Method for locating ancestral haplotype gene of deep hybrid line

Abstract

The invention belongs to the technical field of biological information, and particularly relates to an ancestral haplotype gene positioning method for a deep hybrid line. The method comprises the following steps: obtaining a to-be-analyzed sample ancestral genotype, and further obtaining a genomic segment significantly associated with the phenotype; compared with an existing method, the ancestral haplotype gene positioning method for the deep hybridization line, provided by the invention, can be used for heterosis analysis, capture of dominance and epistatic effects among genetic markers and the like while effectively reducing false positive.

Description

Technical Field

[0001] The invention belongs to the field of bioinformatics technology, and in particular relates to a method for locating ancestral haplotype genes for deep hybrid systems. Background Art

[0002] Deep hybridization refers to the use of two parent populations with large differences to produce offspring, and continue to pass on. In the continuous generation, new recombination is constantly generated, and the accuracy of gene positioning is improved through recombination.

[0003] Local ancestral analysis aims to obtain the ancestral origin of the haplotype of the offspring sample to be tested using single nucleotide genetic markers. This analysis method is of great significance for analyzing the ancestral composition of offspring samples produced by hybrid breeding, the location of key genes, and the source of ancestral heterosis.

[0004] Whole-genome association analysis can effectively capture the correlation between phenotypes and genetic markers. With the improvement of sequencing technology and the reduction of costs, we can obtain a large number of high-density single nucleotide mutations. Compared with the traditional single nucleotide mutation, the whole-genome association analysis of continuous single nucleotide mutations into haplotypes can reduce false positives and capture the interaction of genes within a short distance. However, in some cases, the false positive rate is still high, and there are problems such as low heritability and inability to analyze hybrid vigor. In addition, whole-genome association analysis can only capture additive effects between genetic markers, and cannot capture dominant and epistatic effects.

[0005] Therefore, there is an urgent need for a new method for locating ancestral haplotype genes in deep hybrid lines, which can effectively reduce false positives and can also be used for hybrid vigor analysis, capturing dominance and epistasis between genetic markers, etc. Summary of the invention

[0006] In response to the above problems, the first technical solution of the present application discloses a method for ancestral haplotype gene positioning for deep hybrid lines, including obtaining the ancestral genotype and gene positioning, wherein obtaining the ancestral genotype is: dividing the whole genome of the offspring to be analyzed into N haplotypes, inferring the two ancestral sources based on the haplotypes, and obtaining the ancestral genotype of the whole genome.

[0007] Furthermore, the gene positioning includes whole-genome association analysis, specifically: calculating the kinship matrix between the offspring individuals of the deep hybridization line according to the whole-genome ancestral genotype, and performing whole-genome association analysis based on the ancestral genotype and the kinship matrix, and using the false discovery rate correction method for multiple corrections to obtain the genomic segments that are significantly associated with the phenotype to be analyzed.

[0008] The gene localization method according to claim 2 is characterized in that the whole genome association analysis is performed using a mixed linear model, which is:

[0009] y=Qα+Xβ+g+e

[0010] Among them, y represents the phenotype to be analyzed in the offspring population of the deep hybridization line, Q is the covariance matrix, including sex and batch fixed effects, α is the effect vector of the covariate, X is the encoding vector of the ancestral haplotype of the offspring of the deep hybridization line, β is the effect size of the haplotype of the target site, g is the kinship matrix of the pairwise samples, and e is the residual vector.

[0011] Furthermore, the gene positioning includes the positioning of superior-affinity and intermediate-affinity hybrid vigor genomic segments, specifically: based on the whole genome ancestral genotype and the corrected phenotype, the superior-affinity and intermediate-affinity hybrid vigor detection are performed respectively using the non-parametric Kruskal-Wallis and Steel-Dwass tests to obtain the superior-affinity and intermediate-affinity hybrid vigor genomic segments that are significantly correlated with the phenotype.

[0012] Furthermore, the gene positioning includes the positioning of epistatic interaction genomic segments, specifically: based on the whole genome ancestral genotype and corrected phenotype, epistatic interaction detection is performed using logistic regression, and a mixed linear model is used for simple effect comparison to test the significance of interactions between genotypes, thereby obtaining epistatic interaction genomic segments that are significantly correlated with the phenotype.

[0013] Furthermore, the residual after deducting the fixed effects in the phenotype to be analyzed is used as the corrected phenotype.

[0014] The present invention has the following beneficial effects: the present application obtains the ancestral genotype of the sample to be analyzed, and further obtains the genome segment with significant phenotype association; and then performs whole genome analysis. Compared with the existing methods, the ancestral haplotype gene positioning method for deep hybrid lines provided by the present application can effectively reduce false positives and can also be used for hybrid vigor analysis, capturing dominance and epistatic effects between genetic markers, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Manhattan plot for genome-wide association analysis of ancestral haplotypes;

[0016] Figure 2 Corrected phenotypic and genotypic distribution maps for superior-parent heterosis and intermediate-parent heterosis;

[0017] Figure 3 This is the case of interaction between two ancestral genotypes. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0019] Unless otherwise specifically stated, the numerical expressions and numerical values ​​of the steps set forth in these embodiments do not limit the scope of the application. The technology and methods known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology and methods should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments can have different values.

[0020] The first embodiment of the present application discloses a method for ancestral haplotype gene positioning for deep hybrid lines, including obtaining ancestral genotypes and gene positioning, wherein obtaining the ancestral genotypes is: dividing the whole genome of the offspring to be analyzed into N haplotypes, inferring the two ancestral sources based on the haplotypes, and obtaining the ancestral genotype of the whole genome.

[0021] In this embodiment, based on the haplotype data composed of continuous single nucleotide mutations, a local ancestry inference tool is used to obtain the two ancestral sources of the haplotype of the offspring sample to be analyzed, and the ancestral haplotype information is encoded to obtain the ancestral genotype. Among them, through the local ancestry inference method, all offspring to be analyzed divide the whole genome into several haplotypes with the same fixed number of continuous single nucleotide mutation sites.

[0022] For the diploid deep hybrid system, the haplotype of the offspring sample to be analyzed is used to infer the two ancestral origins. Specifically, let ancestor 1 and ancestor 2 be represented by 0 and 1 respectively.

[0023] Each haplotype ancestral source of each offspring sample to be analyzed can be represented by 0, 1, and 2, also known as the ancestral genotype, which represents the number of copies of the diploid plant and animal offspring sample haplotype from ancestor 1. Therefore, each offspring sample can form an ancestral haplotype vector according to the haplotype sequence, and several offspring samples form an ancestral haplotype matrix. Among them, the offspring haplotypes 0 and 2 to be analyzed represent ancestral homozygotes, and 1 represents ancestral heterozygotes.

[0024] It can be understood that when dividing haplotypes, it is only necessary to divide them according to the fixed frame length of the whole genome. For example, in the following Example 1, the whole genome of chicken is divided into a fixed frame length of 5 single nucleotides. In actual application, it can also be divided into a fixed frame length of 6, 10, etc. single nucleotides according to actual conditions, thereby dividing N (N = number of single nucleotides in the whole genome / number of single nucleotides in the fixed frame length) haplotypes.

[0025] It should be noted that after obtaining the whole-genome ancestral genotype, whole-genome association analysis, superior and intermediate-affinity heterosis genome segment positioning, and epistatic interaction genome segment positioning can be performed accordingly. Compared with the prior art base-based genotype positioning method, the positioning method based on the whole-genome ancestral genotype of the present application can effectively reduce false positives for whole-genome association analysis, and for superior and intermediate-affinity heterosis genome segment positioning, it can capture dominant effects based on ancestral heterosis analysis; for epistatic interaction genome segment positioning, it can capture epistatic effects between genetic markers. Therefore, the whole-genome-based ancestral genotype positioning method of the present application has a wider application range and higher accuracy.

[0026] In a specific embodiment, the gene positioning is a whole-genome association analysis, specifically: calculating the kinship matrix between the offspring individuals of the deep hybridization line according to the whole-genome ancestral genotype, and performing a whole-genome association analysis based on the ancestral genotype and the kinship matrix to obtain the genomic segment that is significantly associated with the phenotype to be analyzed.

[0027] In this embodiment, the whole genome ancestral genotype is used to calculate the kinship matrix of the sample to be analyzed. For the phenotypic data to be analyzed, a mixed linear model is used to perform whole genome association analysis based on the whole genome ancestral genotype and the kinship matrix, and multiple corrections are performed using the false discovery rate correction method to calculate the genomic segments significantly associated with the phenotype. The mixed linear model is:

[0028] y=Qα+Xβ+g+e

[0029] Among them, y represents the phenotype to be analyzed in the offspring population of the deep hybridization line, Q is the covariance matrix, including fixed effects such as sex and batch, α is the effect vector of the covariate, X is the ancestral haplotype encoding matrix of the offspring of the deep hybridization line, β is the effect size of the haplotype of the target site, g is the kinship matrix of the two samples, and e is the residual vector.

[0030] It should be noted that in the method of performing whole-genome association analysis based on the whole-genome ancestral genotype in this embodiment, the kinship matrix g is first calculated based on the ancestral genotype. Compared with the prior art, the calculation model of this application can effectively reduce false positives.

[0031] In a specific embodiment, the gene positioning includes the positioning of superior affinity and intermediate affinity hybrid vigor genomic segments, specifically: according to the whole genome ancestral genotype and the corrected phenotype, the superior affinity and intermediate affinity hybrid vigor detection are performed respectively using the non-parametric Kruskal-Wallis and Steel-Dwass tests to obtain the superior affinity and intermediate affinity hybrid vigor genomic segments that are significantly correlated with the phenotype.

[0032] In this embodiment, the corrected phenotype is to use the residual after deducting the fixed effect in the phenotype to be analyzed as the corrected phenotype. For example, a linear regression model is established with the phenotype to be analyzed as the dependent variable, and the fixed effects such as gender and batch as the independent variables, and the residual of the regression model is extracted as a new phenotype to become the corrected phenotype. Each ancestral haplotype of the sample to be analyzed is coded into three types: 0, 1, and 2, wherein 0 represents the homozygous ancestral haplotype of ancestor 1, 1 represents the heterozygous ancestral haplotype of ancestor 1 and ancestor 2, and 2 represents the homozygous ancestral haplotype of ancestor 2. The corrected phenotype mean of each haplotype and each ancestral genotype is calculated at the level of the offspring population. If the corrected phenotype mean of ancestral genotype 1 is higher than that of ancestral genotypes 0 and 2, the haplotype is considered to be a potential superior parent heterosis haplotype; if the corrected phenotype mean of ancestral genotype 1 is higher than the corrected mean of ancestral genotypes 0 and 2, the haplotype is considered to be a potential intermediate parent heterosis haplotype. The Kruskal-Wallis test was used to test whether there were significant differences between different genotypes and corrected phenotypes overall for potential superior parent heterosis haplotypes and potential intermediate parent heterosis haplotypes, and the Steel-Dwass test was used to test which specific groups had differences.

[0033] In a specific embodiment, the gene positioning includes the positioning of epistatic interaction genomic segments, specifically: based on the whole genome ancestral genotype and corrected phenotype obtained in claim 1, epistatic interaction detection is performed using logistic regression, and a mixed linear model is used for simple effect comparison to test the significance of interaction between genotypes, and obtain epistatic interaction genomic segments that are significantly correlated with the phenotype.

[0034] In this embodiment, the correction phenotype is consistent with the above, and the residual after deducting the fixed effect in the phenotype to be analyzed is used as the correction phenotype. The ancestral genotypes of all samples to be analyzed and the phenotype to be analyzed are used, with fixed effects such as gender and batch as covariates, and the MatrixEpistasis R package is used to perform gene-gene interaction analysis, and the ancestral genotype pairs with significant (P<0.05) interactions are output, represented by ancestral genotype 1 and ancestral genotype 2. For each significant ancestral genotype pair, the correction phenotype is used as the dependent variable, and the ancestral genotype 1, ancestral genotype 2, and the interaction of ancestral genotypes 1 and 2 are called different genotype groups, and different genotype groups are independent variables to establish a linear model. And calculate the least squares mean, perform slice analysis and compare which levels have significant differences, and obtain the epistatic interaction advantage genomic segment significantly associated with the phenotype.

[0035] It should be noted that the existing technology based on base genotype analysis can only analyze the genetic information of the organism itself, but the applicant has found that hybrid vigor, epistatic effects, etc. may have different ancestral sources. Therefore, this application uses ancestral genotypes to analyze hybrid vigor and epistatic effects, which can effectively improve accuracy.

[0036] The following is a more detailed description of the method for locating ancestral haplotype genes in deep hybridization lines through specific examples.

[0037] Example 1

[0038] The fast-growing and fast-growing Lingnan Yellow Chicken A03 line and the Guangdong local breed Huiyang Bearded Chicken were used as parents, and the distant deep hybrid line was constructed by the forward and reverse cross method. The F9 offspring of the deep hybrid line were used as the sample to be analyzed, and the body weight at the 8th week was used as the phenotype to be analyzed (original phenotype), and the ancestral haplotype gene positioning analysis of the deep hybrid line was carried out.

[0039] S1. Obtain ancestral genotype: Divide the whole genome of the offspring to be analyzed into N haplotypes, infer its two ancestral sources based on the haplotypes, and obtain the ancestral genotype of the whole genome.

[0040] RFMix software was used to divide haplotypes based on the A03 line and Huiyang Bearded Chicken as the two parental populations, and the ancestral origin of each haplotype of each sample of the F9 offspring was calculated using a fixed frame length of 5 single nucleotides. In this example, the ancestral population Huiyang Bearded Chicken is represented by 0, and the ancestral population A03 line is represented by 1. The ancestral origin of each haplotype of the F9 offspring can be represented by 0, 1, and 2 as the ancestral genotype, 0 indicates that the ancestral origins of the two haplotypes of a sample are both Huiyang Bearded Chicken, 1 indicates that one haplotype comes from Huiyang Bearded Chicken and the other haplotype comes from the A03 line, and 2 indicates that both haplotypes come from the A03 line.

[0041] S2. Obtain associated genomic segments: Calculate the kinship matrix between individuals of the deep hybridization lineage based on the whole-genome ancestral genotype, and perform whole-genome association analysis based on the ancestral genotype and the kinship matrix. Use the false discovery rate correction method for multiple corrections to obtain genomic segments with significant phenotype association.

[0042] Based on the ancestral genotypes of the F9 offspring group, the kinship matrix of the F9 offspring group was calculated using the GCTA software. For the phenotypic information of the body weight to be analyzed at week 8, a genome-wide association analysis was performed using the mixed linear model in the GCTA software based on the ancestral genotypes of the F9 offspring and the kinship matrix, and multiple corrections were performed using the false discovery rate correction method. Specifically, Figure 1This is the Manhattan plot of the whole genome association analysis of ancestral haplotypes (FDR≤0.05), where the horizontal axis represents the genomic position and the vertical axis represents the significance -log10(p). The haplotype segments significantly associated with the phenotype (adjusted p-value<0.05) were further extracted. Specifically, Table 1 shows the significant results of the whole genome association analysis on chromosome GGA12, and some significant genomic segments were shown for this study. In the table, the first column is the chromosome number, the second column is the haplotype number, the third column is the genomic segment where the corresponding haplotype is located, the fourth column is the P-value hypothesis probability obtained by the mixed dominant model of GCTA software, the fifth column represents the effect value of the ancestral haplotype segment, and the sixth column is the FDR pseudo-discovery rate obtained by multiple correction after false discovery rate correction.

[0043] In this example, the mixed linear model used is:

[0044] y=Qα+Xβ+g+e

[0045] Among them, y represents the phenotype to be analyzed in the F9 population, Q is the covariance matrix, including sex and batch fixed effects, α is the effect vector of the covariate, X is the ancestral genotype matrix of the offspring of the deep hybridization line, β is the effect size of the haplotype of the target site, g is the kinship matrix of the pairwise samples, and e is the residual vector.

[0046] Table 1 Significant genomic segments (part)

[0047]

[0048] Furthermore, the phenotypic data of body weight at week 8 were used as the dependent variable, and gender and batch fixed effects were used as independent variables. The linear model was fitted using the lm function in R language, and the residuals function was used to obtain the residuals of the linear model. The residuals were used as the new phenotype, called the corrected phenotype.

[0049] S3. Genomic segment mapping of superior and intermediate heterosis

[0050] The corrected phenotype was obtained based on the same method as in S2, and the corrected phenotypic mean of each ancestral genotype was calculated at the F9 offspring population level. If the corrected phenotypic mean of ancestral genotype 1 was higher than those of ancestral genotypes 0 and 2, the ancestral genotype 1 was considered to be a potential superior parent hybrid advantage genotype; if the corrected phenotypic mean of ancestral genotype 1 was higher than the corrected mean of ancestral genotypes 0 and 2, the ancestral genotype 1 was considered to be a potential intermediate parent hybrid advantage genotype, and the position information of the above genotypes was recorded. For each potential superior hybrid and neutral hybrid vigor, the corrected phenotype and genotype at the corresponding position of the F9 population were extracted, and the kruskal.test function in R language was used with the corrected phenotype as the dependent variable and the genotype as the independent variable to test whether there was a significant difference in the overall population. If there was a significant difference, the Steel.Dwass function in the steel.Dwass.test package of R language was used with the corrected phenotype as the dependent variable and the genotype as the grouping variable to test and compare the differences between the three genotypes. Specifically, Table 2 shows some of the sections with significant superior and neutral hybrid vigor in this study (in the table, the first column indicates the chromosome number, the second column indicates the haplotype number, the third column corresponds to the genomic section where the haplotype is located, and the fourth column (type) indicates whether the haplotype belongs to superior hybrid vigor or neutral hybrid vigor after identification by the above method). Figure 2 The phenotypes and genotype distribution diagrams of corrected heterosis of superior parents and heterosis of intermediate parents were shown for this study. The left figure shows that the segment was identified as the superior heterosis segment on the haplotype of GGA2_H2082 ((GGA2: 47,996,576_48,032,258bp), red represents ancestral genotype 2 (two ancestral haplotypes come from the A03 population), blue represents ancestral genotype 0 (two ancestral genotypes come from the Huiyang bearded chicken population), and orange represents ancestral genotype 1 (one ancestral haplotype comes from the A03 population, and the other comes from the Huiyang bearded chicken population); It can be seen that the phenotypic value of ancestral genotype 1 is significantly higher than the phenotypic values ​​of ancestral genotypes 1 and 2; the right figure shows the hybrid advantage of the middle parent on GGA4_H132 (GGA4: 74,902,003_74,907,736bp). Orange represents ancestral genotype 1 (one ancestral haplotype comes from the A03 population, and the other ancestral haplotype comes from the Huiyang Bearded Chicken population). Blue represents ancestral genotypes 0 and 2 (two ancestral haplotypes come from the same ancestral population). The vertical axis represents the corrected body weight; it can be seen that the phenotypic value of ancestral genotype 1 is significantly higher than the phenotypic mean of ancestral genotypes 1 and 2.

[0051] Table 2: Sections with significant superior and intermediate heterosis (partial)

[0052] chromosome Haplotype number Genome segment type GGA2 H2080 47,996,576-48,032,258 Heterosis GGA2 H2081 48,032,274-48,055,092 Heterosis GGA2 H2082 48,055,103-48,058,378 Heterosis GGA2 H2083 48,061,736-48,080,667 Heterosis GGA2 H2084 48,088,991-48,094,961 Heterosis GGA2 H2085 48,095,009-48,119,993 Heterosis GGA4 H3132 74,902,003-74,907,736 Heterosis GGA4 H3136 74,932,252-74,952,035 Heterosis GGA4 H3137 74,952,055-74,973,907 Heterosis GGA4 H3138 74,985,794-75,032,073 Heterosis

[0053] S4. Mapping of epistatically interacting genomic segments

[0054] Furthermore, based on the F9 offspring population, the MatrixEpistasis_main function in the R language MatrixEpistasis package was used to perform gene-gene interaction analysis. The interacting genes A and B were the whole genome ancestral genotypes, the phenotypes were the original phenotypes of the body weight at week 8, the covariance matrix was gender and batch, and the ancestral genotype pairs with significant (P<0.05) interactions were output. For each significant ancestral genotype pair, the ancestral genotype A, the ancestral genotype B, and the interaction between the ancestral genotypes A and B were called different genotype groups. The glimmix generalized linear mixed model procedure in the SAS language was used to establish a regression model with the corrected body weight at week 8 as the dependent variable and the three different genotype groups as independent variables, and the parameter estimates for each effect, i.e., the genotype group, were displayed. The least squares means were then calculated, and slice analysis was performed to compare which genotype groups had significant differences, specifically Figure 3 This study shows the interaction between the two ancestral genotypes. The interaction site between the GGA4_H3634 haplotype and the GGA15_H112 haplotype exists (where the vertical axis represents the corrected body weight, 0 represents the ancestral homozygote of the Huiyang Bearded Chicken population, and 2 represents the ancestral homozygote of the A03 population. 1 represents the ancestral heterozygote, one haplotype comes from the Huiyang Bearded Chicken population, and the other haplotype comes from the A03 population. The red column represents Epistatic interaction sites between genotypes 0 and 2 (p<0.01). When GGA4_HAP3634 was homozygous for the ancestral genotype 0 of Huiyang Bearded Chicken and GGA15_HAP11 was homozygous for the ancestral genotype 2 of A03 population, or when GGA4_HAP3634 was homozygous for the ancestral genotype 2 of A03 population and GGA15_HAP112 was homozygous for the ancestral genotype 0 of Huiyang Bearded Chicken, the weight of these individuals increased significantly.

[0055] Furthermore, Table 3 shows some epistatic interaction detection results.

[0056] Table 3 Epistatic interaction detection results (partial)

[0057]

[0058] The first column indicates the chromosome number of the interacting gene A, the second column indicates the haplotype number of the interacting gene A, the third column indicates the chromosome number of the interacting gene B, the fourth column indicates the haplotype number of the interacting gene B, the fifth column indicates the significance of the interaction, and the sixth column indicates which two genotypes have significant interactions. The interacting genotype pairs are separated by “||”.

[0059] The above embodiments demonstrate that after obtaining the whole genome ancestral genotype and corrected phenotype of the deep hybridization line through the technical means of the present application, not only can the false positives that are prone to occur based on base genotype analysis be significantly reduced, but it can also be used for hybrid vigor analysis, capturing dominance and epistatic effects between genetic markers, etc., confirming that hybrid vigor, epistatic effects, etc. may have different ancestral sources, and therefore have higher accuracy than the prior art based on base genotype analysis.

[0060] The above embodiments are only used to illustrate the technical solutions of the present invention, but the protection scope of the present invention is not limited thereto. The embodiments of the present invention and the features in the embodiments can be arbitrarily combined with each other without conflict. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for locating ancestral haplotype genes of deep hybridization lines, characterized in that: The method includes obtaining the ancestral genotype and gene positioning, wherein obtaining the ancestral genotype is: dividing the whole genome of the offspring to be analyzed into N haplotypes, inferring the two ancestral sources based on the haplotypes, and obtaining the ancestral genotype of the whole genome.

2. The gene localization method according to claim 1, characterized in that: The gene positioning includes whole-genome association analysis, specifically: calculating the kinship matrix between the offspring individuals of the deep hybridization line according to the whole-genome ancestral genotype, and performing whole-genome association analysis based on the ancestral genotype and the kinship matrix, and using the false discovery rate correction method for multiple corrections to obtain the genomic segment that is significantly associated with the phenotype to be analyzed.

3. The gene localization method according to claim 2, characterized in that: The genome-wide association analysis was performed using a mixed linear model, which was: y=Qα+Xβ+g+e Among them, y represents the phenotype to be analyzed in the offspring population of the deep hybridization line, Q is the covariance matrix, including sex and batch fixed effects, α is the effect vector of the covariate, X is the encoding vector of the ancestral haplotype of the offspring of the deep hybridization line, β is the effect size of the haplotype of the target site, g is the kinship matrix of the pairwise samples, and e is the residual vector.

4. The gene localization method according to claim 1, characterized in that: The gene positioning includes the positioning of superior and intermediate affinity heterosis genomic segments, specifically: according to the whole genome ancestral genotype and the corrected phenotype, the superior and intermediate affinity heterosis are detected respectively using the non-parametric Kruskal-Wallis and Steel-Dwass tests to obtain the superior and intermediate affinity heterosis genomic segments that are significantly correlated with the phenotype.

5. The gene localization method according to claim 1, characterized in that: The gene positioning includes the positioning of epistatic interaction genomic segments, specifically: based on the whole genome ancestral genotype and the corrected phenotype, epistatic interaction detection is performed using logistic regression, and a mixed linear model is used for simple effect comparison to test the significance of interaction between genotypes, thereby obtaining epistatic interaction genomic segments that are significantly correlated with the phenotype.

6. The gene localization method according to claim 4 or 5, characterized in that: The residual after deducting the fixed effects in the phenotype to be analyzed is used as the corrected phenotype.

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