Multi-trait composite breeding method for feed conversion rate, growth and survival rate of fish

Abstract

The present invention relates to a multi-trait composite breeding method for fish feed conversion rate, growth and survival rate, belonging to the field of genetic breeding of aquatic animals. The method establishes composite traits by using phenotypic correlations, then establishes genomic selection methods for multiple traits, and finally uses genetic correlations to establish a comprehensive genomic breeding value calculation method and a parental selection scheme. The method of the present invention overcomes the problem of excessive subjectivity in artificially setting weighting parameters in traditional methods. Through the comprehensive evaluation of multiple important economic traits, it not only improves the objectivity and accuracy of breeding selection, but also enables more effective resource allocation and higher genetic gain in multi-trait breeding, which is beneficial to the sustainable development of aquaculture.

Description

Technical Field

[0001] The present invention belongs to the field of genetic breeding of aquatic animals, and particularly relates to a multi-trait composite breeding method for feed conversion rate, growth, and survival rate of fish. Background Art

[0002] Fish is one of the important sources of animal protein for humans. In recent years, the aquaculture production in China has been continuously increasing, especially in the context of coping with population growth and increasing food demand. The feed cost accounts for 30% to 70% of the total production cost of fish farming, which is the main expenditure in intensive farming. The feed conversion rate (FCR) is a key feature in aquaculture, directly affecting the profitability and sustainability of the industry. Therefore, breeding new varieties with high FCR can reduce breeding costs and increase profits.

[0003] With the continuous development of the aquaculture industry, the selection and breeding of single traits can no longer meet the diverse market demands for fish quality. Therefore, in recent years, the trend of selection and breeding has started to develop towards multi-trait breeding. However, there is still a lack of efficient composite breeding methods for multi-trait selection. In order to effectively improve the comprehensive production performance of fish, it is urgent to develop an efficient multi-trait composite breeding technology that can take into account key traits such as feed conversion rate, growth rate, and survival rate to achieve better breeding results. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a multi-trait efficient composite breeding method for feed conversion rate, growth, and survival rate of fish, so as to improve breeding efficiency and accelerate the breeding process.

[0005] The present invention is realized by the following technical solutions:

[0006] A multi-trait composite breeding method for feed conversion rate, growth, and survival rate of fish, the method is specifically as follows:

[0007] (1) Establish more than 60 full-sib families of fish, mark the individuals in the families, and count the survival rate during cultivation;

[0008] (2) Randomly select 20 - 30 tails from each family and culture them separately in small net cages. After culturing for a period of time, obtain the body weight and feed conversion rate of the individuals in the families;

[0009] (3) After obtaining the two traits of body weight BW and feed conversion rate FCR, conduct genetic parameter evaluation to obtain the heritability of the traits of fish body weight and feed conversion rate and the phenotypic correlation r XY and genetic correlation r A ;

[0010] (4) Standardize the trait values of body weight and feed conversion rate by Z-score. The standardization formula is:

[0011] Z = (X - μ) / σ, where X is the original value, μ is the mean of the trait, and σ is the standard deviation of the trait;

[0012] (5) Establish a composite trait CT, CT = BW Z值 + FCR Z值 (1 + (1 - r XY ));

[0013] (6) Extract DNA from all individuals, sequence them using a sequencing platform, and obtain the SNP genotypes of each individual;

[0014] (7) For the three traits of BW, FCR, and the composite trait CT, use three genomic selection prediction methods, GBLUP, ssGBLUP, and rrBLUP, to estimate the genomic estimated breeding values (GEBVs), and use the five-fold cross-validation method to compare the prediction accuracies of the three genomic selection prediction methods;

[0015] (8) For each of the three traits of BW, FCR, and the composite trait CT, select the genomic selection prediction method with the highest accuracy, and calculate the individual genomic breeding value GBLUP;

[0016] (9) Establish the composite genomic breeding value CGBLUP and its calculation method;

[0017] CGBLUP = BW GBLUP值 + FCR GBLUP值 (1 + (1 - r A )) + CT GBLUP值 ;

[0018] Sort all individuals according to the level of the composite genomic breeding value, screen the families in the top 20% of the survival rate, and at the same time, the individuals with a higher ranking in the composite genomic breeding value are subjected to individual marking and used as reserve broodstock for subsequent cultivation.

[0019] Furthermore, in step (1), during the family cultivation process, when the individuals grow to the size of commercial fry, randomly select more than 2000 tails for special pond cultivation, and at the same time start recording the death situation and counting the family survival rate.

[0020] Furthermore, in step (2), a weighing operation is required at the beginning of the cultivation. During the cultivation process, keep the cultivation environment consistent, feed to satiety, clean up the residual bait, and record the food intake of each fish every day.

[0021] Furthermore, in step (2), all individuals in the families are marked in separate cages and randomly assigned to each cultivation pond.

[0022] Further, in step (3), an animal model is used to evaluate genetic parameters, considering the common environmental effect, and the heritability h 2 , phenotypic genetic correlation r XY and genetic correlation r A are obtained.

[0023] Further, in step (6), the tissue used for DNA extraction is selected as the fin of the fish to avoid damaging the fish body.

[0024] Further, in step (6), an Illumina sequencing platform or a BGI T7 sequencing platform can be used to sequence the DNA.

[0025] Further, in step (7), the evaluation index is predictive accuracy (PA), which is defined as the correlation between genomic estimated breeding values (GEBVs) and phenotypic values divided by the square root of heritability.

[0026] The beneficial effects of the present invention compared with the prior art: The present invention innovatively proposes a multi-trait efficient composite breeding technology for feed conversion rate, growth, and survival rate of fish. By using phenotypic correlation to establish composite traits and using genetic correlation to calculate comprehensive genomic breeding values, the problem of overly strong subjectivity in artificially setting weighting parameters in traditional methods is overcome. This method not only improves the objectivity and accuracy of breeding selection but also enables more effective resource allocation and higher genetic gain in multi-trait breeding. In addition, through the comprehensive evaluation of multiple important economic traits, the present invention enhances the comprehensive production performance of selected individuals, improves breeding efficiency, makes the breeding work more in line with actual aquaculture needs, and ultimately promotes the sustainable development of aquaculture and the improvement of economic benefits. Specific embodiments

[0027] The technical solution of the present invention will be further explained below through examples, but the protection scope of the present invention is not limited in any form by the examples. In this example, turbot is used as the research object to establish a multi-trait efficient composite breeding technology for feed conversion rate, growth, and survival rate.

[0028] Example 1

[0029] 1. Establishment of families and seedling cultivation

[0030] A full-sib family is established using a mating design of 1 female and 1 male. In 2023, 70 F1 full-sib families were successfully constructed at Yantai Tianyuan Aquatic Products Co., Ltd. Each family was reared in a different culture pond and labeled with a family number. When the families reached 9 months of age, they were used for weight and feed conversion rate measurement experiments.

[0031] 2. Centralized temporary rearing before testing

[0032] Before the start of the test, 1000 fish were randomly selected from each family and temporarily reared in a workshop in pools according to family for 2 weeks. After the temporary rearing, 30 healthy and undamaged juvenile fish were randomly selected from each family and temporarily reared in a small net cage culture system without feeding for 3 days (the small net cage system refers to the patent obtained by the applicant, patent number: 2021102057610, patent name: An apparatus and method for large-scale determination of the individual feed conversion rate of turbot).

[0033] 3. Determination of body weight and feed conversion rate traits

[0034] After the temporary rearing without feeding ended, the body weight of each fish was accurately weighed and recorded (denoted as the initial body weight). The juvenile fish were put back into the small net cages and numbered separately. Starting from the next day, they were fed a commercial feed of uniform specification and fed to satiety. If there was residual bait, it was recovered in time, and the feed intake of each fish per day was recorded, so as to calculate the feed conversion rate of each fish. The experiment lasted for 60 days. During the experiment, the water temperature of each culture pond was kept consistent, the salinity was 30, and the dissolved oxygen was >6.0 mg / L. After the experiment ended, feeding was stopped for 24 hours, and the body weight of each fish was accurately weighed and recorded (denoted as the final body weight). Calculate the feed conversion rate of each individual, and the formula is:

[0035] Feed coefficient ratio (FCR, %) = (Final body weight - Initial body weight) / Total dry matter weight of the input feed × 100%;

[0036] After weighing, a part of the caudal fin of each fish was cut and placed in absolute ethanol and then frozen in a -80 °C refrigerator for future use.

[0037] 4. Evaluation of genetic parameters of body weight and feed conversion rate traits

[0038] Use Excel software to sort and preliminarily analyze the descriptive parameters such as feed conversion rate and body weight of turbot in different families, and perform variance analysis on the growth-related traits of different families through SPSS software, as shown in Table 1. Use the ASReml software package in R software to estimate variance components and heritability, and the heritability is estimated using the individual animal model. Heritability is represented by h 2 The phenotypic correlation and genetic correlation are represented by r XY and genetic correlation r A respectively, as shown in Table 2.

[0039] Table 1 shows the variance components and heritability of body weight and feed conversion rate traits

[0040]

[0041] Table 2 shows the genetic correlation and phenotypic correlation between body weight and feed conversion rate traits

[0042]

[0043] 5 Establish the composite trait CT

[0044] Calculation method of the composite trait: All individuals are Z-score standardized according to the trait values of body weight and feed conversion rate. The standardization method can convert traits in different ranges into standard scores (Z-scores) with the same dimension. The standardization formula is: Z = (X - μ) / σ, where X is the original value, μ is the mean of the trait, and σ is the standard deviation of the trait. Weighting is performed according to the phenotypic correlation obtained by genetic parameter evaluation. The final composite trait CT is the body weight Z value + the feed conversion rate Z value (1 + (1 - phenotypic correlation)), that is, CT = BW Z 值 + FCR Z值 (1 + (1 - r XY ))

[0045] 6. Genotype acquisition

[0046] The library is sequenced using the Illumina sequencing platform, and the sequencing volume requirement is >6G / sample. After sequencing, the raw sequencing data (Raw Reads) are obtained. To ensure the accuracy of subsequent analysis, the raw data need to be quality controlled and then mutation detection is performed. The default parameters of the fastp software are used for sequencing Read quality control, and the data CleanReads are obtained after quality control and cleaning. The Clean Reads are aligned to the reference genome using the BWA software. The alignment results are converted in format using the SAMtools software, and the sam file is converted into a bam file and sorted. Subsequently, the Picard software is used to remove redundancy, and the Qualimap software is used to analyze the results. The GATK software is used for SNP detection, and sites with a missing rate exceeding 20%, more than two completely different genotypes, and a minor allele frequency less than 0.05 are filtered out. (Filtering parameters: QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0). The software Annovar is used to perform gene annotation on the SNP detection results. DNA is quantified and genotyped by whole-genome resequencing, with an average sequencing depth of more than 10×. Sites with a deletion exceeding 0.02 are filtered out, and the software beagle is used for imputation. Minor allele frequencies less than 0.05 (--MAF 0.05) and deviations from Hardy-Weinberg equilibrium (--hwe 1e-5) are filtered out, and the finally obtained SNP sites are used for subsequent analysis

[0047] 7. Genomic selection prediction model and cross-validation

[0048] For body weight, feed conversion rate, and composite traits, three methods, namely GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP, and rrBLUP, were used to estimate GEBVs. Among them, GBLUP and ssGBLUP belong to the direct method, that is, the variance components are estimated by the iterative method, and then the individual estimated breeding values are obtained by solving the mixed linear model. GBLUP replaces the relationship matrix based on pedigree with a molecular relationship matrix constructed by SNPs. ssGBLUP integrates the relationship matrix based on pedigree and the analytical relationship matrix constructed by SNPs to establish a new H relationship matrix. rrBLUP belongs to the indirect method. First, the marker effects are estimated, and the marker effects are accumulated in combination with genotype information to obtain the individual estimated breeding values.

[0049] To evaluate the accuracy of the three methods of GBLUP, ssGBLUP, and rrBLUP, five-fold cross-validation was carried out. The reference population was randomly divided into 5 subsets with the same number. The phenotypes of one subset were set as missing, and the remaining four subsets were used for model training. And so on, each subset was regarded as a missing calculation once. To reduce the random error caused by grouping, the cross-validation results were repeated 50 times and averaged. The evaluation index was the predictive accuracy (PA), which was defined as the correlation (r XY ) between GEBV and phenotypic value divided by the square root of heritability (h).

[0050] 8. Predictive accuracy of different GS models

[0051] The predictive accuracy and bias of the three genomic selection prediction models of GBLUP, ssGBLUP, and rrBLUP were compared by the cross-validation method. As shown in Table 3, the predictive accuracies of the three models were relatively high. For the body weight trait, the accuracy of ssGBLUP was the highest at 0.5411, followed by GBLUP at 0.5325, and rrBLUP was the lowest at 0.5203, and there was no significant difference among them. For the FCR trait, the accuracy of ssGBLUP was the highest at 0.5339, followed by GBLUP at 0.5278, and rrBLUP was the lowest at 0.5223, and there was no significant difference among them. For the composite trait, the accuracy of ssGBLUP was the highest at 0.5405, followed by GBLUP at 0.5316, and rrBLUP was the lowest at 0.5187, and there was no significant difference among them.

[0052] Table 3 Predictive accuracy and bias of GBLUP, ssGBLUP, and rrBLUP models

[0053]

[0054] 9. Establish the Composite Genomic Breeding Value CGBLUP and Its Calculation Method

[0055] Calculation method of the Composite Genomic Breeding Value CGBLUP;

[0056] CGBLUP = BW GBLUP值 + FCR GBLUP值 (1 + (1 - r A )) + CT GBLUP值

[0057] Taking the profit obtained from weight gain - actual feed cost as the cross - validation phenotypic value, the prediction accuracy of ssGBLUP is 0.5526 ± 0.0312, and the bias is 0.9661 ± 0.1253. The bias is close to 1, indicating that the model is an unbiased prediction.

[0058] 10. Selection of Broodstock

[0059] Sort all individuals according to the level of the Composite Genomic Breeding Value, screen the families in the top 20% of survival rate, and at the same time, mark the individuals with higher rankings of the Composite Genomic Breeding Value electronically and use them as broodstock for subsequent cultivation.

[0060] 11. Evaluation of Breeding Effect

[0061] After two consecutive generations of selection, compared with ordinary fry, the growth rate of the selected line has increased by more than 15%, the survival rate in aquaculture has increased by more than 20%, and the feed conversion rate has increased by more than 10%.

Claims

1. A multi-trait composite breeding method for feed conversion rate, growth, and survival rate of turbot, characterized in that, The specific method is as follows: (1) Establish more than 60 full-sib families of turbot, mark the individuals in the families, and count the survival rate during cultivation; (2) Randomly select 20 - 30 individuals from each family and culture them separately in small cages. After a period of cultivation, obtain the body weight and feed conversion rate of the family individuals; (3) After obtaining the two traits of body weight BW and feed conversion rate FCR, genetic parameter evaluation is carried out to obtain the heritability of the body weight and feed conversion rate traits of turbot and the phenotypic correlation r XY and genetic correlation r A ; (4) Standardize the trait values of body weight and feed conversion rate using Z-score. The standardization formula is: Z = (X - μ) / σ, where X is the original value, μ is the mean of the trait, and σ is the standard deviation of the trait; (5) Establish composite trait CT, CT = BW Z值 + FCR Z值 (1 + (1 - r XY )); (6) Extract DNA from all individuals and sequence them using a sequencing platform to obtain the SNP genotypes of each individual; (7) For the three traits of BW, FCR, and the composite trait CT, use three genomic selection prediction methods, GBLUP, ssGBLUP, and rrBLUP, to estimate the genomic estimated breeding values (GEBVs). Use the five-fold cross-validation method to compare the prediction accuracies of the three genomic selection prediction methods; (8) For the three traits of BW, FCR, and the composite trait CT, respectively select the genomic selection prediction method with the highest accuracy and calculate the individual genomic breeding value GBLUP; (9) Establish the composite genomic breeding value CGBLUP and its calculation method; CGBLUP = BW GBLUP值 + FCR GBLUP值 (1 + (1 - r A )) + CT GBLUP值 ; Sort all individuals according to the level of the composite genomic breeding value, screen the families in the top 20% of the survival rate, and at the same time, mark the individuals with a higher ranking in the composite genomic breeding value as reserve broodstock for subsequent cultivation.

2. The method according to claim 1, wherein In step (2), a weighing operation is required at the beginning of cultivation. Keep the cultivation environment consistent during the cultivation process, feed to satiety, clean up the residual bait, and record the daily food intake of each fish.

3. The method according to claim 1, characterized in that, In step (2), all family individuals are marked in separate cages and randomly assigned to each cultivation pond.

4. The method according to claim 1, wherein In step 3), an animal model is used to evaluate genetic parameters, considering the common environmental effect, and the heritability h 2 , phenotypic genetic correlation r XY and genetic correlation r A .

5. The method according to claim 1, wherein In step (7), the evaluation index is prediction accuracy, which is defined as the correlation between the genomic predicted breeding value and the phenotypic value divided by the square root of the heritability.

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