Whole genome selection method based on interaction of cell nucleus and cytoplasm
By adopting a genome-wide selection method based on the interaction between the nucleus and the cytoplasm in crop breeding, the problem that the existing technology is difficult to accurately estimate the nuclear-plasmid interaction effect is solved, and more accurate breeding prediction and efficiency improvement are achieved.
Patent Information
- Application Number
- CN202510320465.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-18
- Publication Date
- 2025-06-27
AI Technical Summary
The prior art is difficult to accurately estimate the nucleoplasmic interaction effect controlled by micro-effect multigenes, which limits its application in crop breeding.
Using a genome-wide selection method based on the interaction between the nucleus and the cytoplasm, genetic parameters are solved to more accurately predict breeding values by establishing positive and negative cross populations and constructing mixed model equations.
This method can more accurately predict the breeding value of the test population, reduce breeding costs, improve breeding efficiency, and more in line with the actual breeding conditions.
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Figure CN120220795A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of biological breeding, and particularly relates to a genome-wide selection method based on the interaction between the nucleus and cytoplasm. Background Art
[0002] Plants contain three sets of genetic systems, namely nuclear genome, mitochondrial genome and chloroplast genome, among which mitochondrial genome and chloroplast genome are collectively called cytoplasmic genome. Since a large number of important agronomic traits of plants, such as yield, plant height, seed vigor, heading date and fertility, etc., are affected by the interaction between nuclear and cytoplasmic genes, it is of great theoretical and application value to accurately estimate the nuclear-cytoplasmic interaction effect. For example, the reciprocal hybrids of crops such as rice, corn and sorghum show different heterosis strengths, which is caused by the interaction between the nucleus and cytoplasm. At present, some statistical genetics methods have been applied to detect the nuclear-cytoplasmic interaction effect (Han Lide, 2007). However, since the nuclear-cytoplasmic interaction effect is mainly controlled by minor polygenes, traditional statistical models are difficult to accurately estimate such effects, thus slowing down their application in breeding.
[0003] In order to genetically improve quantitative traits controlled by minor polygenes, Meuwissen et al. proposed a revolutionary molecular breeding technology, namely genome-wide selection (GS) in 2001. This technology uses high-density molecular markers covering the whole genome to accurately estimate the genetic locus effects controlling quantitative traits (Meuwissen THE, 2001), so it can greatly improve the selection accuracy of target traits, accelerate the breeding process, increase genetic gain and reduce breeding costs, and is widely applied to animal and plant breeding. However, there is currently no GS calculation method based on the interaction between the nucleus and cytoplasm in crop breeding. Therefore, it is urgent to develop a genome-wide selection method based on the interaction between the nucleus and cytoplasm for nuclear-cytoplasmic interaction breeding. Summary of the Invention
[0004] In order to solve the above-mentioned deficiencies existing in the prior art, the purpose of the present invention is to provide a genome-wide selection method based on the interaction between the nucleus and cytoplasm, so as to provide a genome-wide selection method that meets the actual breeding conditions and can more accurately predict the test population, thereby greatly reducing the breeding cost and improving the breeding efficiency.
[0005] The technical solution for the present invention to solve the above technical problems is as follows: Provide a genome-wide selection method based on the interaction between the nucleus and cytoplasm, including the following steps: (1) Genetic mating design: Establish an orthogonal population and a reciprocal population ; (2)Statistical genetic model establishment: Establish a statistical genetic model for genome-wide selection of the interaction between the nucleus and cytoplasm for phenotype prediction; here, only the additive effect is concerned, assuming The sample size of , The sample size of is , and the number of markers is
[0006] where is the phenotypic value vector of the orthogonal population ; is the phenotypic value vector of the reciprocal cross population ; is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix of the orthogonal population ; is the marker matrix of the reciprocal cross population ; is the marker effect vector of the orthogonal population ; is the marker effect vector of the reciprocal cross population ; is the residual vector of the orthogonal population ; is the residual vector of the reciprocal cross population ; is the identity matrix; (3)Construct a mixed model equations system through the genome-wide selection statistical genetic model in step (2) to solve genetic parameters (nuclear, cytoplasmic, and nuclear-cytoplasmic interaction effects); where the mixed model equations system is as follows:
[0007] where ; ; ; ; ; ; ; ; where is the phenotypic value vector; is the orthogonal population 's phenotypic value vector; is the reciprocal cross population 's phenotypic value vector; is the population mean vector; is the orthogonal population 's mean; is the reciprocal cross population 's mean; is the coefficient matrix of fixed effects; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect vector; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix; is the orthogonal population 's marker matrix; is the reciprocal cross population 's marker matrix; is the marker effect vector; is the orthogonal population 's marker effect vector; is the reciprocal cross population 's marker effect vector; is the genetic variance and covariance matrix; and are the genetic variances of the orthogonal population and the reciprocal cross population respectively; is the genetic covariance between the orthogonal population and the reciprocal cross population ; is the residual variance and covariance matrix; are the residual variances of the orthogonal population and the reciprocal cross population respectively; is the residual covariance between the orthogonal population and the reciprocal cross population ; (4)Calculate the breeding values of the test population and based on the genetic parameters obtained in step (3). The calculation formula is as follows: where,
[0008] Among them, is the mean of the orthogonal population ; Is the reciprocal cross population The mean value of Is the cytoplasmic indicator matrix in the orthogonal population Is the cytoplasmic indicator matrix in the reciprocal cross population Is the cytoplasmic effect value in the orthogonal population Is the cytoplasmic effect value in the reciprocal cross population Is the orthogonal population The marker matrix of Is the reciprocal cross population The marker matrix of Is the orthogonal population The marker effect vector of Is the reciprocal cross population The marker effect vector of (5) Rank the breeding values obtained in step (4) for genomic selection.
[0009] Furthermore, in step (1), the orthogonal population And the reciprocal cross population Specifically: Select 2 inbred line parents with significant cytoplasmic differences And , First use As the female parent, As the male parent for hybridization to produce , Obtain the orthogonal population ; Then use As the female parent, As the male parent for hybridization to produce , Obtain the reciprocal cross population .
[0010] Furthermore, in step (2), Follows The normal distribution of Follows The normal distribution of Follows The normal distribution of Follows The normal distribution of
[0011] Furthermore, in step (3), Follows The normal distribution of Follows The normal distribution of
[0012] Furthermore, in step (3), the genetic variance and covariance matrix And the residual variance and covariance matrix , Calculated using the minimum norm quadratic unbiased estimation method, and its calculation formula is as follows:
[0013]
[0014]
[0015] Among them, is the trace of the matrix; is the matrix of variables to be solved; is the matrix of background variables; is the variance or covariance of the variables to be solved; is the intermediate parameter; is the phenotypic vector; is the total variance and covariance matrix; is the matrix transpose; is the fixed effect matrix.
[0016] Furthermore, the marker effect vector and the fixed effect vector in step (3) are calculated by using the best linear unbiased prediction, and their calculation formulas are as follows:
[0017]
[0018]
[0019] Among them, is the phenotypic vector; is the fixed effect vector; is the population mean vector; is the cytoplasmic effect vector; is the fixed effect matrix; is the total variance matrix; is the marker matrix; is the matrix transpose; is the estimated genetic variance; is the intermediate parameter; is the marker matrix; is the matrix transpose.
[0020] The present invention has the following beneficial effects: Since cytoplasm is maternally inherited, that is, only the cytoplasm existing in the female parent will be inherited, in order to detect the interaction effect between the nucleus and the cytoplasm, the present invention adopts a reciprocal cross design. In this way, the cytoplasm within the reciprocal cross population is the same, while there are significant differences in the cytoplasm between the populations. The whole-genome selection method of the present invention is more in line with the actual breeding conditions, can predict the test population more accurately, and can greatly reduce the breeding cost and improve the breeding efficiency. Specific embodiments
[0021] The principles and features of the present invention will be described below in conjunction with embodiments. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. For those conditions not specified in the examples, they shall be carried out according to conventional conditions or conditions recommended by the manufacturer. For reagents or instruments whose manufacturers are not indicated, they are all conventional products that can be obtained through commercial purchase.
[0022] Example 1 A genome-wide selection method based on the interaction between the nucleus and cytoplasm, comprising the following steps: (1) Genetic mating design: Since the cytoplasm is maternally inherited, that is, only the cytoplasm present in the female parent will be inherited, in order to detect the interaction effect between the nucleus and cytoplasm, a reciprocal cross design will be adopted in this method. In this way, the cytoplasm within the reciprocal cross population is the same, while there are significant differences in the cytoplasm between the populations. The method for constructing the population is as follows: Select 2 inbred line parents with significant cytoplasmic differences and . First, use as the female parent and as the male parent to hybridize to produce , and obtain the orthogonal population ; then use as the female parent and as the male parent to hybridize to produce , and obtain the reciprocal cross population ; (2) Establishment of statistical genetic model: Establish a genome-wide selection statistical genetic model for the interaction between the nucleus and cytoplasm (Nucleus-Cytoplasm Interaction Genomic Best Linear Unbiased Prediction, abbreviated as NCIGBLUP) for phenotype prediction; here, only the additive effect is concerned. Assume that has a sample size of , has a sample size of , and the number of markers is . Then the genome-wide selection statistical genetic model for the interaction between the nucleus and cytoplasm is as follows:
[0023] where is the phenotype value vector of the orthogonal population ; is the phenotype value vector of the reciprocal cross population ; is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the orthogonal population 's marker matrix; is the reciprocal cross population 's marker matrix; is the orthogonal population 's marker effect vector, following the normal distribution; is the reciprocal cross population 's marker effect vector, following the normal distribution; is the orthogonal population 's residual vector, following the normal distribution; is the reciprocal cross population 's residual vector, following the normal distribution; is the identity matrix; (3) Construct a mixed model equation (MME) through the whole-genome selection statistical genetic model in step (2) to solve genetic parameters (nuclear, cytoplasmic, and nucleo-cytoplasmic interaction effects); among them, the mixed model equation is as follows:
[0024] Among them, ; ; ; ; ; ; ; ; Among them, is the phenotypic value vector; is the phenotypic value vector of the orthogonal population ; is the phenotypic value vector of the reciprocal cross population ; is the population mean vector; is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the coefficient matrix of the fixed effect; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect vector; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix; is the orthogonal population 's marker matrix; is the reciprocal cross population 's marker matrix; is the marker effect vector; is the orthogonal population 's marker effect vector, which follows the normal distribution; is the reciprocal cross population 's marker effect vector, which follows the normal distribution; is the genetic variance and covariance matrix; and are respectively the genetic variances of the orthogonal population and the reciprocal cross population ; is the genetic covariance between the orthogonal population and the reciprocal cross population ; is the residual variance and covariance matrix; are respectively the residual variances of the orthogonal population and the reciprocal cross population ; is the residual covariance between the orthogonal population and the reciprocal cross population ; (4) To solve the above MME equation, the minimum norm quadratic unbiased estimator (MINQUE) method is needed to calculate the genetic variance and covariance matrix G and the residual variance and covariance matrix D, and their calculation formulas are as follows:
[0025]
[0026]
[0027] where, is the trace of the matrix; is the solution variable matrix; is the background variable matrix; is the solution variable variance or covariance; is the intermediate parameter; is the phenotype vector; is the total variance and covariance matrix; is the matrix transpose; is the fixed effect matrix; (5) Calculate the marker effect vector using best linear unbiased prediction (BLUP) and the fixed effect vector , and their calculation formulas are as follows:
[0028]
[0029]
[0030] where is the phenotypic vector; is the fixed effect vector; is the population mean vector; is the cytoplasmic effect vector; is the fixed effect matrix; is the total variance matrix; is the marker matrix; is the matrix transpose; is the estimated genetic variance; is the intermediate parameter; is the marker matrix; is the matrix transpose; (6) Calculate and the breeding value of the test population , and their calculation formulas are as follows:
[0031] where is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix of the orthogonal population ; is the marker matrix of the reciprocal cross population ; is the marker effect vector of the orthogonal population ; is the marker effect vector of the reciprocal cross population ; (7) Rank the breeding values obtained in step (6) for genomic selection.
[0032] Calculate the simulated population using the whole-genome selection method (NCIGBLUP) of the present invention and the existing GBLUP algorithm respectively. The parameters of the simulated population are as follows: The population size is 600, The population size is 600, there are 6 chromosomes in the nucleus, the number of molecular markers on each chromosome is 300, there is 1 chromosome in the cytoplasm, the number of molecular markers is 200, the genetic distance between adjacent two markers is 1 cM, the marker locus coincides with the minor gene locus controlling the trait, the effects of each minor gene follow the same normal distribution N(0, 1), the true heritability of the trait controlled by the nucleus is 0.5, the true heritability of the trait controlled by the cytoplasm is 0.1, the true heritability of the interaction between the nucleus and the cytoplasm in the population is 0.2, the true heritability of the interaction between the nucleus and the cytoplasm in the population is 0.1, the size of the test population is 100, and the number of simulations is 10 times.
[0033] The results are as follows: The prediction accuracy of NCIGBLUP in the population is 0.77, in the population is 0.64, and the prediction accuracy of GBLUP in and the population is 0.45. The results show that the prediction accuracy of NCIGBLUP is significantly better than that of GBLUP, and the cytoplasm and the interaction between the nucleus and the cytoplasm significantly affect the prediction accuracy.
[0034] Example 2 A whole-genome selection method based on the interaction between the nucleus and the cytoplasm, comprising the following steps: (1) Genetic mating design: Since the cytoplasm is maternally inherited, that is, only the cytoplasm present in the female parent will be inherited, in order to detect the interaction effect between the nucleus and the cytoplasm, a reciprocal cross design will be adopted in this method. In this way, the cytoplasm within the population in the reciprocal cross populations is the same, while there are significant differences in the cytoplasm between the populations. The method for constructing the population is as follows: Select 2 inbred line parents with significant cytoplasmic differences and , first use as the female parent, as the male parent for hybridization to produce , and obtain the orthogonal population ; then use as the female parent, as the male parent for hybridization to produce , and obtain the reciprocal cross population ; (2) Establishment of statistical genetic model: Establish a genome-wide selection statistical genetic model for the interaction between nucleus and cytoplasm (Nucleus-Cytoplasm Interaction Genomic Best Linear Unbiased Prediction, abbreviated as NCIGBLUP) for phenotype prediction; here, only the additive effect is concerned, assuming The number of samples of is The number of samples of is The number of markers is
[0035] where is the phenotypic value vector of the orthogonal population ; is the phenotypic value vector of the reciprocal cross population ; is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix of the orthogonal population ; is the marker matrix of the reciprocal cross population ; is the marker effect vector of the orthogonal population , which follows normal distribution; is the marker effect vector of the reciprocal cross population , which follows normal distribution; is the residual vector of the orthogonal population , which follows normal distribution; is the residual vector of the reciprocal cross population , which follows normal distribution; is the identity matrix; (3)Construct a mixed model equation (MME) through the genome-wide selection statistical genetic model in step (2) to solve genetic parameters (nuclear, cytoplasmic, and nucleus-cytoplasm interaction effects); among them, the mixed model equation is as follows:
[0036] Among them, ; ; ; ; ; ; ; ; Among them, is the phenotypic value vector; is the phenotypic value vector of the orthogonal population ; is the phenotypic value vector of the reciprocal cross population ; is the population mean vector; is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the coefficient matrix of the fixed effect; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect vector; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix; is the marker matrix of the orthogonal population ; is the marker matrix of the reciprocal cross population ; is the marker effect vector; is the marker effect vector of the orthogonal population , which follows the normal distribution; is the marker effect vector of the reciprocal cross population , which follows the normal distribution; is the genetic variance and covariance matrix; and are respectively the genetic variances of the orthogonal population and the reciprocal cross population ; is the genetic covariance between the orthogonal population and the reciprocal cross population ; is the residual variance and covariance matrix; are respectively the residual variances of the orthogonal population and the reciprocal cross population ; is an orthogonal population and the reciprocal cross population of the residual covariance; (4) To solve the above MME equation, the minimum norm quadratic unbiased estimator (MINQUE) method is needed to calculate the genetic variance and covariance matrix G and the residual variance and covariance matrix D. The calculation formulas are as follows:
[0037]
[0038]
[0039] where, is the trace of the matrix; is the variable matrix to be solved; is the background variable matrix; is the variance or covariance of the variable to be solved; is the intermediate parameter; is the phenotype vector; is the total variance and covariance matrix; is the matrix transpose; is the fixed effect matrix; (5) Use the best linear unbiased prediction (BLUP) to calculate the marker effect vector and the fixed effect vector , and the calculation formulas are as follows:
[0040]
[0041]
[0042] where, is the phenotype vector; is the fixed effect vector; is the population mean vector; is the cytoplasmic effect vector; is the fixed effect matrix; is the total variance matrix; is the marker matrix; is the matrix transpose; is the estimated genetic variance; is the intermediate parameter; is the marker matrix; is the matrix transpose; (6) Calculate based on the genetic parameters obtained in step (3) and the breeding value of the test population , and its calculation formula is as follows:
[0043] where, is the mean of the orthogonal population ; is the mean of the reciprocal cross population ; is the cytoplasmic indicator matrix in the orthogonal population; is the cytoplasmic indicator matrix in the reciprocal cross population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the marker matrix of the orthogonal population ; is the marker matrix of the reciprocal cross population ; is the marker effect vector of the orthogonal population ; is the marker effect vector of the reciprocal cross population ; (7) Rank the breeding values obtained in step (6) for genomic selection.
[0044] Calculate the simulated population using the whole genome selection method (NCIGBLUP) of the present invention and the existing GBLUP algorithm respectively. The parameters of the simulated population are as follows: The population size is 1000, The population size is 1000, the cell nucleus has 8 chromosomes, the number of molecular markers on each chromosome is 500, the cytoplasm has 1 chromosome, the number of molecular markers is 300, the genetic distance between adjacent markers is 1 cM, the marker locus coincides with the minor gene locus controlling the trait, the effect of each minor gene follows the same normal distribution N(0,1), the true heritability of the trait controlled by the cell nucleus is 0.39, the true heritability of the trait controlled by the cytoplasm is 0.12, in the true heritability of the interaction between the cell nucleus and the cytoplasm in the population is 0.15, in the true heritability of the interaction between the cell nucleus and the cytoplasm in the population is 0.23, the test population size is 100, and the number of simulations is 10 times.
[0045] The results are as follows: The prediction accuracy of NCIGBLUP in the population is 0.63, and in the population is 0.71. The prediction accuracy of GBLUP in and The prediction accuracy in the population is 0.33 for all. The results show that the prediction accuracy of NCIGBLUP is significantly better than that of GBLUP. The results show that the prediction accuracy of NCIGBLUP is significantly better than that of GBLUP, and cytoplasmic and nucleo-cytoplasmic interactions significantly affect the prediction accuracy.
[0046] The above results indicate that NCIGBLUP has good prediction accuracy, and cytoplasmic and nucleo-cytoplasmic interactions significantly affect the prediction accuracy. Compared with the existing algorithm GBLUP, the beneficial effects of NCIGBLUP of the present invention are: (1) it is more in line with the actual breeding conditions and can predict the test population more accurately; (2) it can greatly reduce the breeding cost and improve the breeding efficiency.
[0047] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.
Claims
1. A genome-wide selection method based on the interaction between the nucleus and the cytoplasm, characterized in that: The following steps are involved: (1) Genetic mating design: establishing orthogonal populations and backcross populations ; (2) Establishment of statistical genetic model: Establish a genome-wide selection statistical genetic model of the interaction between the nucleus and the cytoplasm for phenotype prediction; here, only the additive effect is concerned, assuming The number of samples is , Number of samples , the number of markers is , then the genome-wide selection statistical genetic model of the interaction between the nucleus and the cytoplasm is as follows: in, Orthogonal group phenotypic value vector of ; Backcross group phenotypic value vector of ; Orthogonal group The mean of Backcross group The mean of is the indicator matrix of cytoplasm in the orthogonal population; It is the cytoplasmic indicator matrix in the reciprocal population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; Orthogonal group The marker matrix of Backcross group The marker matrix of Orthogonal group The marker effect vector of ; Backcross group The marker effect vector of ; Orthogonal group The residual vector of Backcross group The residual vector of is the identity matrix; (3) The whole genome selection statistical genetic model of step (2) is used to construct a mixed model equation group for solving genetic parameters; wherein the mixed model equation group is as follows: in, ; ; ; ; ; ; ; ; in, is the phenotype value vector; Orthogonal group phenotypic value vector of ; Backcross group phenotypic value vector of ; is the group mean vector; Orthogonal group The mean of Backcross group The mean of is the coefficient matrix of fixed effects; is the indicator matrix of cytoplasm in the orthogonal population; It is the cytoplasmic indicator matrix in the reciprocal population; is the cytoplasmic effector vector; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; is the labeling matrix; Orthogonal group The marker matrix of Backcross group The marker matrix of is the marker effect vector; Orthogonal group The marker effect vector of ; Backcross group The marker effect vector of ; is the genetic variance and covariance matrix; and Orthogonal groups and backcross populations The genetic variance of Orthogonal group Reciprocal population The genetic covariance of is the residual variance and covariance matrix; Orthogonal groups and backcross populations The residual variance of Orthogonal group Reciprocal population The residual covariance of (4) Calculation of genetic parameters based on step (3) and Breeding values of the test population , and its calculation formula is as follows: in, Orthogonal group The mean of Backcross group The mean of is the indicator matrix of cytoplasm in the orthogonal population; It is the cytoplasmic indicator matrix in the reciprocal population; is the cytoplasmic effect value in the orthogonal population; is the cytoplasmic effect value in the reciprocal cross population; Orthogonal group The marker matrix of Backcross group The marker matrix of Orthogonal group The marker effect vector of ; Backcross group The marker effect vector of ; (5) Sort the breeding values obtained in step (4) by size for genomic selection.
2. The whole genome selection method based on the interaction between the nucleus and the cytoplasm according to claim 1, characterized in that: Establish an orthogonal population as described in step (1) and backcross populations Specifically: select two inbred parents with significant cytoplasmic differences and , first of all For the mother, Produced by hybridization for male parents , to obtain an orthogonal population ; then For the mother, Produced by hybridization for male parents , to obtain a reverse cross population .
3. The whole genome selection method based on the interaction between the nucleus and the cytoplasm according to claim 1, characterized in that: As described in step (2) obey The normal distribution of obey The normal distribution of obey The normal distribution of obey The normal distribution of .
4. The whole genome selection method based on the interaction between the nucleus and the cytoplasm according to claim 1, characterized in that: As described in step (3) obey The normal distribution of obey The normal distribution of .
5. The whole genome selection method based on the interaction between the nucleus and the cytoplasm according to claim 1, characterized in that: The genetic variance and covariance matrix described in step (3) and the residual variance and covariance matrix , calculated using the minimum norm quadratic unbiased estimation method, the calculation formula is as follows: in, is the trace of the matrix; To solve the variable matrix; is the background variable matrix; To solve the variance or covariance of variables; is the intermediate parameter; is the phenotype vector; is the total variance and covariance matrix; Represents matrix transpose; is the fixed effects matrix.
6. The whole genome selection method based on the interaction between the nucleus and the cytoplasm according to claim 1, characterized in that: The marker effect vector described in step (3) is calculated using the best linear unbiased prediction and the fixed effects vector , and its calculation formula is as follows: in, is the phenotype vector; is the fixed effect vector; is the group mean vector; is the cytoplasmic effector vector; is the fixed effect matrix; is the total variance matrix; is the labeling matrix; Transpose the matrix; is the estimated genetic variance; is the intermediate parameter; is the labeling matrix; Transpose the matrix.