CN115298329A

CN115298329A - Litopenaeus vannamei breeding variety identification method based on characteristic SNP marker

Info

Publication number: CN115298329A
Application number: CN202280002538.9A
Authority: CN
Inventors: 曾启繁; 王浩; 包振民; 陆维; 赵宝军; 滕铭轩; 赵明洋
Original assignee: Ocean University of China
Current assignee: Ocean University of China
Priority date: 2022-06-21
Filing date: 2022-06-21
Publication date: 2022-11-04
Anticipated expiration: 2042-06-21
Also published as: WO2023245401A1; CN115298329B

Abstract

The invention provides a method for identifying a litopenaeus vannamei breeding variety based on a characteristic SNP marker, which is a method for identifying the genetic background and variety source of litopenaeus vannamei on a molecular level on the basis of providing a characteristic SNP marker set among litopenaeus vannamei varieties and groups; in the SNP marker set, the SNP locus is the 36 th site of any sequence with the sequence of SEQ ID NO. 1-14974. The SNP marker used by the invention is a full genome scale SNP data set, is different from the prior molecular marker in a specific region of a genome, and is more accurate in identifying the source of a sample. According to the method, principal component analysis is applied to sample clustering, and the difference before the sample is represented by the characteristic vector, so that the data quantification of the sample clustering fuzzy result is realized. The method utilizes the R script to visualize the sample clustering result, and utilizes 95% confidence interval to divide the variety to which the sample belongs, so that the classification result is more visual.

Description

Litopenaeus vannamei breeding variety identification method based on characteristic SNP marker

Technical Field

The invention belongs to the technical field of aquaculture molecular markers, and particularly relates to a litopenaeus vannamei breeding variety identification method based on characteristic SNP markers.

Background

Litopenaeus vannamei (Litopenaeus vannamei or Penaeus vannamei), also known as Penaeus vannamei, belongs to the family Penaeidae, the family Penaeus, the phylum Arthropoda. The litopenaeus vannamei has a bluish blue or pale grey body color and is naturally distributed in the area from sonola in mexico to the tobesi east pacific sea in northern peru.

Litopenaeus vannamei was artificially cultured in the americas since the last 70 s, and initially, wild mated parent shrimps were artificially captured for hatching to produce nauplius larvae for artificial culture. With the expansion of the breeding scale, the shrimp fry technology without Specific-pathogen-free (SPF) rapidly develops in hawaii, florida and the like. Due to the rapid growth speed and strong stress resistance, the litopenaeus vannamei is introduced into Asia in the end of the 80 th century of the last century, gradually develops into one of the most important aquaculture species at present, and has great economic value.

Along with the rapid development of the litopenaeus vannamei industry, the demand for high-quality seedlings is higher and higher. With the perennial fishing, the germplasm resources of the wild litopenaeus vannamei are damaged in the world; under such circumstances, the prawn farming industry has begun to develop genetic improvement and improved variety breeding studies. Compared with wild seedlings, the offspring of the artificially bred excellent seedlings shows better growth speed and disease resistance.

Litopenaeus vannamei has been introduced into China since 1988 and has been cultivated locally for decades. At present, more than 50 ten thousand pairs of parent shrimps are imported from the world every year in China. The imported seed shrimps of the litopenaeus vannamei are not only expensive, but also have the problems of good and irregular quality, unclear genetic background and the like. Therefore, the identification of the variety and source of the parent shrimp is crucial to the continuous development of the litopenaeus vannamei breeding industry.

However, the appearance difference of prawns from different sources is small, and the sources cannot be judged only by the culture experience of technicians. Therefore, a more effective method for identifying the variety of litopenaeus vannamei needs to be provided.

Disclosure of Invention

The invention aims to provide a litopenaeus vannamei breeding variety identification method based on characteristic SNP markers, namely a method for identifying the genetic background and variety source of litopenaeus vannamei is established on a molecular level on the basis of providing a characteristic SNP marker set among litopenaeus vannamei varieties and groups.

The invention firstly provides an SNP marker set, wherein the SNP locus in the SNP marker set is the 36 th site of any sequence with the sequence of SEQ ID NO 1-14974;

one application of the SNP marker set provided by the invention is to identify the variety source of litopenaeus vannamei;

the invention also provides a method for identifying the source of the Litopenaeus vannamei variety, which uses the SNP marker set as a molecular marker for detection;

the method, as a specific description of an embodiment, is a principal component-based clustering analysis method, and includes the following steps:

step 1) acquiring a gene table corresponding to each SNP locus of a sample to be detected by using the SNP marker set;

step 2) preparing an input file of principal component analysis, wherein the input file comprises a reference set and a test set;

step 3), principal component analysis is carried out to determine a feature vector;

and 4) visualizing the characteristic vector to determine the variety source.

Compared with the prior art, the method of the invention has the following advantages:

1. the SNP marker used by the invention is derived from a genome-wide scale SNP data set, is different from the prior molecular marker in a specific region (such as mitochondria, microsatellite markers and the like) of a genome, and is more accurate to identify the source of a sample.

2. According to the method, principal component analysis is applied to sample clustering, and the difference before the sample is represented by the characteristic vector, so that the data quantification of the sample clustering fuzzy result is realized.

3. The method utilizes the R script to visualize the sample clustering result, and utilizes 95% confidence interval to divide the variety to which the sample belongs, so that the classification result is more visual.

Drawings

FIG. 1: and (3) a clustering analysis result graph of 18 litopenaeus vannamei samples with known sources, wherein x and y represent characteristic values of the samples on the first four principal component vectors, points with different shapes represent reference samples of main varieties and groups of litopenaeus vannamei, and black circular points represent target samples to be identified. RH, KH, representing two domestic breeding varieties of Nonhai No. 1 and Kehai No. 1, TA, CP, BMK and SIS respectively represent breeding varieties of four foreign companies of Dingfeng, zhengda, BMK and SIS;

FIG. 2:5 clustering analysis result graphs of litopenaeus vannamei samples with unknown sources.

Detailed Description

Single nucleotide polymorphism SNPs are one of the most common molecular markers in heritable variation, and refer to DNA sequence polymorphisms resulting from single nucleotide variations at the genomic level among individuals or groups. The SNP molecular markers have the characteristics of large quantity, high density and simple type. The source of the prawn is identified by utilizing the difference of genetic components, and a large number of molecular markers with the genome scale are needed, so that the SNP molecular marker is the most effective tool for identifying the source.

At present, methods for obtaining genome SNP markers include whole genome re-sequencing, genome chips, simplified genome sequencing, restriction enzyme sequencing Genotyping (GBS), PCR-based fluorescence labeling high-throughput methods, high-resolution melting curve analysis (HRM), taqMan probe fluorescence quantitative PCR, and the like. Wherein, the SNP marker density obtained by whole genome re-sequencing is the highest,

the invention collects two domestic breeding varieties of Nonhai No. 1 (yellow sea aquatic product institute of Chinese aquatic science institute, hainan aquatic product science and technology limited company, RH) and Konhai No. 1 (ocean institute of Chinese academy of sciences, northwest agroforestry science and technology university, hainan eastern China Marine organism breeding limited company, KH), two parent Shrimp populations of Taiyang (Taifeng aquatic product cultivation limited company, TA) and Zhengda (Zhengda bee group, CP), and two breeding populations of American Shrimp companies of BMK (Benchmark Genetics Shrimp breeding center, BMK) and SIS (Shrimp Improvement Systems company, SIS). The genetic background and variety sources of the litopenaeus vannamei are identified at a molecular level by utilizing a characteristic Single Nucleotide Polymorphism (SNP) marker set of the litopenaeus vannamei varieties and groups obtained by screening.

The present invention will be described in detail below with reference to examples and the accompanying drawings.

Example 1: characteristic SNP marker set for screening and identifying variety source of prawns

The SNP marker set is selected from the whole genome of prawns, and the screening steps are as follows:

a. collecting samples and data: firstly, performing biopsy on all collected prawn samples, performing DNA extraction by using an SDS (sodium dodecyl sulfate) phenol-chloroform cleavage extraction method, constructing a 250-350bp genome re-sequencing library, and then performing Illumina double-end sequencing.

b. Genome alignment: and after removing low-quality reads from all individual sequencing data, comparing the low-quality reads to a Litopenaeus vannamei reference genome, and then sequencing according to genome coordinates, so as to facilitate individual genotype detection.

c. And (3) genotype detection: after PCR repeated reads are removed, each individual variation site is detected. After all individuals are combined, SNP locus filtration is carried out (the filtration locus mainly comprises a sequencing depth smaller than 4, a comparison quality smaller than 20, a secondary allele frequency smaller than 0.05 and deletion in individual typing). After filtering out the low quality sequencing data and outlier samples, a total of 151 samples were used for subsequent analyses, including 25 nonane sea No. 1, 35 cohai No. 1, 23 roof abundance, 25 plus, 16 SIS,27 BMK. And then carrying out quality detection and screening on the whole genome SNP data set so as to obtain a high-quality SNP set.

d. Obtaining a characteristic SNP set between litopenaeus vannamei varieties and groups: the eigenvalues (eigenvalues) of the sites on each principal component were calculated using principal component analysis for data compression and redundant noise cancellation. 14974 SNP markers with the highest characteristic values in the first five principal component directions are obtained by screening and are used as a characteristic SNP set for identifying the variety source; wherein the SNP site is located at the 36 th site of any sequence with the sequence SEQ ID NO 1-14974.

The SNP marker set provided by the invention is used for identifying the variety source of the litopenaeus vannamei and can also be used for genetic breeding of the litopenaeus vannamei.

The invention also provides a method for identifying the source of the Litopenaeus vannamei variety, which is to use the SNP marker set as a molecular marker to carry out the main component clustering analysis method detection.

Example 2: identification of samples of known origin

18 Litopenaeus vannamei from Fenneropenaeus seedling companies of yellow Ye county, hebei province were obtained in 7 months in 2021 as test sets, 3 of each of the 6 varieties were obtained, and biopsy was performed in a laboratory to obtain gill tissues for DNA extraction, followed by DNA re-sequencing library construction and Illumina second generation sequencing. And after genome comparison and genotype detection, acquiring all SNP locus sets of the test set.

Step 1): the method comprises the steps of obtaining genotype information of a Test sample through sample collection, whole genome re-sequencing and genotyping, and obtaining a SNP corresponding site gene table of the sample to be tested by using a sequence table of a screened SNP set as a target reference for anchoring SNP site extraction (taking test.txt as an example, a sample name is named as a letter plus number, such as 'Test 1, test2, \ 8230;, test N') file format requirement refers to the form shown in the following table 1.

Table 1: test sample genotype file format example table

SNP_ID	Test1	Test2	…	TestN
					lg1-67279	CC	CC	…	CC
lg1-414145	GG	GA	…	AA
					lg1-839392	TT	TT	…	CT
lg1-1033093	AA	AA	…	TT
					lg1-1033097	GG	GG	…	CC
lg1-1033101	TT	TT	…	CC
					lg1-1214444	CC	TT	…	CC

Step 2): preparing an input file of principal component analysis, comprising a reference set and a test set: the genotype table of the sample to be tested is merged with a reference set (reference. Txt) and converted into a Plink format (comprising two files: analysis. Ped and analysis. Map) by using a command provided by the method.

a. Preparing a gene type part of the pid file in the Plink format, transversely combining the file of the sample to be tested with the reference set provided by the method, reserving the gene type part and performing row-column transposition to obtain the gene type part (genotype) in the Plink format file:

$$$awk'{$1＝null；print$0}'Test.txt|paste reference.txt-|sed's//\t/g'

|awk'{$1＝null；print$0}'|sed'1d'|awk'{for(i＝1；i<＝NF；

i++){if(NR＝＝1){res[i]＝$i；}else{res[i]＝res[i]""$i}}}END{for(i＝1；

i<＝NF；i++){print res[i]}}'>genotype.txt

b. prepare the Plink format ped file sample information part (sample. Name. Txt):

$$$awk'{$1＝null；print$0}'Test.txt|paste reference.txt-|sed's//\t/g'

|awk'{$1＝null；print$0}'|head-1|awk'{for(i＝1；i<＝NF；

i++){if(NR＝＝1){res[i]＝$i；}else{res[i]＝res[i]""$i}}}END{for(i＝1；

i<＝NF；i++){print res[i]}}'>sample.name.txt

c. and merging to obtain a ped file:

$$$awk'{print$1,$1,"0","0","0","-9"}'sample.name.txt|paste–

genotype.txt|sed's/\t//'>analysis.ped

d. preparing a map file in a Plunk format:

$$$sed'1d'reference.txt|cut-f 1|sed's/-//'|awk'{print

$1,$1"-"$2,$2,$2}'>analysis.map

step 3): principal component analysis determines feature vectors: principal component analysis was performed using Plink software under linux system to obtain a feature vector file (merge. Eigenvec) for each sample: the first two columns are the IDs of the samples, the following columns are the values of the feature vectors on each principal component.

$$$plink--file analysis--pca tabs--allow-extra-chr--out merge

Step 4), visualizing the feature vector, and determining the variety source:

a. add breed name to the input file merge. Eigenvec, and data table header:

$$$cut-f 1-6 merge.eigenvec|sed's/[0-9]*\t/\t/'>merge.pca

$$$printf“TYPE\tINDV\tPC1\tPC2\tPC3\tPC4\n”|cat-merge.pca.ref>

merge.PCA.txt

merge.

Starting the R procedure

And determining the variety source of the test sample according to the sample feature vector distribution diagram (figure 1).

And visualizing the clustering result of the principal component sample by using a scatter diagram distribution form, and distinguishing the sample source according to 95% confidence intervals of various groups. As shown in the feature vector distribution of fig. 1, 18 black solid dots correspond to 18 test samples, gray symbols of different properties represent different reference samples, and a dotted ellipse is a 95% confidence interval of the feature vector distribution of the corresponding variety. According to the distribution of the feature vectors of each sample, the six varieties in the reference sample are clearly layered on the first four principal components. The spatial distribution of the feature vectors of 18 test samples from 6 varieties on the first principal component (1 a) and the second principal component (1 a) and the spatial distribution of the feature vectors on the third principal component (1 b) and the fourth principal component (1 b) are similar to those of corresponding reference samples, and the vector distributions fall within respective 95% confidence intervals, which indicates that the credibility of the sample identification result is more than 95%.

Example 3: variety identification of unknown samples:

5 litopenaeus vannamei from unknown sources were obtained from the market for demonstration analysis. Through sample collection, whole-gene re-sequencing and genotype detection, characteristic SNP set sites of a sample to be detected are extracted, data of two groups of samples and data of a reference sample are respectively merged, and principal component clustering analysis is carried out (the specific steps are the same as those in example 2).

As shown in fig. 2, the 5 black solid dots represent 5 test samples, the gray symbols of different properties represent different reference samples, and the dashed ellipse is the 95% confidence interval of the corresponding breed feature vector distribution. According to the distribution of each feature vector shown in the figure, the six varieties in the reference sample are clearly layered on the first four principal components. The feature vectors of 2 test samples in 5 unknown test samples are spatially distributed in 95% confidence intervals of the variety Nonoea (RH), and 3 test samples are distributed in 95% confidence intervals of the variety Koghai (KH), which indicates that the test samples are from two varieties of Nonoea and Koghai, and the credibility of the identification result is more than 95%.

Claims

1. An SNP marker set, characterized in that the SNP locus in the SNP marker set is located at the 36 th site of any one sequence with SEQ ID NO. 1-14974.

2. The application of the SNP marker set of claim 1 in identifying the variety source of litopenaeus vannamei.

3. The use of the SNP marker set according to claim 1 for genetic breeding of litopenaeus vannamei.

4. A method for identifying the source of a variety of Litopenaeus vannamei, which is characterized in that the method uses the SNP marker set of claim 1 as a molecular marker to identify the source of the variety of Litopenaeus vannamei.

5. The method of claim 4, wherein the method is a principal component clustering analysis method.

6. The method of claim 5, wherein the method comprises the steps of:

step 1): acquiring a gene table corresponding to each SNP locus of a sample to be detected by using the SNP marker set according to claim 1;

step 2): preparing an input file for principal component analysis, wherein the file comprises a reference set and a test set;

step 3): principal component analysis determines a feature vector;

step 4): and visualizing the characteristic vector to determine the variety source.