Construction method and application of fruit tree F1 population genetic map

Abstract

The invention relates to the technical field of plant breeding, in particular to a construction method and application of a genetic map of a fruit tree F1 population. The construction method comprises the following steps: mining and screening whole genome sequencing data of a fruit tree F1 group and parents thereof to obtain SNP loci; the sites in accordance with the lmxll or nnxnp separation type in the SNP sites are taken to construct an MNP molecular marker; dividing bins for each chromosome according to a predetermined length, and endowing each bin with a corresponding separation type according to the separation type of the MNP molecular marker in the bins; and constructing a genetic map according to the separation type data of each bin. The construction method provided by the invention can correct errors and deletion of SNP, has high marker detection and positioning precision, consistent linkage relationship and recombination rate, can significantly improve the breeding efficiency when applied to plant breeding, and has significantly high practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of plant breeding, and in particular to a method for constructing a genetic map of a fruit tree F1 population and an application thereof. Background Art

[0002] Genetic map construction is an important tool in biological and genetic research. It can help identify genes or loci related to specific traits (such as disease resistance, yield, fruit quality, etc.), provide clues for further gene cloning and functional analysis, and is one of the underlying technologies for the construction of molecular-assisted breeding technology systems. Accurate construction of genetic maps is of great significance for gene positioning, functional analysis, and the construction of molecular-assisted breeding technology systems.

[0003] Fruit trees are perennial, highly heterozygous, and have a complex genetic background. Unlike field crops, their F1 population can produce a wide range of trait separations and can be used for genetic map construction, which is called the CP population. Molecular markers commonly used for genetic map construction include simple sequence repeat (SSR) markers, amplified fragment length polymorphism (AFLP) markers, and randomly amplified polymorphic DNA (RAPD) markers. In recent years, with the decline in sequencing costs, single nucleotide polymorphism (SNP) molecular markers based on low-coverage second-generation whole-genome sequencing have been increasingly used in genetic map construction, but this method has the problems of inaccurate SNPs and widespread site missing, and the huge amount of data brings trouble to the calculation and mapping of genetic distances, and also leads to inaccurate genetic maps. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a method for constructing a genetic map of a fruit tree F1 population and application thereof.

[0005] The present invention obtains MNP molecular markers by combining SNP sites of fruit tree F1 population and its parents, and constructs a genetic map based on these MNP molecular markers, which has significantly better accuracy and construction efficiency.

[0006] In a first aspect, the present invention provides a method for constructing a genetic map of a fruit tree F1 population, comprising: The whole genome sequencing data of the fruit tree F1 population and its parents are mined and screened to obtain SNP sites; the sites that meet the lmxll or nnxnp separation type among these SNP sites are selected to construct MNP molecular markers; bins are divided according to a predetermined length for each chromosome, and the corresponding separation type is assigned to each bin according to the separation type of the MNP molecular marker in the bin; a genetic map is constructed based on the separation type data of each bin; The construction method of the MNP molecular marker includes: combining N tandem SNP sites within the range of 1000-10000 bp to obtain the MNP molecular marker; wherein N is an odd number and ≥3.

[0007] Furthermore, the construction method of the MNP molecular marker also includes: (1) Obtain the theoretical genotype values ​​of all MNP molecular markers Separation type statistics were performed for each MNP molecular marker, and the top two separation types that met the following characteristics were used as the theoretical genotype values ​​of the MNP molecular marker: i) The number exceeds 1 / 2 of the population size; ii) It does not contain...; iii) The corresponding SNP sites are different from each other; (2) Determine the genotypes of all MNP molecular markers Fuzzy matching is performed for each MNP molecular marker. When the genotypes of more than half of the SNP sites in the MNP molecular marker are completely consistent with one of its genotype theoretical values, the genotype of the MNP molecular marker is determined to be the matched genotype theoretical value, otherwise the MNP molecular marker is determined to be missing; Remove MNP molecular markers with a deletion rate exceeding 10%; A chi-square test was performed, and the MNP molecular markers whose p-values ​​met the predetermined requirements were retained.

[0008] Since the SNPs in the MNPs do not have complete typing information (Unphased), the theoretical value of the offspring MNP genotype cannot be directly obtained through the parental MNP genotypes, but each MNP should produce two types of separation in a 1:1 ratio in the offspring. Therefore, the present invention infers the two genotype theoretical values ​​through the MNP genotypes in the offspring, and then corrects the errors and deletions in the offspring MNPs through fuzzy matching. When the genotypes of more than half of the SNP sites in the offspring MNP sites correspond one-to-one to one of the two genotype theoretical values, it is determined to be the genotype, otherwise the MNP molecular marker is determined to be missing; the probability of an SNP error is assumed to be 1%, and the maximum completely random missing rate allowed for each site is 25%, then the probability that the five independent sites are not missing and are all correct is 31.85%. If the five sites are regarded as one MNP marker, as long as at least three sites are not missing and are correct, the MNP marker is considered to be accurate, and the accuracy of each MNP marker is 88.84%. It can be seen that the accuracy of converting SNP markers into MNP markers can be greatly improved, or the MNP markers play a role in error correction and omission correction for SNP markers. Through this process, a genetic map with a correlation coefficient of 1 with the physical map can be obtained, that is, the positional relationship between the obtained genetic map and the physical map is completely consistent.

[0009] Furthermore, the p value may be predetermined in advance, for example, p>0.01 is considered to meet the requirements.

[0010] Furthermore, the lmxll or nnxnp segregation type described in the present invention is a parental segregation type, for example, lmxll means that the mother is heterozygous (lm) and the father is homozygous (ll), which can be used to construct the maternal genetic map, while nnxnp means that the mother is homozygous (nn) and the father is heterozygous (np), which can be used to construct the paternal genetic map.

[0011] Furthermore, the SNP loci that meet the lmxll or nnxnp separation type of the present invention are determined by the following method: The separation types of each SNP site were counted, and the top two separation types were retained. If the ratio of these two separation types was between 0.6 and 1.5, and the total number exceeded 1 / 2 of the population size, it was judged to meet the lmxll or nnxnp separation type.

[0012] Furthermore, the group whose number exceeds 1 / 2 of the group size is the aforementioned fruit tree F1 group.

[0013] Furthermore, the whole genome sequencing data is prepared by the following method: Sequencing is performed based on the DNA of the fruit tree F1 population and its parents, and the sequencing mode includes: generating double-end reads of PE150, the progeny sequencing depth is not less than 6× coverage, and the parent sequencing depth is not less than 10× coverage.

[0014] Preferably, the parent sequencing depth is not less than 50× coverage.

[0015] Furthermore, the mining includes: comparing the whole genome sequencing data with the reference genome of the fruit tree or the haploid genome of the parent to obtain the SNP sites of the parent and the SNP sites of the offspring.

[0016] Furthermore, the screening includes: The SNP sites of the parents are filtered by the following method: i) Eliminate rows with missing genotypes, ii) Remove the lines with * in ALT. iii) Remove SNP sites with coverage lower than 10×, iv) Eliminate sites where both the paternal and maternal genes are homozygous; The SNP sites of the offspring are filtered by the following method: SNP sites with coverage lower than 3× were removed; The intersection of the SNP sites of the parents and the SNP sites of the offspring is taken, and the SNP sites in which the number of genotype missing in the offspring exceeds a predetermined ratio are removed.

[0017] Furthermore, the predetermined proportion may be pre-divided, for example, 10%, 15%, 20%, 25% or 30%.

[0018] Furthermore, the predetermined length of the bins is 10-100 kb.

[0019] Furthermore, assigning a corresponding separation type to each bin according to the separation type of the MNP molecular marker in the bins includes: The number of MNP genotypes in the bins was counted, and the separation type of the bins was determined according to the principle of minority obeying the majority; Bins with missing rates less than 10% and p-values ​​meeting the predetermined requirements are retained.

[0020] Further, constructing a genetic map according to the separation type data of each bin includes: The separation type data of each bin is output, different colors are used to represent different separation types, and the genetic spectrum is constructed after manual correction.

[0021] Output the classification type data of each bin of parents and offspring, based on which a genetic map can be constructed (for example using existing software).

[0022] In a second aspect, the present invention provides the application of the construction method in improving the accuracy and positioning precision of genetic maps.

[0023] The present invention has the following beneficial effects: The present invention obtains MNP molecular markers by combining SNP sites of fruit tree F1 population and its parents, and constructs a genetic map based on these MNP molecular markers. The accuracy of the genetic map constructed based on this method is significantly improved, and the positional relationship is completely consistent with the physical map. It can be further applied to selective breeding, variety improvement, gene positioning, adaptability research, diversity analysis, evolution and phylogeny research of fruit trees, etc., and has important significance in the field of genetic research of fruit trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a schematic diagram of the construction principle of the genetic map of the fruit tree F1 population provided in Example 1 of the present invention.

[0026] Figure 2 It is a flow chart for constructing the genetic map of the fruit tree F1 population provided in Example 1 of the present invention.

[0027] Figure 3 This is the grape F1 population genetic map constructed based on the method of Example 1 provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The experimental methods involved in the following examples, unless otherwise mentioned, are all conventional methods in the art, for example, reference may be made to experimental manuals in the art, or the conditions recommended in the manufacturer's instructions.

[0030] Unless otherwise specified, the experimental materials and reagents involved in the following examples can be obtained from commercial sources.

[0031] Example 1 The present invention provides a method for constructing a genetic map of a fruit tree F1 population (the construction principle is as follows Figure 1 As shown, the construction process is as follows Figure 2 shown), including the following: 1. Whole genome resequencing First, DNA was extracted from young leaves of all individuals in the F1 population and parents, and the second-generation whole-genome sequencing library was constructed and sequenced on the machine. The sequencing mode was PE150, and the sequencing depth of the offspring was not less than 6× coverage (if the genome size was 0.5 Gb, the offspring sequencing data volume was not less than 3 Gb), and the sequencing depth of the parents was not less than 50× coverage (if the genome size was 0.5 Gb, the parent sequencing data volume was not less than 25 Gb).

[0032] 2. Variant site mining After quality control, the data from the machine is compared with the paternal or maternal haploid genome. The reference genome of the species can also be used, but the comparison effect is better using the parental haploid genome. The parental genetic variation site VCF file and the offspring genetic variation site VCF file are obtained.

[0033] 3. Variant site screening First, filter the parent VCF file to get the filtered.parents.vcf file. The filtering conditions are: a. Eliminate rows with missing genotypes (rows containing . / .); b. Remove the rows with * in ALT to ensure the accuracy and uniqueness of sequencing data alignment; c. Filter out sites with parental coverage lower than 10×; d. Eliminate the sites where both the paternal and maternal parent are homozygous; Afterwards, the offspring VCF file was filtered, requiring the minimum coverage of the site to be 4, and the filtered.offsprings.vcf file was obtained.

[0034] VCF files contain a lot of information, which makes the file very large, and thus file operations are inefficient. You can extract CHROM, POS, REF, ALT, and GT information from the VCF file to generate csv files, and get filtered.parents.csv and filtered.offsprings.csv files.

[0035] Afterwards, the parental and offspring variant sites are intersected to obtain the merged.variant.csv file.

[0036] After that, filter out sites where the number of missing genotypes (. / .) in the offspring exceeds a certain ratio, such as sites where more than 50 are missing in a population of 200 plants. Output to filtered.merged.variant.csv 4. Construction of MNP molecular markers Based on the principle of 'pseudo double test cross', the lmxll and nnxnp segregation type loci can be used to construct genetic maps of the two parents respectively. For example, lmxll means that the mother is heterozygous (lm) and the father is homozygous (ll), which can be used to construct the maternal genetic map, while nnxnp can be used to construct the paternal genetic map.

[0037] Take the construction of maternal genetic map of lmxll segregation type as an example: First, filter the filtered.merged.variant.csv file to retain only the sites where the father is homozygous, and obtain the filtered.merged.lmxll.csv file.

[0038] Afterwards, in order to improve efficiency, MNP molecular markers were constructed by chromosome, and the filtered.merged.lmxll.csv file was split according to the chromosome ID, for example, chr1.filtered.merged.lmxll.csv, chr2.filtered.merged.lmxll.csv, ..., until the last chromosome.

[0039] After that, the segregation ratio of each locus was filtered. According to Mendel's law of independent segregation, the segregation ratio of lmxll should be 1:1. The number of genotypes at each locus was counted and sorted in descending order, and only the loci with the ratio of the first two segregation types in the range of 0.6-1.5 (and the sum of the number of these two segregation types exceeded 1 / 2 of the population size) were retained.

[0040] Afterwards, the MNP molecular marker is constructed with 5 tandem SNPs within the range of 5000 bp. 5000 bp is very small for a genome, so the linkage is very strong. In practical applications, an odd number of SNPs can be selected for tandem, and 5 is preferred in the present invention.

[0041] Afterwards, since the SNPs in the MNP do not have complete typing information (Unphased), the theoretical value of the offspring MNP genotype cannot be directly obtained through the parental MNP genotype, but each MNP should produce two types of separation in a 1:1 ratio in the offspring. Therefore, the two genotype theoretical values ​​are inferred from the MNP genotypes in the offspring, and then the errors and omissions in the offspring MNP are corrected through fuzzy matching. When the genotypes of more than half of the SNP sites in the offspring MNP sites correspond one-to-one to one of the two genotype theoretical values, it is determined to be the genotype, otherwise the MNP molecular marker is judged to be missing. In this way, data errors and omissions can be corrected, and the accuracy of molecular markers and data availability can be improved.

[0042] Afterwards, the MNP sites with a missing rate of more than 10% were filtered out.

[0043] Afterwards, a chi-square test was performed and only sites with p>0.01 were output.

[0044] 5. Bins construction First, each chromosome is divided into bins with a step length of 50 kb, the number of MNP genotypes in the bins is counted, and the separation type of the bins is determined according to the principle of minority obeys majority. This step can further correct outliers.

[0045] Afterwards, a chi-square test was performed on the results of the bins, and only sites with less than 10% missingness and a p-value greater than 0.05 were retained.

[0046] 6. Data output Output the bins molecular marker separation type data to an .xlsx file. Each chromosome is an independent worksheet. Set the column width to 2.5 characters and use different background colors to display the two separation types. For example, if the cell content is ll, set the cell background color to light yellow. If the cell content is lm, set the cell background color to light red. The purpose is to visualize the separation type.

[0047] 7. Manual correction Although this process can correct data errors and omissions, in order to achieve the best results, manual correction of the output files is required. Upstream and downstream separation type information can be used to correct omissions and abnormal separation types, and some abnormal plants and sites can also be removed.

[0048] 8. Genetic map construction The manually corrected output file can be used for genetic map construction, such as importing it into Joinmap software for map construction.

[0049] Example 2 Construction of genetic map of grape F1 population The present invention uses 'color fragrant' as the female parent and 'black crisp seedless' as the male parent for hybridization, and constructs an F1 segregation population consisting of 198 true hybrids. The construction method provided in Example 1 is used to construct a genetic map, and the specific operation process is as follows: 1. DNA extraction The leaves of the parents and 198 offspring were collected for DNA extraction. The DNA extraction can be performed using a kit or the CTAB method, for example: add liquid nitrogen to grind the sample, take 0.5g of powder in a 1.5mL centrifuge tube; add 600μL of preheated CTAB extraction solution, shake and vortex vigorously, add 0.2μL of RNAase (10mg / ml), and place in a 65℃ water bath for 15min; cool to room temperature, add an equal volume of chloroform and gently invert to mix, centrifuge at 12000rpm for 3~5min; transfer the supernatant (about 600μL) to a new centrifuge tube, add an equal volume of chloroform and gently invert to mix, centrifuge at 12000rpm for 3~5min; transfer the supernatant to a new centrifuge tube and repeat the extraction once; centrifuge at 12000rpm for 3~5min, take the supernatant, add 2 times the volume of anhydrous ethanol, there is a flocculent precipitate after mixing, centrifuge at 12000rpm for 2min and discard the supernatant; first use 400μL The precipitate was washed with 70% ethanol, and then washed once with 400 μL of anhydrous ethanol, the liquid was discarded, and the precipitate was dried until there was no ethanol smell, and then dissolved with 40 μL of sterile ultrapure water.

[0050] 2. Library construction and sequencing 2.1 DNA quality assessment The quality of isolated genomic DNA was verified using a combination of the following three methods: DNA degradation and contamination were monitored using 1% agarose gel; The OD 260 / 280 ratio was measured using Nanodrop to check DNA purity; DNA concentration was measured by the Qubit® DNA Assay Kit using a Qubit® 3.0 Fluorometer (Invitrogen, USA).

[0051] 2.2 Library preparation 0.2 μg of DNA was used as the starting material for library preparation for each sample. Sequencing libraries were generated using the Rapid Plus DNA Library Preparation Kit (Illumina, RK20208) according to the manufacturer's recommendations, and index codes were added for each sample. Briefly, genomic DNA samples were processed into 350 bp fragments by sonication. The DNA fragments were then end-modified, A-tailed, and ligated with full-length adapters for Illumina sequencing, followed by PCR amplification. After PCR products were purified using the AMPure XP system (Beckman Coulter, Beverly, USA), DNA concentration was measured using a Qubit® 3.0 Fluorometer (Invitrogen, USA), and the libraries were analyzed for fragment size distribution using an Agilent 2100 Bioanalyzer and quantified by real-time PCR (>2 nM).

[0052] 2.3 Clustering and sequencing Clustering of index-encoded samples was performed on the cBotCluster generation system using the Illumina PE Cluster Kit (Illumina, USA) according to the manufacturer’s instructions. After cluster generation, DNA libraries were sequenced on the Illumina platform to generate 150 bp paired-end reads, 25 Gb of parental sequencing data, and 3 Gb of progeny sequencing data.

[0053] 3. Variant site mining The data was filtered and quality controlled using fastp software, with default parameters. The hap1 haplotype genome of the maternal parent 'Colored Fragrant' was used as the reference genome, and the bwa software was used to align the reads with the reference genome to obtain a bam file. The samtools sort command was then used to sort the bam file to obtain the sort.bam file. The gatkMarkDuplicates command was then used to mark PCR duplications. The gatk HaplotypeCaller command was then used to analyze the site variation information to obtain a g.vcf file. The gatk CombineGVCFs command was then used to merge the g.vcf files of the offspring and parents, respectively. The gatk GenotypeGVCFs command was then used to extract the variation sites to obtain a vcf file.

[0054] 4. Variant site screening First, use a self-written Python script to filter the parent VCF file to obtain the filtered.parents.vcf file. The script implements the following filtering: a. Eliminate rows with missing genotypes (rows containing . / .); b. Remove the rows with * in ALT to ensure the accuracy and uniqueness of sequencing data alignment; c. Filter out sites with parental coverage lower than 10×; d. Eliminate the sites where both the paternal and maternal parent are homozygous; Afterwards, the offspring VCF files were filtered using a self-written python script, which filtered the sites with a minimum coverage of 4 to obtain the filtered.offsprings.vcf file.

[0055] VCF files contain a lot of information, which makes the file very large, and thus file operations are inefficient. Use the bcftools query-f '%CHROM;%POS;%REF;%ALT[;%GT]\n' command to convert them into smaller csv files, and get filtered.parents.csv and filtered.offsprings.csv files.

[0056] Afterwards, use the awk command to take the intersection of the parental and offspring variant sites to obtain the merged.variant.csv file.

[0057] After that, a self-written python script was used to filter out sites with more than 50 missing genotypes (. / .) in the offspring, and output to filtered.merged.variant.csv.

[0058] 5. Construction of MNP molecular markers Based on the principle of 'pseudo double test cross', the lmxll and nnxnp segregation type loci can be used to construct genetic maps of the two parents respectively. For example, lmxll means that the mother is heterozygous (lm) and the father is homozygous (ll), which can be used to construct the maternal genetic map, while nnxnp can be used to construct the paternal genetic map.

[0059] Take the construction of maternal genetic map of lmxll segregation type as an example: First, a self-written python script was used to filter the filtered.merged.variant.csv file, retaining only the sites where the male parent'black crisp seedless' was homozygous, and the genotype separators / and | were removed using the sed command to obtain the filtered.merged.lmxll.csv file.

[0060] Afterwards, in order to improve efficiency, the MNP molecular marker construction was carried out by chromosome. A self-written python script was used to split the filtered.merged.lmxll.csv file according to the chromosome ID, for example, chr1.filtered.merged.lmxll.csv, chr2.filtered.merged.lmxll.csv, ..., until the last chromosome.

[0061] After that, a self-written python script was used to filter the segregation ratio of each locus. According to Mendel's law of independent segregation, the segregation ratio of lmxll should be 1:1. The number of genotypes at each locus was counted and sorted in descending order, and only the loci with the ratio of the first two segregation types in the range of 0.6-1.5 (and the sum of the number of these two segregation types exceeded 1 / 2 of the population size) were retained.

[0062] Afterwards, a self-written python script was used to construct the MNP molecular marker with five tandem SNPs within a range of 5000 bp. 5000 bp is very small for the genome, so the linkage is very strong.

[0063] Afterwards, a self-written python script was used to count the molecular marker separation types of the offspring MNPs, extracting the top two numbers whose total was more than 100 and could not contain... and creating a dictionary for the types of the corresponding sites with different pairs. The key was type, and the key values ​​were lm and ll, respectively. The type with the largest number was lm, and the second largest was ll.

[0064] Afterwards, the offspring MNP genotype was determined using a self-written fuzzy matching function. When three or more of the five SNP sites corresponded to the SNP genotypes corresponding to 1m or 11, the MNP molecular marker genotype was determined to be 1m or 11. This can correct data errors and omissions, improve the accuracy of molecular markers and data availability.

[0065] Afterwards, a self-written python script was used to filter out MNP sites with a missing rate of more than 10% in the determination results.

[0066] Afterwards, a self-written python script was used to perform a chi-square test, and only sites with p>0.01 were output.

[0067] 6. Bins construction First, a self-written python script was used to divide bins and determine the separation type. Specifically, each chromosome was divided into bins with a step size of 50kb (this parameter can be adjusted according to actual conditions), the number of MNP genotypes in the bins was counted, and the separation type of the bins was determined according to the principle of minority obeying majority. This step can further correct outliers.

[0068] Afterwards, a self-written python script was used to perform a chi-square test on the results of the bins, and only sites with less than 10% missing and a p-value greater than 0.05 were retained.

[0069] 7. Data output Use a self-written python script to output the bins molecular marker separation type data to an .xlsx file. Each chromosome is an independent worksheet. The column width is set to 2.5 characters, and the two separation types are displayed with different background colors. For example, if the content in the cell is ll, the cell background color is set to light yellow. If the content in the cell is lm, the cell background color is set to light red. The purpose is to visualize the separation type.

[0070] 8. Manual correction Although this process can correct data errors and omissions, in order to achieve the best results, manual correction of the output files is required. Upstream and downstream separation type information can be used to correct omissions and abnormal separation types, and some abnormal plants and sites can also be removed.

[0071] 9. Genetic map construction The output file after manual correction can be used to construct a genetic map. The output file contains the classification type information of the parent and offspring bins. Importing this data into the Joinmap software can realize the construction of the genetic map. The final genetic map is as follows Figure 3As shown, the average map distance is about 0.6 cM, and the correlation coefficient with the physical map is 1.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a genetic map of a fruit tree F1 population, characterized in that: include: Mining and screening the whole genome sequencing data of the fruit tree F1 population and its parents to obtain SNP sites; Among these SNP sites, sites that meet the lmxll or nnxnp separation type are selected to construct MNP molecular markers; bins are divided according to a predetermined length for each chromosome, and a corresponding separation type is assigned to each bin according to the separation type of the MNP molecular marker in the bin; Construct a genetic map based on the segregation type data of each bin; The method for constructing the MNP molecular marker includes: combining N tandem SNP sites within the range of 1000 to 10000 bp to obtain the MNP molecular marker; The N is an odd number and is ≥3.

2. The construction method according to claim 1, characterized in that: The construction method of the MNP molecular marker also includes: (1) Obtain the theoretical genotype values ​​of all MNP molecular markers Separation type statistics were performed for each MNP molecular marker, and the top two separation types that met the following characteristics were used as the theoretical genotype values ​​of the MNP molecular marker: i) The number exceeds 1 / 2 of the population size; ii) It does not contain...; iii) The corresponding SNP sites are different from each other; (2) Determine the genotypes of all MNP molecular markers Fuzzy matching is performed for each MNP molecular marker. When the genotypes of more than half of the SNP sites in the MNP molecular marker are completely consistent with one of its genotype theoretical values, the genotype of the MNP molecular marker is determined to be the matched genotype theoretical value, otherwise the MNP molecular marker is determined to be missing; Remove MNP molecular markers with a deletion rate exceeding 10%; A chi-square test was performed, and the MNP molecular markers whose p-values ​​met the predetermined requirements were retained.

3. The construction method according to claim 1 or 2, characterized in that: The whole genome sequencing data is prepared by the following method: Sequencing is performed based on the DNA of the fruit tree F1 population and its parents, and the sequencing modes include: PE150 double-end sequencing, the progeny sequencing depth is not less than 6× coverage, and the parent sequencing depth is not less than 50× coverage.

4. The construction method according to claim 1 or 2, characterized in that: The mining includes: comparing the whole genome sequencing data with the reference genome of the fruit tree or the haploid genome of the parent to obtain the SNP sites of the parent and the SNP sites of the offspring.

5. The construction method according to claim 4, characterized in that: The screening includes: The SNP sites of the parents are filtered by the following method: i) Eliminate rows with missing genotypes, ii) Remove the lines with * in ALT. iii) Remove SNP sites with coverage lower than 10×, iv) Eliminate sites where both the paternal and maternal genes are homozygous; The SNP sites of the offspring are filtered by the following method: SNP sites with coverage lower than 3× were removed; The intersection of the SNP sites of the parents and the SNP sites of the offspring is taken, and the SNP sites in which the number of genotype missing in the offspring exceeds a predetermined ratio are removed.

6. The construction method according to any one of claims 1 to 5, characterized in that: The predetermined length of the bins is 10-100 kb.

7. The construction method according to any one of claims 1 to 5, characterized in that: The step of assigning a corresponding separation type to each bin according to the separation type of the MNP molecular markers in the bins includes: The number of MNP genotypes in the bins was counted, and the separation type of the bins was determined according to the principle of minority obeying the majority; Bins with missing rates less than 10% and p-values ​​meeting the predetermined requirements are retained.

8. The construction method according to any one of claims 1 to 7, characterized in that: The step of constructing a genetic map according to the separation type data of each bin comprises: The separation type data of each bin is output, different colors are used to represent different separation types, and the genetic spectrum is constructed after manual correction.

9. Use of the construction method according to any one of claims 1 to 8 in improving the accuracy and positioning precision of genetic maps.

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