A method and system for electronic simulation of maize marker-assisted backcross breeding

Abstract

The present application relates to the technical field of plant breeding, and provides a corn molecular marker assisted backcross breeding electronic simulation method and system, the method comprising: collecting whole genome molecular marker data of a first group of single plants carrying known target genes which exhibit new traits; collecting whole genome molecular marker data of a donor parent of the known target genes; collecting whole genome molecular marker data of a recipient parent of the known target genes; based on the whole genome molecular marker data, deducing one or more candidate chromosome regions by analyzing chromosome fragments commonly inherited from the donor parent in the genome of the first group of single plants; selecting representative molecular markers from the candidate chromosome regions as proxy targets; taking the proxy targets and the whole genome molecular marker data of the first group of single plants as selection targets, performing electronic simulation of subsequent backcross breeding, and outputting the results of the electronic simulation. The present application has the effect of improving breeding efficiency and decision-making scientificity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plant breeding, and particularly relates to a corn molecular marker-assisted backcrossing electronic simulation method and system. BACKGROUND

[0002] In modern corn breeding practice, Marker-Assisted Backcrossing (MABC) is a widely used technique, aiming to efficiently introduce excellent genes carried by donor parents into the excellent genetic background of recipient parents. Breeding institutions usually plan and optimize the backcrossing breeding process with the help of electronic simulation systems, which can predict the gene introduction efficiency and background genome recovery process based on the genotype information of parents and breeding goals.

[0003] However, in the actual breeding process, especially in the backcrossing generation, new traits with potential breeding value may be accidentally observed in offspring carrying known target genes. The system cannot preliminarily infer the chromosomal region that may be associated with the new trait based on the specific single data of a few plants showing new traits. The lack of this ability makes it difficult for breeders to quickly and scientifically decide whether and how to integrate the new trait into the current breeding plan when facing such unexpected beneficial discoveries, which may miss breeding opportunities or fail to adjust breeding strategies in time.

[0004] The prior art needs to be improved in view of the above problems. SUMMARY

[0005] The purpose of the present application is to provide a corn molecular marker-assisted backcrossing electronic simulation method and system, which has the advantages of being able to include new traits with unknown genetic basis into the electronic simulation of backcrossing breeding, and improving breeding efficiency and decision-making scientificity.

[0006] The present application provides a corn molecular marker-assisted backcrossing electronic simulation method, and the technical solution is as follows:

[0007] Comprising:

[0008] Collecting whole genome molecular marker data of the first group of single plants carrying known target genes showing new traits;

[0009] Collecting whole genome molecular marker data of the donor parent of the known target gene; collecting whole genome molecular marker data of the recipient parent of the known target gene;

[0010] Based on all the whole genome molecular marker data, one or more candidate chromosomal regions are inferred by analyzing the chromosomal fragments commonly inherited from the donor parent in the genomes of the first group of single plants, and / or by comparing the genotype differences between the first group of single plants and the preset control group.

[0011] selecting a representative molecular marker from the candidate chromosomal region as a proxy target;

[0012] using the proxy target and the whole genome molecular marker data of the first set of single plants as selection targets, performing electronic simulation of subsequent backcross breeding, and outputting results of the electronic simulation.

[0013] By the above scheme, the newly-born trait with unknown genetic basis can be included in the electronic simulation of backcross breeding, and the breeding efficiency and decision-making scientificity are improved.

[0014] Optionally, the step of inferring one or more candidate chromosomal regions by analyzing the chromosomal fragments commonly inherited from the donor parent in the genomes of the first set of single plants comprises:

[0015] based on the whole genome molecular marker data of the first set of single plants, determining the donor fragment containing the known target gene and the chromosomal region adjacent to the donor fragment in the genome of each single plant in the first set of single plants;

[0016] after excluding the donor fragment and the adjacent chromosomal region in the genomes of the first set of single plants, counting the co-occurrence frequency corresponding to the other chromosomal fragments derived from the donor parent;

[0017] the other chromosomal fragments derived from the donor parent with a co-occurrence frequency higher than a preset threshold are taken as one or more candidate chromosomal regions.

[0018] By the above scheme, a specific method for inferring candidate regions based on the co-occurrence frequency of donor fragments is provided, and the accuracy of the inference is improved.

[0019] Optionally, the step of selecting a representative molecular marker from the candidate chromosomal region as a proxy target comprises:

[0020] obtaining association data between each candidate molecular marker in all candidate chromosomal regions and the newly-born trait;

[0021] obtaining genetic linkage analysis parameters of the candidate molecular marker and the molecular marker of the known target gene;

[0022] obtaining breeding application convenience evaluation information of the candidate molecular marker;

[0023] generating evaluation results of each candidate molecular marker according to the association data, the genetic linkage analysis parameters and the breeding application convenience evaluation information;

[0024] selecting one or a group of molecular markers with evaluation results meeting a preset condition from all candidate molecular markers as a proxy target.

[0025] The evaluation criteria and method for selecting the agent target are provided, and the selected marker is ensured to be representative and practical.

[0026] Optionally, in the genomes of the first group of single strains, after excluding the donor fragment and the adjacent chromosome region, the step of counting the co-occurrence frequency corresponding to the other chromosome fragments derived from the donor parent includes:

[0027] For each single strain in the first group of single strains, based on the donor fragment and the adjacent chromosome region, other chromosome fragments outside the donor fragment and the adjacent chromosome region and derived from the donor parent are identified;

[0028] The first co-occurrence frequency of the other chromosome fragments is counted.

[0029] The second co-occurrence frequency of the chromosome fragments corresponding to the other chromosome fragments in the second group of single strains of the preset control group is obtained.

[0030] Based on the first co-occurrence frequency and the second co-occurrence frequency, the difference significance is evaluated, and a difference significance evaluation result is generated.

[0031] The first co-occurrence frequency that meets the preset condition in the difference significance evaluation result is taken as the co-occurrence frequency of the first group of single strains.

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

[0033] Optionally, the step of evaluating the difference significance based on the first co-occurrence frequency and the second co-occurrence frequency to generate a difference significance evaluation result includes:

[0034] For the first group of single strains and the second group of single strains, the presence state data of each other chromosome fragment is obtained.

[0035] The presence state data of the two groups of single strains is merged to generate a merged state data set.

[0036] The merged state data set is randomly reassigned to generate a plurality of pairs of simulated state data sets; each pair of simulated state data sets includes a simulated first group state data set and a simulated second group state data set.

[0037] According to the simulated state data set, the simulated first co-occurrence frequency and the simulated second co-occurrence frequency are calculated to obtain a simulated frequency difference value.

[0038] Based on the simulated frequency difference value, a frequency difference reference distribution of the other chromosome fragments is constructed.

[0039] The actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency is calculated, the actual frequency difference value is compared with the frequency difference reference distribution, and statistical significance is determined as a difference significance evaluation result.

[0040] Through the above scheme, a difference significance evaluation method based on random redistribution is provided, and the rigor of statistical inference is enhanced.

[0041] Optionally, the step of calculating the actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency, comparing the actual frequency difference value with the frequency difference reference distribution, and determining the statistical significance as the difference significance evaluation result comprises:

[0042] According to the actual frequency difference value and the frequency difference reference distribution, an initial statistical significance indication value is determined.

[0043] Based on the number of other chromosome fragments and a preset overall false judgment control parameter, a corrected significance judgment threshold is calculated.

[0044] The initial statistical significance indication value is compared with the corrected significance judgment threshold to determine whether there is statistical significance, and the determination result is taken as the difference significance evaluation result.

[0045] Through the above scheme, a statistical significance judgment method based on the corrected significance judgment threshold is provided, and the false judgment rate is reduced.

[0046] Optionally, the step of determining the initial statistical significance indication value according to the actual frequency difference value and the frequency difference reference distribution comprises:

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

[0048] The proportion of the total number in the total number of simulations in the frequency difference reference distribution is calculated.

[0049] The proportion is taken as the initial statistical significance indication value of the other chromosome fragments.

[0050] Through the above scheme, a specific method for calculating the initial statistical significance indication value is provided.

[0051] Optionally, the step of calculating the corrected significance judgment threshold based on the number of other chromosome fragments and the preset overall false judgment control parameter comprises:

[0052] Based on the frequency difference reference distribution and the simulated state data set, the simulated initial statistical significance indication value of the other chromosome fragments is calculated and the minimum value is recorded.

[0053] The minimum values in all simulations are summarized to form a minimum value set.

[0054] The preset specified output judgment threshold is outputted as the corrected saliency judgment threshold based on the minimum value set and the preset overall false judgment control parameter.

[0055] The above scheme provides a specific method for calculating the corrected saliency judgment threshold, and improves the accuracy of judgment.

[0056] Optionally, the step of outputting the preset specified output judgment threshold comprises:

[0057] The preset overall false judgment control parameter is taken as a target proportion;

[0058] When the proportion of the minimum values in the minimum value set that are less than or equal to the preset threshold is equal to the target proportion, the preset threshold is taken as the judgment threshold.

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

[0060] A corn molecular marker assisted backcross breeding electronic simulation system is used to perform corn molecular marker assisted backcross breeding electronic simulation, and comprises:

[0061] The first group of single plant gene data acquisition modules are used to acquire whole genome molecular marker data of the first group of single plants carrying known target genes and exhibiting new traits;

[0062] The donor and recipient gene data acquisition modules are used to acquire whole genome molecular marker data of the donor parent of the known target gene and whole genome molecular marker data of the recipient parent of the known target gene;

[0063] 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 the chromosome fragments commonly inherited from the donor parent in the genomes of the first group of single plants and / or by comparing the genotype differences between the first group of single plants and a preset control group;

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

[0065] The electronic simulation output module is used to take the proxy targets and the whole genome molecular marker data of the first group of single plants as selection targets to perform subsequent backcross breeding electronic simulation and output the results of the electronic simulation.

[0066] From the above, the application provides a corn molecular marker assisted backcross breeding electronic simulation method and system, by collecting the genomic data of specific single plants showing new traits, inferring the candidate chromosome regions associated with the new traits, and selecting proxy markers therefrom, the proxy markers are included in the backcross breeding electronic simulation together with known target genes, so that the new traits with unknown genetic basis can be included in the backcross breeding electronic simulation, and the breeding efficiency and decision-making scientificity are improved. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0068] Figure 2 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0069] Figure 3 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0070] Figure 4 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0071] Figure 5 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0072] Figure 6 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0073] Figure 7 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0074] Figure 8 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0075] Figure 9 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure.

[0076] Figure 10 For one embodiment of the corn molecular marker assisted backcross breeding electronic simulation method of the application, a method flowchart is shown in the figure. DETAILED DESCRIPTION

[0077] The technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0078] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0079] The conventional existing electronic simulation method of maize molecular marker assisted backcross breeding has the problems that when dealing with new traits that occur unexpectedly in the breeding process, the phenotype and genotype information of a small number of specific single plants cannot be effectively utilized, the candidate chromosome region related to the new traits cannot be quickly inferred, and in the case where the genetic basis is unknown, the new traits are integrated into the electronic simulation and evaluation of backcross selection as proxy targets together with the original breeding targets. This limits the response ability of breeders to unexpected beneficial variations.

[0080] For example, assuming that in the process of introducing a known target gene from a donor parent into a recipient parent in a maize molecular marker assisted backcross breeding project, in the backcross second generation population, the user observes a new trait different from the parent in the first group of single plants carrying the known target gene. The genetic basis of the new trait is not clear, but preliminary analysis shows that it has potential breeding value. The existing electronic simulation system usually relies on known genes or positioned quantitative trait locus information for simulation selection. In the face of this new trait with unknown genetic basis, the system cannot directly infer the candidate chromosome region related to the trait based on the whole genome molecular marker data of the first group of single plants that exhibit the new trait. Further, the system cannot also integrate the inferred candidate region or its representative marker as a proxy target together with the original known target gene into the subsequent backcross breeding electronic simulation to evaluate the impact of selecting both targets on the breeding process.

[0081] To this end, the present application provides a maize molecular marker assisted backcross breeding electronic simulation method, which combines Figure 1 as shown, comprising:

[0082] S1, collecting whole genome molecular marker data of the first group of single plants carrying known target genes and exhibiting new traits;

[0083] S2, collecting whole genome molecular marker data of the donor parent of the known target genes; collecting whole genome molecular marker data of the recipient parent of the known target genes;

[0084] S3, based on all whole genome molecular marker data, deducing one or more candidate chromosome regions by analyzing the chromosome fragments commonly inherited from the donor parent in the genome of the first group of single plants, and / or by comparing the genotypes of the first group of single plants with a pre-set control group;

[0085] S4, selecting a representative molecular marker from the candidate chromosome region as a proxy target;

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

[0087] Wherein, the first group of single plants refers to a specific collection of individuals carrying known target genes and exhibiting new traits during the backcross breeding process, which aims to provide genetic material with new traits to be analyzed; whole genome molecular marker data refers to the genotype information of molecular markers covering the entire genome of corn, which can be obtained using SNP chips, sequencing, etc., which aims to provide comprehensive genetic background information; known target genes refer to genes that are initially planned to be introduced in breeding projects and have a clear genetic basis, which aims to serve as the original target of breeding; new traits refer to traits that are accidentally observed during the backcross breeding process, which are not stably expressed in parents and have unknown genetic basis, which aims to serve as new targets that need to be analyzed and processed in this method; donor parents refer to parent materials that provide known target genes, which aims to provide genetic information of known target genes; recipient parents refer to excellent genetic background materials that accept the introduction of known target genes, which aims to provide background genomic information for breeding; candidate chromosome regions refer to genomic fragments that may be related to new traits deduced by analysis, which aims to narrow the scope of subsequent analysis and selection; proxy targets refer to molecular markers selected from candidate chromosome regions to represent new traits for subsequent simulation, which aims to convert new traits with unknown genetic basis into operable molecular marker information; electronic simulation refers to using computer programs to simulate the transmission and selection of genes during backcross breeding, which aims to predict the breeding process and results under different selection strategies.

[0088] In some preferred embodiments, the method can be implemented in the following way. First, whole genome genotyping is performed on the first set of single plants, the donor parent and the recipient parent using high-density SNP chips to obtain whole genome molecular marker data. Next, using bioinformatics software, by analyzing the chromosomal fragments shared by the first set of single plants and the donor parent, or performing whole genome association analysis on the control group of single plants that do not exhibit the newly generated trait, the chromosomal regions significantly associated with the newly generated trait are identified as candidate regions. Then, within the candidate regions, molecular markers with high association degree with the newly generated trait, moderate genetic distance and easy to detect in breeding are selected as proxy targets. Finally, the genotype information of these proxy targets is input into the backcross breeding electronic simulation platform, the corresponding selection parameters are set, the multi-generation backcross process is simulated, the key indicators such as the introduction efficiency of the target gene and the newly generated trait, the recovery rate of the background genome and the breeding cycle are evaluated under the condition of simultaneously selecting the known target gene and the proxy target, and the simulation report is output.

[0089] Through the above technical solutions, the method can use the phenotype and genotype data of the newly generated trait accidentally discovered in the backcross breeding process and unknown in the genetic basis to quickly infer the potential genetic correlation region and convert it into an operable molecular marker proxy target. This enables breeders to include the newly generated trait with potential value into the electronic simulation evaluation system together with the original breeding target, so that the impact of integrating the newly generated trait on the overall breeding process can be scientifically evaluated, data support can be provided for breeding decisions, the loss of beneficial variation can be avoided, and the flexibility and efficiency of breeding are improved.

[0090] Optionally, in combination with Figure 2 As shown in FIG. 3, the step of inferring one or more candidate chromosomal regions in S3 by analyzing the chromosomal fragments commonly inherited from the donor parent in the genomes of the first set of single plants includes:

[0091] S31, based on the whole genome molecular marker data of the first set of single plants, determining the donor fragment containing the known target gene and the chromosomal region adjacent to the donor fragment in the genome of each single plant in the first set of single plants;

[0092] S32, after excluding the donor fragment and the adjacent chromosomal region in the genomes of the first set of single plants, counting the co-occurrence frequency corresponding to the other chromosomal fragments derived from the donor parent;

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

[0094] wherein the donor segment refers to a chromosome segment inherited from the donor parent to the progeny single plant genome, which can be realized by a method of determining the parent origin of a single plant based on molecular marker genotyping data, such as by comparing the genotype of a single plant at a specific marker locus with the genotypes of the donor parent and the recipient parent to determine the parent origin of the chromosome segment to which the locus belongs, with the purpose of identifying which part of the single plant genome is derived from the donor parent; the adjacent chromosome region refers to a region closely linked to the donor segment containing the known target gene in terms of physical or genetic distance of the genome, which can be defined by extending a predetermined genetic distance (e.g. centiMorgan, cM) or physical distance (e.g. base pair, bp) to both sides of the donor segment containing the known target gene based on genetic linkage map or physical map after determining the position of the donor segment, with the purpose of treating the known target gene and its closely linked region as a whole; the co-occurrence frequency refers to the proportion of a specific chromosome segment appearing in the first group of single plants, which can be calculated by dividing the number of single plants in the first group of single plants having the specific chromosome segment by the total number of the first group of single plants, with the purpose of quantifying the degree of association of a certain chromosome segment with the first group of single plants (exhibiting the newly emerged trait).

[0095] In some preferred embodiments, specifically, based on the whole genome SNP (single nucleotide polymorphism) marker data of the first group of single plants, the position of the known target gene in the donor parent genome can be located first, and the boundary of the donor segment containing the known target gene can be determined according to the SNP genotype of the first group of single plants in this region. For example, the marker loci on both sides of the region where the genotype changes from donor homozygous to heterozygous or recipient homozygous can be identified to define the donor segment. Then, according to the genetic linkage map of corn, a certain distance (e.g. 5 cM) can be extended to both sides of the donor segment to define the chromosome region adjacent to the donor segment. Next, in the genomes of the first group of single plants, all chromosome regions outside the above-mentioned determined donor segment and its adjacent region are scanned to identify the segments derived from the donor parent. For each identified other donor segment, the number of single plants in which it appears in the first group of single plants is counted to calculate its co-occurrence frequency. Finally, a preset threshold, for example 75%, is set to determine the other donor segments with a co-occurrence frequency higher than 75% as candidate chromosome regions.

[0096] By the above technical solutions, the chromosome region potentially associated with the newly emerged trait can be more accurately identified from the complex backcross progeny genome, effectively excluding the interference of the known target gene and its linked region, improving the precision of candidate region inference, providing more reliable input information for subsequent electronic simulation of molecular marker-assisted backcross breeding of the newly emerged trait, and thus improving the effectiveness and guiding value of electronic simulation in dealing with traits with unknown genetic basis.

[0097] Optionally, in combination with Figure 3 As shown in FIG. 4, the step of selecting a representative molecular marker from the candidate chromosome region as a proxy target includes:

[0098] S41, obtaining association data of each candidate molecular marker in the candidate chromosome region and the new trait;

[0099] S42, obtaining genetic linkage analysis parameters of the candidate molecular marker and the molecular marker of the known target gene;

[0100] S43, obtaining breeding application convenience evaluation information of the candidate molecular marker;

[0101] S44, generating evaluation results of each candidate molecular marker according to the association data, the genetic linkage analysis parameters and the breeding application convenience evaluation information;

[0102] S45, selecting one or a group of molecular markers with evaluation results satisfying a preset condition from all the candidate molecular markers as the proxy target.

[0103] The association data of each candidate molecular marker in all candidate chromosome regions and the new trait refers to data obtained by analyzing whether there is a significant correlation between the molecular marker genotype and the new trait phenotype by statistical methods, which can be P value, effect value or correlation coefficient calculated by whole genome association analysis (GWAS) or quantitative trait locus (QTL) positioning method, etc. The purpose is to quantify the association strength of each candidate molecular marker and the new trait. The genetic linkage analysis parameter of the candidate molecular marker and the molecular marker of the known target gene refers to a parameter reflecting the location distance and genetic recombination probability of two molecular markers on the chromosome, which can be linkage distance (in centimorgan, cM) or linkage disequilibrium (LD) coefficient calculated by analyzing population genetic data. The purpose is to evaluate the genetic correlation between the candidate molecular marker and the molecular marker of the known target gene. The breeding application convenience evaluation information of the candidate molecular marker refers to information measuring the ease of use of the molecular marker in actual breeding operation, which can include the type of molecular marker (such as single nucleotide polymorphism SNP, insertion and deletion InDel), the maturity of detection method, detection cost, detection throughput, result stability and whether it is easy to carry out high-throughput detection, etc. The purpose is to select those markers that have more operation advantages in laboratory detection and breeding population screening. The evaluation result of each candidate molecular marker refers to the quantitative index of each candidate molecular marker suitability after considering the association data, genetic linkage analysis parameter and breeding application convenience evaluation information, which can be a comprehensive score or ranking calculated by weighted summation, sorting, machine learning model or other multi-index comprehensive evaluation method. The purpose is to provide a unified evaluation standard for subsequent marker selection. The preset condition refers to the screening standard set when selecting the proxy target, which can be a threshold (such as a score higher than a certain score), a number (such as selecting the top N markers with the best evaluation result) or a combination standard. The purpose is to determine the final proxy target set according to the specific breeding demand and resource limitation.

[0104] In some preferred embodiments, it is assumed that a candidate chromosome region containing 100 candidate SNP markers is inferred by analyzing the first set of individual genomic data. First, the association data of each candidate SNP marker in the region with the light yellow seed trait is obtained, for example, by performing whole genome association analysis on specific individuals with light yellow seeds and control individuals, and the P value and effect value of each SNP are calculated. Then, the genetic linkage analysis parameters of these candidate SNP markers and the molecular markers M1, M2 of the known disease resistance gene R are obtained, for example, the linkage distance between them is calculated. At the same time, the breeding application convenience evaluation information of each candidate SNP marker is obtained, for example, whether it is suitable for KASP detection, detection cost, and result stability, etc. are evaluated. Then, according to the association data, genetic linkage analysis parameters and breeding application convenience evaluation information, the evaluation results of each candidate SNP marker are generated, for example, a weighted scoring formula is used to comprehensively calculate the P value, linkage distance and convenience score to obtain the comprehensive score of each SNP. Finally, from the 100 candidate SNP markers, the markers whose evaluation results meet the preset conditions are selected as the proxy target of the light yellow seed trait, for example, the preset condition is set to select the top 5 SNP markers with the highest comprehensive score.

[0105] Optionally, in combination with Figure 4 As shown in S32, in the genomes of the first set of individuals, after excluding the donor fragment and the adjacent chromosome region, the step of counting the co-occurrence frequency corresponding to the other chromosome fragments derived from the donor parent includes:

[0106] S321, for each individual of the first set of individuals, based on the donor fragment and the adjacent chromosome region, identifying other chromosome fragments outside the donor fragment and the adjacent chromosome region and derived from the donor parent;

[0107] S322, counting the first co-occurrence frequency of the other chromosome fragments;

[0108] S323, obtaining the second co-occurrence frequency of the chromosome fragments corresponding to the other chromosome fragments in the second set of individuals of the preset control group;

[0109] S324, based on the first co-occurrence frequency and the second co-occurrence frequency, evaluating the difference significance and generating a difference significance evaluation result;

[0110] S325, taking the first co-occurrence frequency that meets the preset condition of the difference significance evaluation result as the co-occurrence frequency of the first set of individuals.

[0111] The other chromosome fragment outside the donor fragment and adjacent chromosome region and originating from the donor parent refers to determining which regions in the genome of the first set of individuals originate from the donor parent by analyzing the genomic molecular marker data of the first set of individuals, combined with the genotype information of the known donor parent and recipient parent, and excluding the donor fragment containing the known target gene and its immediately adjacent chromosome region, which can be achieved by a genomic fragment tracing method based on molecular marker typing, and the purpose is to determine the donor-derived fragment of the non-target gene region that needs to be statistically analyzed for co-occurrence frequency; the first co-occurrence frequency refers to calculating the proportion of the appearance of a specific other chromosome fragment in the first set of individual populations, which can be achieved by traversing the genotype data of the first set of individuals and counting the number of individuals containing the fragment, and the purpose is to quantify the universality of the fragment in the individual population carrying the target gene; the second set of individuals of the preset control group refers to a predetermined individual population, which has a similar genetic background to the first set of individuals, but does not carry the known target gene or exhibit the newly generated trait, which can be constructed by screening individuals that do not carry the target gene from the original recipient parent population or its backcross offspring, and the purpose is to provide a benchmark population for comparison to distinguish between co-occurrence associated with the target gene linkage and co-occurrence associated with the population background; obtaining the second co-occurrence frequency refers to calculating the proportion of the appearance of a specific other chromosome fragment in the second set of individual populations of the preset control group, which can be achieved by traversing the genotype data of the second set of individuals and counting the number of individuals containing the fragment, and the purpose is to quantify the universality of the fragment in the control population; evaluating the significance of the difference refers to comparing whether there is a statistically significant difference between the first co-occurrence frequency and the second co-occurrence frequency using statistical methods, which can be achieved by using methods such as chi-square test, Fisher's exact test or permutation test, and the purpose is to determine whether the high-frequency co-occurrence of a specific fragment in the first set of individuals is statistically meaningful or accidental; the difference significance evaluation result refers to the statistical index obtained after evaluating the difference significance, such as P value or statistical quantity, which can be represented by the numerical value or judgment mark output by the statistical test software or algorithm, and the purpose is to provide a quantitative basis to judge whether the difference is significant; the preset condition refers to the standard used to judge whether the difference significance evaluation result meets the requirements, which can be achieved by setting a significance level threshold (such as P value less than 0.05), and the purpose is to screen out those fragments that are considered to be significantly associated with the newly generated trait in statistics.

[0112] In some preferred embodiments, specifically, assuming that the first group of individuals contains 100 individuals that exhibit the newly emerged trait and carry the target gene, and the second group of individuals in the control group contains 200 individuals that do not exhibit the newly emerged trait and do not carry the target gene but have similar genetic backgrounds. First, for each individual in the first group, by analyzing its whole genome molecular marker data, identify all other chromosomal fragments derived from the donor parent except for the known donor fragment and adjacent regions. For example, identify fragment X, fragment Y and fragment Z. Then, count the occurrence frequency of these fragments in the 100 individuals in the first group, for example, fragment X occurs in 85 individuals, the first co-occurrence frequency is 0.85; fragment Y occurs in 60 individuals, the first co-occurrence frequency is 0.60; fragment Z occurs in 30 individuals, the first co-occurrence frequency is 0.30. At the same time, obtain the occurrence frequency of these fragments in the 200 individuals in the control group, for example, fragment X occurs in 120 individuals, the second co-occurrence frequency is 0.60; fragment Y occurs in 80 individuals, the second co-occurrence frequency is 0.40; fragment Z occurs in 70 individuals, the second co-occurrence frequency is 0.35. Then, based on the first co-occurrence frequency and the second co-occurrence frequency, evaluate the difference significance of each fragment. For example, perform statistical test on fragment X, compare the difference between 0.85 and 0.60 to obtain a P value; perform statistical test on fragment Y, compare the difference between 0.60 and 0.40 to obtain a P value; perform statistical test on fragment Z, compare the difference between 0.30 and 0.35 to obtain a P value. Finally, take the first co-occurrence frequency that meets the preset condition (for example, P value is less than 0.05) as the co-occurrence frequency of the first group of individuals. If the P values of fragment X and fragment 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 0.85 of fragment X and the first co-occurrence frequency 0.60 of fragment Y are adopted for subsequent candidate chromosomal region inference.

[0113] Optionally, in combination with Figure 5 As shown in S324, based on the first co-occurrence frequency and the second co-occurrence frequency, evaluate the difference significance, and generate difference significance evaluation results. The step of generating difference significance evaluation results includes:

[0114] S3241, obtain the presence state data of each other chromosomal fragment for the first group of individuals and the second group of individuals;

[0115] S3242, merge all the presence state data of the two groups of individuals to generate a merged state data set;

[0116] S3243, perform random reassignment processing on the merged state data set to generate a plurality of pairs of simulated state data sets; each pair of simulated state data sets includes a simulated first group state data set and a simulated second group state data set;

[0117] S3244, according to the simulation state data set, calculate the simulation frequency difference value between the simulation first co-occurrence frequency and the simulation second co-occurrence frequency;

[0118] S3245, based on the simulation frequency difference value, construct the frequency difference reference distribution of other chromosome fragments;

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

[0120] Wherein, the presence state data refers to the data used to indicate whether a specific chromosome fragment exists in a single single genome, which can be represented by binary numerical value; the merged state data set refers to the data set formed by collecting all presence state data of the first group of single plants and the second group of single plants together; the random reassignment processing refers to a statistical resampling technique, which simulates the data distribution under different grouping by randomly grouping or arranging the data in the merged state data set, which can be realized by permutation test or bootstrap method; the simulation state data set refers to a pair of virtual data sets generated after random reassignment processing, representing the state data of the simulated first group and the simulated second group of single plants respectively; the simulation first co-occurrence frequency and the simulation second co-occurrence frequency refer to the occurrence frequency of a specific chromosome fragment under simulated grouping calculated according to the simulation state data set; the simulation frequency difference value refers to the numerical difference between the simulation first co-occurrence frequency and the simulation second co-occurrence frequency; the frequency difference reference distribution refers to the collection or statistical distribution of simulation frequency difference values obtained by multiple random reassignment processing and calculating simulation 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 according to the original presence state data of the first group of single plants and the second group of single plants; the statistical significance refers to the probability of observing the actual frequency difference value under the assumption that there is no real difference between the two groups; the difference significance evaluation result refers to the conclusion about whether the frequency difference between the two groups has statistical significance based on the statistical significance.

[0121] In some preferred embodiments, specifically for the first set of singletons and the second set of singletons, the presence status data of each of the other chromosome segments can be recorded in binary form, for example, for a certain other chromosome segment, if present in a singleton, record as 1, if not present, record as 0. Merge the presence status data of all the segments (e.g. a vector) of the first set of singletons with the presence status data of all the segments (e.g. another vector) of the second set of singletons to form a longer merged status data set vector. Perform random reassignment processing on this merged status data set vector, for example, perform ten thousand random permutations. Each permutation will randomly shuffle the order of the elements in the merged vector, and then re-divide it into two simulated status data sets according to the original proportion of the two sets of singletons, representing the simulated first set and the simulated second set respectively. For each simulated status data set generated by each permutation, calculate the frequency of the specific segment in the simulated first set and the frequency in the simulated second set, and calculate the simulated frequency difference value between the two. Repeat the permutation ten thousand times to obtain ten thousand simulated frequency difference values, which constitute the frequency difference reference distribution of the segment. At the same time, calculate the actual frequency difference value of the segment in the original first set and the second set of singletons. Compare this actual frequency difference value with the reference distribution composed of ten thousand simulated frequency difference values, for example, calculate how many simulated frequency difference values in the reference distribution have an absolute value greater than or equal to the absolute value of the actual frequency difference value, and this proportion can be used as a preliminary statistical significance indication. In this way, it can be determined whether the actual observed frequency difference is within a reasonable range of random fluctuations.

[0122] Optionally, in combination with Figure 6 As shown, S3246, calculate the actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency, compare the actual frequency difference value with the frequency difference reference distribution, determine the statistical significance, and take it as the difference significance evaluation result. The step of

[0123] A1, according to the actual frequency difference value and the frequency difference reference distribution, determine an initial statistical significance indication value;

[0124] A2, based on the number of other chromosome segments and a preset overall false judgment control parameter, calculate a corrected significance judgment threshold;

[0125] A3, compare the initial statistical significance indication value with the corrected significance judgment threshold, determine whether it has statistical significance, and take the judgment result as the difference significance evaluation result.

[0126] wherein the number of other chromosome fragments refers to the total number of other chromosome fragments from the donor parent in the first set of single genomes, excluding the donor fragment and the adjacent chromosome region. The preset overall false call control parameter refers to the overall probability level of false positive results allowed to occur when performing multiple comparison statistical test. The corrected significance judgment threshold refers to the standard used to judge whether the difference in chromosome fragments has statistical significance after multiple comparison correction.

[0127] In specific implementation, to ensure the statistical stability of the frequency difference reference distribution, preferably, the random reassignment operation should be repeated at least 1000 times, more preferably 10000 times, to construct a sufficiently accurate simulated reference distribution. The setting of the simulation rounds can be flexibly adjusted according to the computing resources or statistical accuracy requirements, and whether it has converged can be monitored based on the stability of the distribution. The operation of excluding the donor fragment and the adjacent region is preferably realized by setting a window range, for example, for a donor fragment containing a target gene, a 1-2 Mb window interval is set upstream and downstream as the adjacent region, and the remaining part after exclusion is used as the other candidate chromosome region. The window size can be parameterized and adjusted according to the length of the donor fragment and the recombination density of the chromosome. For the setting of the corrected significance judgment threshold, if the number of other fragments N is small (such as N≤20), it is recommended to preferentially use the Bonferroni method, which has a simple correction process and conservative results; if the number of other fragments is large (such as 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 repeatability and transparency of the overall system, it is recommended to output the number of simulations, the actual frequency difference value, the p-value calculation method, the correction threshold, and the final judgment logic at the same time as the output of the significance evaluation results, for breeders to trace and interpret the results.

[0128] In some preferred embodiments, the application is implemented as follows. Assuming that, for a certain chromosome segment to be evaluated, the actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency is calculated by the foregoing method, and the frequency difference reference distribution of the segment is constructed based on random reassignment. According to the actual frequency difference value and the frequency difference reference distribution, an initial statistical significance indication value can be determined, for example, by obtaining a p-value through the proportion of simulated frequency difference values in the statistical reference distribution that are greater than or equal to the actual frequency difference value. Assuming that the number of other chromosome segments to be evaluated is N, and the preset overall false positive control parameter is a. The Bonferroni correction method can be used to calculate the corrected significance judgment threshold, which can be a / N. Alternatively, the minimum value based simulation distribution method can be used, that is, for each random reassignment simulation, the simulated initial statistical significance indication values of all other chromosome segments are calculated, and the minimum value among them is recorded. After repeating multiple simulations, arrange the set of these minimum values in ascending order, and take the (simulation total number * a) value as the corrected significance judgment threshold. Then, compare the initial statistical significance indication value of the chromosome segment with the calculated corrected significance judgment threshold. If the initial statistical significance indication value is less than or equal to the corrected significance judgment threshold, it is determined that the segment has statistical significance; otherwise, it is determined that it does not have statistical significance. The judgment result is taken as the difference significance evaluation result of the chromosome segment.

[0129] Optionally, in combination with Figure 7 As shown, the step of A1 of determining the initial statistical significance indication value according to the actual frequency difference value and the frequency difference reference distribution includes:

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

[0131] A12, calculating the proportion of the total number in the total simulation number in the frequency difference reference distribution;

[0132] A13, taking the proportion as the initial statistical significance indication value of the other chromosome segment.

[0133] The scheme of the present application calculates the proportion of the total number of simulated frequency difference values greater than or equal to the actual frequency difference value in the total number of simulated times in the frequency difference reference distribution, and takes the proportion as the initial statistical significance indication value of the other chromosomal segment. This process utilizes the frequency difference reference distribution, which is constructed based on the simulated frequency difference values calculated from a number of simulated state data sets generated by random reassignment processing on the merged state data set. The actual frequency difference value is calculated according to the first co-occurrence frequency and the second co-occurrence frequency. By calculating the proportion of the simulated distribution greater than or equal to the actual observed value, the probability of equal to or greater than the actual difference under random conditions is essentially calculated. This probability value reflects the statistical significance of the actual difference: the smaller the probability, the less likely the difference is caused by random factors, and the more significant it is. Taking this probability value as the initial statistical significance indication value provides an accurate quantitative basis for subsequent comparison and judgment based on the indication value and the corrected significance judgment threshold. This method avoids subjective judgment, utilizes the reference distribution constructed from large-scale simulation data, and can objectively and accurately evaluate the statistical significance of the actual frequency difference, thereby improving the reliability of identifying candidate chromosomal regions associated with new traits.

[0134] In some preferred embodiments, the specific implementation is as follows: assuming that for a certain specific other chromosomal segment, the calculated actual frequency difference value is 0.3. The frequency difference reference distribution of this segment has been constructed through random reassignment processing and simulation calculation, which contains 10,000 simulated frequency difference values obtained by simulation. Now the initial statistical significance indication value of the actual frequency difference value 0.3 needs to be determined. First, count how many of the 10,000 simulated frequency difference values are greater than or equal to 0.3. Assuming that the statistical result shows that there are 50 simulated frequency difference values greater than or equal to 0.3. Then, calculate the proportion of this total number 50 in the total number of simulations 10,000, i.e. 50 / 10,000 = 0.005. Finally, take the calculated proportion 0.005 as the initial statistical significance indication value of the other chromosomal segment. This indication value 0.005 reflects the probability of equal to or greater than 0.3 frequency difference under random conditions.

[0135] Optionally, in combination with Figure 8 As shown in FIG. 2, A2, based on the number of other chromosomal segments and the preset overall false judgment control parameter, the step of calculating the corrected significance judgment threshold value includes:

[0136] A21, based on the frequency difference reference distribution and the simulated state data set, calculate the simulated initial statistical significance indication value of the other chromosomal segment and record the minimum value;

[0137] A22, aggregate all the minimum values in the simulation to form a minimum value set;

[0138] A23, based on the minimum value set and a preset overall false rejection control parameter, outputs a preset specified judgment threshold value as a corrected significance judgment threshold value.

[0139] wherein the frequency difference reference distribution refers to a distribution constructed by simulated frequency difference values calculated from a plurality of simulated state data sets generated by randomly reassigning the merged state data set, and the purpose is to provide a distribution range and probability of the frequency difference value that may occur under the zero hypothesis (i.e. the two groups of single plants have no difference in the presence state of other chromosome fragments); the simulated state data set refers to a data set for simulating the presence state distribution of the two groups of single plants under the zero hypothesis, which is obtained by randomly reassigning the merged state data set, and the purpose is to provide basic data for constructing the frequency difference reference distribution; the other chromosome fragments refer to the chromosome fragments derived from the donor parent identified in the genome of the first group of single plants, excluding the donor fragment containing the known target gene and the adjacent chromosome region of the donor fragment, and the purpose is to focus on analyzing the donor fragment that may be associated with the new trait and has nothing to do with the known target gene and the adjacent region; the simulated initial statistical significance indicator value refers to the initial statistical significance indicator value calculated for the other chromosome fragments under the simulated state data set, and the purpose is to simulate the significance level that can be obtained by statistical test under the condition of no real difference; the minimum value refers to the minimum value of the simulated initial statistical significance indicator value of all other chromosome fragments in each simulation, and the purpose is to capture the minimum p value (or equivalent indicator) that may occur under the multiple test background even if the zero hypothesis is true; the minimum value set refers to a set formed by summarizing all the minimum values recorded in the simulation, and the purpose is to provide an empirical distribution of the minimum significance indicator value under the conditions of multiple testing and zero hypothesis; the preset overall false rejection control parameter refers to a parameter preset by the user or the system for controlling the probability of overall false rejection (e.g. at least one false rejection of the zero hypothesis) when multiple hypothesis testing is performed, which can be a numerical value, such as 0.05, and the purpose is to control the overall false judgment rate when multiple statistical tests are performed; the preset specification refers to the rules or algorithms for determining the judgment threshold value according to the minimum value set and the preset overall false rejection control parameter, which can adopt different statistical correction methods corresponding to the rules, such as Bonferroni correction, Sidak correction or correction method based on resampling (such as permutation test), and the purpose is to provide a basis for determining the corrected significance judgment threshold value; the judgment threshold value refers to a numerical value calculated according to the preset specification, which is used for comparison with the initial statistical significance indicator value to judge the statistical significance, and the purpose is to provide a judgment standard after multiple test correction; the corrected significance judgment threshold value refers to the significance judgment threshold value after multiple hypothesis test correction, and the purpose is to reduce the false rejection probability caused by multiple testing and improve the accuracy of judgment.

[0140] In some preferred embodiments, the application is implemented as follows: suppose that the frequency difference reference distribution for a plurality of other chromosome segments and the simulated state data set used to construct the distribution have been obtained. For each simulated state data set, the respective simulated initial statistical significance indicator value can be calculated for all other chromosome segments contained therein according to the method of determining the initial statistical significance indicator value (e.g., the proportion of the total number of simulated frequency difference values that are greater than or equal to the simulated frequency difference value in the total number of simulations). Then, the smallest one of these simulated initial statistical significance indicator values is found and recorded. This process is repeated a plurality of times (e.g., 1000 simulations), and the smallest simulated initial statistical significance indicator value among all segments is recorded each time. The 1000 recorded smallest values are then collected to form a set of minimum values. Suppose that the preset overall false positive control parameter is set to 0.05, and the preset provision is to use the quantile-based method to determine the threshold value. Then, the set of minimum values can be sorted to find the value at the 5th percentile (i.e., the proportion of the total number of minimum values that are less than or equal to this value is 0.05), and this value is taken as the corrected significance judgment threshold value. Finally, the initial statistical significance indicator value actually calculated for the other chromosome segment is compared with the corrected significance judgment threshold value, and if the initial statistical significance indicator value is less than or equal to the corrected significance judgment threshold value, it is considered that the frequency difference of the segment has statistical significance.

[0141] Optionally, in combination with Figure 9 As shown in FIG. 23, the step of outputting the judgment threshold value according to the preset provision in A23 includes:

[0142] A231, taking the preset overall false positive control parameter as the target proportion;

[0143] A232, when the proportion of the minimum values in the set of minimum values that are less than or equal to the preset threshold value is equal to the target proportion, taking the preset threshold value as the judgment threshold value.

[0144] The preset overall false judgment control parameter refers to a probability value preset for controlling the overall error rate when performing multiple statistical tests, which can be realized by using a parameter value for controlling the family-wise error rate (FWER) or the false discovery rate (FDR), and the purpose is to limit the probability of at least one false positive result or the proportion of expected false positive results in all tested chromosome fragments. The target proportion refers to a specific value that the proportion of minimum values in the minimum value set that are less than or equal to the threshold value reaches in determining the threshold value, and the specific value is equal to the preset overall false judgment control parameter. The minimum value set refers to a set composed of the minimum values of the simulation initial statistical significance indicators of all other chromosome fragments in each simulation obtained through multiple random simulations, which reflects the distribution of the most extreme (most significant) statistical indicators that may appear under random distribution. The preset threshold value refers to a temporary or to-be-tested value used for comparison with the elements in the minimum value set in the process of determining the final judgment threshold value, which can be determined by iteration or search between 0 and 1.

[0145] The scheme of the present application sets the preset overall false positive control parameter as a target proportion, and based on the minimum value set, finds a preset threshold, so that the proportion of the minimum values in the minimum value set that are less than or equal to the preset threshold is exactly equal to the target proportion, thereby determining the final judgment threshold. This process is essentially using the minimum p-value method (or its variants) based on permutation test to determine the significance threshold after multiple test correction. The distribution of the minimum value set reflects the distribution of the most significant statistical results that can be observed through multiple simulations under the condition that the null hypothesis (i.e., other chromosome fragments are not associated with the newly born trait) is true. By finding the quantile corresponding to the target proportion (i.e., the preset overall false positive control parameter) in this distribution, the quantile can be used as the judgment threshold. This means that if the actual calculated initial statistical significance indicator value of a certain other chromosome fragment 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 preset overall false positive control parameter, so the null hypothesis can be rejected, and it is considered that the fragment is statistically associated with the newly born trait. This method makes full use of the frequency difference reference distribution constructed from simulated data, and can effectively control the overall probability of false positives when multiple chromosome fragments are tested simultaneously, thereby improving the accuracy and reliability of identifying candidate chromosome regions that are truly associated with the newly born trait. The scheme is closely integrated with the process of constructing the frequency difference reference distribution, calculating the initial statistical significance indicator value, and calculating the corrected significance judgment threshold in the previous step, and together constitutes a complete and statistically rigorous candidate region identification process, providing reliable basic data for subsequent inclusion of the newly born trait as a proxy target into electronic simulation.

[0146] In some preferred embodiments, specifically, assuming the preset overall false alarm control parameter setting is 0.05, i.e., the target ratio is 0.05. First, an initial preset threshold can be set, for example, 0.01. Then, it is counted how many minimum values in the minimum value set are less than or equal to 0.01, and the ratio of this number to the total number of minimum values in the minimum value set is calculated. If the ratio is less than 0.05, it means that the current preset threshold is too small and needs to be increased; if the ratio is greater than 0.05, it means that the preset threshold is too large and needs to be reduced. The bisection method or the step-by-step adjustment method can be used to iteratively update the preset threshold. For example, if the ratio is less than 0.05, the preset threshold can be increased to 0.02, and the ratio is recalculated; if the ratio is greater than 0.05, the preset threshold can be reduced to 0.005, and the ratio is recalculated. Repeat this process until a preset threshold is found, which makes the proportion of minimum values in the minimum value set that are less than or equal to the threshold very close to or equal to the target ratio 0.05. Once such a threshold is found, it is determined as the final judgment threshold. For example, after several iterations, it is found that when the preset threshold is 0.008, the proportion of minimum values in the minimum value set that are less than or equal to 0.008 is exactly 0.05, then 0.008 is determined as the judgment threshold.

[0147] A corn molecular marker assisted backcross breeding electronic simulation system for performing corn molecular marker assisted backcross breeding electronic simulation, comprising Figure 10 as shown, comprising:

[0148] The first group of single gene data acquisition module is used for collecting whole genome molecular marker data of the first group of single plants carrying known target genes showing new traits;

[0149] The donor and recipient gene data acquisition module is used for collecting whole genome molecular marker data of the donor parent of the known target gene; collecting whole genome molecular marker data of the recipient parent of the known target gene;

[0150] The candidate chromosome region inference module is used for inferring one or more candidate chromosome regions based on all whole genome molecular marker data by analyzing the chromosome fragments commonly inherited from the donor parent in the genome of the first group of single plants, and / or by comparing the genotype differences between the first group of single plants and the preset control group;

[0151] The proxy target selection module is used for selecting a representative molecular marker from the candidate chromosome region as a proxy target;

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

[0153] The first group of single plant gene data acquisition module refers to a unit for acquiring whole genome molecular marker data of the first group of single plants showing new traits, which can be realized by genotyping equipment, data interface, and data storage unit. The donor and recipient gene data acquisition module refers to a unit for acquiring whole genome molecular marker data of the donor parent and recipient parent of the known target gene, which can be realized by genotyping equipment, data interface, and data storage unit. The candidate chromosome region inference module refers to a unit for inferring one or more candidate chromosome regions based on all whole genome molecular marker data, which can be realized by a data processing unit, an analysis algorithm, and a storage unit. The proxy target selection module refers to a unit for selecting representative molecular markers from the candidate chromosome region as proxy targets, which can be realized by a data processing unit, a selection algorithm, and a storage unit. The electronic simulation output module refers to a unit for performing electronic simulation of backcross breeding with the proxy targets and the whole genome molecular marker data of the first group of single plants as selection targets, and outputting the electronic simulation results, which can be realized by a simulation calculation unit, a result generation unit, and an output interface.

[0154] The scheme of the present application realizes the complete process of electronic simulation of maize molecular marker assisted backcross breeding by setting multiple functional modules. First, the first group of single plant gene data acquisition module and the donor and recipient gene data acquisition module are responsible for acquiring the basic genomic information required for simulation, including the molecular marker data of the specific single plants showing new traits and the backcross parents. After these collected data are gathered, the candidate chromosome region inference module performs in-depth analysis. This module identifies one or more chromosome regions that may be associated with new traits by comparing the genotype differences between the specific single plants and the parents or the control group, or analyzing the co-occurrence patterns of genetic fragments. Subsequently, the proxy target selection module intervenes, and according to certain standards, selects representative molecular markers from the inferred candidate regions, and determines these markers as proxy targets for subsequent simulation. Finally, the electronic simulation output module receives these proxy targets and the original first group of single plant gene data as selection criteria in the simulation process, performs electronic simulation calculation of backcross breeding, and generates simulation results. The entire system converts the original genetic data into simulation results that can be used to guide breeding practice through sequential execution and data transfer between modules, thereby solving the problem of lacking a specific implementation system for the execution method.

[0155] In some preferred embodiments, the electronic simulation system for maize marker-assisted backcross breeding of the present application can be embodied as a software system running on one or more computers. The first set of individual plant genetic data collection module and the donor and recipient genetic data collection module can be manifested 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 proxy target selection module can be composed of a series of pre-set bioinformatics analysis algorithms and statistical models, which are executed on the processing unit of the computer to calculate and analyze the imported genetic data, identify candidate regions and screen proxy markers. The electronic simulation output module can include a genetic simulation engine, which iteratively calculates genotype changes and trait performance during multiple generations of backcross breeding according to the breeding scheme set by the user, using the determined proxy targets and individual plant genetic data, and presents the final simulation results to the user in the form of a graphical interface or a report file.

[0156] Through the above technical solutions, an electronic simulation system for maize marker-assisted backcross breeding is provided, which integrates data collection, candidate region inference, proxy target selection, and electronic simulation output function modules to provide a platform capable of performing electronic simulation of maize marker-assisted backcross breeding. This enables breeders to input genetic data collected during actual breeding, especially individual plant data related to newly observed traits, into the system for processing and analysis. The system can automatically infer candidate chromosome regions related to newly observed traits and select representative molecular markers as proxy targets, which are then included in subsequent backcross breeding electronic simulation along with the original breeding targets. This solves the problem of being unable to actually perform simulation by only using methods, provides a specific tool to complete the electronic simulation process, especially for handling newly observed traits with unknown genetic basis, and provides breeders with the ability to perform simulation and decision support based on actual data, thereby enabling more effective utilization of beneficial variations occurring during breeding and optimization of breeding strategies.

[0157] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for electronic simulation of maize marker assisted backcross breeding, characterized in that, The method comprises the following steps: Collecting whole genome molecular marker data of a first group of single plants carrying a known target gene and exhibiting a newly generated trait; Collecting whole genome molecular marker data of a donor parent of the known target gene and collecting whole genome molecular marker data of a recipient parent of the known target gene; Based on all the whole genome molecular marker data, one or more candidate chromosome regions are inferred by analyzing chromosome fragments commonly inherited from the donor parent in the genomes of the first group of single plants and / or by comparing the genotypes of the first group of single plants with a preset control group; The step of inferring one or more candidate chromosome regions by analyzing chromosome fragments commonly inherited from the donor parent in the genomes of the first group of single plants comprises the following steps: based on the whole genome molecular marker data of the first group of single plants, determining the donor fragment containing the known target gene and the adjacent chromosome region of the donor fragment in the genome of each single plant in the first group of single plants; after excluding the donor fragment and the adjacent chromosome region in the genomes of the first group of single plants, counting the co-occurrence frequency of other chromosome fragments derived from the donor parent; and taking the other chromosome fragments derived from the donor parent with a co-occurrence frequency higher than a preset threshold as one or more candidate chromosome regions; Selecting a representative molecular marker from the candidate chromosome region as a proxy target; the step of selecting a representative molecular marker from the candidate chromosome region as a proxy target comprises the following steps: obtaining association data of each candidate molecular marker in all the candidate chromosome regions with the newly generated trait; obtaining genetic linkage analysis parameters of the candidate molecular marker and the molecular marker of the known target gene; obtaining breeding application convenience evaluation information of the candidate molecular marker; generating evaluation results of each candidate molecular marker according to the association data, the genetic linkage analysis parameters and the breeding application convenience evaluation information; and selecting one or a group of molecular markers with evaluation results meeting preset conditions from all the candidate molecular markers as proxy targets; Taking the proxy target and the whole genome molecular marker data of the first group of single plants as selection targets, performing electronic simulation of subsequent backcross breeding, and outputting the results of the electronic simulation.

2. A method for electronic simulation of marker assisted backcross breeding in maize as described in claim 1, wherein, The step of counting the co-occurrence frequency of other chromosome fragments derived from the donor parent after excluding the donor fragment and the adjacent chromosome region in the genomes of the first group of single plants comprises the following steps: For each single plant in the first group of single plants, identifying other chromosome fragments derived from the donor parent outside the donor fragment and the adjacent chromosome region based on the donor fragment and the adjacent chromosome region; Counting the first co-occurrence frequency of the other chromosome fragments; Obtaining the second co-occurrence frequency of the chromosome fragments corresponding to the other chromosome fragments in a second group of single plants of the preset control group; Based on the first co-occurrence frequency and the second co-occurrence frequency, evaluating the difference significance and generating a difference significance evaluation result; and The first co-occurrence frequency satisfying the preset condition is taken as the co-occurrence frequency of the first group of single plants.

3. A method for electronic simulation of marker assisted backcross breeding in maize as described in claim 2, wherein, The step of evaluating the difference significance based on the first co-occurrence frequency and the second co-occurrence frequency to generate a difference significance evaluation result comprises: For the first group of single plants and the second group of single plants, obtain the presence state data of each of the other chromosome fragments; Merge all the presence state data of the two groups of single plants to generate a merged state data set; Randomly reassign the merged state data set to generate a plurality of pairs of simulated state data sets; each pair of simulated state data sets comprises a simulated first group state data set and a simulated second group state data set; According to the simulated state data set, calculate the simulated frequency difference value between the simulated first co-occurrence frequency and the simulated second co-occurrence frequency; Based on the simulated frequency difference value, construct a frequency difference reference distribution of the other chromosome fragments; Calculate the actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency, compare the actual frequency difference value with the frequency difference reference distribution, and determine the statistical significance as the difference significance evaluation result.

4. A computer-implemented method for electronic simulation of marker assisted backcross breeding of Zea mays as defined in claim 3, wherein, The step of calculating the actual frequency difference value between the first co-occurrence frequency and the second co-occurrence frequency, comparing the actual frequency difference value with the frequency difference reference distribution, and determining the statistical significance as the difference significance evaluation result comprises: According to the actual frequency difference value and the frequency difference reference distribution, determine an initial statistical significance indication value; Based on the number of the other chromosome fragments and a preset overall false judgment control parameter, calculate a corrected significance judgment threshold value; Compare the initial statistical significance indication value with the corrected significance judgment threshold value to determine whether there is statistical significance, and take the determination result as the difference significance evaluation result.

5. A computer-implemented method for electronic simulation of marker assisted backcrossing in maize as defined in claim 4, wherein, The step of determining an initial statistical significance indication value according to the actual frequency difference value and the frequency difference reference distribution comprises: Statistically count the total number of simulated frequency difference values in the frequency difference reference distribution that are greater than or equal to the actual frequency difference value; Calculate the proportion of the total number in the total number of simulations in the frequency difference reference distribution; Take the proportion as the initial statistical significance indication value of the other chromosome fragments.

6. A computer-implemented method for electronic simulation of marker assisted backcrossing in maize as defined in claim 4, wherein, The step of calculating a corrected significance judgment threshold value based on the number of the other chromosome fragments and a preset overall false judgment control parameter comprises: Based on the frequency difference reference distribution and the simulated state data set, calculate the simulated initial statistical significance indication value of the other chromosome fragments and record the minimum value; Summarize the minimum values in all simulations to form a minimum value set; Based on the minimum value set and the preset overall false judgment control parameter, output a predetermined specified judgment threshold value as the corrected significance judgment threshold value.

7. A computer-implemented method for electronic simulation of a molecular marker assisted backcross breeding of Zea mays as defined in claim 6, wherein, The step of outputting a predetermined specified judgment threshold value comprises: Take the preset overall false judgment control parameter as the target proportion; When the proportion of minimum values in the minimum value set that are less than or equal to a preset threshold value is equal to a target proportion, the preset threshold value is taken as a judgment threshold value.

8. 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, The method comprises the following steps: A first group of single-plant gene data acquisition modules are used to acquire whole genome molecular marker data of a first group of single plants carrying a known target gene that exhibits a new trait; A donor and recipient gene data acquisition module is used to acquire whole genome molecular marker data of a donor parent of the known target gene; and acquire whole genome molecular marker data of a recipient parent of the known target gene; A 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 fragments in the genomes of the first group of single plants that are inherited from the donor parent in common, and / or by comparing genotype differences between the first group of single plants and a preset control group; The step of inferring one or more candidate chromosome regions by analyzing chromosome fragments in the genomes of the first group of single plants that are inherited from the donor parent in common comprises the following steps: determining donor fragments containing the known target gene and adjacent chromosome regions of the donor fragments in the genomes of each single plant in the first group of single plants based on the whole genome molecular marker data of the first group of single plants; and after excluding the donor fragments and the adjacent chromosome regions from the genomes of the first group of single plants, counting co-occurrence frequencies of other chromosome fragments derived from the donor parent. A proxy target selection module is used to select representative molecular markers from the candidate chromosome regions as proxy targets; the step of selecting representative molecular markers from the candidate chromosome regions as proxy targets comprises the following steps: obtaining association data of each candidate molecular marker in all the candidate chromosome regions with the new trait; obtaining genetic linkage analysis parameters of the candidate molecular markers and molecular markers of the known target gene; obtaining breeding application convenience evaluation information of the candidate molecular markers; generating evaluation results of each candidate molecular marker according to the association data, the genetic linkage analysis parameters and the breeding application convenience evaluation information; and selecting one or a group of molecular markers whose evaluation results meet preset conditions from all the candidate molecular markers as proxy targets. An electronic simulation output module is used to take the proxy targets and the whole genome molecular marker data of the first group of single plants as selection targets to perform electronic simulation of subsequent backcross breeding, and output results of the electronic simulation.

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