A method, device and storage medium for joint genetic evaluation of multiple varieties of genomes

Abstract

The present invention provides a multi-variety genome joint genetic evaluation method, device and storage medium, which relates to the field of genome prediction technology. The method includes: constructing a multi-variety joint evaluation model according to prior distribution parameters, the prior distribution parameters include the prior effect variance of genotype markers; obtaining multi-variety reference group information; obtaining posterior distribution parameters according to the multi-variety reference group information and the prior distribution parameters, the posterior distribution parameters include the posterior effect variance of genotype markers; configuring the multi-variety joint evaluation model according to the posterior distribution parameters; the multi-variety joint evaluation model is used to obtain predicted phenotypic information according to the input genotype information. The multi-variety joint evaluation model is constructed by associating the effect variance based on genotype information with the genome block position divided based on linkage disequilibrium information, fully considering the differences in gene effects between different varieties, enabling information sharing between different varieties, and facilitating the improvement of the accuracy of genome prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of genome prediction, and in particular to a method, device and storage medium for joint genetic evaluation of multiple varieties of genomes. Background Art

[0002] Genomic prediction is a method that uses genetic markers across the entire genome to obtain genomic estimated breeding values ​​(GEBVs). GEBVs can use genomic information to quantify the potential value of individuals in breeding. The key to achieving prediction accuracy in genomic prediction is to construct a reference population with genotypic and phenotypic information, and based on the information of the reference population, predict the GEBVs of the population with only genotypic information.

[0003] Genomic prediction has achieved remarkable results in animal and plant breeding. However, for small-scale varieties, it is difficult to establish a large enough reference group for genomic prediction due to the limited population size. In addition, even if the population size is large enough, there are situations where some traits that are difficult to measure or costly to measure (such as slaughter or feed efficiency traits) need to be bred, and there is also the problem of difficulty in establishing a reference group of sufficient size. In these cases, due to the limited size of the reference group, the accuracy of GEBVs prediction decreases. When the reference population of the target variety is relatively small, information from multiple varieties is usually used to establish a reference group of sufficient size for genomic prediction (Multi-Breeding Group Prediction, MBGP).

[0004] In the prior art, MBGP generally regards different breeds as a single purebred population and applies a common single-trait model. However, due to differences in linkage disequilibrium (LD) phase and allele frequency between breeds, simply merging different breeds does not necessarily lead to higher prediction accuracy. On the contrary, it may make the accuracy of the combined genomic prediction of multiple breeds lower than that of the genomic prediction within a breed.

[0005] The multi-trait model provides a more accurate way to predict the genome. By treating the same traits of different varieties as different but possibly genetically related traits of the same variety, the genetic correlation between the traits can be flexibly considered. However, the multi-trait model in the prior art usually assumes that the effects of genotype markers are the same at different locations throughout the genome, but this assumption is inconsistent with the current results of multi-variety joint association analysis. The difference in the distribution of genes in the genomes of different varieties indicates that the degree of genetic association between varieties in different genomic blocks varies, resulting in poor prediction accuracy of the multi-trait model.

[0006] Therefore, when combining genes from multiple varieties for genome prediction, how to fully consider the differences in gene effects among varieties and improve the accuracy of genome prediction is an urgent problem to be solved. Summary of the invention

[0007] The present invention provides a multi-variety genome joint genetic evaluation method, equipment and storage medium to solve the above-mentioned defects in the prior art.

[0008] The present invention provides a multi-variety genome joint genetic evaluation method, comprising:

[0009] Obtaining prior distribution parameters, and constructing a multi-variety joint evaluation model according to the prior distribution parameters, wherein the prior distribution parameters include the prior effect variance of each genotype marker in each genomic block corresponding to the variety;

[0010] Obtain multi-variety reference group information;

[0011] According to the multi-variety reference group information and the prior distribution parameters, the posterior distribution parameters are obtained, wherein the posterior distribution parameters include the posterior effect variance of each genotype marker in each genomic block of all varieties;

[0012] Configuring the multi-variety joint evaluation model according to the posterior distribution parameters;

[0013] Among them, the multi-variety joint evaluation model is used to obtain predicted phenotypic information based on the input genotype information, and the predicted phenotypic information is used to obtain the genome estimated breeding value.

[0014] According to a multi-variety genome joint genetic evaluation method provided by the present invention, the multi-variety reference group information includes genotype information and phenotypic information; before obtaining the posterior distribution parameters according to the multi-variety reference group information and the prior distribution parameters, it also includes:

[0015] According to the genotype information, obtaining a linkage disequilibrium measure between genotype markers;

[0016] Calculating a hypothetical breakpoint linkage value based on the linkage disequilibrium measure between the markers, wherein the hypothetical breakpoint linkage value represents the degree of linkage between two genomic fragments obtained by taking the position of the genotype marker as the breakpoint;

[0017] According to the assumed breakpoint linkage value, obtaining the actual breakpoint of the block partition;

[0018] According to the actual breakpoints of the block division, the genome in the genotype information is divided into genome blocks.

[0019] According to a multi-variety genome joint genetic evaluation method provided by the present invention, obtaining the actual breakpoints of block division according to the assumed breakpoint linkage values ​​includes:

[0020] forming a hypothetical breakpoint linkage vector according to the positional order of the genotyping markers and the hypothetical breakpoint linkage values ​​of each of the genotyping markers;

[0021] Using a cubic spline function to interpolate the assumed breakpoint linkage vector to form an assumed breakpoint linkage smoothing vector;

[0022] According to the assumed breakpoint chain smoothing vector, the position corresponding to the minimum value is determined as the actual breakpoint of the block division.

[0023] According to a multi-variety genome joint genetic evaluation method provided by the present invention, the multi-variety reference group information includes the first genotype information and the first phenotype information of the target variety and the second genotype information, the second phenotype information and the missing phenotype information of the auxiliary variety; the posterior distribution parameters are obtained according to the multi-variety reference group information and the prior distribution parameters, including:

[0024] Generate sampling phenotypic information according to the first genotypic information, the first phenotypic information, the second genotypic information and the second phenotypic information, wherein the sampling phenotypic information corresponds to the same trait as the missing phenotypic information;

[0025] The posterior distribution parameters are obtained according to the first genotype information, the first phenotype information, the second genotype information, the second phenotype information and the sampled phenotype information in combination with the prior distribution parameters.

[0026] According to a multi-variety genome joint genetic evaluation method provided by the present invention, in the multi-variety joint evaluation model constructed according to the prior distribution parameters, the multi-variety joint evaluation model is as follows:

[0027]

[0028] Among them, l is the variety number, y l is the phenotypic vector of variety l, X l is the association matrix between variety l and fixed effects, b is the fixed effect vector, s is the number of genomic blocks, m i is the number of genotype markers in genomic block i, Z ijl is the association matrix of genotype marker j in genome block i of variety l, a ijl is the additive genetic effect value of genotype marker j in genomic block i of variety l, e l is the residual vector of variety l.

[0029] According to a multi-variety genome joint genetic evaluation method provided by the present invention, the multi-variety joint evaluation model is constructed according to the prior distribution parameters, comprising:

[0030] According to the prior distribution parameters, the fixed effect vector is configured to obey a uniform distribution;

[0031] According to the prior distribution parameters, the additive genetic effect values ​​of the genotype markers are configured to obey a normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency;

[0032] According to the prior distribution parameters, the residual vector is configured to obey the normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency.

[0033] According to a multi-variety genome joint genetic evaluation method provided by the present invention, in calculating the assumed breakpoint linkage value according to the linkage disequilibrium metric value between the markers, the assumed breakpoint linkage value is calculated by the following expression:

[0034]

[0035] Among them, w k is the assumed breakpoint linkage value of the kth genotype marker, M i is the i-th column in the genetic variation matrix, i = min{1, kN},…, k; M j is the jth column in the genetic variation matrix, j = i+1,…,min{k+N,N}, Corr[M i ,M j ] is M i With M j The correlation coefficient between them, N is the preset parameter value, m k is the total number of all vectors within the range of i and j.

[0036] According to a multi-variety genome joint genetic evaluation method provided by the present invention, after configuring the multi-variety joint evaluation model according to the posterior distribution parameters, the method further includes:

[0037] Acquiring genotype information to be predicted, wherein the genotype information to be predicted includes genotype information of at least two varieties;

[0038] Inputting the genotype information to be predicted into the multi-variety joint evaluation model to obtain predicted phenotypic information, wherein the predicted phenotypic information includes phenotypic values ​​of at least two traits;

[0039] According to the predicted phenotypic information, the genomic estimated breeding value is obtained.

[0040] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a multi-variety genome joint genetic evaluation method as described in any one of the above methods is implemented.

[0041] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-variety genome joint genetic evaluation method as described in any one of the above.

[0042] The present invention provides a method, device and storage medium for joint genetic evaluation of multiple varieties of genomes, which have at least the following beneficial effects: a multi-variety joint evaluation model is constructed according to prior distribution parameters, and the prior distribution parameters are used to set model parameters based on theory and experience, wherein the prior effect variance in the prior distribution parameters is used to consider the difference in genotype marker effects in different genomic blocks of different varieties, that is, the effects of genotype markers at different genomic block positions may be different, and different varieties can share information through the covariance of marker effect distribution. After constructing the multi-variety joint evaluation model, the posterior distribution parameters that more accurately reflect the relationship between the actual genotype information and the phenotypic information are obtained based on the multi-variety reference group information obtained by actual observation and combined with the prior distribution parameters, and the multi-variety joint evaluation model is updated and configured based on the posterior distribution parameters, so that the multi-variety joint evaluation model can accurately output the predicted phenotypic information based on the input genotype information, and then obtain the genome estimated breeding value based on the predicted phenotypic information, so as to achieve the purpose of genome prediction. In this way, by associating the effect variance based on genotype information with the location of the genomic block, a multi-variety joint evaluation model is constructed, which can fully consider the differences in genetic effects between different varieties, allowing information sharing between different varieties, which is conducive to improving the accuracy of genome prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 This is one of the flow charts of a multi-variety genome joint genetic evaluation method provided by the present invention;

[0045] Figure 2 This is the second flow chart of a multi-variety genome joint genetic evaluation method provided by the present invention;

[0046] Figure 3This is the third flow chart of a multi-variety genome joint genetic evaluation method provided by the present invention;

[0047] Figure 4 This is the fourth flow chart of a multi-variety genome joint genetic evaluation method provided by the present invention;

[0048] Figure 5 This is the fifth flow chart of a multi-variety genome joint genetic evaluation method provided by the present invention;

[0049] Figure 6 A schematic diagram of a process for calculating a hypothetical breakpoint linkage value in one embodiment of the present invention;

[0050] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Current multi-trait models usually assume that the genetic variance and covariance of the genotype marker (usually SNP) effect are the same at different locations throughout the genome, an assumption that is inconsistent with the results of multi-breed joint association analysis. For example, in the association analysis conducted in multiple beef cattle breeds, the proportion of common associated genes between different breeds is small (8-22%), and the difference in the distribution of these genes in the genomes of different breeds indicates that there are changes in the degree of genetic association between breeds in different genomic blocks. Considering the differences in variance and covariance of heterogeneous inheritance between breeds in different genomic blocks in the joint prediction model allows information sharing between different breeds, which can further obtain higher genomic prediction accuracy.

[0053] For the existing situation that does not take into account the differences in variance and covariance of heterogeneous genetics, which limits the accuracy of genomic prediction, refer to Figures 1 to 5 The present invention proposes a multi-variety genome joint genetic evaluation method, comprising:

[0054] S100: Obtaining a priori distribution parameters, and constructing a multi-variety joint evaluation model according to the priori distribution parameters, wherein the priori distribution parameters include a priori effect variance of each genotype marker in each genomic block corresponding to the variety;

[0055] S110: Acquire multi-variety reference group information;

[0056] S120: acquiring posterior distribution parameters according to the multi-variety reference group information and the prior distribution parameters, wherein the posterior distribution parameters include the posterior effect variance of each genotype marker in each genomic block of all varieties;

[0057] S130: configuring the multi-variety joint evaluation model according to the posterior distribution parameters;

[0058] Among them, the multi-variety joint evaluation model is used to obtain predicted phenotypic information based on the input genotype information, and the predicted phenotypic information is used to obtain the genome estimated breeding value.

[0059] A multi-variety joint evaluation model is constructed according to the prior distribution parameters. The prior distribution parameters set the model parameters based on theory and experience. The prior effect variance in the prior distribution parameters can be used to consider the differences in the effects of genotype markers in different genomic blocks of different varieties, that is, the effects of genotype markers at different genomic block positions can be different. After the multi-variety joint evaluation model is constructed, the multi-variety reference group information obtained by actual observation is combined with the prior distribution parameters to obtain the posterior distribution parameters that more accurately reflect the relationship between the actual genotype information and the phenotypic information. The multi-variety joint evaluation model is updated and configured based on the posterior distribution parameters, so that the multi-variety joint evaluation model can accurately output the predicted phenotypic information based on the input genotype information, and then obtain the genome estimated breeding value based on the predicted phenotypic information, so as to achieve the purpose of genome prediction. In this way, the multi-variety joint evaluation model constructed by associating the effect variance of the genotype information with the location of the genomic block can fully consider the differences in gene effects between different varieties, so that different varieties can share information, which is conducive to improving the accuracy of genome prediction.

[0060] In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, in S100, the multi-variety joint evaluation model is as follows:

[0061]

[0062] Among them, l is the variety number, y l is the phenotypic vector of variety l, X l is the association matrix between variety l and fixed effects, b is the fixed effect vector, s is the number of genomic blocks, m i is the number of genotype markers (SNPs) in genomic block i, Z ijl is the association matrix of genotype marker j in genome block i of variety l, a ijl is the additive genetic effect value of genotype marker j in genomic block i of variety l, e l is the residual vector of variety l.

[0063] The multi-variety joint evaluation model is improved based on the multi-trait Bayesian model, and further considers the difference in the effect of genotype markers in different genomic blocks. In the model, it is reflected in the accumulation of each genomic block and the accumulation of the additive genetic effect value of each genotype marker in each genomic block. The phenotype of the trait is affected by both the gene effect and the environmental effect, that is, the gene and the environment affect the phenotype of the trait. In the model, the fixed effect vector b corresponds to the effect of the difference between different species on the phenotype, and the additive genetic effect value a ijl Reflects the effect of gene effect on phenotype, residual vector e l The multi-variety joint evaluation model comprehensively considers various factors that affect the phenotype of the trait, and further considers the differences in gene effects at different locations. Different varieties can share information through the covariance of marker effect distribution, which is conducive to more accurate acquisition of predicted phenotypic information.

[0064] In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, S100 includes:

[0065] According to the prior distribution parameters, the fixed effect vector is configured to obey a uniform distribution;

[0066] According to the prior distribution parameters, the additive genetic effect values ​​of the genotype markers are configured to obey a normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency;

[0067] According to the prior distribution parameters, the residual vector is configured to obey the normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency.

[0068] According to the idea of ​​Bayesian theorem, a multi-variety joint evaluation model is constructed using prior distribution parameters obtained based on theory and experience. The fixed effect vector b is configured to obey a uniform distribution, and the additive genetic effect value a ijl Configured to follow the normal distribution N(0,G i ), the residual vector e l Configured to follow normal distribution

[0069] G i is the dispersion parameter of the effect of all genotype markers in a genomic block, G i The distribution is the inverse Wishart distribution IW(df,B i ), df=4+p is the degree of freedom, p is the number of varieties, is the ratio of genetic variance to phenotypic variance (i.e., the heritability of the trait), which can be set to 0.5. A is a diagonal matrix, and the elements on the diagonal of the matrix are the phenotypic variance of each variety on the trait. j is the gene frequency of marker j in block i. R 0 It also follows the inverse Wishart distribution IW(df,R p ),in df=4+p is the degree of freedom, p is the number of varieties, is the ratio of genetic variance to phenotypic variance.

[0070] In the process of constructing a multi-variety joint evaluation model based on prior distribution parameters, it can be assumed that the parameters are independent, as shown below:

[0071]

[0072] p(R 0 ∣v+p,R p )

[0073] Among them, b is the fixed effect vector value, a is the additive genetic effect value, G is the dispersion parameter, and the meaning of the remaining characters can refer to the explanation of the meaning of the characters in the above-mentioned multi-variety joint evaluation model.

[0074] The multi-variety reference group information includes genotype information and phenotypic information, which can be divided into the first genotype information and the first phenotypic information of the target variety, and the second genotype information and the second phenotypic information of the auxiliary variety according to the variety classification.

[0075] refer to Figure 2 In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, the multi-variety reference group information includes the first genotype information and the first phenotype information of the target variety and the second genotype information, the second phenotype information and the missing phenotype information of the auxiliary variety; S120 includes:

[0076] S121: generating sampling phenotypic information according to the first genotype information, the first phenotypic information, the second genotype information and the second phenotypic information, wherein the sampling phenotypic information corresponds to the same trait as the missing phenotypic information;

[0077] S122: Acquire the posterior distribution parameters according to the first genotype information, the first phenotype information, the second genotype information, the second phenotype information and the sampled phenotype information in combination with the prior distribution parameters.

[0078] The multi-variety joint evaluation model regards the same trait of different varieties as different but possibly genetically related traits of the same population. For a given auxiliary variety, it only has the phenotypic information corresponding to one of the traits in the multi-trait model, while the phenotypic information of other traits is marked as missing, that is, missing phenotypic information. According to the phenotype corresponding to the same trait, sampling phenotypic information is formed by sampling to complete the missing phenotypic information of the auxiliary variety, that is, the missing phenotypic information is replaced with the sampling phenotypic information. In this way, the multi-variety reference group information can be more complete, which is conducive to improving the accuracy of phenotypic value prediction.

[0079] In some embodiments of the present invention, the sampled phenotypic information that replaces the missing phenotypic information may be obtained by sampling using the following normal distribution:

[0080]

[0081] Among them, k is the observed individual number, is the sampled phenotype value that replaces the missing, is the phenotypic value of observed individual k in the auxiliary variety, z k and m′ p are subsets of the fixed effect association matrix X and gene effect association matrix Z mentioned above, b is the fixed effect value vector, a is the additive genetic effect value vector, G is the dispersion parameter, and the meanings of the remaining characters can refer to the character explanations in the above multi-variety joint evaluation model.

[0082] After replacing the missing phenotypic information with the sampled phenotypic information, the posterior distribution parameters are obtained by combining the prior distribution parameters, specifically:

[0083] The posterior distribution of the fixed effect vector b can be derived as:

[0084]

[0085] Among them, y * It is given by the formula The calculated corrected phenotypic value vector, M is the allele content matrix of all individuals, I p It is a p-dimensional identity matrix, where p is the number of varieties and a is the vector of additive genetic effect values.

[0086] Additive genetic effectsa ij The posterior distribution of can be derived as:

[0087]

[0088] where y § is the corrected phenotypic value vector calculated by the following formula:

[0089]

[0090] in, is the allele content matrix of all markers in block t, is the allele content matrix of marker u in block i.

[0091] Discreteness matrix G i The posterior distribution of can be derived as:

[0092]

[0093] Discreteness matrix R 0 The posterior distribution of can be derived as:

[0094]

[0095] where e k is the residual effect vector of individual k, and n is the number of individuals in the reference group.

[0096] The multi-variety joint evaluation model is configured through the above-mentioned posterior distribution parameters, so that the multi-variety joint evaluation model can output predicted phenotypic information based on the input genotype information, and then obtain group-based estimated breeding values ​​to achieve the purpose of genome prediction.

[0097] The multi-variety joint evaluation model of the present invention further improves the accuracy of genome prediction based on the association between the effect variance of genotype information and the location of the genomic block, wherein the division of genomic blocks also has an impact on the accuracy of prediction.

[0098] refer to Figure 3 In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, the multi-variety reference group information includes genotype information and phenotypic information; before S120, it also includes:

[0099] S010: obtaining linkage disequilibrium measurement values ​​between genotype markers according to the genotype information;

[0100] S020: calculating a hypothetical breakpoint linkage value according to the linkage disequilibrium measure between the markers, wherein the hypothetical breakpoint linkage value represents the degree of linkage between two genomic fragments obtained by taking the position of the genotype marker as the breakpoint;

[0101] S030: Obtaining actual breakpoints of block partitioning according to the assumed breakpoint linkage value;

[0102] S040: Divide the genome in the genotype information into genome blocks according to the actual breakpoints of the block division.

[0103] In the process of genome inheritance, there is a linkage relationship between gene fragments. The higher the degree of linkage, the more likely it is to be inherited together by the offspring. Therefore, by obtaining the linkage disequilibrium measure between genotype markers, assuming that the position of the genotype marker is used as the breakpoint, the degree of linkage between the two gene fragments formed by this division is calculated, that is, the assumed breakpoint linkage value can reflect whether the position of the corresponding gene marker is suitable as a breakpoint. In this way, by calculating the assumed breakpoint linkage value and obtaining the actual breakpoint of the block division, the genome can be divided more reasonably to form genome blocks, which is conducive to further improving the accuracy of the prediction.

[0104] In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, in S020, the assumed breakpoint linkage value is calculated by the following expression:

[0105]

[0106] Among them, w k is the assumed breakpoint linkage value of the kth genotype marker, M i is the i-th column in the genetic variation matrix, i = min{1, kN},…, k; M j is the jth column in the genetic variation matrix, j = i+1,…,min{k+N,N}, Corr[M i ,M j ] is M i With M j The correlation coefficient between them, N is the preset parameter value (can be set to 50), m k is the total number of all vectors within the range of i and j.

[0107] refer to Figure 6 , is a process for calculating the assumed breakpoint linkage value in one embodiment of the present invention, wherein the SNP-ID matrix is ​​a genetic variation matrix, the SNP-SNP matrix is ​​a linkage disequilibrium measure between genotype markers (SNPs), and w is the assumed breakpoint linkage value corresponding to the genotype marker (SNP). Based on the curve graph formed by the assumed breakpoint linkage value, the degree of linkage between the two gene fragments obtained after taking the genotype marker (SNP) as the breakpoint can be intuitively known.

[0108] refer to Figure 4 In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, S030 includes:

[0109] S031: forming a hypothetical breakpoint linkage vector according to the position sequence of the genotyping markers and the hypothetical breakpoint linkage value of each of the genotyping markers;

[0110] S032: using a cubic spline interpolation function to interpolate the breakpoint chain vector to form a breakpoint chain smoothing vector;

[0111] S033: According to the breakpoint chain smoothing vector, determine the position corresponding to the minimum value as the actual breakpoint of the block division.

[0112] Considering that in some cases, the best position of the actual breakpoint of block division may not coincide with the position of the genotype marker, that is, it may be between two genotype markers. Therefore, the assumed breakpoint linkage vector formed by the assumed breakpoint linkage value is interpolated by the cubic spline function, that is, interpolated between genotype markers to make the assumed breakpoint linkage vector smoother to form a breakpoint linkage smooth vector, and then take the position corresponding to the minimum value of the breakpoint linkage smooth vector as the actual breakpoint of block division. In this way, through the interpolation and smoothing method, it is helpful to obtain a more accurate position with a smaller assumed breakpoint linkage value as the actual breakpoint of block division.

[0113] In some embodiments of the present invention, the breakpoint linkage vector is interpolated using a cubic spline interpolation function, specifically:

[0114] Assuming that the number of genotype markers in a genome is n, construct a matrix Q with n rows and n-2 columns. The value q at the j column of the i-th row in the matrix is i,j for:

[0115]

[0116] Construct a matrix R with n-2 rows and n-2 columns. The value at the jth column of the i-th row in the matrix is ​​r. i,j :

[0117]

[0118] The following formula is used to obtain the breakpoint linkage smoothing vector w′ after the assumed breakpoint linkage vector w is processed by cubic spline interpolation:

[0119] w′=w-αQ(αQ T Q+R) -1 Q T w

[0120] Among them, α is a parameter that controls smoothness and can be set to 780.

[0121] refer to Figure 5 In some embodiments of the multi-variety genome joint genetic evaluation method of the present invention, after S130, the method further includes:

[0122] S200: Acquire genotype information to be predicted, wherein the genotype information to be predicted includes genotype information of at least two varieties;

[0123] S210: Inputting the genotype information to be predicted into the multi-variety joint evaluation model to obtain predicted phenotypic information, wherein the predicted phenotypic information includes phenotypic values ​​of at least two traits;

[0124] S220: Obtaining genomic estimated breeding values ​​based on the predicted phenotypic information.

[0125] After configuring the multi-variety joint evaluation model with the posterior distribution parameters, the genotype information to be predicted can be input into the multi-variety joint evaluation model. The multi-variety joint evaluation model comprehensively considers the relationship between the effect variance of the genotype information and the position of the genomic block, obtains the output phenotypic value as the predicted phenotypic information, and then calculates the corresponding genome estimated breeding value based on it to achieve the effect of genome prediction.

[0126] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the above-mentioned multi-variety genome joint genetic evaluation method.

[0127] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0128] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute a multi-variety genome joint genetic evaluation method provided by the above methods.

[0129] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0130] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A method for joint genetic evaluation of multiple varieties of genomes, characterized in that: include: Obtaining prior distribution parameters, and constructing a multi-variety joint evaluation model according to the prior distribution parameters, wherein the prior distribution parameters include the prior effect variance of each genotype marker in each genomic block corresponding to the variety; Obtain multi-variety reference group information; According to the multi-variety reference group information and the prior distribution parameters, the posterior distribution parameters are obtained, wherein the posterior distribution parameters include the posterior effect variance of each genotype marker in each genomic block of all varieties; Configuring the multi-variety joint evaluation model according to the posterior distribution parameters; Wherein, the multi-variety joint evaluation model is used to obtain predicted phenotypic information based on the input genotype information, and the predicted phenotypic information is used to obtain the genome estimated breeding value; The multi-variety reference group information includes genotype information and phenotypic information; before obtaining the posterior distribution parameters according to the multi-variety reference group information and the prior distribution parameters, it also includes: According to the genotype information, obtaining a linkage disequilibrium measure between genotype markers; Calculating a hypothetical breakpoint linkage value according to the linkage disequilibrium metric, wherein the hypothetical breakpoint linkage value represents the degree of linkage between two genomic fragments obtained by taking the position of the genotype marker as the breakpoint; According to the assumed breakpoint linkage value, obtaining the actual breakpoint of the block partition; According to the actual breakpoints of the block division, the genome in the genotype information is divided into genome blocks; The step of obtaining the actual breakpoint of the block partition according to the assumed breakpoint linkage value includes: According to the position sequence of each of the genotype markers and the assumed breakpoint linkage value, forming a putative breakpoint linkage vector; Using a cubic spline function to interpolate the assumed breakpoint linkage vector to form an assumed breakpoint linkage smoothing vector; According to the assumed breakpoint chain smoothing vector, the position corresponding to the minimum value is determined as the actual breakpoint of the block division.

2. A multi-variety genome joint genetic evaluation method according to claim 1, characterized in that: The multi-variety reference group information includes first genotype information and first phenotype information of the target variety and second genotype information, second phenotype information and missing phenotype information of the auxiliary variety; The step of obtaining the posterior distribution parameters according to the multi-variety reference group information and the prior distribution parameters includes: Generate sampling phenotypic information according to the first genotypic information, the first phenotypic information, the second genotypic information and the second phenotypic information, wherein the sampling phenotypic information corresponds to the same trait as the missing phenotypic information; The posterior distribution parameters are obtained according to the first genotype information, the first phenotype information, the second genotype information, the second phenotype information and the sampled phenotype information in combination with the prior distribution parameters.

3. The multi-variety genome joint genetic evaluation method according to claim 1, characterized in that: In constructing a multi-variety joint evaluation model according to the prior distribution parameters, the multi-variety joint evaluation model is as follows: Among them, l is the variety number, y l is the phenotypic vector of variety l, X l is the association matrix between variety l and fixed effects, b is the fixed effect vector, s is the number of genomic blocks, m i is the number of genotype markers in genomic block i, Z ijl is the association matrix of genotype marker j in genome block i of variety l, a ijl is the additive genetic effect value of genotype marker j in genomic block i of variety l, e l is the residual vector of variety l.

4. A multi-variety genome joint genetic evaluation method according to claim 3, characterized in that: The multi-variety joint evaluation model is constructed according to the prior distribution parameters, including: According to the prior distribution parameters, the fixed effect vector is configured to obey a uniform distribution; According to the prior distribution parameters, the additive genetic effect values ​​of the genotype markers are configured to obey a normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency; According to the prior distribution parameters, the residual vector is configured to obey the normal distribution, and the dispersion of the normal distribution is determined according to the number of varieties, the phenotypic variance of each variety, and the gene frequency.

5. A multi-variety genome joint genetic evaluation method according to any one of claims 1 to 4, characterized in that: After configuring the multi-variety joint evaluation model according to the posterior distribution parameters, the method further includes: Acquiring genotype information to be predicted, wherein the genotype information to be predicted includes genotype information of at least two varieties; Inputting the genotype information to be predicted into the multi-variety joint evaluation model to obtain predicted phenotypic information, wherein the predicted phenotypic information includes phenotypic values ​​of at least two traits; According to the predicted phenotypic information, the genomic estimated breeding value is obtained.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, a multi-variety genome joint genetic evaluation method as described in any one of claims 1 to 5 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a multi-variety genome joint genetic evaluation method as described in any one of claims 1 to 5 is implemented.

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