Genetic correlation algorithm between animal binding gametes

Abstract

The invention discloses a genetic correlation algorithm between animal combined gametes, which comprises the steps of collecting pedigree data of animal individuals, acquiring phenotype data of the individuals, preprocessing the pedigree data, associating the phenotype data with the pedigree data, constructing input data required by genetic correlation analysis between the gametes, and deploying a distributed computing framework. The method comprises the steps of designing and building a distributed storage system, developing a parallelized genetic correlation algorithm, distributing genetic correlation calculation tasks to a plurality of nodes in a distributed calculation cluster for parallel processing, integrating and summarizing genetic correlation results obtained by calculation of the distributed calculation nodes, and performing statistical analysis and visualization on the results. Performance optimization and tuning are carried out on a distributed computing system, an algorithm is tuned and optimized according to actual requirements, indexes such as computing complexity and memory consumption are optimized, the effect of a breeding scheme is monitored and evaluated, and adjustment and optimization are carried out according to requirements.

Description

Technical Field

[0001] The invention relates to the technical field of biological genetic algorithms, in particular to a genetic correlation algorithm between animal combining gametes. Background Art

[0002] In the field of animal breeding, genetic relatedness algorithms between gametes play an important role, especially in selecting appropriate genetic resources, improving breeding efficiency, and achieving genetic improvement.

[0003] Genetic evaluation and selection: Genetic correlation algorithms between gametes can be used to evaluate the genetic similarities or differences between different individuals, thereby helping breeders conduct genetic evaluation and selection. By calculating the genetic correlation coefficient between gametes, individuals with higher genetic correlation can be identified as breeding targets.

[0004] Pedigree analysis and genetic improvement: Pedigree analysis in animal breeding is an important method that uses genetic correlation information between gametes to reconstruct family relationships, infer genetic transmission, and evaluate genetic contributions. Pedigree analysis based on genetic correlation algorithms can help breeders select suitable parents for mating, optimize breeding programs, and achieve genetic improvement.

[0005] Genomic selection and breeding strategies: Genetic correlation algorithms between gametes also play a key role in genomic selection. By evaluating the genetic correlation between gametes, the genetic performance of offspring can be more accurately predicted, and optimized breeding strategies can be formulated to accelerate the genetic evolution process and improve breeding efficiency and genetic improvement effects.

[0006] Population genetic structure analysis: The genetic correlation algorithm between gametes can also be used to analyze the genetic structure of animal populations. By understanding the genetic correlation between individuals within a population, we can better manage genetic diversity, prevent genetic drift and genetic decline, and maintain the genetic health and genetic progress of the population.

[0007] In the field of animal breeding, there are many existing technologies for collecting, analyzing and applying genotypic data in combination with algorithms for genetic relatedness between gametes.

[0008] 1. Genome sequencing technology: High-throughput sequencing technologies such as next-generation sequencing (NGS) and third-generation sequencing (such as PacBio and Oxford Nanopore) make it easier and more economical to obtain genomic data of individual animals. These technologies can provide rich genotype information for calculating genetic correlations between gametes.

[0009] 2. SNP Genotyping: Single nucleotide polymorphism (SNP) genotyping technology can quickly and accurately genotype large-scale SNP markers. These data can be used to assess the genetic correlation between individuals and support breeding decisions based on genetic correlation algorithms between gametes.

[0010] 3. Continuous readout technology: Continuous readout technology can connect the genotype information of a specific individual through long DNA fragments or continuous SNP markers on chromosomes. These continuous readout data help to more accurately evaluate family relationships and calculate the genetic correlation between gametes.

[0011] Genomic selection: Genomic selection is a method that uses genotype data to guide breeding. By evaluating the genetic correlation between gametes, individuals with excellent genotypes can be more accurately selected as breeding targets, thereby improving breeding efficiency and genetic improvement effects.

[0012] Population genetics analysis tools: Population genetics analysis tools (such as PLINK, STRUCTURE, etc.) can be used to evaluate the genetic structure of animal populations and the genetic correlation between individuals. These tools provide rich statistical analysis functions to support breeders in the interpretation and application of genotype data.

[0013] 4. Genetic linkage map construction: Genetic linkage map is a graphical tool that describes the relative position and genetic linkage relationship between different loci on the genome. By constructing a genetic linkage map, we can better understand the genotype data and optimize the genetic correlation algorithm between gametes.

[0014] Although these existing technologies provide rich genotype data and analytical tools in the field of animal breeding, they also have some shortcomings:

[0015] Genome Sequencing Technology:

[0016] 1. High cost: High-throughput sequencing technology usually requires expensive instruments and consumes a lot of time and resources, so it is costly and may not be practical for large-scale animal breeding projects.

[0017] 2. Complexity: The processing and analysis of sequencing data requires professional skills and software tools, which may have a certain learning curve for ordinary breeders.

[0018] SNP Genotyping:

[0019] 1. Limited information: Although SNP markers are a powerful genotyping tool, their information content is still limited and cannot fully represent the entire genetic information of an individual. Therefore, it may not be possible to fully evaluate the genetic correlation between gametes.

[0020] 2. Marker selection bias: The selection of SNP markers may be biased and may have different effects on different species or populations. It is necessary to carefully select a suitable marker set.

[0021] Continuous reading technology:

[0022] 1. High cost: Compared with other genotyping technologies, the cost of continuous reading technology is relatively high, especially for large sample groups. The cost may limit its popularity in practical applications.

[0023] 2. Difficulty in data analysis: The amount of continuous reading data is large, and processing and analysis may be complex, requiring efficient data processing tools and computing resources.

[0024] Genomic Selection:

[0025] 1. Loss of genetic diversity: Genomic selection may lead to the loss of genetic diversity. Long-term selection may lead to genetic impoverishment of the population and increase the risk of genetic degeneration.

[0026] 2. Unable to fully predict genetic performance: Although genomic selection can select individuals more accurately, there is still a certain degree of uncertainty and it is impossible to fully predict the genetic performance of offspring, especially for the prediction of complex traits.

[0027] Population genetics analysis tools:

[0028] 1. Limitations of model assumptions: Many population genetics analysis tools are based on specific statistical models and assumptions, which may have certain limitations on data requirements and scope of application, and the results may be affected by model assumptions.

[0029] Difficulty in interpreting results: The analysis results usually require professional knowledge to interpret and understand, and it may be difficult for non-professionals to understand their meaning and application. Therefore, an improved technology is urgently needed to solve this problem in the existing technology. Summary of the invention

[0030] The object of the present invention is to provide an algorithm for the genetic correlation between animal combining gametes to solve the problems raised in the above-mentioned background technology.

[0031] To achieve the above object, the present invention provides the following technical solution: an algorithm for the genetic correlation between animal combined gametes, the genetic correlation algorithm process between animal combined gametes includes:

[0032] Collect pedigree data of individual animals and obtain individual phenotypic data;

[0033] Preprocess the pedigree data, associate the phenotypic data with the pedigree data, and construct the input data required for genetic correlation analysis between gametes;

[0034] Deploy distributed computing frameworks, design and build distributed storage systems;

[0035] Develop a parallel genetic correlation algorithm to distribute genetic correlation calculation tasks to multiple nodes in a distributed computing cluster for parallel processing;

[0036] Integrate and summarize the genetic correlation results calculated by distributed computing nodes, and perform statistical analysis and visualization on the results;

[0037] Optimize and tune the performance of distributed computing systems, tune algorithms according to actual needs, and optimize indicators such as computing complexity and memory consumption;

[0038] Monitor and evaluate the performance of breeding programs and make adjustments and optimizations as needed.

[0039] Preferably, the genetic correlation algorithm between the animal's combined gametes pre-processes the pedigree data of individual animals, including data cleaning, formatting and standardization;

[0040] Associate phenotypic data with pedigree data to build data structures suitable for distributed computing.

[0041] Preferably, the distributed computing framework includes Apache Hadoop and Apache Spark.

[0042] Preferably, the parallelized genetic correlation algorithm is developed to divide the genetic correlation calculation task into multiple subtasks and distribute them to multiple nodes in a distributed computing cluster for parallel processing;

[0043] After parallel processing, the divided subtasks will classify and count the processed data results, and generate data graphs for the divided and processed task data.

[0044] Preferably, the distributed computing framework performs appropriate configuration on each computing node, including the settings of the hardware configuration CPU, memory, storage and software environment (such as operating system, distributed computing framework).

[0045] Preferably, a suitable task scheduling strategy is designed during the genetic correlation algorithm process between the animal binding gametes to monitor the operating status of the system in real time.

[0046] Preferably, the distributed computing system performs performance optimization and tuning on task scheduling, resource management, and data transmission.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) Distributed computing can greatly accelerate the speed of genetic correlation analysis, allowing breeders to more quickly evaluate the genetic correlation between gametes, which helps to accelerate the breeding process, shorten the breeding cycle, and improve breeding efficiency;

[0049] (2) Gamete genetic correlation analysis through distributed computing can more accurately evaluate the genetic relationship between different gametes, which helps breeders to select gametes with excellent genetic background more accurately, thereby improving the genetic quality of offspring. Distributed computing enables breeders to more comprehensively analyze large-scale phenotypic data and pedigree information, better understand the genetic relationship between individuals, and thus optimize genetic improvement strategies, which helps to formulate more effective breeding plans and promote the development of the breeding industry;

[0050] (3) Divide the genetic correlation calculation task into multiple subtasks and distribute the genetic correlation calculation task to multiple nodes in the distributed computing cluster for parallel processing. Compared with traditional single-machine computing, distributed computing can dynamically allocate computing resources according to demand, make full use of the processing power of multiple computing nodes, and reduce computing costs. This enables breeders to obtain more computing resources at a lower cost and improve economic benefits;

[0051] (4) The genetic correlation algorithm between animal gametes uses distributed computing to calculate the genetic correlation between gametes based on pedigree, which can accelerate the breeding process, accurately select and match, optimize genetic improvement strategies, and reduce costs. It helps to accelerate the breeding process, shorten the breeding cycle, and improve breeding efficiency. It can more accurately evaluate the genetic relationship between different gametes, enhance data processing capabilities, and enable breeders to better cope with the needs of large-scale data processing. At the same time, the enhanced data processing capabilities have brought many beneficial effects to the field of animal breeding. DETAILED DESCRIPTION

[0052] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0053] The present invention provides a technical solution: a genetic correlation algorithm between animal combining gametes, wherein the genetic correlation algorithm process between animal combining gametes includes:

[0054] Collect the pedigree data of individual animals and obtain the phenotypic data of individuals. Collect the pedigree data of individual animals, including parent information, offspring information and family relationships, and obtain the phenotypic data of individuals, which can be obtained through sequencing and other technologies.

[0055] The genetic correlation algorithm between animal gametes pre-processes the pedigree data of individual animals, including data cleaning, formatting and standardization, to ensure the quality and consistency of the data, associate the phenotypic data with the pedigree data, and construct a data structure suitable for distributed computing. Distributed computing can greatly accelerate the speed of genetic correlation analysis, allowing breeders to more quickly evaluate the genetic correlation between gametes, which helps to accelerate the breeding process, shorten the breeding cycle and improve breeding efficiency.

[0056] By conducting gamete genetic correlation analysis through distributed computing, the genetic relationship between different gametes can be evaluated more accurately, which helps breeders to select gametes with excellent genetic background more accurately, thereby improving the genetic quality of offspring. Distributed computing enables breeders to analyze large-scale phenotypic data and pedigree information more comprehensively, and can better understand the genetic relationship between individuals, thereby optimizing genetic improvement strategies, which helps to formulate more effective breeding plans and promote the development of the breeding industry.

[0057] The pedigree data are preprocessed, including cleaning, formatting and standardization, to associate the phenotypic data with the pedigree data and construct the input data required for genetic correlation analysis between gametes.

[0058] Deploy distributed computing frameworks, including Apache Hadoop and Apache Spark, to support parallel distributed processing of large-scale data, design and build distributed storage systems to store and manage large amounts of genealogical and phenotypic data, and configure each computing node appropriately, including hardware configuration of CPU, memory, storage, and software environment (such as operating system, distributed computing framework).

[0059] Distributed computing enables breeders to analyze large-scale phenotypic data and pedigree information more comprehensively, which can better understand the genetic relationships between individuals and optimize genetic improvement strategies. This helps to develop more effective breeding plans and promote the development of the breeding industry.

[0060] Develop a parallelized genetic correlation algorithm, divide the genetic correlation calculation task into multiple subtasks, and distribute the genetic correlation calculation task to multiple nodes in a distributed computing cluster for parallel processing. Compared with traditional single-machine computing, distributed computing can dynamically allocate computing resources according to demand, make full use of the processing power of multiple computing nodes, and reduce computing costs. This enables breeders to obtain more computing resources at a lower cost and improve economic benefits.

[0061] After parallel processing, the divided subtasks will classify and count the processed data results, and generate data graphs for the divided and processed task data.

[0062] The genetic correlation results calculated by distributed computing nodes are integrated and summarized, and the results are statistically analyzed and visualized to understand the genetic correlation patterns and trends among different gametes.

[0063] Optimize and tune the performance of distributed computing systems, tune algorithms according to actual needs, optimize indicators such as computational complexity and memory consumption, monitor and evaluate the effectiveness of breeding programs, and adjust and optimize as needed.

[0064] The field of animal breeding usually involves a large amount of phenotypic data and pedigree information. Traditional single-machine computing often finds it difficult to handle such a large amount of data. The use of distributed computing can make full use of the processing power of multiple computing nodes, enhance data processing capabilities, and enable breeders to better cope with the needs of large-scale data processing.

[0065] In the process of genetic correlation algorithm between animal gametes, appropriate task scheduling strategies are designed, the system operation status is monitored in real time, and the distributed computing system optimizes and tunes the performance of task scheduling, resource management, and data transmission to ensure that the system can run efficiently and stably.

[0066] Embodiment 1,

[0067] First, the pedigree data of individual animals are preprocessed, including data cleaning, formatting and standardization, and the phenotypic data are associated with the pedigree data to construct a data structure suitable for distributed computing.

[0068] Develop a parallel genetic correlation calculation algorithm to adapt to the distributed computing environment, divide the genetic correlation calculation task into multiple subtasks, and distribute them to multiple nodes in the distributed computing cluster for parallel processing.

[0069] Deploy and configure computing nodes to ensure that the nodes can communicate and collaborate with each other, and properly configure each computing node, including the settings of hardware configuration (such as CPU, memory, storage) and software environment (such as operating system, distributed computing framework).

[0070] Design appropriate task scheduling strategies to ensure that tasks can be completed on time and computing resources can be fully utilized, monitor the system's operating status in real time, promptly discover and handle potential problems and anomalies, integrate and summarize the genetic correlation results calculated by distributed computing nodes, and perform statistical analysis and visualization of the results so that breeders can understand and apply them.

[0071] Optimize and tune the system's performance, including optimization of task scheduling and resource management, tune the algorithm according to actual needs, and optimize indicators such as computational complexity and memory consumption.

[0072] Through the above implementation methods, a complete pedigree-based distributed computing system can be constructed for gamete genetic correlation analysis, thereby providing effective technical support for the field of animal breeding.

[0073] The genetic correlation algorithm between animal combined gametes uses distributed computing to calculate the genetic correlation between gametes based on pedigree, which can accelerate the breeding process, accurately select and match, optimize genetic improvement strategies, and reduce costs. It helps to accelerate the breeding process, shorten the breeding cycle, and improve breeding efficiency. It can more accurately evaluate the genetic relationship between different gametes, enhance data processing capabilities, enable breeders to better cope with the needs of large-scale data processing, and at the same time enhance data processing capabilities, bringing many beneficial effects to the field of animal breeding.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An algorithm for the genetic correlation between animal gametes, characterized in that: The genetic correlation algorithm process between the animal's combined gametes includes Collect pedigree data of individual animals and obtain individual phenotypic data; Preprocess the pedigree data, associate the phenotypic data with the pedigree data, and construct the input data required for genetic correlation analysis between gametes; Deploy distributed computing frameworks, design and build distributed storage systems; Develop a parallel genetic correlation algorithm to distribute genetic correlation calculation tasks to multiple nodes in a distributed computing cluster for parallel processing; Integrate and summarize the genetic correlation results calculated by distributed computing nodes, and perform statistical analysis and visualization on the results; Optimize and tune the performance of distributed computing systems, tune algorithms according to actual needs, and optimize indicators such as computing complexity and memory consumption; Monitor and evaluate the performance of breeding programs and make adjustments and optimizations as needed.

2. The genetic correlation algorithm between animal combining gametes according to claim 1, characterized in that: Preprocessing the pedigree data of individual animals in the genetic correlation algorithm between the combined gametes of the animals, including data cleaning, formatting and standardization; Associate phenotypic data with pedigree data to build data structures suitable for distributed computing.

3. The genetic correlation algorithm between animal combining gametes according to claim 1, characterized in that: The distributed computing framework includes Apache Hadoop and Apache Spark.

4. The genetic correlation algorithm between animal fusing gametes according to claim 1, characterized in that: The parallel genetic correlation algorithm is developed to divide the genetic correlation calculation task into multiple subtasks and distribute them to multiple nodes in a distributed computing cluster for parallel processing; After parallel processing, the divided subtasks will classify and count the processed data results, and generate data graphs for the divided and processed task data.

5. The genetic correlation algorithm between animal combining gametes according to claim 1, characterized in that: The distributed computing framework performs appropriate configuration on each computing node, including the hardware configuration of CPU, memory, storage and the setting of software environment (such as operating system, distributed computing framework).

6. The genetic correlation algorithm between animal mating gametes according to claim 1, characterized in that: In the process of the genetic correlation algorithm between the animal combining gametes, a suitable task scheduling strategy is designed to monitor the operating status of the system in real time.

7. The genetic correlation algorithm between animal fusing gametes according to claim 1, characterized in that: The distributed computing system performs performance optimization and tuning on task scheduling, resource management and data transmission.