Breeding selection optimization method and system based on BLUP model, device and medium

Abstract

The present invention relates to the technical field of animal breeding. Disclosed are a breeding selection optimization method and system based on a BLUP model, a device and a medium. The method comprises: constructing a BLUP model on the basis of animal pedigree information; and using a partitioning method to perform optimization solution on the BLUP model to obtain an unbiased optimal prediction value of an animal genetic potential, the unbiased optimal prediction value is used for breeding selection, and the optimization solution process of the BLUP model comprises two matrix inversion operations, one being the inversion operation of a numerator relationship matrix, and the other being the inversion operation of a larger matrix containing a fixed effect. By using an iterative partitioning calculation method, the present invention can significantly reduce the memory load in the solution process of the BLUP model, improve the operation efficiency, and keep the accuracy of breeding value estimation undamaged.

Description

Breeding selection optimization methods, systems, equipment, and media based on BLUP model Technical Field

[0001] This invention relates to the field of animal breeding technology, and in particular to a method, system, equipment and medium for optimizing breeding selection based on the BLUP model. Background Technology

[0002] Animal breeding emphasizes the improvement and enhancement of genetic structure. The scientific theory of modern breeding can be traced back to the rediscovery of Mendel's laws of inheritance in 1900, which allowed breeding work to be carried out according to scientific genetic laws. Systematic breeding of domesticated animals made a huge leap after World War II. The BLUP (Best Linear Unbiased Prediction) model, developed by American statistician CRHenderson in the 1960s, made a decisive contribution to this at the computational level. The BLUP model optimizes breeding selection by estimating the unbiased best predictor value (EBV), and using BLUP to assess the genetic potential of animals is a standard method adopted by countries around the world.

[0003] As the cost of sequencing technology decreases, the amount of data obtained from gene chip sequencing is increasing, leading to the wider application of GBLUP models in animal breeding. However, this increased data volume also brings new challenges: when the number of animals becomes extremely large, traditional computational methods face enormous computational pressure and resource constraints. Processing large-scale datasets requires substantial computational resources, especially when complex genetic evaluations and large matrix operations are involved. Therefore, researching how to improve the computational efficiency of BLUP models on large-scale datasets while maintaining their accuracy has become an urgent problem to be solved.

[0004] Traditional BLUP models suffer from computational inefficiency when solving for EBV on large-scale datasets, primarily due to the involvement of two matrix inversion operations: one for the inverse of the kinship matrix and the other for the inverse of a larger matrix containing the fixed effects. Matrix inversion is a computationally intensive step, therefore optimizing this process is crucial for improving the efficiency of BLUP model solutions.

[0005] In ABLUP, the first matrix inversion is the inversion of matrix A. In 1975, Henderson proposed that A could be directly constructed from a genealogy. -1 A simple method, the iterative method, directly constructs A -1This avoids the need for the first matrix inversion, thus improving the computational efficiency of ABLUP to some extent. In GBLUP, the Apy algorithm proposes dividing the population into a core population (c) and a non-core population (n), allowing for the direct construction of GBLUP with some data loss. -1 The construction process involves inverting a core population matrix. Reducing the number of core populations decreases the accuracy of estimated breeding values, and inverting the core matrix remains difficult in large-scale populations. Existing algorithms such as Apy and ssGBLUP cannot avoid inverting large matrices and also suffer some loss in the accuracy of estimated breeding values. Summary of the Invention

[0006] The purpose of this invention is to provide a breeding selection optimization method, system, device and medium based on the BLUP model, which can significantly reduce the memory load in the BLUP model solution process by adopting an iterative block calculation method, while improving the computational efficiency and maintaining the accuracy of breeding value estimation.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A breeding selection optimization method based on the BLUP model includes:

[0009] BLUP model constructed based on animal pedigree information;

[0010] The BLUP model is optimized using a block-based method to obtain the unbiased optimal prediction value of animal genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

[0011] Optionally, the basic equations of the BLUP model are:

[0012] Where X is the fixed effects matrix; X' is the transpose of X; Z is the random effects matrix, and Z' is the transpose of Z; y is the observation matrix; G is the kinship matrix, with a size of n*n; n is the number of animal individuals; G -1 Let G be the inverse matrix; k represents the heritability of the phenotype, which is a constant;

[0013] The inversion operation of the kinship matrix is ​​the inversion of matrix G, where G... -1 Calculation;

[0014] The inversion operation of the larger matrix containing the fixed effects is as follows:

[0015] in, Let be the fixed effect value to be solved. The value of the random effect to be solved.

[0016] Optionally, before optimizing the BLUP model using the block-based method, the method further includes: setting the G matrix in the BLUP model to be (n*n), and dividing the G matrix into four equal parts:

[0017] Where G1 represents the first part matrix; G2 represents the second part matrix; G3 represents the third part matrix; and G4 represents the fourth part matrix.

[0018] Optionally, the optimization solution of the BLUP model using the block method includes single inversion and iterative inversion multiple times.

[0019] Optionally, the single inversion is calculated according to the following formula:

[0020] The global inversion of the G matrix is ​​simplified to the G1 matrix and Finding the inverse of two smaller matrices, the computational complexity of inverting matrix G is O(n). 3 The computational complexity of the two smaller matrices is 2*(n / 2). 3 ;

[0021] The iterative process involves multiple inversions, and the leftmost mixing matrix of the fundamental equation is calculated using the following formula:

[0022] Where A represents the first submatrix of the mixture matrix; B represents the second submatrix of the mixture matrix; C represents the third submatrix of the mixture matrix; and D represents the fourth submatrix of the mixture matrix.

[0023] This invention also provides a breeding selection optimization system based on the BLUP model, comprising:

[0024] The model building module is used to build BLUP models based on animal pedigree information;

[0025] The model solving module is used to optimize the BLUP model using a block-based method to obtain the unbiased optimal prediction value of animal genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization solution process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

[0026] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the breeding selection optimization method based on the BLUP model described above.

[0027] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the BLUP-based breeding selection optimization method as described above.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] This invention discloses a breeding selection optimization method, system, device, and medium based on a BLUP model. The method includes constructing a BLUP model based on animal pedigree information; optimizing the BLUP model using a block-based method to obtain an unbiased optimal predicted value of the animal's genetic potential; the unbiased optimal predicted value is used for breeding selection; the optimization solution process of the BLUP model includes two matrix inversion operations: one inversion of the kinship matrix and the other inversion of a larger matrix containing fixed effects. This invention significantly reduces the memory load in the BLUP model solution process by employing an iterative block-based computation method, while improving computational efficiency and maintaining the accuracy of breeding value estimation. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 is a schematic diagram of the breeding selection optimization process based on the BLUP model of this invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] The purpose of this invention is to provide a breeding selection optimization method, system, device and medium based on the BLUP model, which can significantly reduce the memory load in the BLUP model solution process by adopting an iterative block calculation method, while improving the computational efficiency and maintaining the accuracy of breeding value estimation.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] The BLUP method was initially applied to animal breeding. Traditional animal models solve mixed model equations (MMEs) based on a pedigree matrix (also known as the A matrix) constructed from pedigree information, hence the name ABLUP. The ABLUP model uses animal pedigree information to estimate EBV. Pedigree information has traditionally been used in animal breeding to estimate genetic parameters, but it provides only limited genetic information because it relies primarily on family relationships rather than specific genetic markers.

[0036] With the rapid development of biotechnology, especially the advancement of microarray sequencing technology, the GBLUP (Genomic BLUP) model has emerged. Utilizing dense genetic marker data obtained from gene chips, the GBLUP model can more accurately estimate genetic potential compared to ABLUP because gene chips provide more detailed genetic information.

[0037] However, while microarray sequencing provides dense information, its high cost limits the number of measurements that can be performed. To obtain genetic estimates for the entire population, the ssGBLUP (Single-Step GBLUP) model, which combines traditional pedigree information with gene sequencing data, was proposed. The ssGBLUP model considers both animal genotype and pedigree information, providing a comprehensive genetic assessment.

[0038] Based on the above, and in order to solve the specific problems described in the background section, this invention provides a breeding selection optimization method based on the BLUP model, as shown in Figure 1, comprising:

[0039] Step 100: Construct a BLUP model based on animal pedigree information;

[0040] Step 200: Optimize the BLUP model using the block method to obtain the unbiased optimal prediction value of the animal's genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

[0041] The BLUP model involves two matrix inversion operations: one is the inverse of the kinship matrix, and the other, taking GBLUP as an example, is solving for G... -1 matrix.

[0042] Another instance is the inverse of a larger matrix containing fixed effects, which is the inverse of the rightmost mixing matrix in the equation below.

[0043] The technical principles of this implementation plan are as follows:

[0044] Taking the G matrix (n*n) as an example, divide it into four equal parts:

[0045] (1) Inverse calculation in one step

[0046] The inverse of a single iteration is calculated using the following formula:

[0047] This formula simplifies the global inversion of the G matrix to the G1 matrix and... Finding the inverse of two smaller matrices, the computational complexity of inverting matrix G is O(n). 3 The computational complexity of the two smaller matrices is 2*(n / 2). 3 .

[0048] (2) Iterate through multiple inversions

[0049] Based on a single block partitioning, if the G1 matrix and The matrix is ​​still too large, so we can continue to solve it by matrix partitioning. The second inversion is a composite matrix inversion.

[0050] The inversion operation of the larger matrix containing the fixed effects is as follows:

[0051] That is, the following part of the calculation:

[0052] This matrix can be viewed as a single matrix, which can be broken down into ABCD, and then inverted piece by piece:

[0053] Based on the above specific process, this embodiment has the following beneficial effects:

[0054] With n individual animals and m fixed-effects one-hot coding classifications, the computational complexity of the first inversion of the kinship matrix in the traditional BLUP model's direct inversion method is O(n). 3 The computational complexity of inverting the second mixed matrix is ​​(n+m). 3 Performing matrix partitioning once has a computational complexity of 2*(n / 2). 3 And 2*[(n+m) / 2] 3As the number of block iterations increases, the computational complexity of a single inversion becomes lower, thus significantly reducing the overall computation time. Compared to the direct inversion method in the traditional BLUP model, the block inversion technique used in this invention greatly reduces computation time.

[0055] This embodiment significantly improves computational efficiency when processing large-scale datasets, providing a more efficient and energy-saving solution for complex genetic evaluations and breeding value estimations. Furthermore, by reducing computation time, this method also helps reduce energy consumption, further alleviating the need for high-performance computing resources and facilitating more environmentally friendly and cost-effective scientific computing.

[0056] Furthermore, this invention also provides a breeding selection optimization system based on the BLUP model, comprising:

[0057] The model building module is used to build BLUP models based on animal pedigree information;

[0058] The model solving module is used to optimize the BLUP model using a block-based method to obtain the unbiased optimal prediction value of animal genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization solution process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

[0059] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the breeding selection optimization method based on the BLUP model described above.

[0060] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the BLUP-based breeding selection optimization method as described above.

[0061] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0062] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A breeding selection optimization method based on the BLUP model, characterized in that, include: BLUP model constructed based on animal pedigree information; The BLUP model is optimized using a block-based method to obtain the unbiased optimal prediction value of animal genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

2. The breeding selection optimization method based on the BLUP model according to claim 1, characterized in that, The basic equations of the BLUP model are: Where X is the fixed effects matrix; X' is the transpose of X; Z is the random effects matrix, and Z' is the transpose of Z; y is the observation matrix; G is the kinship matrix, with a size of n*n; n is the number of animal individuals; G -1 Let G be the inverse matrix; k represents the heritability of the phenotype, which is a constant; The inversion operation of the kinship matrix is ​​the inversion of matrix G, where G... -1 Calculation; The inversion operation of the larger matrix containing the fixed effects is as follows: in, Let be the fixed effect value to be solved. The value of the random effect to be solved.

3. The breeding selection optimization method based on the BLUP model according to claim 2, characterized in that, Before optimizing the BLUP model using the block-based method, the process includes: setting the G matrix in the BLUP model to be (n*n), and dividing the G matrix into four equal parts: Where G1 represents the first part matrix; G2 represents the second part matrix; G3 represents the third part matrix; and G4 represents the fourth part matrix.

4. The breeding selection optimization method based on the BLUP model according to claim 3, characterized in that, The optimization solution of the BLUP model using the block method includes single inversion and iterative inversion multiple times.

5. The breeding selection optimization method based on the BLUP model according to claim 4, characterized in that, The single inversion is calculated using the following formula: The global inversion of the G matrix simplifies to the G1 matrix and Finding the inverse of two smaller matrices, the computational complexity of inverting matrix G is O(n). 3 The computational complexity of the two smaller matrices is 2*(n / 2). 3 ; The iterative process involves multiple inversions, and the leftmost mixing matrix of the fundamental equation is calculated using the following formula: Where A represents the first submatrix of the mixture matrix; B represents the second submatrix of the mixture matrix; C represents the third submatrix of the mixture matrix; and D represents the fourth submatrix of the mixture matrix.

6. A breeding selection optimization system based on the BLUP model, characterized in that, include: The model building module is used to build BLUP models based on animal pedigree information; The model solving module is used to optimize the BLUP model using a block-based method to obtain the unbiased optimal prediction value of animal genetic potential; the unbiased optimal prediction value is used for breeding selection; the optimization solution process of the BLUP model includes two matrix inversion operations, one is the inversion operation of the kinship matrix, and the other is the inversion operation of a larger matrix containing fixed effects.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-5.

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