Crossbreeding method for procypris merus and crucian

Abstract

The invention discloses a hybrid breeding method for procypris merus and crucian carps, and relates to the technical field of freshwater fish hybridization. According to the method, experience-to-precision crossing of parent matching is realized, a parent combination with the best genetic complementarity and the highest hybridization success rate can be scientifically selected through the multi-character comprehensive scoring model and the hybridization affinity prediction model, the hybridization efficiency and the target character directional polymerization capability are improved from the source, and the method is suitable for large-scale popularization and application. The optimal insemination opportunity is accurately locked through an ovum quality evaluation algorithm, and an optimized insemination program is combined, so that the reproductive disorder of distant hybridization is effectively overcome, and then abnormal embryos are automatically removed in an early stage by utilizing an embryo health degree evaluation model, so that the labor burden is greatly reduced, and the efficiency is improved. And the quality of offspring seed groups is ensured.

Description

Technical Field

[0001] This invention relates to the field of freshwater fish hybridization technology, specifically a method for hybrid breeding of carp and crucian carp. Background Technology

[0002] Rice paddy carp (usually referring to a carp variant with specific regional characteristics, known for its tender and delicious meat) and crucian carp (characterized by strong adaptability and relatively more intramuscular bones) are important freshwater economic fish species. Combining the rapid growth and excellent meat quality of rice paddy carp with the strong resistance of crucian carp through distant hybridization is an effective way to cultivate new aquatic varieties.

[0003] However, existing methods for hybrid breeding of rice paddy carp and crucian carp have the following limitations:

[0004] Parent selection relies on experience: parents are usually selected only based on size or appearance, lacking in-depth understanding of genetic background, unable to predict the performance after hybridization, and are largely blind, resulting in large fluctuations in hybridization success rate (fertilization rate, hatching rate).

[0005] Reproductive isolation barriers: Carp and crucian carp belong to different genera and exhibit severe reproductive isolation. Conventional hybridization methods result in low fertilization rates and low rates of normal embryonic development, leading to high rates of offspring malformation. This necessitates numerous fertilization experiments and frequent manual removal of abnormal embryos under a microscope, resulting in a massive workload.

[0006] The breeding cycle for superior traits is long: the superior traits of offspring (such as growth rate and number of intermuscular spines) can only be accurately measured after they have been raised to a certain size, resulting in low screening efficiency and a long breeding cycle.

[0007] Lack of precise decision-making tools: From parent selection and timing of fertilization to early seedling screening, the entire process lacks quantitative standards and algorithmic support, making it difficult to achieve standardized and efficient precision breeding.

[0008] Therefore, there is an urgent need in this field for a new precision hybridization breeding method that can overcome reproductive isolation, improve hybridization efficiency, and accelerate the selection process for superior traits. Summary of the Invention

[0009] The purpose of this invention is to provide a method for hybrid breeding of rice paddy carp and crucian carp to solve the technical problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for hybrid breeding of rice paddy carp and crucian carp, comprising at least the following steps:

[0011] S1: Build a multi-trait comprehensive selection module for parental lines, collect multiple phenotypic data and genotype numbers of candidate parents from the basic populations of rice carp and crucian carp, and calculate the breeding value of each candidate parent through a comprehensive parental scoring model;

[0012] S2: Establish the optimal hybridization combination matching decision. Pair the top-quality rice carp and crucian carp parents selected in step S1 for simulation. Based on a hybridization affinity prediction model, calculate the expected hybridization success rate, offspring deformity rate and genetic gain of the target trait for each pair of combinations, so as to select the optimal hybridization combination.

[0013] S3: Make precise decisions and operations on the timing of artificial insemination, monitor the oocyte development status of the female parent in the optimal combination, determine the best time point for artificial insemination through an oocyte quality assessment algorithm based on image recognition and physiological parameters, and use optimized sperm activation solution and insemination procedure for hybridization insemination;

[0014] S4: Intelligent screening of early embryos and fry: The hybrid fertilized eggs are monitored throughout the process. Using a computer vision-based embryo development health assessment model, embryos with abnormal or arrested development are automatically identified and removed in the early stage (before the gastrulation stage). In the fry cultivation stage, behavioral analysis algorithms are used to quantify the growth rate and vitality of fry and screen individuals with growth advantages in advance.

[0015] Furthermore, the phenotypic data includes at least body length, weight, body size coefficient, and body color characteristics, and the genotypic data includes at least SNP sites related to growth, disease resistance, and intermuscular spurs.

[0016] Furthermore, the parental multi-trait comprehensive selection module in S1 defines the parental comprehensive breeding value. for:

[0017]

[0018] in:

[0019] This represents the phenotypic or genotypic value of the i-th candidate parent on the j-th trait;

[0020] The function normalizes the trait value to the interval [0,1].

[0021] The weighting coefficients for this trait are determined by analytic hierarchy process or principal component analysis. The weighting vectors are set to favor complementary superior traits of carp and crucian carp, such as growth rate, fewer intermuscular spines, and cold resistance.

[0022] Furthermore, the hybridization affinity prediction model is a machine learning model based on gradient boosting decision tree, and its input feature vector includes: parental genetic distance (calculated based on SNP data), complementarity of specific genotypes of male and female parents (such as immunocompatibility genes), parental genotype difference ratio, and historical records of successful hybridization of similar combinations.

[0023] The hybridization affinity prediction model outputs an affinity index between 0 and 1. ,choose The combination with the highest genetic gain of the target trait (greater than a preset threshold, such as 0.75) is the best hybridization combination.

[0024] Furthermore, the oocyte quality assessment algorithm in step S3 is specifically as follows:

[0025] The diameter, uniformity, and transparency of the eggs were obtained through microscopic imaging.

[0026] Define egg quality maturity score :

[0027]

[0028] in, It is the normalized egg diameter; It refers to the uniformity of the eggs; It is the transparency coefficient; As weight;

[0029] when Three consecutive measurements exceeding the threshold At that time, the system determines that the optimal insemination window has been entered.

[0030] Furthermore, the embryonic development health assessment model in step S4 is based on a deep learning convolutional neural network;

[0031] Train an image classification model using a large number of labeled normal and abnormal embryo images;

[0032] The embryonic development health assessment model analyzes real-time acquired embryonic microscopic images and outputs a confidence level representing developmental health.

[0033] Embryos with a confidence level <0.95 are automatically removed to ensure the overall quality of the hatching population;

[0034] During the seedling stage, by tracking the swimming trajectory of fry in a specific area, their average swimming speed and activity entropy are calculated to screen out high-activity individuals with fast swimming speed and moderate activity entropy.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. This invention achieves a leap from empirical to precise parental selection. Through a multi-trait comprehensive scoring model and a hybridization affinity prediction model, it can scientifically select parental combinations with the best genetic complementarity and the highest hybridization success rate, thereby improving the efficiency of hybridization and the directional aggregation of target traits from the source.

[0037] 2. This invention significantly improves the hybridization fertilization rate and the normal embryo rate: By accurately identifying the optimal fertilization time through an egg quality assessment algorithm and combining it with an optimized fertilization procedure, it effectively overcomes the reproductive barriers of distant hybridization. Furthermore, by using an embryo health assessment model, it automatically removes abnormal embryos in the early stages, greatly reducing the human burden and ensuring the quality of the seedling population.

[0038] 3. This invention enables early prediction and efficient screening of superior seedlings. Growth traits that traditionally require months of rearing for evaluation can be predicted early using behavioral indicators (swimming speed, vitality) during the seedling stage. This allows for rapid screening of individuals with growth potential at the larval stage, significantly shortening the breeding cycle and improving breeding efficiency. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of 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.

[0040] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0042] See Figure 1 A method for crossbreeding rice paddy carp and crucian carp includes at least the following steps:

[0043] S1: Build a multi-trait comprehensive selection module for parental lines, collect multiple phenotypic data and genotype numbers of candidate parents from the basic populations of rice carp and crucian carp, and calculate the breeding value of each candidate parent through a comprehensive parental scoring model;

[0044] S2: Establish the optimal hybridization combination matching decision. Pair the top-quality rice carp and crucian carp parents selected in step S1 for simulation. Based on a hybridization affinity prediction model, calculate the expected hybridization success rate, offspring deformity rate and genetic gain of the target trait for each pair of combinations, so as to select the optimal hybridization combination.

[0045] S3: Make precise decisions and operations on the timing of artificial insemination, monitor the oocyte development status of the female parent in the optimal combination, determine the best time point for artificial insemination through an oocyte quality assessment algorithm based on image recognition and physiological parameters, and use optimized sperm activation solution and insemination procedure for hybridization insemination;

[0046] S4: Intelligent screening of early embryos and fry: The hybrid fertilized eggs are monitored throughout the process. Using a computer vision-based embryo development health assessment model, embryos with abnormal or arrested development are automatically identified and removed in the early stage (before the gastrulation stage). In the fry cultivation stage, behavioral analysis algorithms are used to quantify the growth rate and vitality of fry and screen individuals with growth advantages in advance.

[0047] Furthermore, the phenotypic data includes at least body length, weight, body size coefficient, and body color characteristics, and the genotypic data includes at least SNP sites related to growth, disease resistance, and intermuscular spurs.

[0048] Furthermore, the parental multi-trait comprehensive selection module in S1 defines the parental comprehensive breeding value. for:

[0049]

[0050] in:

[0051] This represents the phenotypic or genotypic value of the i-th candidate parent on the j-th trait;

[0052] The function normalizes the trait value to the interval [0,1].

[0053] The weighting coefficients for this trait are determined by analytic hierarchy process or principal component analysis. The weighting vectors are set to favor complementary superior traits of carp and crucian carp, such as growth rate, fewer intermuscular spines, and cold resistance.

[0054] Furthermore, the hybridization affinity prediction model is a machine learning model based on gradient boosting decision tree, and its input feature vector includes: parental genetic distance (calculated based on SNP data), complementarity of specific genotypes of male and female parents (such as immunocompatibility genes), parental genotype difference ratio, and historical records of successful hybridization of similar combinations.

[0055] The hybridization affinity prediction model outputs an affinity index between 0 and 1. ,choose The combination with the highest genetic gain of the target trait (greater than a preset threshold, such as 0.75) is the best hybridization combination.

[0056] Furthermore, the oocyte quality assessment algorithm in step S3 is specifically as follows:

[0057] The diameter, uniformity, and transparency of the eggs were obtained through microscopic imaging.

[0058] Define egg quality maturity score :

[0059]

[0060] in, It is the normalized egg diameter; It refers to the uniformity of the eggs; It is the transparency coefficient; As weight;

[0061] when Three consecutive measurements exceeding the threshold At that time, the system determines that the optimal insemination window has been entered.

[0062] Furthermore, the embryonic development health assessment model in step S4 is based on a deep learning convolutional neural network;

[0063] Train an image classification model using a large number of labeled normal and abnormal embryo images;

[0064] The embryonic development health assessment model analyzes real-time acquired embryonic microscopic images and outputs a confidence level representing developmental health.

[0065] Embryos with a confidence level <0.95 are automatically removed to ensure the overall quality of the hatching population;

[0066] During the seedling stage, by tracking the swimming trajectory of fry in a specific area, their average swimming speed and activity entropy are calculated to screen out high-activity individuals with fast swimming speed and moderate activity entropy.

[0067] Based on the above embodiments, a precision breeding system can be proposed, including:

[0068] Parental data management platform for storing phenotypic and genotypic data;

[0069] The intelligent decision-making center has the aforementioned parental scoring model, affinity prediction model, and insemination timing decision algorithm built-in.

[0070] Automated monitoring and screening devices, including microscopic imaging systems and behavior analysis software;

[0071] The user interface is used to display decision results and filtering suggestions.

[0072] An intelligent breeding pipeline has been constructed: This invention connects various stages through algorithms and models, forming a closed-loop data flow and decision chain from parent management to seedling selection. This system makes the hybridization breeding process quantifiable, predictable, and controllable, providing a model for the intelligent upgrading of the aquaculture breeding industry.

[0073] It has strong scalability: The core algorithm framework of this method (such as parent scoring, affinity prediction, and early screening) can be widely applied to the distant hybridization breeding of other aquatic animals, and has significant industry promotion value.

[0074] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for hybrid breeding of rice paddy carp and crucian carp, characterized in that: At least the following steps are included: S1: Build a multi-trait comprehensive selection module for parental lines, collect multiple phenotypic data and genotype numbers of candidate parents from the basic populations of rice carp and crucian carp, and calculate the breeding value of each candidate parent through a comprehensive parental scoring model; S2: Establish the optimal hybridization combination matching decision. Pair the top-quality rice carp and crucian carp parents selected in step S1 for simulation. Based on a hybridization affinity prediction model, calculate the expected hybridization success rate, offspring deformity rate and genetic gain of the target trait for each pair of combinations, so as to select the optimal hybridization combination. S3: Make precise decisions and operations on the timing of artificial insemination, monitor the oocyte development status of the female parent in the optimal combination, determine the best time point for artificial insemination through an oocyte quality assessment algorithm based on image recognition and physiological parameters, and use optimized sperm activation solution and insemination procedure for hybridization insemination; S4: Intelligent screening of early embryos and fry: The hybrid fertilized eggs are monitored throughout the process. An embryo development health assessment model based on computer vision is used to automatically identify and remove embryos with abnormal or arrested development in the early stage. In the fry cultivation stage, behavioral analysis algorithms are used to quantify the growth rate and vitality of fry and screen individuals with growth advantages in advance.

2. The method for hybrid breeding of carp and crucian carp according to claim 1, characterized in that: The phenotypic data includes at least body length, weight, body size coefficient, and body color characteristics, and the genotypic data includes at least SNP sites related to growth, disease resistance, and intermuscular thorns.

3. The method for hybrid breeding of carp and crucian carp according to claim 1, characterized in that: The parental multi-trait comprehensive optimization module in S1 defines the parental comprehensive breeding value. for: in: This represents the phenotypic or genotypic value of the i-th candidate parent on the j-th trait; The function normalizes the trait value to the interval [0,1]. The weight coefficients for this trait are determined by the analytic hierarchy process (AHP) or principal component analysis. The weight vectors are set to favor the complementary superior traits of the Chinese carp and crucian carp.

4. The method for hybrid breeding of carp and crucian carp according to claim 1, characterized in that: The hybridization affinity prediction model is a machine learning model based on gradient boosting decision tree. Its input feature vector includes: parental genetic distance, complementarity of specific genotypes of male and female parents, parental genotype difference ratio, and historical records of successful hybridization of similar combinations. The hybridization affinity prediction model outputs an affinity index between 0 and 1. ,choose The combination that exceeds the preset threshold and has the highest genetic gain of the target trait is selected as the best hybridization combination.

5. The method for hybrid breeding of carp and crucian carp according to claim 1, characterized in that: The oocyte quality assessment algorithm in step S3 is as follows: The diameter, uniformity, and transparency of the eggs were obtained through microscopic imaging. Define egg quality maturity score : in, It is the normalized egg diameter; It refers to the uniformity of the eggs; It is the transparency coefficient; As weight; when Three consecutive measurements exceeding the threshold At that time, the system determines that the optimal insemination window has been entered.

6. The method for hybrid breeding of carp and crucian carp according to claim 1, characterized in that: The embryonic development health assessment model in step S4 is based on a deep learning convolutional neural network. Train an image classification model using a large number of labeled normal and abnormal embryo images; The embryonic development health assessment model analyzes real-time acquired embryonic microscopic images and outputs a confidence level representing developmental health. Embryos with a confidence level <0.95 are automatically removed to ensure the overall quality of the hatching population; During the seedling stage, by tracking the swimming trajectory of fry in a specific area, their average swimming speed and activity entropy are calculated, and high-activity individuals with fast swimming speed and moderate activity entropy are selected.

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