Corn molecular marker assisted backcross breeding electronic simulation method and system

Abstract

The invention relates to the technical field of plant breeding, and provides a corn molecular marker-assisted backcross breeding electronic simulation method and system.The method comprises the steps that whole genome molecular marker data showing newborn characters are collected for a first set of single plants carrying known target genes; collecting whole genome molecular marker data of the donor parent of the known target gene; collecting whole genome molecular marker data of a receptor parent of a known target gene; deducing one or more candidate chromosome regions by analyzing chromosome segments commonly inherited from a donor parent in a first group of single plant genomes based on all whole genome molecular marker data; selecting a representative molecular marker from the candidate chromosome region as a proxy target; and taking the proxy target and the whole genome molecular marker data of the first group of single plants as selection targets, carrying out electronic simulation of subsequent backcross breeding, and outputting an electronic simulation result. The method has the effects of improving the breeding efficiency and the decision-making scientificity.

Description

Technical Field

[0001] This invention relates to the technical field of plant breeding, specifically to an electronic simulation method and system for marker-assisted backcross breeding of maize. Background Technology

[0002] In modern maize breeding practices, marker-assisted backcrossing (MABC) is a widely used technique aimed at efficiently introducing superior genes carried by donor parents into the favorable genetic background of recipient parents. Breeding institutions typically use electronic simulation systems to plan and optimize the backcross breeding process. These systems can predict gene introduction efficiency and background genome recovery progress based on parental genotype information and breeding objectives.

[0003] However, in actual breeding processes, especially in backcross generations, new traits with potential breeding value may be unexpectedly observed in offspring carrying known target genes. The system cannot, based on data from these few specific individuals exhibiting the new trait, preliminarily infer the chromosomal regions that may be associated with it. This lack of capability makes it difficult for breeders to quickly and scientifically decide whether and how to integrate this new trait into current breeding plans when faced with such unexpected and beneficial discoveries, potentially leading to missed breeding opportunities or an inability to adjust breeding strategies in a timely manner.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide an electronic simulation method and system for marker-assisted backcross breeding of maize, which has the advantages of being able to incorporate emerging traits with unknown genetic basis into the electronic simulation of backcross breeding, thereby improving breeding efficiency and the scientific nature of decision-making.

[0006] This application provides an electronic simulation method for marker-assisted backcross breeding of maize, the technical solution of which is as follows:

[0007] include:

[0008] For the first group of single plants carrying the known target gene, whole-genome molecular marker data showing the newborn trait were collected;

[0009] Collect whole-genome molecular marker data of donor parents with known target genes; collect whole-genome molecular marker data of recipient parents with known target genes;

[0010] Based on the complete genome molecular marker data, one or more candidate chromosome regions are inferred by analyzing chromosome segments in the genome of the first group of individual plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of individual plants and the preset control group.

[0011] Representative molecular markers were selected from candidate chromosomal regions to serve as proxy targets;

[0012] The whole genome molecular marker data of the proxy target and the first group of single plants were used as selection targets to conduct electronic simulations for subsequent backcross breeding, and the results of the electronic simulations were output.

[0013] The above scheme can incorporate newly emerging traits with unknown genetic basis into electronic simulations of backcross breeding, thereby improving breeding efficiency and the scientific nature of decision-making.

[0014] Optionally, the step of inferring one or more candidate chromosomal regions by analyzing chromosomal segments commonly inherited from the donor parent in the genomes of the first group of individual plants includes:

[0015] Based on the whole genome molecular marker data of the first group of single plants, the donor fragments of the known target gene and the chromosomal regions adjacent to the donor fragments in the genome of each single plant in the first group were identified.

[0016] In the genomes of the first group of individual plants, after excluding donor fragments and adjacent chromosomal regions, the co-occurrence frequencies of other chromosomal fragments derived from the donor parent were counted.

[0017] Other chromosome fragments derived from the donor parent whose co-occurrence frequency is higher than a preset threshold are selected as one or more candidate chromosome regions.

[0018] The above scheme provides a specific method for inferring candidate regions based on the co-occurrence frequency of donor fragments, thereby improving the accuracy of the inference.

[0019] Optionally, the step of selecting representative molecular markers from candidate chromosomal regions as proxy targets includes:

[0020] Obtain the association data between each candidate molecular marker and neonatal trait in all candidate chromosomal regions;

[0021] Obtain genetic linkage analysis parameters for candidate molecular markers and molecular markers of known target genes;

[0022] Obtain information on the ease of use of candidate molecular markers in breeding;

[0023] Based on the association data, genetic linkage analysis parameters, and breeding application convenience assessment information, the evaluation results of each candidate molecular marker are generated;

[0024] Select one or a group of molecular markers from all candidate molecular markers that meet the preset conditions for evaluation, and use them as proxy targets.

[0025] The above scheme provides evaluation criteria and methods for selecting proxy targets, ensuring that the selected targets are representative and practical.

[0026] Optionally, in the genome of the first group of individual plants, after excluding donor fragments and adjacent chromosomal regions, the step of calculating the co-occurrence frequency of other chromosomal fragments derived from the donor parent includes:

[0027] For each individual plant in the first group, based on the donor fragment and the adjacent chromosome region, other chromosome fragments originating from the donor parent but outside of the donor fragment and the adjacent chromosome region are identified.

[0028] Calculate the first co-occurrence frequency of other chromosome segments;

[0029] Obtain the second co-occurrence frequency of chromosome segments corresponding to other chromosome segments in the second group of single plants of the preset control group;

[0030] Based on the first co-occurrence frequency and the second co-occurrence frequency, the significance of the difference is assessed, and the significance of the difference assessment results are generated.

[0031] The first co-occurrence frequency that meets the preset conditions in the significance assessment of differences is taken as the co-occurrence frequency of the first group of single plants.

[0032] The above scheme provides a method for excluding known target gene regions and using control group data to perform co-occurrence frequency statistics and significance assessment, thereby improving the reliability of the inference.

[0033] Optionally, the step of assessing the significance of the difference based on the first co-occurrence frequency and the second co-occurrence frequency, and generating the significance assessment result, includes:

[0034] For the first and second groups of single plants, obtain the existence status data of each other chromosome segment;

[0035] The two sets of single plant data are merged to generate a merged state dataset.

[0036] The merged state dataset is randomly redistributed to generate several pairs of simulated state datasets; each pair of simulated state datasets includes a simulated first group of state datasets and a simulated second group of state datasets.

[0037] Based on the simulated state dataset, the simulated frequency difference between the simulated first co-occurrence frequency and the simulated second co-occurrence frequency is calculated.

[0038] Based on simulated frequency difference values, construct a reference distribution of frequency differences for other chromosome segments;

[0039] Calculate the actual frequency difference between the first co-occurrence frequency and the second co-occurrence frequency, compare the actual frequency difference with the frequency difference reference distribution, determine the statistical significance, and use it as the result of the difference significance assessment.

[0040] The above scheme provides a method for assessing the significance of differences based on random redistribution, thereby enhancing the rigor of statistical inference.

[0041] Optionally, the steps of calculating the actual frequency difference between the first and second co-occurrence frequencies, comparing the actual frequency difference with a frequency difference reference distribution, and determining statistical significance as the result of the difference significance assessment include:

[0042] The initial statistical significance index value is determined based on the actual frequency difference value and the frequency difference reference distribution;

[0043] Based on the number of other chromosome segments and the preset overall misjudgment control parameters, the significance judgment threshold for correction is calculated;

[0044] The initial statistical significance index is compared with the corrected significance threshold to determine whether there is statistical significance. The result of the determination is used as the result of the difference significance assessment.

[0045] The above scheme provides a statistical significance judgment method based on the corrected significance judgment threshold, which reduces the false judgment rate.

[0046] Optionally, the step of determining the initial statistical significance index value based on the actual frequency difference value and the frequency difference reference distribution includes:

[0047] The total number of simulated frequency difference values ​​that are greater than or equal to the actual frequency difference value in the statistical frequency difference reference distribution;

[0048] Calculate the proportion of the total number of simulations to the total number of simulations in the frequency difference reference distribution;

[0049] The proportion was used as an initial statistical significance indicator for other chromosome segments.

[0050] The above scheme provides a specific method for calculating the initial statistical significance indicator value.

[0051] Optionally, the step of calculating the significance threshold for correction based on the number of other chromosome segments and a preset overall misjudgment control parameter includes:

[0052] Based on the frequency difference reference distribution and the simulated state dataset, the simulated initial statistical significance index values ​​of other chromosome segments are calculated and the minimum value is recorded;

[0053] Summarize the minimum values ​​from all simulations to form a set of minimum values;

[0054] Based on the minimum value set and the preset overall misjudgment control parameters, the judgment threshold is output according to the preset regulations and used as the correction significance judgment threshold.

[0055] The above scheme provides a specific method for calculating the significance threshold for correction, thereby improving the accuracy of the judgment.

[0056] Optionally, the step of outputting a judgment threshold according to a preset specification includes:

[0057] The preset overall misjudgment control parameter is used as the target ratio;

[0058] When the proportion of minimum values ​​less than or equal to a preset threshold in the set of minimum values ​​is equal to the target proportion, the preset threshold is used as the judgment threshold.

[0059] The above scheme provides specific criteria for determining the judgment threshold.

[0060] An electronic simulation system for maize molecular marker-assisted backcross breeding, used to perform electronic simulations of maize molecular marker-assisted backcross breeding, comprising:

[0061] The first group of single-plant gene data acquisition modules is used to collect whole-genome molecular marker data of the first group of single plants carrying known target genes, which exhibit newborn traits.

[0062] The donor and recipient gene data acquisition module is used to acquire whole-genome molecular marker data of donor parents with known target genes; and to acquire whole-genome molecular marker data of recipient parents with known target genes.

[0063] The candidate chromosome region inference module is used to infer one or more candidate chromosome regions based on the complete genome molecular marker data by analyzing chromosome segments in the genome of the first group of single plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of single plants and the preset control group.

[0064] The surrogate target selection module is used to select representative molecular markers from candidate chromosomal regions as surrogate targets;

[0065] The electronic simulation output module is used to select the whole genome molecular marker data of the surrogate target and the first group of single plants as the target for subsequent backcross breeding electronic simulation, and output the results of the electronic simulation.

[0066] As can be seen from the above, the electronic simulation method and system for maize molecular marker-assisted backcross breeding provided in this application infers candidate chromosomal regions associated with new traits by collecting genomic data of specific single plants exhibiting new traits, selecting surrogate markers from them, and incorporating the surrogate markers along with known target genes into the electronic simulation of backcross breeding. This enables the inclusion of new traits with unknown genetic basis into the electronic simulation of backcross breeding, thereby improving breeding efficiency and the scientific nature of decision-making. Attached Figure Description

[0067] Figure 1 This is a flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in one embodiment of the present invention.

[0068] Figure 2 This is one of the flowcharts of an electronic simulation method for marker-assisted backcross breeding of maize according to another embodiment of the present invention;

[0069] Figure 3 This is a second flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0070] Figure 4 This is the third flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0071] Figure 5 This is the fourth flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0072] Figure 6 This is the fifth flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0073] Figure 7 This is the sixth flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0074] Figure 8 This is the seventh flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0075] Figure 9 This is the eighth flowchart of an electronic simulation method for marker-assisted backcross breeding of maize, as described in another embodiment of the present invention.

[0076] Figure 10 This is a system block diagram of an electronic simulation system for maize molecular marker-assisted backcross breeding, as described in another embodiment of the present invention. Detailed Implementation

[0077] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0078] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0079] Traditional marker-assisted backcross breeding electronic simulation methods for maize have limitations when dealing with unexpected new traits that emerge during the breeding process. These limitations include the inability to effectively utilize the phenotypic and genotypic information of a few specific individual plants to quickly infer candidate chromosomal regions associated with the new trait, and the inability to integrate this new trait as a proxy target into the electronic simulation and evaluation of backcross selection when the genetic basis is unknown. This restricts breeders' ability to respond to unexpected beneficial variations.

[0080] For example, suppose that in a marker-assisted backcross breeding project for maize, where a known target gene is introduced from a donor parent into a recipient parent, a new trait different from the parent is observed in the first group of plants carrying the known target gene in the second generation of the backcross population. The genetic basis of this new trait is unclear, but preliminary analysis shows that it has potential breeding value. Existing electronic simulation systems typically rely on known genes or pre-located quantitative trait loci for simulated selection. Faced with this new trait with an unknown genetic basis, the system cannot directly infer candidate chromosomal regions associated with this trait based on the whole-genome molecular marker data of these few plants exhibiting the new trait. Furthermore, the system cannot use the inferred candidate regions or their representative markers as proxy targets, incorporating them along with the original known target gene into subsequent backcross breeding electronic simulations to assess the impact of simultaneously selecting these two targets on the breeding process.

[0081] To address this, this application proposes an electronic simulation method for marker-assisted backcross breeding of maize, combined with... Figure 1 As shown, it includes:

[0082] S1: Collect genome-wide molecular marker data for the first group of single plants carrying the known target gene, which exhibit newborn traits.

[0083] S2, Collect whole-genome molecular marker data of donor parents with known target genes; collect whole-genome molecular marker data of recipient parents with known target genes;

[0084] S3, based on the complete genome molecular marker data, by analyzing the chromosome segments in the genome of the first group of single plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of single plants and the preset control group, one or more candidate chromosome regions are inferred;

[0085] S4: Select representative molecular markers from candidate chromosomal regions as surrogate targets;

[0086] S5 uses the whole genome molecular marker data of the surrogate target and the first group of single plants as selection targets to perform electronic simulation of subsequent backcross breeding and outputs the results of the electronic simulation.

[0087] The first group of individual plants refers to a specific set of individuals carrying known target genes and exhibiting new traits during backcross breeding, with the aim of providing genetic material with new traits to be analyzed; whole-genome molecular marker data refers to genotypic information of molecular markers covering the entire maize genome, which can be obtained using SNP chips, sequencing, and other technologies, with the aim of providing comprehensive genetic background information; known target genes refer to genes with a clear genetic basis that were initially planned to be introduced in the breeding project, with the aim of serving as the original breeding target; new traits refer to traits unexpectedly observed during backcross breeding that are not stably expressed in the parents and whose genetic basis is unknown, with the aim of serving as new targets to be analyzed and processed by this method; donor parents refer to... Providing parental materials with known target genes aims to provide genetic information about those genes. Recipient parents refer to superior genetic background materials that have received the known target gene, aiming to provide background genomic information for breeding. Candidate chromosomal regions refer to genomic fragments that may be associated with new traits, inferred through analysis, aiming to narrow down the scope of subsequent analysis and selection. Surrogate targets refer to molecular markers selected from candidate chromosomal regions that represent new traits for subsequent simulation, aiming to transform new traits with unknown genetic basis into operable molecular marker information. Electronic simulation refers to using computer programs to simulate gene transfer and selection during backcross breeding, aiming to predict the breeding process and results under different selection strategies.

[0088] In some preferred embodiments, this method can be implemented as follows: First, a high-density SNP chip is used to perform whole-genome genotyping on the first group of individual plants, the donor parent, and the recipient parent to obtain whole-genome molecular marker data. Next, using bioinformatics software, genome-wide association analysis is performed on chromosome segments shared with the donor parent in the genome of the first group of individual plants, or on control group plants that did not exhibit the new trait, to identify chromosomal regions significantly associated with the new trait as candidate regions. Then, within the candidate regions, molecular markers with high association with the new trait, moderate genetic distance, and easy detection in breeding are selected as surrogate targets. Finally, the genotypic information of these surrogate targets is input into a backcross breeding electronic simulation platform, and corresponding selection parameters are set to simulate a multi-generation backcross process. The efficiency of introducing the target gene and the new trait, the background genome recovery rate, and the breeding cycle are evaluated when both known target genes and surrogate targets are selected simultaneously, and a simulation report is output.

[0089] Through the aforementioned technical solution, this method can rapidly infer the potential genetic association regions of newly emerging traits with unknown genetic basis, which are unexpectedly discovered during backcross breeding, and transform them into actionable molecular marker surrogate targets. This allows breeders to incorporate potentially valuable new traits into the electronic simulation evaluation system along with existing breeding goals, thereby scientifically assessing and integrating the impact of these new traits on the overall breeding process, providing data support for breeding decisions, avoiding the loss of beneficial variations, and improving the flexibility and efficiency of breeding.

[0090] Optional, combined Figure 2 As shown, the steps in S3 to infer one or more candidate chromosomal regions by analyzing chromosomal segments in the genomes of the first group of individual plants that are commonly inherited from the donor parent include:

[0091] S31, Based on the whole genome molecular marker data of the first group of single plants, determine the donor fragments of the known target gene and the chromosomal regions adjacent to the donor fragments in the genome of each single plant in the first group of single plants.

[0092] S32, in the genome of the first group of single plants, after excluding donor segments and adjacent chromosomal regions, the co-occurrence frequencies of other chromosomal segments derived from the donor parent were counted.

[0093] S33, other chromosome fragments derived from the donor parent with a co-occurrence frequency higher than a preset threshold are selected as one or more candidate chromosome regions.

[0094] In this context, the donor segment refers to a chromosomal region in the genome of a single offspring that is inherited from the donor parent. This can be determined using methods based on molecular marker genotype data to identify the parental origin. For example, comparing the genotype of a single plant at a specific marker locus with the genotypes of the donor and recipient parents can determine the parental origin of the chromosomal region at that locus. The aim is to identify which parts of the single plant's genome originate from the donor parent. The adjacent chromosomal region refers to a region that is physically or genetically connected to the donor segment containing the known target gene. This can be determined using methods based on genetic linkage maps or physical maps. After determining the location of the donor fragment containing the known target gene, a predetermined genetic distance (e.g., centimole, cM) or physical distance (e.g., base pair, bp) is extended to both sides to define the region. The purpose is to treat the known target gene and its closely linked regions as a whole. Co-occurrence frequency refers to the proportion of a specific chromosome fragment appearing in the first group of individual plants. It can be calculated by counting the number of individual plants in the first group that have the specific chromosome fragment and dividing it by the total number of individual plants in the first group. The purpose is to quantify the degree of association between a certain chromosome fragment and the first group of individual plants (which exhibit the neonatal trait).

[0095] In some preferred embodiments, specifically, based on the whole-genome SNP (single nucleotide polymorphism) marker data of the first group of individual plants, the location of the known target gene in the donor parent genome can be first located, and the boundary of the donor fragment containing the known target gene can be determined according to the SNP genotype of the first group of individual plants in that region. For example, marker sites on both sides of the region that transition from a homozygous donor genotype to a heterozygous or homozygous recipient genotype can be identified to define the donor fragment. Then, according to the maize genetic linkage map, a certain distance (e.g., 5 cM) can be extended on both sides of the donor fragment, and this range can be defined as the chromosomal region adjacent to the donor fragment. Next, in the genome of the first group of individual plants, all chromosomal regions other than the aforementioned identified donor fragment and its adjacent regions are scanned to identify fragments originating from the donor parent. For each other identified donor fragment, the number of individual plants appearing in the first group of individual plants is counted, and its co-occurrence frequency is calculated. Finally, a preset threshold is set, such as 75%, and other donor fragments with a co-occurrence frequency higher than 75% are identified as candidate chromosomal regions.

[0096] The above technical solutions can more accurately identify chromosomal regions potentially associated with new traits from the complex genomes of backcross progeny, effectively eliminate interference from known target genes and their linked regions, improve the accuracy of candidate region inference, and provide more reliable input information for the subsequent inclusion of new traits in molecular marker-assisted backcross breeding electronic simulations, thereby enhancing the effectiveness and guiding value of electronic simulations in dealing with unknown genetic basic traits.

[0097] Optional, combined Figure 3 As shown, the steps in S4 for selecting representative molecular markers from candidate chromosomal regions as proxy targets include:

[0098] S41, obtain the association data between each candidate molecular marker and neonatal trait in all candidate chromosomal regions;

[0099] S42, Obtain genetic linkage analysis parameters for candidate molecular markers and molecular markers of known target genes;

[0100] S43, Obtain information on the ease of use of candidate molecular markers in breeding;

[0101] S44, based on association data, genetic linkage analysis parameters, and breeding application ease of assessment information, generate evaluation results for each candidate molecular marker;

[0102] S45: Select one or a group of molecular markers whose evaluation results meet the preset conditions from all candidate molecular markers as proxy targets.

[0103] The acquisition of association data between each candidate molecular marker and the newly formed trait across all candidate chromosomal regions refers to data obtained through statistical analysis of whether there is a significant correlation between the molecular marker genotype and the newly formed trait phenotype. Specifically, this can be data such as P-values, effect values, or correlation coefficients calculated using methods like genome-wide association studies (GWAS) or quantitative trait loci (QTL) mapping. The purpose is to quantify the association strength between each candidate molecular marker and the newly formed trait. Acquiring genetic linkage analysis parameters between candidate molecular markers and known target genes refers to parameters reflecting the distance between the two molecular markers on the chromosome and the probability of genetic recombination. Specifically, this can be linkage distance (in centimoles, cM) or linkage disequilibrium (LD) coefficients calculated by analyzing population genetic data. The purpose is to assess the genetic association between candidate molecular markers and known target genes. Acquiring information on the ease of use of candidate molecular markers in breeding refers to information measuring the ease of use of molecular markers in actual breeding operations. Specifically, this can include molecular... The selection criteria include marker type (e.g., single nucleotide polymorphism, SNP, insertion / deletion), maturity of detection methods, detection cost, detection throughput, result stability, and ease of high-throughput detection. The aim is to select markers that offer greater operational advantages in laboratory testing and breeding population screening. The evaluation results for each candidate molecular marker refer to a quantitative indicator of suitability for each candidate molecular marker, considering association data, genetic linkage analysis parameters, and ease-of-use assessment information for breeding applications. Specifically, this can be a comprehensive score or ranking calculated using weighted summation, ranking, machine learning models, or other multi-indicator comprehensive evaluation methods. The purpose is to provide a unified evaluation standard for subsequent marker selection. Preset conditions refer to the screening criteria set when selecting surrogate targets. Specifically, this can be a threshold (e.g., an evaluation result higher than a certain score), a quantity (e.g., selecting the top N markers with the best evaluation results), or a combination of criteria. The purpose is to determine the final set of surrogate targets based on specific breeding needs and resource constraints.

[0104] In some preferred embodiments, it is assumed that a candidate chromosomal region containing 100 candidate SNP markers is inferred by analyzing the genome data of a first set of individual plants. First, association data between each candidate SNP marker in this region and the light yellow seed phenotype is obtained. For example, genome-wide association analysis is performed on specific plants exhibiting light yellow seeds and control plants to calculate the p-value and effect value of each SNP. Next, genetic linkage analysis parameters between these candidate SNP markers and molecular markers M1 and M2 of the known disease resistance gene R are obtained; for example, the linkage distance between them is calculated. Simultaneously, information on the ease of use in breeding for each candidate SNP marker is obtained, such as assessing its suitability for KASP testing, testing cost, and result stability. Then, based on this association data, genetic linkage analysis parameters, and ease of use assessment information, evaluation results for each candidate SNP marker are generated. For example, a weighted scoring formula is used to comprehensively calculate the p-value, linkage distance, and ease of use score to obtain a comprehensive score for each SNP. Finally, from these 100 candidate SNP markers, the markers whose evaluation results meet the preset conditions are selected as surrogate targets for the light yellow grain trait. For example, the preset condition is to select the 5 SNP markers with the highest comprehensive scores.

[0105] Optional, combined Figure 4 As shown in S32, the steps for calculating the co-occurrence frequencies of other chromosome segments derived from the donor parent after excluding donor segments and adjacent chromosomal regions in the genome of the first group of single plants include:

[0106] S321, for each individual plant in the first group, based on the donor fragment and the adjacent chromosome region, identifies other chromosome fragments that are outside the donor fragment and the adjacent chromosome region and originate from the donor parent.

[0107] S322, Count the first co-occurrence frequency of other chromosome segments;

[0108] S323, obtain the second co-occurrence frequency of chromosome segments corresponding to other chromosome segments in the second group of single plants of the preset control group;

[0109] S324, Based on the first co-occurrence frequency and the second co-occurrence frequency, assess the significance of the difference and generate the significance of the difference assessment results;

[0110] S325, the first co-occurrence frequency that meets the preset conditions for the difference significance assessment results is taken as the co-occurrence frequency of the first group of single plants.

[0111] Identifying chromosomal fragments originating from the donor parent, excluding the donor fragment and adjacent chromosomal regions, involves analyzing the genomic molecular marker data of the first group of individual plants, combined with known genotype information of the donor and recipient parents, to determine which regions in the genome of the first group of individual plants originate from the donor parent, excluding donor fragments containing known target genes and their adjacent chromosomal regions. This can be achieved using a genomic fragment tracing method based on molecular marker typing. Its purpose is to identify donor-origin fragments from non-target gene regions requiring co-occurrence frequency statistics. Statistical analysis of the first co-occurrence frequency refers to calculating the specific other chromosome... The proportion of the fragment appearing in the first group of individual plants can be determined by traversing the genotype data of the first group of individual plants and counting the number of individual plants containing the fragment. The purpose is to quantify the prevalence of the fragment in the population of individual plants carrying the target gene. The second group of individual plants, which is a pre-determined population, has a similar genetic background to the first group of individual plants but does not carry the known target gene or exhibits a new trait. This second group can be constructed by screening individual plants that do not carry the target gene from the original recipient parent population or its backcross progeny. Its purpose is to provide a benchmark population for comparison to distinguish it from the target gene population. Linkage-related co-occurrence and population-background-related co-occurrence; obtaining the second co-occurrence frequency refers to calculating the proportion of a specific other chromosome segment appearing in the second group of individual plants in the pre-defined control group. This can be achieved by traversing the genotype data of the second group of individual plants and counting the number of individual plants containing the segment. The purpose is to quantify the prevalence of the segment in the control population; assessing the significance of differences refers to using statistical methods to compare whether there is a statistically significant difference between the first and second co-occurrence frequencies. This can be achieved using methods such as the chi-square test, Fisher's exact test, or permutation test. The purpose is to determine whether a specific segment is present in the first or second co-occurrence frequency. Whether the high-frequency co-occurrence in a group of individual plants is statistically significant, rather than accidental; the result of the significance assessment refers to the statistical indicators obtained after assessing the significance of the difference, such as the p-value or statistic, which can be expressed by the numerical value or judgment label output by statistical testing software or algorithms. Its purpose is to provide a quantitative basis for judging whether the difference is significant; the preset conditions refer to the standards used to judge whether the result of the significance assessment of the difference meets the requirements. It can be achieved by setting a significance level threshold (e.g., p-value less than 0.05). Its purpose is to screen out those segments that are statistically considered to be significantly associated with the new traits.

[0112] In some preferred embodiments, specifically, assuming a first group of individual plants contains 100 individuals exhibiting the neonatal trait and carrying the target gene, and a second group of individual plants (presumably a control group) contains 200 individuals not exhibiting the neonatal trait and not carrying the target gene but with similar genetic backgrounds. First, for each individual plant in the first group, its whole-genome molecular marker data is analyzed to identify all other chromosomal segments derived from the donor parent, excluding known donor segments and adjacent regions. For example, segments X, Y, and Z are identified. Next, the frequency of these segments in the first group of 100 individual plants is calculated; for example, segment X appears in 85 individual plants with a first co-occurrence frequency of 0.85; segment Y appears in 60 individual plants with a first co-occurrence frequency of 0.60; and segment Z appears in 30 individual plants with a first co-occurrence frequency of 0.30. Simultaneously, the occurrence frequencies of these fragments in the control group of 200 individual plants were obtained. For example, fragment X appeared in 120 individual plants with a second co-occurrence frequency of 0.60; fragment Y appeared in 80 individual plants with a second co-occurrence frequency of 0.40; and fragment Z appeared in 70 individual plants with a second co-occurrence frequency of 0.35. Then, based on the first and second co-occurrence frequencies, the significance of differences for each fragment was assessed. For example, a statistical test was performed on fragment X, comparing the differences between 0.85 and 0.60 to obtain a p-value; for fragment Y, comparing 0.60 and 0.40 to obtain a p-value; and for fragment Z, comparing 0.30 and 0.35 to obtain a p-value. Finally, the first co-occurrence frequency whose significance assessment results met the preset conditions (e.g., p-value less than 0.05) was used as the co-occurrence frequency of the first group of individual plants. If the p-values ​​of fragments X and Y are both less than 0.05, while the p-value of fragment Z is greater than 0.05, then only the first co-occurrence frequency of fragment X (0.85) and the first co-occurrence frequency of fragment Y (0.60) are adopted for subsequent candidate chromosome region inference.

[0113] Optional, combined Figure 5 As shown, S324, the step of assessing the significance of differences based on the first co-occurrence frequency and the second co-occurrence frequency, and generating the significance assessment result, includes:

[0114] S3241, for the first group of single plants and the second group of single plants, obtain the existence status data of each other chromosome segment;

[0115] S3242, merge all the existence status data of the two groups of single plants to generate a merged status dataset;

[0116] S3243, Randomly redistribute the merged state dataset to generate several pairs of simulated state datasets; each pair of simulated state datasets includes a simulated first group of state datasets and a simulated second group of state datasets.

[0117] S3244, Based on the simulation state dataset, calculate the simulation frequency difference between the first simulation co-occurrence frequency and the second simulation co-occurrence frequency;

[0118] S3245, Based on simulated frequency difference values, construct a reference distribution of frequency differences for other chromosome segments;

[0119] S3246, calculate the actual frequency difference between the first co-occurrence frequency and the second co-occurrence frequency, compare the actual frequency difference with the frequency difference reference distribution, determine the statistical significance, and use it as the result of the difference significance assessment.

[0120] Among them, existence status data refers to data used to indicate the presence or absence of a specific chromosome segment in the genome of a single plant, which can be represented by binary values; the merged status dataset refers to the data set formed by combining all existence status data of the first group and the second group of plants; random redistribution is a statistical resampling technique that simulates the data distribution under different groups by randomly grouping or arranging the data in the merged status dataset, which can be implemented using methods such as permutation tests or bootstrap methods; the simulated status dataset refers to a pair of virtual datasets generated after random redistribution, representing the status data of the simulated first group and the simulated second group of plants, respectively; the simulated first co-occurrence frequency and the simulated second co-occurrence frequency refer to the data calculated based on the simulated status dataset. The frequency of a specific chromosome segment is calculated under simulated grouping; the simulated frequency difference value refers to the numerical difference between the simulated first co-occurrence frequency and the simulated second co-occurrence frequency; the frequency difference reference distribution refers to the set of simulated frequency difference values ​​or its statistical distribution obtained after multiple random redistribution processes and calculation of simulated frequency difference values; the actual frequency difference value refers to the numerical difference between the first co-occurrence frequency and the second co-occurrence frequency calculated based on the original data on the existence status of individual plants in the first and second groups; statistical significance refers to the probability of the observed actual frequency difference value occurring under the assumption that there is no real difference between the two groups; the difference significance assessment result refers to the conclusion drawn based on the statistical significance judgment regarding whether the frequency difference between the two groups is statistically significant.

[0121] In some preferred embodiments, specifically for the first and second groups of individual plants, the presence status data of each other chromosome segment can be recorded in binary form. For example, for a specific other chromosome segment, if it exists in a single plant, it is recorded as 1; if it does not exist, it is recorded as 0. The presence status data of all segments in the first group of individual plants (e.g., a vector) are merged with the presence status data of all segments in the second group of individual plants (e.g., another vector) to form a longer merged state dataset vector. This merged state dataset vector is then randomly redistributed, for example, through 10,000 random permutations. Each permutation randomly shuffles the elements in the merged vector and then re-divides it into two simulated state datasets according to the original proportions of the two groups of individual plants, representing the simulated first group and the simulated second group, respectively. For each simulated state dataset generated by each permutation, the frequency of the specific segment in the simulated first group and the frequency in the simulated second group are calculated, and the simulated frequency difference value between the two is calculated. Repeating the permutation 10,000 times yields 10,000 simulated frequency difference values, which constitute the frequency difference reference distribution for that segment. Simultaneously, the actual frequency difference of this segment among the original first and second group of individual plants is calculated. This actual frequency difference is compared with a reference distribution consisting of ten thousand simulated frequency difference values. For example, the proportion of simulated frequency difference values ​​in the reference distribution whose absolute values ​​are greater than or equal to the absolute values ​​of the actual frequency difference can serve as a preliminary indicator of statistical significance. In this way, it can be determined whether the observed frequency difference is within a reasonable range of random fluctuations.

[0122] Optional, combined Figure 6 As shown in S3246, the steps of calculating the actual frequency difference between the first co-occurrence frequency and the second co-occurrence frequency, comparing the actual frequency difference with the frequency difference reference distribution, and determining statistical significance as the result of the difference significance assessment include:

[0123] A1. Determine the initial statistical significance index value based on the actual frequency difference value and the frequency difference reference distribution;

[0124] A2, based on the number of other chromosome segments and the preset overall misjudgment control parameters, calculate the correction significance judgment threshold;

[0125] A3. Compare the initial statistical significance indicator value with the corrected significance judgment threshold to determine whether there is statistical significance, and use the judgment result as the result of the difference significance assessment.

[0126] The number of other chromosome segments refers to the total number of chromosome segments originating from the donor parent in the first group of single-plant genomes, excluding the donor segment and adjacent chromosome regions. The preset overall misclassification control parameter refers to the overall probability level of false positive results allowed during multiple comparison statistical tests. The significance correction threshold refers to the standard used to determine whether the differences in chromosome segments are statistically significant after multiple comparison correction.

[0127] In practical implementation, to ensure the statistical stability of the frequency difference reference distribution, the random redistribution operation should preferably be repeated at least 1000 times, more preferably 10000 times, to construct a sufficiently accurate simulated reference distribution. The number of simulation rounds can be flexibly adjusted according to computational resources or statistical accuracy requirements, and convergence can be monitored based on distribution stability. The operation of excluding donor fragments and adjacent regions is preferably implemented by setting a window range. For example, for a donor fragment containing the target gene, a window interval of 1 to 2 Mb is set upstream and downstream as the adjacent region, and the remaining part after exclusion is regarded as other candidate chromosome regions. The window size can be parameterized and adjusted according to the length of the donor fragment and the chromosome recombination density. Regarding the setting of the significance judgment threshold, if the number of other fragments N is small (e.g., N≤20), the Bonferroni method is recommended, as its correction process is simple and the results are conservative; if the number of other fragments is large (e.g., N>50), the simulated minimum set method is preferred, which can more accurately reflect the influence of extreme value distribution on significance judgment. To improve the reproducibility and transparency of the overall system, it is recommended that the output of significance assessment results be accompanied by the number of simulations, actual frequency difference values, p-value calculation method, correction threshold, and final judgment logic, so that breeders can trace the results and interpret them.

[0128] In some preferred embodiments, this application is implemented as follows. Assume that, using the aforementioned method, for a chromosome segment to be evaluated, the actual frequency difference between its first and second co-occurrence frequencies is calculated, and a frequency difference reference distribution for the segment is constructed based on random redistribution. Based on this actual frequency difference value and the frequency difference reference distribution, an initial statistical significance indicator value can be determined. For example, a p-value can be obtained by the proportion of simulated frequency difference values ​​greater than or equal to the actual frequency difference value in the statistical reference distribution. Assume the number of other chromosome segments to be evaluated is N, and the preset overall misjudgment control parameter is α. The Bonferroni correction method can be used to calculate the correction significance threshold, which can be α / N. Alternatively, a method based on the minimum value of the simulated distribution can be used. That is, for each random redistribution simulation, the simulated initial statistical significance indicator values ​​for all other chromosome segments are calculated, and the minimum value is recorded. After repeating the simulation multiple times, the set of these minimum values ​​is arranged in ascending order, and the (total number of simulations * α)th value is taken as the correction significance threshold. Then, the initial statistical significance indicator value of the chromosome segment is compared with the calculated correction significance threshold. If the initial statistical significance index is less than or equal to the corrected significance threshold, the segment is considered statistically significant; otherwise, it is considered not statistically significant. This determination is used as the result of the differential significance assessment of the chromosome segment.

[0129] Optional, combined Figure 7 As shown in Figure A1, the steps for determining the initial statistical significance index value based on the actual frequency difference value and the frequency difference reference distribution include:

[0130] A11, The total number of simulated frequency difference values ​​that are greater than or equal to the actual frequency difference value in the statistical frequency difference reference distribution;

[0131] A12, calculate the proportion of the total number of simulations to the total number of simulations in the frequency difference reference distribution;

[0132] A13 uses the proportion as an initial statistical significance indicator for other chromosome segments.

[0133] The proposed method calculates the proportion of simulated frequency differences greater than or equal to the actual frequency difference value in a statistical frequency difference reference distribution relative to the total number of simulations in the distribution. This proportion is then used as an initial statistical significance indicator for other chromosomal segments. This process utilizes a frequency difference reference distribution constructed from several pairs of simulated state datasets generated by randomly redistributing merged state datasets. The actual frequency difference value is calculated based on a first co-occurrence frequency and a second co-occurrence frequency. By calculating the proportion of simulated differences greater than or equal to the actual observed values, the probability of an equal or greater difference occurring under random conditions is essentially calculated. This probability reflects the statistical significance of the actual difference: the smaller the probability, the less likely the difference is to be caused by random factors, and therefore the more significant it is. Using this probability value as an initial statistical significance indicator provides an accurate quantitative basis for subsequent comparisons with a corrected significance threshold. This method avoids subjective judgment, utilizes a reference distribution constructed from large-scale simulated data, and can objectively and accurately assess the statistical significance of actual frequency differences, thereby improving the reliability of identifying candidate chromosomal regions related to neonatal traits.

[0134] In some preferred embodiments, the implementation is as follows: Suppose that for a specific other chromosome segment, the calculated actual frequency difference value is 0.3. A reference distribution of the frequency difference for this segment has been constructed through random redistribution and simulation calculations, containing simulated frequency difference values ​​obtained from 10,000 simulations. Now, it is necessary to determine the initial statistical significance indicator value for this actual frequency difference value of 0.3. First, count how many of these 10,000 simulated frequency difference values ​​are greater than or equal to 0.3. Suppose the statistical results show that there are 50 simulated frequency difference values ​​greater than or equal to 0.3. Then, calculate the proportion of this total of 50 to the total number of simulations of 10,000, i.e., 50 / 10,000 = 0.005. Finally, use the calculated proportion of 0.005 as the initial statistical significance indicator value for this other chromosome segment. This indicator value of 0.005 reflects the probability of a frequency difference equal to or greater than 0.3 occurring under random conditions.

[0135] Optional, combined Figure 8 As shown in Figure A2, the steps for calculating the significance threshold for correction, based on the number of other chromosome segments and the preset overall misjudgment control parameters, include:

[0136] A21, based on the frequency difference reference distribution and the simulated state dataset, calculate the simulated initial statistical significance index values ​​for other chromosome segments and record the minimum value;

[0137] A22, summarize the minimum values ​​from all simulations to form a set of minimum values;

[0138] A23, based on the minimum value set and the preset overall misjudgment control parameters, outputs a judgment threshold according to the preset regulations and uses it as the judgment threshold for correction significance.

[0139] The frequency difference reference distribution refers to the distribution constructed by randomly redistributing the merged state dataset to generate several pairs of simulated state datasets, and calculating the simulated frequency difference values ​​based on these simulated state datasets. Its purpose is to provide a possible distribution range and probability of frequency difference values ​​under the null hypothesis (i.e., no difference in the presence of other chromosome segments between the two groups of individuals). The simulated state dataset refers to the dataset obtained after randomly redistributing the merged state dataset, used to simulate the presence state distribution of the two groups of individuals under the null hypothesis. Its purpose is to provide basic data for constructing the frequency difference reference distribution. Other chromosome segments refer to the first group... In the genome of a single plant, after excluding donor segments containing known target genes and chromosomal regions adjacent to donor segments, the identified chromosomal segments originating from the donor parent are used to focus on analyzing donor segments that are unrelated to known target genes and adjacent regions but may be associated with the new trait. The simulated initial statistical significance index (MSI) refers to the initial statistical significance index calculated for other chromosomal segments in the simulated dataset. Its purpose is to simulate the significance level that can be obtained through statistical testing in the absence of real differences. The minimum value refers to the smallest of the simulated initial MSI values ​​for all other chromosomal segments in each simulation. Its purpose is to capture the minimum value obtained in each simulation. In the context of multiple tests, the minimum p-value (or equivalent indicator) that may occur even when the null hypothesis is true; the minimum set refers to the set of minimum values ​​recorded in all simulations, whose purpose is to provide an empirical distribution of the minimum significance indicator under multiple tests and the null hypothesis; the preset population misjudgment control parameter refers to a parameter pre-set by the user or system to control the probability of a population misjudgment (e.g., at least one incorrect rejection of the null hypothesis) during multiple hypothesis testing. It can be a numerical value, such as 0.05, and its purpose is to control the overall misjudgment rate when performing multiple statistical tests; the preset specification refers to the requirement based on the minimum set and the preset population misjudgment control parameter. The rules or algorithms used to determine the judgment threshold can employ different statistical correction methods, such as Bonferroni correction, Sidak correction, or correction methods based on resampling (e.g., permutation test). The purpose is to provide a basis for determining the judgment threshold for corrected significance. The judgment threshold is a value calculated according to preset rules, used to compare with the initial statistical significance indicator value to determine statistical significance. Its purpose is to provide a judgment standard after multiple test corrections. The corrected significance judgment threshold is the significance judgment threshold after multiple hypothesis testing corrections. Its purpose is to reduce the probability of misjudgment caused by multiple tests and improve the accuracy of the judgment.

[0140] In some preferred embodiments, this application is implemented as follows: Assume that a reference distribution of frequency differences for multiple other chromosome segments and a simulated state dataset for constructing this distribution have been obtained. For each simulated state dataset, for all other chromosome segments contained therein, their respective simulated initial statistical significance indicators can be calculated using a method for determining initial statistical significance indicators (e.g., counting the proportion of simulated frequency differences greater than or equal to the simulated frequency difference value to the total number of simulations). Then, the smallest value among these simulated initial statistical significance indicators is found and recorded. This process is repeated multiple times (e.g., performing 1000 simulations), recording the smallest simulated initial statistical significance indicator value among all segments in each simulation. These 1000 recorded minimum values ​​are aggregated to form a minimum value set. Assume that the preset overall misjudgment control parameter is set to 0.05, and the preset rule is to use a quantile-based method to determine the threshold. Then, the minimum value set can be sorted, and the value located at the 5th percentile position in the set (i.e., the proportion of minimum values ​​less than or equal to this value to the total is 0.05) can be used as the correction significance judgment threshold. Finally, the initial statistical significance index values ​​of other chromosome segments obtained from actual calculations are compared with this corrected significance threshold. If the initial statistical significance index value is less than or equal to the corrected significance threshold, the frequency difference of the segment is considered to be statistically significant.

[0141] Optional, combined Figure 9 As shown, the steps in A23 to output the judgment threshold according to the preset rules include:

[0142] A231, using the preset overall misjudgment control parameter as the target ratio;

[0143] A232, when the proportion of minimum values ​​less than or equal to a preset threshold in the set of minimum values ​​is equal to the target proportion, the preset threshold is used as the judgment threshold.

[0144] The preset overall misjudgment control parameter refers to a probability value pre-set to control the overall error rate during multiple statistical tests. It can be achieved by controlling the Family-wise Error Rate (FWER) or the False Discovery Rate (FDR). Its purpose is to limit the probability of at least one false positive result or the expected proportion of false positive results among all tested chromosome segments. The target proportion refers to a specific value that is desired when determining the judgment threshold, and the proportion of minimum values ​​less than or equal to the threshold in the minimum value set is equal to the preset overall misjudgment control parameter. The minimum value set refers to the set of minimum values ​​among the simulated initial statistical significance indicators of all other chromosome segments in each simulation, obtained through multiple random simulations. It reflects the possible distribution of the most extreme (most significant) statistical indicator value under random distribution. The preset threshold is a temporary or test value used to compare with the elements in the minimum value set during the determination of the final judgment threshold. It can be determined by iterative or search methods between 0 and 1.

[0145] The proposed solution sets a target proportion as a predefined overall misjudgment control parameter and, based on a set of minimum values, finds a predefined threshold such that the proportion of minimum values ​​less than or equal to this predefined threshold in the set of minimum values ​​is exactly equal to the target proportion, thus determining the final judgment threshold. This process essentially utilizes the minimum p-value method (or a variant thereof) based on permutation tests to determine the significance threshold after multiple test correction. The distribution of the minimum value set reflects the distribution of the most significant statistical result that can be observed through multiple simulations, assuming the null hypothesis (i.e., other chromosome segments are not associated with the newborn trait) is true. By finding the quantile corresponding to the target proportion (i.e., the predefined overall misjudgment control parameter) in this distribution, this quantile can be used as the judgment threshold. This means that if the initial statistical significance indicator value of a certain other chromosome segment is less than or equal to this judgment threshold, then under the null hypothesis, the probability of observing such an extreme or more extreme result is less than or equal to the predefined overall misjudgment control parameter. Therefore, the null hypothesis can be rejected, and it can be considered that the segment is statistically associated with the newborn trait. This method fully utilizes the frequency difference reference distribution constructed from simulated data, effectively controlling the overall probability of false positives when simultaneously examining multiple chromosome segments, thereby improving the accuracy and reliability of identifying candidate chromosomal regions truly related to neonatal traits. This approach, closely integrated with the preceding steps of constructing the frequency difference reference distribution, calculating the initial statistical significance indicator value, and calculating the corrected significance threshold, constitutes a complete and statistically rigorous candidate region identification process, providing reliable foundational data for subsequently incorporating neonatal traits as surrogate targets into electronic simulations.

[0146] In some preferred embodiments, specifically, assuming the preset overall misjudgment control parameter is set to 0.05, i.e., the target proportion is 0.05. First, an initial preset threshold can be set, for example, 0.01. Then, count how many minimum values ​​in the set of minimum values ​​are less than or equal to 0.01, and calculate the proportion of this number to the total number of minimum values ​​in the set. If this proportion is less than 0.05, it means the current preset threshold is too small and needs to be increased; if the proportion is greater than 0.05, it means the preset threshold is too large and needs to be decreased. A binary search method or a stepwise adjustment method can be used to iteratively update the preset threshold. For example, if the proportion is less than 0.05, the preset threshold can be increased to 0.02, and the proportion can be recalculated; if the proportion is greater than 0.05, the preset threshold can be decreased to 0.005, and the proportion can be recalculated. This process is repeated until a preset threshold is found such that the proportion of minimum values ​​less than or equal to this threshold in the set of minimum values ​​is very close to or equal to the target proportion of 0.05. Once such a threshold is found, it is determined as the final judgment threshold. For example, after multiple iterations, it was found that when the preset threshold was 0.008, the proportion of the minimum value less than or equal to 0.008 in the minimum value set was exactly 0.05. Therefore, 0.008 was determined as the judgment threshold.

[0147] An electronic simulation system for maize molecular marker-assisted backcross breeding, used to perform electronic simulations of maize molecular marker-assisted backcross breeding, combined with... Figure 10 As shown, it includes:

[0148] The first group of single-plant gene data acquisition modules is used to collect whole-genome molecular marker data of the first group of single plants carrying known target genes, which exhibit newborn traits.

[0149] The donor and recipient gene data acquisition module is used to acquire whole-genome molecular marker data of donor parents with known target genes; and to acquire whole-genome molecular marker data of recipient parents with known target genes.

[0150] The candidate chromosome region inference module is used to infer one or more candidate chromosome regions based on the complete genome molecular marker data by analyzing chromosome segments in the genome of the first group of single plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of single plants and the preset control group.

[0151] The surrogate target selection module is used to select representative molecular markers from candidate chromosomal regions as surrogate targets;

[0152] The electronic simulation output module is used to select the whole genome molecular marker data of the surrogate target and the first group of single plants as the target for subsequent backcross breeding electronic simulation, and output the results of the electronic simulation.

[0153] The system comprises the following modules: The first group of single-plant gene data acquisition module is used to acquire the whole-genome molecular marker data of the first group of single plants exhibiting the newborn trait. This module can be implemented using genotyping equipment, a data interface, and a data storage unit. The donor and recipient gene data acquisition module is used to acquire the whole-genome molecular marker data of the donor and recipient parents with known target genes. This module can be implemented using genotyping equipment, a data interface, and a data storage unit. The candidate chromosome region inference module is used to infer one or more candidate chromosome regions based on the complete whole-genome molecular marker data. This module can be implemented using data processing units, analysis algorithms, and storage units. The surrogate target selection module is used to select representative molecular markers from the candidate chromosome regions as surrogate targets. This module can be implemented using data processing units, selection algorithms, and storage units. The electronic simulation output module is used to perform electronic simulations of subsequent backcross breeding using the surrogate targets and the whole-genome molecular marker data of the first group of single plants as selection targets, and output the electronic simulation results. This module can be implemented using simulation calculation units, result generation units, and an output interface.

[0154] This application's scheme realizes a complete workflow for electronic simulation of maize molecular marker-assisted backcross breeding by setting up multiple functional modules. First, the first set of single-plant gene data acquisition modules and the donor and recipient gene data acquisition modules are responsible for acquiring the basic genomic information required for simulation, including molecular marker data of specific single plants exhibiting the new trait and the backcross parents. After these acquired data are aggregated, they are analyzed in depth by the candidate chromosome region inference module. This module identifies one or more chromosome regions that may be associated with the new trait by comparing the genotypic differences between specific single plants and parents or control groups, or by analyzing the co-occurrence patterns of genetic segments. Subsequently, the surrogate target selection module intervenes, selecting representative molecular markers from these inferred candidate regions according to certain criteria, and determining these markers as surrogate targets for subsequent simulations. Finally, the electronic simulation output module receives these surrogate targets and the original first set of single-plant gene data, using them as the selection basis in the simulation process, executing the electronic simulation calculation for backcross breeding, and generating simulation results. The entire system, through the sequential execution and data transfer between modules, transforms the original gene data into simulation results that can be used to guide breeding practices, thus solving the problem of a lack of specific implementation systems with execution methods.

[0155] In some preferred embodiments, the electronic simulation system for maize molecular marker-assisted backcross breeding of this application can be specifically implemented as a software system running on one or more computers. The first set of single-plant gene data acquisition modules and donor and recipient gene data acquisition modules can function as data input interfaces, allowing users to import molecular marker data files from genotyping platforms or databases. The candidate chromosome region inference module and the surrogate target selection module can consist of a series of preset bioinformatics analysis algorithms and statistical models. These algorithms are executed on the computer's processing unit to calculate and analyze the imported gene data, identify candidate regions, and screen surrogate markers. The electronic simulation output module can include a genetic simulation engine. This engine, based on the user-defined breeding scheme, uses determined surrogate targets and single-plant gene data to perform iterative calculations, simulating genotypic changes and phenotypic expressions during multiple generations of backcrossing, and presents the final simulation results to the user through a graphical interface or report file.

[0156] The above technical solution provides an electronic simulation system for maize marker-assisted backcross breeding. This system integrates modules for data acquisition, candidate region inference, surrogate target selection, and electronic simulation output, offering a platform capable of performing electronic simulations for maize marker-assisted backcross breeding. This allows breeders to input gene data collected during actual breeding processes, especially single-plant data related to unexpectedly observed new traits, into the system for processing and analysis. The system can automatically infer candidate chromosomal regions related to new traits and select representative molecular markers as surrogate targets, incorporating these surrogate targets along with the original breeding targets into subsequent backcross breeding electronic simulations. This solves the problem of the difficulty in practically executing simulations using only methods, providing a concrete tool to complete the electronic simulation process, particularly capable of handling new traits with unknown genetic basis. It provides breeders with the ability to conduct simulations and provide decision support based on actual data, thereby enabling them to more effectively utilize beneficial variations that occur during breeding and optimize breeding strategies.

[0157] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An electronic simulation method for marker-assisted backcross breeding of maize, characterized in that, include: For the first group of single plants carrying the known target gene, whole-genome molecular marker data showing the newborn trait were collected; Collect whole-genome molecular marker data of the donor parent of the known target gene; collect whole-genome molecular marker data of the recipient parent of the known target gene; Based on all the whole-genome molecular marker data, one or more candidate chromosomal regions are inferred by analyzing the chromosomal segments in the genome of the first group of individual plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of individual plants and the preset control group. Representative molecular markers were selected from the candidate chromosomal regions as proxy targets; The whole genome molecular marker data of the surrogate target and the first group of single plants are used as selection targets to perform electronic simulations for subsequent backcross breeding, and the results of the electronic simulations are output.

2. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 1, characterized in that, The step of inferring one or more candidate chromosomal regions by analyzing chromosomal segments in the genomes of the first group of single plants that are commonly inherited from the donor parent includes: Based on the whole genome molecular marker data of the first group of single plants, it was determined that the genome of each single plant in the first group contains the donor fragment of the known target gene and the chromosomal region adjacent to the donor fragment. In the genome of the first group of single plants, after excluding the donor fragment and the adjacent chromosomal regions, the co-occurrence frequency of other chromosomal fragments derived from the donor parent is calculated; Other chromosome fragments derived from the donor parent whose co-occurrence frequency is higher than a preset threshold are selected as one or more candidate chromosome regions.

3. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 1, characterized in that, The step of selecting representative molecular markers from the candidate chromosomal regions as proxy targets includes: Obtain the association data between each candidate molecular marker in all candidate chromosomal regions and the neonatal trait; Obtain the genetic linkage analysis parameters of the candidate molecular markers and the molecular markers of the known target genes; Obtain information on the ease of use of the candidate molecular markers in breeding; Based on the association data, the genetic linkage analysis parameters, and the breeding application convenience assessment information, the evaluation results for each of the candidate molecular markers are generated; One or a group of molecular markers whose evaluation results meet the preset conditions are selected from all the candidate molecular markers and used as proxy targets.

4. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 2, characterized in that, The step of calculating the co-occurrence frequency of other chromosome segments derived from the donor parent after excluding the donor segment and the adjacent chromosomal regions in the genome of the first group of single plants includes: For each individual plant in the first group, based on the donor fragment and the adjacent chromosome region, other chromosome fragments originating from the donor parent but outside of the donor fragment and the adjacent chromosome region are identified; Calculate the first co-occurrence frequency of the other chromosome segments; Obtain the second co-occurrence frequency of chromosome segments corresponding to the other chromosome segments in the second group of single plants of the preset control group; Based on the first co-occurrence frequency and the second co-occurrence frequency, the significance of the difference is evaluated, and the significance of the difference evaluation result is generated; The first co-occurrence frequency that meets the preset conditions based on the significance of the difference assessment results is taken as the co-occurrence frequency of the first group of individual plants.

5. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 4, characterized in that, The step of assessing the significance of the difference based on the first co-occurrence frequency and the second co-occurrence frequency, and generating a significance assessment result, includes: For the first group of single plants and the second group of single plants, obtain the existence status data of each of the other chromosome segments; The two sets of single plant data are merged to generate a merged state dataset. The merged state dataset is randomly redistributed to generate several pairs of simulated state datasets; each pair of simulated state datasets includes a simulated first group of state datasets and a simulated second group of state datasets. Based on the simulated state dataset, the simulated frequency difference value between the simulated first co-occurrence frequency and the simulated second co-occurrence frequency is calculated; Based on the simulated frequency difference values, a reference distribution of frequency differences for the other chromosome segments is constructed; Calculate the actual frequency difference between the first co-occurrence frequency and the second co-occurrence frequency, compare the actual frequency difference with the frequency difference reference distribution, determine the statistical significance, and use it as the significance evaluation result of the difference.

6. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 5, characterized in that, The step of calculating the actual frequency difference between the first co-occurrence frequency and the second co-occurrence frequency, comparing the actual frequency difference with the frequency difference reference distribution, and determining statistical significance as the result of the difference significance assessment includes: Based on the actual frequency difference value and the frequency difference reference distribution, determine the initial statistical significance indicator value; Based on the number of other chromosome segments and the preset overall misjudgment control parameters, the correction significance judgment threshold is calculated; The initial statistical significance indicator value is compared with the corrected significance judgment threshold to determine whether there is statistical significance, and the judgment result is used as the difference significance evaluation result.

7. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 6, characterized in that, The step of determining the initial statistical significance indicator value based on the actual frequency difference value and the frequency difference reference distribution includes: The total number of simulated frequency difference values ​​that are greater than or equal to the actual frequency difference value in the frequency difference reference distribution is counted. Calculate the proportion of the total number to the total number of simulations in the frequency difference reference distribution; The aforementioned proportion will be used as an initial statistical significance indicator for the other chromosome segments.

8. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 6, characterized in that, The step of calculating the significance judgment threshold based on the number of other chromosome segments and a preset overall misjudgment control parameter includes: Based on the frequency difference reference distribution and the simulated state dataset, calculate the simulated initial statistical significance index value of the other chromosome segments and record the minimum value; Summarize the minimum values ​​from all simulations to form a set of minimum values; Based on the set of minimum values ​​and the preset overall misjudgment control parameters, a judgment threshold is output according to the preset rules and used as the correction significance judgment threshold.

9. The electronic simulation method for marker-assisted backcross breeding of maize as described in claim 8, characterized in that, The step of outputting the judgment threshold according to the preset rules includes: The preset overall misjudgment control parameter is used as the target ratio; When the proportion of minimum values ​​less than or equal to a preset threshold in the set of minimum values ​​is equal to the target proportion, the preset threshold is used as the judgment threshold.

10. An electronic simulation system for maize molecular marker-assisted backcross breeding, used to perform electronic simulation of maize molecular marker-assisted backcross breeding, characterized in that, include: The first group of single-plant gene data acquisition modules is used to collect whole-genome molecular marker data of the first group of single plants carrying known target genes, which exhibit newborn traits. The donor and recipient gene data acquisition module is used to acquire whole-genome molecular marker data of the donor parent of the known target gene; and to acquire whole-genome molecular marker data of the recipient parent of the known target gene. The candidate chromosome region inference module is used to infer one or more candidate chromosome regions based on all the whole genome molecular marker data by analyzing chromosome segments in the genome of the first group of single plants that are commonly inherited from the donor parent, and / or by comparing the genotypic differences between the first group of single plants and the preset control group. The proxy target selection module is used to select representative molecular markers from the candidate chromosomal regions as proxy targets; The electronic simulation output module is used to select the surrogate target and the whole genome molecular marker data of the first group of single plants as the selection targets, perform electronic simulation of subsequent backcross breeding, and output the results of the electronic simulation.

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