A method for constructing an optimal evaluation parameter of inbreeding coefficient of shrimp individual based on long homozygous fragments

Abstract

The application discloses a long homozygous fragment-based shrimp individual inbreeding coefficient optimal evaluation parameter construction method, and belongs to the technical field of aquatic animal molecular breeding, which comprises the following steps: (1) the resequencing data of the shrimp sample is used for SNP screening and typing by using the corresponding shrimp reference genome, and the corresponding number of whole genome SNP markers is obtained; (2) non-genome-specific parameter setting and simulation sliding window database establishment; (3) genome-specific parameter setting; (4) optimal evaluation parameter summary. The application does not depend on the quality of the reference genome, and under the current conditions of "fragmentation" and "low splicing quality", the optimal parameters can still be found, the ROH fragment can be accurately detected, and the true inbreeding coefficient can be accurately evaluated; meanwhile, the method can be popularized to other reference genomes with different splicing qualities and the corresponding optimal parameters can be obtained.

Description

Technical Field

[0001] This invention belongs to the field of molecular breeding technology for aquatic animals, specifically relating to a method for constructing optimal evaluation parameters for the inbreeding coefficient of shrimp individuals based on long homozygous fragments. Background Technology

[0002] Inbreeding is the process of mating between related individuals. This process often exposes recessive harmful genes, leading to a significant reduction in economically valuable traits; this phenomenon is known as inbreeding depression. In breeding, controlling inbreeding is a core element in developing mating programs to avoid germplasm degradation and trait reduction. The inbreeding level is commonly measured using the inbreeding coefficient (IBC). F The inbreeding coefficient is traditionally estimated using the pedigree method. F PED This "probability estimation" can only estimate the average value of a family / population, not down to the individual level; furthermore, it is prone to errors due to recording mistakes. With the widespread application of molecular marker technology, marker-based inbreeding coefficient estimation methods (…) F HOM The method of estimating inbreeding levels at the individual level using molecular markers has been gradually adopted, significantly improving accuracy. However, this method estimates inbreeding coefficients based on allele homozygosity (i.e., state homology, IBS) rather than kinship homology (iBD), thus overestimating the inbreeding coefficient. In recent years, with advancements in genomics technology, more precise estimations of individual genomic inbreeding coefficients have emerged by directly detecting long homozygous fragments (ROH) produced by inbreeding. F ROH The ROH-based inbreeding estimation method has begun to be widely used in animal husbandry. It has demonstrated extremely high accuracy and reliability in species such as domestic pigs, horses, beef cattle, sheep, and broilers.

[0003] Litopenaeus vannamei is the world's highest-value single aquatic species and one of my country's core aquaculture species. Selection breeding techniques based on large-scale families and quantitative genetics have been widely applied to its breeding. However, during mating, the assessment and monitoring of inbreeding levels still rely on pedigree records. F PED Or based on molecular markers F HOMIn fact, not only in this species, but also in the entire aquatic animal population, research on and application of ROH (Reproductive Hazards) is limited. This is mainly due to the lack of ROH identification methods suitable for the genomic characteristics of aquatic animals. On the one hand, directly applying general detection parameters from livestock species will result in obvious errors. This is because the genomic characteristics of aquatic species such as shrimp, including marker distribution and heterozygosity, differ greatly from those of livestock species. Current reference parameters for livestock species are inaccurate, and the results are unreliable. On the other hand, the academic community generally believes that the accuracy of ROH detection depends on the assembly quality of the reference genome. In particular, the identification of long-fragment ROH requires extremely high levels of genome continuity (Contig N50) and integrity (chromosome level). However, the shrimp genome is currently far from reaching the chromosome level. This concern about low genome assembly quality also limits the application of ROH inbreeding assessment in aquatic animals. Summary of the Invention

[0004] To address the shortcomings of existing ROH detection methods and parameters that are unsuitable for aquatic animals such as shrimp, the present invention aims to construct an optimal evaluation parameter for the inbreeding coefficient of shrimp individuals based on long homozygous fragments. This method does not depend on the quality of the reference genome and can still find the optimal parameters, accurately detect ROH fragments, and precisely evaluate the true inbreeding coefficient under current conditions of "fragmentation" and "low splicing quality". At the same time, this method can be extended to other reference genomes with different splicing qualities and obtain the corresponding optimal parameters.

[0005] A method for constructing optimal evaluation parameters for inbreeding coefficients of shrimp individuals based on long homozygous fragments includes the following steps: (1) The resequencing data of shrimp samples were screened and genotyped using the corresponding shrimp reference genome to obtain the corresponding number of whole-genome SNP markers; (2) Non-genome-specific parameter settings and establishment of a simulated sliding window database: Using resequencing data as the analysis object, the parameters in PLINK software were set as follows: Calculate and obtain the minimum number of SNPs that make up the ROH fragment; Set the minimum SNP parameter for each sliding window to the same value as the minimum number of SNPs that make up the ROH fragment; For each sample, a simulated sliding window database was established by using a Python script to segment whole-genome SNP markers according to the minimum number of SNPs that make up each consecutive ROH fragment as a sliding window. Calculate and obtain the sliding window threshold parameter; The number of heterozygous genotypes allowed by the sliding window is set according to the sequencing error rate characteristics of the corresponding shrimp species during resequencing. (3) Genome-specific parameter settings: The parameter for the number of missing genotypes allowed in the sliding window: by analyzing the simulated sliding window database, the average value of the actual missing genotypes of all simulated sliding windows is taken, and then rounded down to the nearest whole number. The minimum SNP density parameter for ROH fragments: By analyzing the simulated sliding window database, starting with the lowest SNP density among all simulated sliding windows, this parameter can cover 100% of the simulated sliding windows; gradually tighten and increase this parameter, and select the parameter when the coverage of the simulated sliding windows decreases to 99.5% as the optimal density parameter; The maximum SNP interval parameter for ROH fragments: Based on the analysis of the whole genome marker database, starting from the maximum interval of all adjacent markers, this parameter can cover 100% of the markers. The parameter is gradually tightened and increased in increments of 10 kb, and the parameter when the marker interval coverage drops to above 99.99% is selected as the optimal interval parameter. The minimum fragment length parameter for composing ROH: The linkage disequilibrium half-life distance is calculated using sequencing data of the corresponding shrimp species. For genomes whose assembly quality does not reach the chromosome level, 1 / 3 of this half-life distance is taken as the optimal length parameter; for genomes whose assembly quality reaches the chromosome level, this half-life distance is taken as the optimal length parameter. (4) Summary of optimal evaluation parameters: Summarize the parameters constructed in steps (2) to (3) to obtain the optimal evaluation parameters for the inbreeding coefficient of the corresponding shrimp individual based on the long homozygous fragment, including the minimum number of SNPs constituting the ROH fragment, the minimum number of SNPs in each sliding window, the sliding window threshold parameter, the number of heterozygous genotypes allowed in the sliding window, the number of missing genotypes allowed in the sliding window, the minimum SNP density parameter of the ROH fragment, the maximum SNP interval parameter of the ROH fragment, and the minimum fragment length parameter constituting the ROH.

[0006] Further, in step (2), the minimum number of SNPs constituting the ROH fragment is calculated according to the following formula: in, L Indicates the minimum number of SNPs. n s This represents the number of SNPs for each individual. n i Indicates the number of individuals. α The percentage of false positive ROH. het This represents the average heterozygosity of all SNPs.

[0007] Further, in step (2), the sliding window threshold parameter is calculated according to the following formula: Where t represents the sliding window threshold parameter; N out This is the final number of external SNPs required on both sides of the homozygous fragment, set according to the characteristics of shrimp; This is the scanning window size; 1 indicates that ROH will tolerate the first SNP, and 3 indicates that the result will be retained to three decimal places.

[0008] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention, by analyzing the characteristics of shrimp reference genomes and sequencing data, constructs a method for determining optimal ROH (Reproductive Hybrid) evaluation parameters for shrimp reference genomes with low splicing quality. This method is independent of the quality of the reference genome and can still find optimal parameters, accurately detect ROH fragments, and precisely evaluate the true inbreeding coefficient under current conditions of "fragmentation" and "low splicing quality." This method can obtain a set of optimal parameters for the corresponding publicly available shrimp genome version, including the minimum number of SNPs constituting the ROH fragment, the minimum number of SNPs per sliding window, the sliding window threshold, the number of heterozygous genotypes allowed in the sliding window, the number of deleted genotypes allowed in the sliding window, the minimum SNP density of the ROH fragment, the maximum SNP interval of the ROH fragment, and the minimum fragment length constituting the ROH, totaling eight parameters. Using this reference genome and this set of parameters, the total ROH length and genome inbreeding coefficient of each individual can be accurately estimated. F ROH The optimal evaluation parameters were effectively validated in five self-crossed first-generation families and non-inbred parents. Furthermore, using the optimal evaluation parameter construction method of this invention, the optimal evaluation parameters can also be determined and accurately evaluated in other reference genomes with varying splicing qualities. F ROH This verifies the effectiveness and applicability of the method of the present invention. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 For the 50 inbred individuals in Example 1 of this invention F ROH Distribution map; Figure 2 For the 50 inbred individuals in Example 1 of this invention F ROH andF HOM Correlation analysis plot; Figure 3 This is from Example 2 of the present invention, which uses 50 inbred individuals from two genomes. F ROH Distribution diagram, where Reference A is the genome in Example 1 and Reference B is the genome in Example 2; Figure 4 This is for Example 2 of the present invention, which uses two different reference genomes. F ROH The results are shown in the correlation analysis diagram, where Reference A is the genome from Example 1 and Reference B is the genome from Example 2. Figure 5 This is a comparison chart of the number and average length of ROH fragments in two different reference genomes used in Example 2 of the present invention, where Reference A is the genome in Example 1 and Reference B is the genome in Example 2. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0011] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0012] Establish a set of optimal evaluation parameters for the first Litopenaeus vannamei genome (ASM378908V1) in the NCBI database, including the following steps: (1) The resequencing data of Litopenaeus vannamei samples were used to screen and genotype SNPs using the Litopenaeus vannamei genome (ASM378908V1) to obtain the corresponding number of whole-genome SNP markers; (2) Non-genome-specific 4-parameter settings and establishment of a simulated sliding window database: Using resequencing data as the analysis object, the parameters in PLINK software were set as follows: The minimum number of SNPs that make up a ROH fragment ( --homozyg-snp The value is calculated according to the following formula, based on the calculation results of different populations of Litopenaeus vannamei, and set to 110.

[0013] in L Indicates the minimum number of SNPs. n s This represents the number of SNPs for each individual. n i Indicates the number of individuals. α The percentage of false positive ROH (fixed value 0.05). het This represents the average heterozygosity of all SNPs; Minimum SNP parameters for each sliding window ( --homozyg-window-snp ), set to the minimum number of SNPs that make up the ROH fragment ( --homozyg-snp The same value is fixed at 110 for Litopenaeus vannamei; For each sample, a simulated sliding window database was created by using a Python script to divide the whole genome SNP markers into sliding windows of 110 consecutive markers. The sliding window threshold parameter is calculated using the following formula ( --homozyg-window-threshold ),in N out Based on the characteristics of shrimp, the threshold is set to 4, therefore this threshold parameter is fixed at 0.02 through calculation.

[0014] Where t represents the sliding window threshold parameter. N out It is the final number of external SNPs required on both sides of the homozygous segment. This is the scanning window size. "1" indicates that ROH will tolerate the first SNP, and "3" indicates that the result will be retained to three decimal places.

[0015] The parameter for the number of heterozygous genotypes allowed by the sliding window ( --homozyg-window-het Based on the characteristics of the sequencing error rate of resequencing, it is fixed at 3; (3) Setting four genome-specific parameters: The parameter for the number of missing genotypes allowed in the sliding window ( --homozyg-window-missing The value is set to 5. The specific method is to take the average value of the actual missing genotypes of all simulated sliding windows based on the above simulated sliding window database analysis, and then retain it to the single digit using the "rounding up method".

[0016] The lowest SNP density parameter of the ROH fragment ( --homzoyg-densityThe value was set to 0.25 (1 SNP / 0.25kb), which was also obtained from the analysis of the simulated sliding window database mentioned above. The specific method is as follows: start from the lowest density among all simulated sliding windows, at which point the parameter can cover 100% of the simulated sliding windows; gradually tighten and increase the parameter, and select the parameter when the coverage of the simulated sliding windows decreases to 99.5% as the optimal density parameter.

[0017] The maximum SNP interval parameter of the ROH fragment ( --homozyg-gap The value was set to 20 kb, obtained from analysis of a whole-genome marker database. The specific method was as follows: starting from the maximum interval (70 kb) of all adjacent markers, this parameter could cover 100% of the markers; the parameter was gradually tightened and increased in increments of 10 kb, and the parameter at which the marker interval coverage dropped to above 99.99% was selected as the optimal interval parameter.

[0018] The minimum segment length parameter that makes up a ROH ( --homozyg-kb The value was set to 10 kb. The specific calculation method was as follows: the linkage disequilibrium (LD) half-life distance was calculated using sequencing data. For Litopenaeus vannamei, the calculated value was 30 kb. For genomes whose assembly quality did not reach the chromosome level (such as ASM378908V1), 1 / 3 of this half-life distance was taken as the optimal length parameter; for genomes whose assembly quality reached the chromosome level, this half-life distance was taken as the optimal length parameter.

[0019] (4) Summary of optimal evaluation parameters: Summarize the parameters constructed in steps (2) to (3) to obtain the optimal evaluation parameters for the inbreeding coefficient of the corresponding shrimp individuals, including the minimum number of SNPs in the ROH fragment, the minimum number of SNPs in each sliding window, the sliding window threshold parameter, the number of heterozygous genotypes allowed in the sliding window, the number of missing genotypes allowed in the sliding window, the minimum SNP density parameter of the ROH fragment, the maximum SNP interval parameter of the ROH fragment, and the minimum fragment length parameter in the ROH fragment.

[0020] The sample materials used in Examples 1 and 2 of this invention were derived from the self-bred Litopenaeus vannamei "Kuai Da" strain, which has been continuously bred for 6 years and exhibits stable genetic performance. In May 2023, female and male shrimp from the same full-sib family (offspring produced by the same female and male shrimp) were mated through artificial insemination, i.e., in-family sibling mating, constructing a total of 11 theoretical inbreeding coefficients. F PED=0.25 self-crossed F1 families. When the self-crossed families grew to the adult shrimp stage (20 g), 50 individuals from 5 families (10 individuals from each family) were randomly selected for 10× deep resequencing as the inbreeding experimental group; 10 individuals from the father and mother of each family were sampled for 10× deep resequencing as the non-inbreeding control group for analysis.

[0021] Example 1: Optimal parameters were determined using the reference genome ASM378908V1 (the first publicly published Litopenaeus vannamei genome). F ROH estimate.

[0022] (1) Determination of optimal parameters for ROH assessment The first step was genotyping of the samples: The resequencing data of 50 inbred experimental group samples and 10 control group samples were used to screen and genotype SNPs using the first publicly published Litopenaeus vannamei genome (ASM378908V1), and a total of 32,240,946 whole-genome SNP markers were obtained.

[0023] The second step involves setting four non-genome-specific parameters: the minimum number of SNPs required to form the ROH and sliding window is set to 110; the sliding window threshold is set to 0.02; and the number of heterozygous genotypes allowed by the sliding window is set to 3.

[0024] The third step involves constructing a simulated sliding window database: For each sample, the samples are sorted according to whole-genome markers, and a sliding window database is constructed with 110 markers per sample. A total of 293,119 simulated sliding windows are included.

[0025] The fourth step involved setting four parameters for genome specificity: The average number of genotype deletions across all sliding windows was calculated to be 4.214; therefore, the allowed number of deleted genotypes for each sliding window was set to 5. The SNP density distribution across all sliding windows ranged from 0.583 to 582.011 SNPs / kb. Coverage was initially set at 0.5 SNPs / kb with 100% coverage, gradually increasing to 20 SNPs / kb. Genome coverage and the performance of the experimental and control groups were also considered. F ROH As shown in Table 1, based on a coverage standard of approximately 99.5%, the minimum density was set to 4 SNPs / kb, which translates to a PLINK parameter value of 0.25 (1 SNP / 0.25 kb). The intervals of the 32,240,909 markers ranged from 0.001 to 69.498 kb. Coverage was 100% starting from a maximum interval of 70 kb, gradually decreasing the parameter to 1 kb. Genome coverage and the experimental and control groups were also considered. F ROHAs shown in Table 2, the maximum interval parameter was set to 20 kb according to the coverage standard of more than 99.99%. The minimum fragment length parameter for the ROH was set to 10 kb, which is 1 / 3 of the linkage disequilibrium (LD) half-life distance (30 kb) of Litopenaeus vannamei, since the contig N50 of the reference genome is about 87 kb, which does not reach the chromosome level.

[0026] Table 1. Genome coverage of simulated sliding windows under different density parameters and experimental and control groups. F ROH Wherein: Genome coverage is the percentage that can be covered by 293,119 simulated sliding windows under this parameter; F ROH In the results, the same superscript letter in the same column indicates no significant difference.

[0027] Table 2. Genomic coverage of markers under different interval parameters and experimental and control groups. F ROH Where: genome coverage is the percentage that can be covered by 32,240,909 marker intervals under this parameter; F ROH In the results, the same superscript letter in the same column indicates no significant difference.

[0028] (2) Genomic inbreeding coefficients of the inbred group and the control group ( F ROH )calculate Based on the confirmed parameters in (1), the optimal ROH screening parameters for the reference genome ASM378908V1 are shown in Table 3. Using these parameters, ROH screening was performed on 50 inbred individuals and 10 control parents. The ROH length of each individual was calculated by dividing the total ROH length by the total length of the reference genome. F ROH ,like Figure 1 As shown, 50 inbred individuals F ROH The coefficient of variation ranged from 0.155 to 0.368, with an average of 0.238 and a coefficient of variation of 0.195. In contrast, the average of the 10 parents in the control group was... F ROH The result is only 0.056. This result shows that the eight optimal parameters identified using the method of this invention can accurately assess the genomic inbreeding coefficient of inbred individuals, while the inbreeding coefficient of the control group is controlled at a low level, indicating that the set of parameters effectively filters out false positive results.

[0029] Table 3. Optimal ROH assessment parameters for the Litopenaeus vannamei reference genome (ASM378908V1) (3) Inbred group F ROH Comparison with other inbreeding coefficient calculation methods The homozygosity of 50 individuals in the inbred group was assessed using a marker-based homozygosity method. F HOM and compared with those evaluated using the method of the present invention. F ROH The association analysis was performed, and the results are as follows: Figure 2 As shown, the ROH-based genomic inbreeding coefficient and the marker-based genomic inbreeding coefficient results are highly consistent at the individual level, with a correlation coefficient of 0.944 and a highly significant P-value. This result indicates that... F ROH The reliability and accuracy of the results.

[0030] Example 2: Determining optimal parameters using a chromosome-level reference genome F ROH Estimation and comparison and verification of results.

[0031] (1) Adjustment of optimal parameters for ROH assessment based on the characteristics of the new reference genome Using a self-assembled, high-quality (chromosome-level) Litopenaeus vannamei genome, and based on the characteristics of the genome, the method of this invention was used to construct a set of optimal ROH evaluation parameters suitable for the characteristics of this genome. The two genomes were then compared. F ROH The results were compared and verified.

[0032] In the first step, the resequencing data of 50 inbred experimental group samples and 10 control group samples were used to screen and genotype SNPs using the new Litopenaeus vannamei genome, and a total of 33,226,287 whole-genome SNP markers were obtained.

[0033] In the second step, the four non-genome-specific parameters remained unchanged: the minimum number of SNPs required to form the ROH and sliding window was set to 110; the sliding window threshold was set to 0.02; and the allowed number of heterozygous genotypes in the sliding window was set to 3. A sliding window database was constructed, containing a total of 302,080 simulated sliding windows.

[0034] The third step involves setting four genome-specific parameters: The allowed number of deleted genotypes in the sliding window is set to 5 based on the calculated average number of genotype deletions in the sliding window; the SNP density distribution of all sliding windows ranges from 0.026 to 1486.486 SNPs / kb, starting with 0.02 SNPs / kb for 100% coverage, gradually increasing the parameter to 0.5 SNPs / kb to achieve approximately 99.5% coverage, thus setting the minimum density to 0.5 SNPs / kb, which is equivalent to a parameter value of 2 (1 SNP / 2 kb); the intervals for the 33,226,240 markers range from 0.001 to 687.56 kb, starting with a maximum interval of 700 kb for 100% coverage, gradually decreasing the parameter to 80 kb while still maintaining above 99.99% coverage, thus setting the maximum interval parameter to 80 kb; and the minimum fragment length parameter for the ROH, since the contig N50 of this reference genome is greater than 10. Mb represents a high-quality genome at the chromosome level; therefore, the minimum ROH fragment length parameter is set to 30 kb, which corresponds to the linkage disequilibrium (LD) half-life distance. A set of optimal parameters adjusted based on the new reference genome is shown in Table 4.

[0035] Table 4 Optimal ROH Evaluation Parameters for the New Reference Genome of Litopenaeus vannamei (2) Based on the new genome F ROH Calculation results and comparison Based on the total ROH length determined by the new reference genome optimal parameters, the lengths of 50 inbred individuals were calculated. F ROH The values ​​ranged from 0.152 to 0.379, with an average of 0.237; the average of 10 non-inbred parents was... F ROH The value was 0.049. The new genome was used to analyze 50 inbred individuals. F ROH The violin scatter plot of the evaluation results, and a comparison with the results of the reference genome in Example 1, are shown below. Figure 3 As shown.

[0036] For using two genomes F ROH Correlation analysis of the evaluation results showed a correlation of 0.953 between the two results, indicating a very strong correlation. Figure 4 As shown. This illustrates that even when using different reference genomes, the optimal parameters found in this invention yield the desired results. F ROH The results were consistent and accurate.

[0037] (3) Characterization of ROH fragments identified by screening from two genomes Although two completely different reference genomes were used, the total ROH length and F ROH The results showed a very high degree of consistency, but further analysis revealed significant differences in the number and average length of ROH fragments identified using the two reference genomes. Figure 5 As shown: using a self-assembled high-quality reference genome (Reference B), the longest ROH fragment was 13.793 Mb, and the average length was 174.970 kb; using the first publicly published reference genome (Reference A), the longest ROH fragment was 1.072 Mb, and the average length was 41.580 kb. The number of ROH fragments found using the two genomes differed by a factor of 1.

[0038] This demonstrates that even if the assembly of the reference genome A in Example 1 is relatively "fragmented" and the found ROH fragment is not of complete length, the method and parameters of this invention can still be used to obtain a complete ROH fragment with a consistent total length. F ROH The results obtained in both cases were almost identical to those of the high-quality genome (Reference B in Example 2). This further confirms that the method is independent of the quality of the reference genome and can still find optimal parameters, accurately detect ROH fragments, and precisely assess the true inbreeding coefficient under current conditions of "fragmentation" and "low splicing quality".

[0039] The above description constitutes an embodiment of the present invention. The foregoing descriptions are preferred embodiments of the present invention. Unless there is a clear contradiction or a prerequisite for a particular preferred embodiment, the preferred embodiments can be arbitrarily combined and used. The embodiments and specific parameters described are merely for clearly illustrating the verification process of the invention and are not intended to limit the scope of patent protection of the present invention. The scope of patent protection of the present invention is still determined by its claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention should also be included within the scope of protection of the present invention.

Claims

1. A method for constructing optimal evaluation parameters for inbreeding coefficients of shrimp individuals based on long homozygous fragments, characterized in that, Includes the following steps: (1) The resequencing data of shrimp samples were screened and genotyped using the corresponding shrimp reference genome to obtain the corresponding number of whole-genome SNP markers; (2) Non-genome-specific parameter settings and establishment of a simulated sliding window database: Using resequencing data as the analysis object, the parameters in PLINK software were set as follows: Calculate and obtain the minimum number of SNPs that make up the ROH fragment; Set the minimum SNP parameter for each sliding window to the same value as the minimum number of SNPs that make up the ROH fragment; For each sample, a simulated sliding window database was established by using a Python script to segment whole-genome SNP markers according to the minimum number of SNPs that make up each consecutive ROH fragment as a sliding window. Calculate and obtain the sliding window threshold parameter; The number of heterozygous genotypes allowed by the sliding window is set according to the sequencing error rate characteristics of the corresponding shrimp species during resequencing. (3) Genome-specific parameter settings: The parameter for the number of missing genotypes allowed in the sliding window: by analyzing the simulated sliding window database, the average value of the actual missing genotypes of all simulated sliding windows is taken, and then rounded down to the nearest whole number. The minimum SNP density parameter for ROH fragments: By analyzing the simulated sliding window database, we started with the lowest SNP density among all simulated sliding windows. At this point, using the lowest SNP density parameter could cover 100% of the simulated sliding windows. We gradually tightened and increased the lowest SNP density parameter, and selected the parameter when the coverage of the simulated sliding windows decreased to 99.5% as the optimal density parameter. The maximum SNP interval parameter for the ROH fragment: Through analysis of the whole genome marker database, starting from the maximum interval of all adjacent markers, the maximum SNP interval parameter can cover 100% of the markers at this point; the maximum SNP interval parameter is gradually tightened and increased in increments of 10 kb, and the parameter when the marker interval coverage drops to above 99.99% is selected as the optimal interval parameter; The minimum fragment length parameter for composing ROH: The linkage disequilibrium half-life distance is calculated using sequencing data of the corresponding shrimp species. For genomes whose assembly quality does not reach the chromosome level, 1 / 3 of this half-life distance is taken as the optimal length parameter; for genomes whose assembly quality reaches the chromosome level, this half-life distance is taken as the optimal length parameter. (4) Summary of optimal evaluation parameters: Summarize the parameters constructed in steps (2) to (3) to obtain the optimal evaluation parameters for the individual inbreeding coefficient of the corresponding shrimp based on long homozygous fragments, including the minimum number of SNPs in the ROH fragment, the minimum number of SNPs in each sliding window, the sliding window threshold parameter, the number of heterozygous genotypes allowed in the sliding window, the number of missing genotypes allowed in the sliding window, the minimum SNP density parameter of the ROH fragment, the maximum SNP interval parameter of the ROH fragment, and the minimum fragment length parameter in the ROH fragment.

2. The method for constructing optimal evaluation parameters for inbreeding coefficients of shrimp individuals based on long homozygous fragments as described in claim 1, characterized in that, In step (2), the minimum number of SNPs constituting the ROH fragment is calculated according to the following formula: ; in, L Indicates the minimum number of SNPs. n s This represents the number of SNPs for each individual. n i Indicates the number of individuals. α The percentage of false positive ROH. het This represents the average heterozygosity of all SNPs.

3. The method for constructing optimal evaluation parameters for inbreeding coefficients of shrimp individuals based on long homozygous fragments according to claim 1, characterized in that, In step (2), the sliding window threshold parameter is calculated according to the following formula: ; Where t represents the sliding window threshold parameter; N out This is the final number of external SNPs required on both sides of the homozygous fragment, set according to the characteristics of shrimp; This is the scanning window size; 1 indicates that ROH will tolerate the first SNP, and 3 indicates that the result will be retained to three decimal places.

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