made at a rate not lower than The probability of producing at least For each offspring sample, the background reversion rate is greater than or equal to the target background reversion rate threshold. Minimum number of sub-algebras required as a subalgebra ,Will , , Substitution The calculation satisfies Algebra The minimum value is used as the minimum number of sub-algebras. ; Indicates from In the offspring The individual background response rate is greater than the target background response rate threshold. The number of combinations.

[0037] Step 4.9: Selecting Subalgebras sum subalgebra The larger one is the next generation (the first generation). Subalgebra of (a) .

[0038] Step 4.10, Record the next generation (the...) (The target background response rate threshold) algebra Add 1, return to step 4.2, until... .

[0039] The purpose of backcrossing is to ensure that the selected materials carry the target superior genes corresponding to the gene loci of interest while increasing the background reversion rate to reach the target background reversion rate threshold. In backcross breeding, each generation t is the sample with the highest background reversion rate selected from the offspring samples of the previous generation t-1 when the parent is used. During this breeding process, the background reversion rate of the selected samples in each offspring population will continuously increase until the breeding target is achieved (generally, the background reversion rate requirement in the target is 98.5%). This invention sets a background reversion rate threshold th that this offspring sample needs to achieve for each backcross, which facilitates calculation and decision-making and reduces the blindness and cost of actual experiments.

[0040] Step 5: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, formulate a multi-gene aggregation breeding scheme by referring to the crop genetic map; Step 5.1: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, arrange the gene loci of interest in ascending order according to chromosome number. For gene loci of interest with the same chromosome number, arrange them in ascending order according to physical location to obtain the gene loci of interest sequences. For example, the gene loci of interest sequences of the 6 gene loci of interest selected in Step 1 are (1_52537654, 1_203127776, 1_293704709, 2_149143204, 6_93373988, 8_122430511).

[0041] Step 5.2: In the sequence of gene loci of interest, if adjacent gene loci of interest are gene loci of interest on the same chromosome, calculate the genetic distance between adjacent gene loci of interest based on the crop computational genetic map. If adjacent loci of interest are loci on different chromosomes, then the genetic distance between adjacent loci of interest is infinite. For example, the calculation results for the 6 gene loci in the previous step correspond to... ,in and These represent the genetic distances between loci of interest 1_52537654 and 1_203127776, and between loci of interest 1_203127776 and 1_293704709, respectively.

[0042] Step 5.3: Select the gene loci of interest from those adjacent loci of interest in Step 5.2, from smallest to largest genetic distance, and perform aggregation operations. Determine each aggregation generation, the number of aggregation operations for each generation, and the gene loci of interest aggregated in each aggregation operation. An aggregation generation may involve multiple aggregation operations. Each aggregation operation selects two adjacent gene loci of interest with the smallest genetic distance, or adjacent gene loci and clusters of interest with the smallest genetic distance, or two adjacent clusters of interest with the smallest genetic distance, to aggregate and obtain a new cluster. A cluster is the set of gene loci of interest after aggregation. The genetic distance between a gene locus of interest and a cluster is the minimum genetic distance between all gene loci in the cluster. The genetic distance between two clusters is the minimum genetic distance between gene loci in one cluster and gene loci in the other cluster. For example, the order of aggregation operations for the six gene loci of interest involved in the above steps is as follows: {AG1: (1_52537654, 1_203127776), AG2: ((1_52537654, 1_203127776), 1_293704709), AG3_1:( (1_52537654, 1_203127776, 1_293704709), 2_149143204), AG3_2:(6_93373988, 8_122430511), AG4: ((1_52537654, 1_203127776, 1_293704709, 2_149143204), (6_93373988, 8_122430511))}.

[0043] The above order of events can be explained as follows: ① For each aggregation generation AG t If there is only one aggregation operation, such as AG1, the statement "AG1: (1_52537654, 1_203127776)" means that two experimental materials carrying the target superior genes corresponding to the gene loci of interest 1_52537654 and 1_203127776 are hybridized so that the two superior genes appear simultaneously in the same offspring experimental material, that is, after aggregation, experimental materials carrying (1_52537654, 1_203127776) are produced; ② For each aggregation generation AG t If there is more than one aggregation operation, such as AG3, the statement "AG3_1:((1_52537654,1_203127776,1_293704709),2_149143204), AG3_2:( The meaning of "6_93373988,8_122430511)" is that in the AG3 generation, there are two aggregation operations, which respectively combine the target superior genes from two experimental materials carrying (1_52537654,1_203127776,1_293704709,2_149143204) and two experimental materials carrying 6_93373988,8_122430511 into the offspring, producing experimental materials carrying 4 target superior genes (1_52537654,1_203127776,1_293704709,2_149143204) and experimental materials carrying 2 target superior genes (6_93373988,8_122430511).

[0044] Step 5.4: In this example, based on the aggregation operations of each aggregation generation determined in Step 5.3, the probability of generating selected materials carrying the target superior genes is calculated. probability The initial value is 1, and the probability is updated with each aggregation operation. Update probability The following situations need to be considered: Scenario 1: In this aggregation operation, the target superior genes at the gene loci of interest will be located on two homologous chromosomes of the same chromosome after aggregation. For example, in the AG2 generation described in step 5.3, (1_52537654, 1_203127776) and 1_293704709 will be located on two homologous chromosomes of chromosome 1 in the selected materials after aggregation. In this case, the probability... Updated to .

[0045] Scenario 2: In this aggregation operation, it is necessary to recombine the target superior genes located on two different homologous chromosomes in the previous generation into the same homologous chromosome. For example, in the AG2 generation, (1_52537654, 1_203127776) need to be recombinated into the same homologous chromosome. The probability of this is... Updated to ,in, This refers to the genetic distance between the two gene loci of interest involved in this aggregation operation. Here, is a natural constant. and is the ordinal number, which is a natural number including 0, and ! is the factorial operator.

[0046] Scenario 3: This aggregation operation involves different chromosomes, and there are target superior genes located on a pair of homologous chromosomes that have not aggregated to the same homologous chromosome. For example, in "AG3_1:( (1_52537654, 1_203127776, 1_293704709), 2_149143204)," chromosomes 1 and 2 are involved. In this case, it is necessary to consider that the target superior genes at the two gene loci of interest, 1_52537654 and 1_203127776, located on chromosome 1, will still be located on the same homologous chromosome on chromosome 1 after aggregation. However, in this case, only 1_52537654 and 1_203127776 are located on the same homologous chromosome of chromosome 1, 1_293704709 is located on another homologous chromosome of chromosome 1, and 2_149143204 is located on chromosome 2. Updated to , This represents the genetic distance between two gene loci of interest located on homologous chromosomes. Here, is a natural constant. and is the ordinal number, which is a natural number including 0, and ! is the factorial operator.

[0047] Step 5.5, each aggregation operation Produced in offspring The selected experimental materials carrying the target superior gene at the gene locus of interest follow a binomial distribution. Calculate the probability that a single experimental material carries the target superior gene at the gene locus of interest. ( The gene loci involved in this aggregation operation are located at On two different chromosomes, for example, in step 5.3, the operation "AG3_1:( (1_52537654,1_203127776,1_293704709),2_149143204)" involves two different chromosomes, chromosomes 1 and 2. ,Will , , Substitute into the equation Calculate the equation that satisfies The smallest subalgebra , Indicates from Produced in offspring The number of combinations of offspring carrying the target superior genes of all gene loci of interest involved in this aggregation operation.

[0048] Step 6: Organize the entire process and visualize the backcross breeding scheme and multi-gene aggregation breeding scheme to the display module. In the display module, the backcross breeding scheme and multi-gene aggregation breeding scheme are displayed in the form of images. The horizontal axis is marked with the gene loci that need attention. On the vertical axis, BC represents the backcross generation, and the subscript of BC is the backcross generation number. AG represents the aggregation generation, and the subscript of AG is the aggregation generation number. The numbers in the figure are the number of offspring corresponding to the backcross generation or aggregation generation.

[0049] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0050] Example 2:

[0051] In this embodiment, a computer device is also provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0052] Example 3:

[0053] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0054] Example 4:

[0055] In this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] It should be noted that the embodiments described in this invention are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains can make various modifications or additions to the described embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops, characterized in that, Includes the following steps: Step 1: Select a crop RIL reference map; Step 2: Import the crop computational genetic map. The crop computational genetic map contains gene loci selected or interpolated from the crop RIL reference map. Step 3: Determine the backcross hyperparameters, including the minimum number of selected materials in each generation. Fault tolerance Backcrossing maximum algebra ; Step 4: Develop a backcross breeding program: Calculate the left genetic distance of the gene loci of interest by referring to the genetic map of the crop. And right genetic distance Further calculate the number of child algebras required to break the left chain. Number of child algebras required to break the chain to the right Based on the number of child generations required to break the left chain Number of child algebras required to break the chain to the right Computing subalgebras ; Calculate the mean background response rate of the next generation Variance of background response rate And the distribution of the next generation of background reactivity, calculating the background reactivity greater than or equal to the target background reactivity threshold. Minimum number of sub-algebras required as a subalgebra Selecting subalgebras sum subalgebra The larger one is the offspring number of the next generation. ; Step 5: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, formulate a multi-gene aggregation breeding scheme by referring to the crop's calculated genetic map.

2. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 1, characterized in that, Step 4, which involves developing a backcross breeding program, includes: Step 4.1: Calculate the background response rate of the F1 generation. , replacing serial number ; Step 4.2, currently... substitute or Generation: Calculate the genetic distance between each gene locus of interest and pre- and post-defined distance gene loci from the crop computational genetic map file. and Proceed to step 4.3; if the current is The maximum generation of the backcross At this time: proceed to step 4.6; Step 4.3: Calculate the left genetic distance of the gene loci of interest. And right genetic distance Substitute each value into the formula for breaking the chain to calculate the probability of breaking the chain on the left. Probability of right-side disconnection ; Step 4.4: Calculate the number of subalgebras required for left-breaking chains based on the subalgebra calculation formula. Number of child algebras required to break the chain to the right ; Step 4.5, currently at number [number missing] When choosing the leftmost child algebra, select the number of child algebras required to break the chain. Number of child algebras required to break the chain to the right The larger value is used as the subalgebra ; Currently When choosing the leftmost child algebra, select the number of child algebras required to break the chain. Number of child algebras required to break the chain to the right The smaller value is used as the subalgebra The current number is generation, and Subalgebra Set to 0; Step 4.6: Use the average background reversion rate of the current generation of experimental materials as the background reversion rate of the next generation of parents. and use Further calculate the mean of the next generation background response rate ,use Calculate the standard deviation of the next generation background response rate , Using the weighted parameters, the distribution of background reversion rate of the next generation of experimental materials is fitted with a normal distribution. ; Step 4.7: Calculate the distribution of background recurrence rate of the next generation of experimental materials. The median value is greater than the target background recovery rate threshold. probability ,in It is the cumulative distribution function of the standard normal distribution. This is the square root of the variance; Step 4.8: Calculate the probability Minimum quantity of materials selected in each generation Fault tolerance Substitution The calculation satisfies Algebra The minimum value is used as the subalgebra ; Indicates from In the offspring The individual background response rate is greater than the target background response rate threshold. The number of combinations; Step 4.9: Selecting Subalgebras sum subalgebra The larger one is the offspring number of the next generation. ; Step 4.10: Record the target background response rate threshold for the next generation. algebra Add 1, return to step 4.2, until... .

3. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 2, characterized in that, The formula for calculating the disconnection chain is: ； ； and The operation number, and All are natural numbers including 0. Left genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of breaking the chain on the left Right genetic distance Substitute into the genetic distance parameter Calculate the probability of disconnection The probability of right-side disconnection .

4. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 3, characterized in that, The probability of disconnection The probability of breaking the chain on the left When, the probability of breaking the chain will be Minimum quantity of materials selected in each generation Fault tolerance Substituting into the subalgebra calculation formula, the calculation is based on a value not less than... The probability of producing at least The number of sub-generations required for a disconnected experimental material The minimum value is used as the number of child algebras required to break the left chain. ; Probability of Disconnection The probability of right-side disconnection When, the probability of breaking the chain will be Minimum quantity of materials selected in each generation Fault tolerance Substituting into the subalgebra calculation formula, the calculation is based on a value not less than... The probability of producing at least The number of sub-generations required for a disconnected experimental material The minimum value is used as the number of child algebras required to break the right chain. ; The formula for calculating subalgebras is: , For the sequence number parameter, Indicates from In the offspring The number of combinations of disconnected offspring.

5. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 4, characterized in that, The target background recovery rate threshold .

6. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 1, characterized in that, Step 5, which involves developing a multi-gene aggregation breeding program, includes: Step 5.1: Based on the last generation of crops obtained from the backcross breeding scheme in Step 4, arrange the gene loci of interest in ascending order according to chromosome number. For gene loci of interest with the same chromosome number, arrange them in ascending order according to physical location to obtain the gene loci of interest sequence. Step 5.2: In the sequence of gene loci of interest, if adjacent gene loci of interest are on the same chromosome, calculate the genetic distance between adjacent gene loci of interest based on the crop genetic map; if adjacent gene loci of interest are on different chromosomes, calculate the genetic distance between adjacent gene loci of interest as infinite. ; Step 5.3: Select the gene loci of interest from the adjacent loci of interest in Step 5.2, from smallest to largest, and perform aggregation operations. Determine each aggregation generation, the number of aggregation operations for each generation, and the gene loci of interest aggregated in each aggregation operation. Each aggregation operation selects two adjacent gene loci of interest with the smallest genetic distance, or adjacent gene loci and clusters of interest with the smallest genetic distance, or two adjacent clusters of interest with the smallest genetic distance, to aggregate and obtain a new cluster. Calculate the new cluster and other gene loci of interest. The distance between them Step 5.4: Calculate the probability of generating selected materials carrying the target superior genes based on each polymerization operation of each polymerization generation determined in Step 5.

3. probability The initial value is 1, and the probability is updated with each aggregation operation. Update probability Based on the following circumstances: Scenario 1: In this aggregation operation, the target superior genes at the gene loci of interest will be located on two homologous chromosomes of the same chromosome after aggregation, with a probability of... Updated to ; Scenario 2: In this aggregation operation, it is necessary to recombine the target superior genes located at different gene loci of interest on two homologous chromosomes in the previous generation into the same homologous chromosome. The probability is... Updated to ,in, This refers to the genetic distance between two loci of interest involved in this aggregation operation, or the genetic distance between a locus of interest and an aggregation cluster, or the genetic distance between aggregation clusters. It is a natural constant. and The operation number, and All are natural numbers including 0; ! is the factorial operator. Scenario 3: This aggregation operation involves different chromosomes, and there are target superior genes located on a pair of homologous chromosomes that have not aggregated to the same homologous chromosome. The probability is... Updated to , It represents the genetic distance between two gene loci of interest located on the same homologous chromosome; Step 5.5: The probability of each aggregation operation calculated in Step 5.

4. Further calculation of subalgebras : Calculate the probability that a single experimental material carries the target superior gene at the gene locus of interest. , The probability is calculated based on the number of gene loci located on different chromosomes involved in this aggregation operation. Minimum quantity of materials selected in each generation Fault tolerance Substitution The calculation satisfies The smallest subalgebra , Indicates from In the offspring The number of combinations of disconnected offspring.

7. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 6, characterized in that, The new clusters and other gene loci of interest The distance between them is the distance between each gene locus of interest in the new cluster. The minimum distance between two clusters is the genetic distance between the gene loci of one cluster and the gene loci of the other cluster.

8. The method for backcrossing improvement and multi-gene aggregation breeding optimization of diploid crops according to claim 6, characterized in that, It also includes visualizing backcross breeding schemes and multigene aggregation breeding schemes to the display module. In the display module, the backcross breeding schemes and multigene aggregation breeding schemes are displayed in the form of images. The horizontal axis is marked with the gene loci of interest, and BC on the vertical axis represents the backcross generation, with the subscript of BC indicating the backcross generation number. AG represents the aggregation generation, with the subscript of AG indicating the aggregation generation number. The numbers in the figure represent the number of offspring corresponding to the backcross generation or aggregation generation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the diploid crop backcrossing improvement and multi-gene aggregation breeding optimization method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the diploid crop backcrossing improvement and multi-gene aggregation breeding optimization method according to any one of claims 1 to 8.

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