Peanut high-oil germplasm resource numerical prediction data model and construction method and application thereof

Abstract

The invention discloses a peanut high-oil germplasm resource numerical prediction data model and a construction method and application thereof, and relates to the cross technical field of bioinformatics and peanut breeding. Obtaining peanut germplasm resources and / or peanut germplasm resource combinations with high genetic potential in high oil content characters on the basis of a pre-constructed peanut high oil germplasm resource numerical prediction data model, and further breeding high oil content improved varieties of peanuts. According to the method, early prediction of the oil content character of the peanuts can be realized, and excellent peanut germplasm with high oil content can be screened in the early planting stage of the peanuts, so that the peanut breeding period is shortened, the peanut breeding intensity is improved, and the breeding process of the high-oil-content germplasm of the peanuts is accelerated. The method has dual important values of scientific research and application.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics and peanut breeding intersection technology, and in particular to a peanut high-oil germplasm resource numerical prediction data model, a construction method thereof, and its application in the breeding of high-oil peanut varieties. Background Art

[0002] Peanut (Arachis hypogaea L.), as an important source of vegetable oil, is an important oil crop in the world. China is the world's largest peanut producer and consumer. 55% of the total peanut production is used for oil extraction. Peanut oil is the second largest source of domestic vegetable oil after rapeseed, which is of great significance to the security of edible oil supply in my country. However, my country's peanut oil production is far from meeting market demand, and the import volume of peanut oil has increased year by year. In order to ensure the security of edible oil supply, increasing peanut oil production by increasing the oil content of peanuts is the primary goal of peanut breeders.

[0003] Peanut oil content is a complex quantitative genetic trait that is controlled by multiple genes, multiple levels, and multiple pathways. It has an extremely complex genetic regulation mechanism. It is difficult for traditional breeding techniques to achieve precise improvement of such traits. With the development of molecular biology and genomics, molecular marker-assisted selection (MAS) technology and association analysis methods have overcome the limitations of traditional breeding techniques and can significantly improve the positioning accuracy of genes related to quantitative traits. In the post-genomic era, the peanut genome sequencing and assembly have been completed (Bertioli et al. 2019; Zhuang et al. 2019; Chen et al. 2019), and the cost of high-throughput sequencing has dropped significantly, laying the foundation for in-depth revelation of the genetic mechanism of peanut quantitative traits and providing important genetic resources for peanut genetic improvement and breeding. However, peanut is an allotetraploid species with high homology between subgenomes and low heterozygosity. The two subgenomes show asymmetric evolution during evolution. Gene editing technology still has certain challenges in genetic improvement of related traits. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a numerical prediction data model for high-oil peanut germplasm resources and a construction method and application thereof.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] The method for breeding high-oil-content peanut varieties is based on peanut germplasm resources and / or peanut germplasm resource combinations with high genetic potential for high-oil-content traits, and is used to select and breed high-oil-content peanut varieties.

[0007] As a preferred technical solution of the present invention, the peanut germplasm resource combination specifically includes but is not limited to the following germplasm resources: Ji 0607-17; Jihua No. 9; Ji 0212-4; Zhonghua No. 10; Jihua No. 6; Huayu No. 16; Tanghua No. 10; Jiyou No. 8; Jihua No. 8.

[0008] As a preferred technical solution of the present invention, the peanut germplasm resource combination specifically includes: any germplasm or any combination of germplasms of the peanut germplasm resource combination.

[0009] As a preferred technical solution of the present invention, the genetic potential of peanut for the high oil content trait is specifically constructed by using molecular marker information covering the entire genome to model the genetic breeding value of peanut individuals for the oil content trait and to quantify and digitize the genetic potential defined by the model evaluation value.

[0010] As a preferred technical solution of the present invention, high-oil-content peanut varieties are bred based on the peanut germplasm resources and / or peanut germplasm resource combinations. The specific breeding method can adopt the existing traditional breeding methods and / or molecular breeding methods, and the breeding is carried out according to the operating procedures of each breeding method.

[0011] The numerical prediction data model of the oil content trait of peanut germplasm resources contains the following interactive data modules:

[0012] A. Direct-indirect data interaction module, which uses direct and indirect data paths to quantify the genetic potential of peanut oil content and allows coordinated cross-data verification of the direct and indirect data paths;

[0013] Data object set module; this module takes the optimal SNP genotype site set as its data content.

[0014] As a preferred technical solution of the present invention, the direct-indirect data interaction module has two data paths as its data content, specifically including:

[0015] A-1, direct data path, this data path estimates the relationship between individuals by constructing a genome relationship matrix, and uses this matrix to predict the genetic potential of peanut oil content of individuals; this data path directly uses genotype and phenotype data to estimate breeding values ​​without pre-estimating the effect of each marker, puts phenotyped and non-phenotyped individuals in the same model and simultaneously measures their genetic potential of peanut oil content, and performs a self-verification of the accuracy of the genetic potential value of peanut oil content;

[0016] A-2, indirect data path, this data path estimates the effect of each marker in the reference population and combines these effects with the genotype information of the prediction population to obtain the estimated genetic potential value of peanut oil content of the individuals in the prediction population. This data path solves the collinearity problem in the marker effect estimation by constructing a loss function and adding a regularization term to the loss function, and performs a generalized and enhanced peanut oil content genetic potential value measurement;

[0017] As a preferred technical solution of the present invention, the optimal SNP genotype site set in the data object set module is specifically constructed as a peanut SNP genotype site set with a number of 18k.

[0018] The method for constructing a numerical prediction data model for the oil content trait of peanut germplasm resources first performs genome resequencing and genotyping on peanut germplasm populations to obtain a high-quality SNP site set, then performs genome-wide association analysis on the peanut oil content trait based on the obtained high-quality SNP site set and in combination with the oil content trait phenotypic data of the peanut germplasm population to obtain different SNP genotype site sets, then uses a certain proportion of the peanut germplasm population as a training population, and uses the oil content trait phenotypic data of several peanut germplasms, several genome-wide selection statistical models, and several SNP genotype site data sets in the training population to collaboratively establish a numerical prediction data model for the oil content trait of peanut germplasm resources.

[0019] As a preferred technical solution of the present invention, the method specifically comprises the following steps:

[0020] ① Resequencing and genotyping of peanut genome to obtain high-quality SNP loci;

[0021] ②Based on the obtained high-quality SNP loci and combined with the peanut oil content phenotypic data, a genome-wide association analysis of the peanut oil content trait was performed to obtain the SNP genotype locus set;

[0022] ③Build an algorithm based on the data obtained in the previous step and perform cross-validation:

[0023] ③-A, construct a crossover algorithm: First, randomly divide the data set into n subsets of roughly equal size, such as setting n=5, and then select one of them as a subset used to evaluate the model performance as a validation set, and use the remaining four as subsets for training the model as training sets, then train the model on the training set and evaluate the model performance on the validation set and record the corresponding evaluation indicators, repeat the above steps several times so that each subset of roughly equal size and as one of the division results has a chance to be used as a validation set for evaluating the model performance, and finally calculate the average of the above several evaluation results and use it as the final performance indicator of the model evaluated in this way;

[0024] ③-B. Based on the above crossover algorithm, 80% of the peanut population was used as the training population; in the training population, the peanut oil content trait phenotypic data, the whole genome selection statistical model and different SNP genotype locus data sets were used to establish a peanut oil content trait data interaction module;

[0025] ④ The peanut oil content trait data interaction module obtained in the previous step is verified and screened, and finally the optimal prediction system for peanut oil content trait consisting of the optimal whole genome selection statistical model, i.e., the optimal data interaction module, and the optimal SNP genotype site set, i.e., the optimal data interaction module, is obtained.

[0026] As a preferred technical solution of the present invention, the optimal data interaction module is constructed as a direct-indirect data interaction module, which performs numerical measurement of the genetic potential of peanut oil content through direct and indirect data paths respectively, and allows coordinated cross-data verification of the direct and indirect data paths.

[0027] As a preferred technical solution of the present invention, the SNP genotype site set is constructed to include: a significantly associated SNP genotype site set obtained by whole genome association analysis, a set of SNP genotype sites ranked in ascending order by P-value in ... Genotype site set, the top 11k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 12k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 13k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 14k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 15k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 16k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 17k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 18k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 19k SNP genotype site set with the highest P-value in genome-wide association analysis, the top 20k SNP genotype site set with the highest P-value in genome-wide association analysis.

[0028] As a preferred technical solution of the present invention, the optimal data object set module uses the optimal SNP genotype site set as data content, and specifically adopts an 18k SNP genotype site set.

[0029] As a preferred technical solution of the present invention, the method for collecting phenotypic data of peanut oil content trait is: after shelling, the full kernels of each strain are selected and ground, and the oil content is determined by Minispec mq-20NMR; the full kernels of each family are divided into three parts, that is, three values ​​are measured, and the average is taken to represent the oil content of the family.

[0030] The use of the numerical prediction data model for the oil content trait of peanut germplasm resources is used to obtain peanut germplasm resources with high genetic potential for high oil content traits.

[0031] The uses of the numerical prediction data model for the oil content trait of peanut germplasm resources include: being used for the breeding of high-oil-content peanut varieties.

[0032] The beneficial effects of the above technical solutions are: in general, the present invention can achieve early prediction of peanut oil content traits, and can screen out excellent peanut germplasm with high oil content in the early stage of peanut planting, thereby shortening the peanut breeding cycle, increasing the intensity of peanut breeding, and accelerating the breeding process of high-oil peanut germplasm. Specifically, the present invention identifies SNP sites significantly associated with peanut oil content traits through whole genome association analysis, and compares the accuracy of whole genome selection prediction by screening different numbers of SNP genotype site data sets and using different statistical models, and establishes an optimal whole genome selection prediction system based on the best linear unbiased prediction model and peanut oil content trait significantly associated sites; wherein the optimal whole genome selection prediction system can quickly and accurately estimate the breeding value of oil content traits in peanut breeding populations, and its prediction accuracy reaches 0.9138. The present invention has both scientific research and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a phenotypic distribution diagram of the oil content trait of the peanut training population in an embodiment of the present invention.

[0034] Figure 2 This is a statistical graph of SNP information of natural peanut population resequencing in an embodiment of the present invention.

[0035] Figure 3 This is a genome-wide association analysis diagram of the peanut oil content trait in an embodiment of the present invention.

[0036] Figure 4 This is a statistical graph of the accuracy of genome-wide selection prediction of peanut oil content traits based on 5 genome-wide selection statistical models and 20 SNP genotype site data sets in an embodiment of the present invention.

[0037] Figure 5 This is a density distribution diagram of the optimal SNP genotype site set 18k on the chromosome in Example 1 of the present invention. DETAILED DESCRIPTION

[0038] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than for limitation, so as to thoroughly understand the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application. It should be understood that when used in the present application specification and the attached claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present application specification and the attached claims refers to any combination of one or more of the items listed in association and all possible combinations, and includes these combinations.

[0039] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when..." or "once" or "in response to determination" or "in response to detection" according to the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to determination" or "once [the described condition or event] is detected" or "in response to detection [the described condition or event]" according to the context. In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference "one embodiment" or "some embodiments" described in the specification of the present application means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Thus, the sentences "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "include", "comprising", "having" and variations thereof all mean "including but not limited to", unless specifically emphasized otherwise.

[0040] The 239 peanut germplasm resources selected in the present invention were all grown at the Di Shang Experimental Station of the Institute of Cereals and Oil Crops of the Hebei Academy of Agricultural and Forestry Sciences. These peanut germplasm resources include the main cultivated varieties and local varieties promoted over the years and have no direct relationship with each other. The experiment adopted a randomized block design, with a row spacing of 40 cm and a row length of 5.6 m, and 31 plants were planted in each strain.

[0041] Example 1

[0042] At harvest, 10 plants were sampled from each plot for indoor testing. The peanut pods were naturally air-dried. After shelling, the seeds of each strain were selected and ground into full kernels. The oil content was uniformly determined using a Minispec mq-20 nuclear magnetic resonance (NMR) instrument. The full kernels (80-100 g) of each family were divided into three parts, that is, three values ​​were measured, and the average represented the oil content of the family. The psych package of R-4.3.2 software was used for phenotypic statistical analysis of peanut oil content traits, including the maximum value, minimum value, mean, median, standard deviation, skewness, kurtosis, and coefficient of variation of the population. The ggplot2 package in R was used to draw the histogram of the oil content density distribution of the peanut population, and the lme4 package was used to calculate the broad heritability of the oil content trait of the peanut population.

[0043] The results are shown in Table 1. The average oil content of the natural peanut population is 51.63%, and the oil content range is 44.19% to 60.04%. There are obvious differences between strains, and there is a large selection space. The oil content of the peanut population basically conforms to the normal distribution (such as Figure 1 ), and its broad-sense heritability was 0.842, indicating that peanut oil content trait is a typical quantitative trait, and its phenotype is mainly regulated by genes.

[0044] Table 1 Statistical analysis of phenotypic data of oil content in peanut training population

[0045] Characteristics Minimum Maximum average value median Standard Deviation Coefficient of variation Kurtosis Skewness Oil content 44.19 60.04 51.63 51.61 2.95 5.71 -0.22 0.18

[0046] Example 2

[0047] 1. Genome resequencing and genotyping

[0048] The Illumina HiSeq6000 high-throughput sequencer was used to perform WGS genome double-end PE150 resequencing of the natural peanut population. The resequencing data was quality controlled by FastQC (www.bioinformatics.babraham.ac.uk / projects / fastqc), and the sequencing data were linked back to the Peanut reference genome (the Peanut reference genome sequence version number is GCA_004170445.1_ASM417044v1) using the default parameters of the BWA tool (bio-bwa.sourceforge.net). The HaplotypeCaller of the GATK software (software.broadinstitute.org / gatk) was then used for SNP identification and InDel detection. The obtained variant result files were then initially filtered by GATK's VariantFiltration based on sequencing depth and quality indicators to screen out false positives and pseudo-variants. Then, PLINK and VCFtools software were used to strictly filter the genotype files. The filtering criteria were set as sequencing depth greater than 5X, completeness greater than 0.8, MAF minimum allele frequency not less than 0.05, missing rate less than 0.2, and conforming to the Hardy-Weinberg law. ANNOVAR software was used to annotate and predict the functions of the selected high-quality SNPs, and finally 8,610,286 high-quality SNP sites were obtained for subsequent genetic analysis.

[0049] 2. Genome-wide association analysis of peanut oil content traits

[0050] Based on the 8610286 high-quality SNP loci obtained above, principal component analysis and kinship analysis were performed on the natural peanut population using PLINK and GCTA software, respectively, to obtain the PCA matrix of the eigenvectors and the Kinship matrix of the kinship coefficients of the population. Combined with the phenotypic data of the peanut oil content trait, the GEMMA software was used to perform a mixed linear model analysis with the population structure as the fixed effect and the kinship as the random effect to obtain the P-value associated with each SNP and the phenotypic trait. The threshold was set by Bonferroni correction, and a total of 823 significantly associated SNP loci were detected. The 823 SNP genotype site sets with significant whole-genome association analysis, the top 2k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 3k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 4k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 5k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 6k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 7k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 8k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 9k SNP genotype site sets with the top P-value in the whole-genome association analysis, the top 10k SNP genotype site sets with the top P-value in the whole-genome association analysis, and the top The top 11k SNP genotype site sets, the top 12k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 13k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 14k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 15k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 16k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 17k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 18k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, the top 19k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, and the top 20k SNP genotype site sets ranked from small to large by P-value in whole-genome association analysis, constitute 20 SNP genotype site sets for model construction and prediction.

[0051] Table 2 SNP loci significantly associated with peanut oil content detected by genome-wide association analysis

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Example 3

[0070] 1. Phenotype and genotype file organization

[0071] Before the whole genome selection prediction, the missing values ​​of the oil content phenotypic and genotypic data of the training population were filled or deleted and the format was sorted. The oil content phenotypic values ​​of 239 peanut germplasm resources were used as the phenotypic data for the whole genome selection prediction of peanut oil content.

[0072] 2. Whole genome selection model parameter setting

[0073] Mainly, the following are constructed: ① direct data path, which estimates the relationship between individuals by constructing a genomic relationship matrix, and uses this matrix to predict the genetic potential of peanut oil content of individuals; this data path directly uses genotype and phenotype data to estimate breeding values ​​without pre-estimating the effect of each marker, puts phenotyped and non-phenotyped individuals in the same model and simultaneously calculates their genetic potential of peanut oil content, and performs a self-check on the accuracy of the genetic potential value of peanut oil content; ② indirect data path, which estimates the effect of each marker in the reference population and accumulates these effects in combination with the genotype information of the prediction population to obtain the estimated genetic potential value of peanut oil content of the individuals in the prediction population. This data path solves the collinearity problem in the marker effect estimation by constructing a loss function and adding a regularization term to the loss function, and performs a generalized and enhanced calculation of the genetic potential value of peanut oil content. Specifically, five different whole-genome selection statistical models are used, including genome best linear unbiased prediction GBLUP, ridge regression best linear unbiased prediction rrBLUP, Bayesian model BayesA, Bayesian least absolute shrinkage and selection operator BL (Bayesian LASSO), and support vector machine classification SVC (Support Vector Classification), which are mainly divided into three categories: BLUP series, Bayesian series, and machine learning series.

[0074] For each genomic selection statistical model, 20 different SNP genotype site data sets were set to evaluate their impact on the accuracy of whole-genome selection prediction. These 20 different SNP genotype site data sets were from the whole genome

[0075] A data set of 823 significantly associated SNP loci obtained by group selection association analysis, as well as the top 2k, 3k, 4k, 5k, 6k, 7k, 8k, 9k, 10k, 11k, 12k, 13k, 14k, 15k, 16k, 17k, 18k, 19k, and 20k SNP genotype loci data sets selected according to the P-value sorting from small to large.

[0076] 3. Determine the optimal model for whole genome selection of peanut oil content traits

[0077] 3-A, Crossover algorithm: First, randomly divide the data set into n subsets of roughly equal size, such as setting n=5, and then select one of them as a subset used to evaluate the performance of the model as the validation set, and use the remaining four as subsets for training the model as the training set, then train the model on the training set and evaluate the model performance on the validation set and record the corresponding evaluation indicators, repeat the above steps several times so that each subset of roughly equal size and as one of the division results has a chance to be used as a validation set for evaluating the performance of the model, and finally calculate the average of the above several evaluation results and use it as the final performance indicator of the model evaluated in this way;

[0078] 3-B. Based on the above crossover algorithm, 80% of the natural population of peanuts was used as the training population, and the remaining 20% ​​was used as the prediction population. In the training population, a whole genome selection prediction system for peanut oil content traits was established using phenotypic data, 5 whole genome selection statistical models, and 20 different SNP genotype site data sets. The genotype data and prediction model were then used to estimate the breeding value of the peanut oil content trait in the validation population, and the Pearson correlation coefficient between the predicted population breeding value and the actual observed value was used as an indicator to evaluate the accuracy of the whole genome selection prediction. Finally, the optimal whole genome selection statistical model and the optimal SNP genotype site data set were determined through this evaluation standard. The optimal whole genome selection statistical model and the optimal SNP genotype site data set constitute the optimal prediction system for the whole genome selection of peanut oil content traits.

[0079] Five different genome-wide selection statistical models and 20 different SNP genotype loci were used to conduct genome-wide selection prediction analysis on peanut oil content.

[0080] The results are as follows Figure 4As shown in the figure, the two models of genomic best linear unbiased prediction GBLUP and ridge regression best linear unbiased prediction rrBLUP showed significantly higher prediction accuracy (the highest prediction accuracy r = 0.9138), which is significantly superior to the Bayesian series model and the machine learning series model (the highest prediction accuracy r = 0.8961), indicating that the prediction model based on artificial intelligence has improved the prediction accuracy of peanut whole genome selection. In the whole genome selection prediction model, using the trait significant correlation loci obtained by whole genome association analysis as the SNP genotype data set can greatly improve the prediction accuracy of whole genome selection. From 823 to 20k markers, the two best linear unbiased prediction models of GBLUP and rrBLUP showed the best GS prediction model characteristics, and their prediction accuracy gradually increased from 0.8012 to 0.9138. The other three prediction models are ranked from high to low in terms of prediction accuracy: BayesA (0.8079-0.8961), BL (Bayesian LASSO, 0.8089-0.8934), SVC (Support Vector Classification, 0.7377-0.8327). The accuracy of the BLUP series of models is higher than that of the Bayesian series of models, and the accuracy of the Bayesian series of models is higher than that of the machine learning series of models SVC (Support Vector Classification). After the number of SNPs reaches 18k, the prediction accuracy of each model tends to be flat, and the prediction accuracy of the GBLUP and rrBLUP models is the highest, reaching 0.9138. The distribution of 18k SNP sites on peanut chromosomes is shown in the figure. Figure 5 As shown, it indicates that the distribution of these SNPs across the genome is relatively uniform.

[0081] Embodiment 4:

[0082] According to the optimal prediction system of the whole genome selection of peanut oil content trait, the breeding value GEBV (Genomic Estimated Breeding Value) of the peanut breeding population oil content trait was estimated using the GBLUP, rrBLUP models and a SNP genotype locus set of 18k. In order to screen high-oil content peanut germplasm, the breeding values ​​of the peanut population were arranged in descending order, and the top 10 peanut germplasms were selected as excellent germplasm materials for subsequent peanut high-oil content breeding and molecular mechanism research, as shown in the table below.

[0083] Table 3 The top ten high oil content germplasms in breeding value in peanut breeding population

[0084] serial number Actual Observation Value Breeding value (GBLUP) Breeding value (rrBLUP) JI0607-17 55.0146666666667 3.8604 55.4754 Jihua No.9 55.2412 3.7895 55.3046 JI0212-4 54.13246667 3.7948 55.2917 8528-1-1 53.28816667 3.3367 54.8229 Zhonghua No.10 55.4578 3.1406 54.6424 Jihua No.6 55.66396667 2.7886 54.1862 Huayu No.16 50.16643333 1.6277 53.2128 Tanghua No.10 52.3621 1.5411 53.1488 Jiyou No.8 54.47296667 1.4691 52.8717 Jihua No.8 54.77553333 1.1442 52.7623

[0085] Based on the peanut germplasm resources with high genetic potential for the high oil content trait obtained above or their further planting combinations, the existing traditional breeding methods and / or molecular breeding methods are then used, and breeding is carried out according to the operating procedures of each breeding method, so as to select high-oil content peanut varieties.

[0086] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] In the data processing link, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also instruct the relevant hardware to complete it through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiments. . Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memories, read-only memories (ROM), random access memories (RAM), electric carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0088] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for breeding high-oil-content peanut varieties, characterized in that: Based on peanut germplasm resources and / or peanut germplasm resource combinations with high genetic potential for high oil content traits, high-oil content peanut varieties are bred.

2. The method for breeding high-oil-content peanut varieties according to claim 1, characterized in that: The peanut germplasm resource combination specifically includes but is not limited to the following germplasm resources: Ji 0607-17; Jihua No. 9; Ji 0212-4; Zhonghua No. 10; Jihua No. 6; Huayu No. 16; Tanghua No. 10; Jiyou No. 8; Jihua No.

8.

3. The method for breeding high-oil-content peanut varieties according to claim 2, characterized in that: The peanut germplasm resource combination specifically includes: any germplasm or any combination of germplasms of the peanut germplasm resource combination according to claim 2.

4. The method for breeding high-oil-content peanut varieties according to claim 1, characterized in that: The genetic potential of peanut for high oil content trait is specifically constructed by using molecular marker information covering the entire genome to conduct a modeled single numerical evaluation of the genetic breeding value of peanut individuals for oil content trait and to quantify and digitize the genetic potential defined by the model evaluation value.

5. The method for breeding high-oil-content peanut varieties according to claim 1, characterized in that: Based on the peanut germplasm resources and / or peanut germplasm resource combinations, high-oil-content peanut varieties are bred. The specific breeding method can adopt the existing traditional breeding method and / or molecular breeding method, and the breeding is carried out according to the operating procedures of each breeding method.

6. A numerical prediction data model for the oil content trait of peanut germplasm resources, characterized by: The data model contains the following interactive data modules: A. Direct-indirect data interaction module, which uses direct and indirect data paths to quantify the genetic potential of peanut oil content and allows coordinated cross-data verification of the direct and indirect data paths; Data object collection module; This module uses the optimal SNP genotype site set as data content.

7. The numerical prediction data model for the oil content trait of peanut germplasm resources according to claim 6, characterized in that: The direct-indirect data interaction module has two data paths as its data content, specifically including: A-1, direct data path, this data path estimates the relationship between individuals by constructing a genome relationship matrix, and uses this matrix to predict the genetic potential of peanut oil content of individuals; this data path directly uses genotype and phenotype data to estimate breeding values ​​without pre-estimating the effect of each marker, puts phenotyped and non-phenotyped individuals in the same model and simultaneously measures their genetic potential of peanut oil content, and performs a self-verification of the accuracy of the genetic potential value of peanut oil content; A-2. Indirect data path. This data path estimates the effect of each marker in the reference population and accumulates these effects in combination with the genotype information of the prediction population to obtain the estimated genetic potential value of peanut oil content of the individuals in the prediction population. This data path constructs a loss function and adds a regularization term to the loss function to solve the collinearity problem in the marker effect estimation, and performs generalized and enhanced genetic potential value calculation of peanut oil content.

8. The numerical prediction data model for the oil content trait of peanut germplasm resources according to claim 6, characterized in that: The optimal SNP genotype site set in the data object set module is specifically constructed as a peanut SNP genotype site set with a number of 18k.

9. The use of the numerical prediction data model for the oil content trait of peanut germplasm resources is used to obtain peanut germplasm resources with high genetic potential for high oil content traits.

10. The uses of the numerical prediction data model for the oil content of peanut germplasm resources include: Used for the breeding of high-oil content peanut varieties.

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