CN115298329B - Litopenaeus vannamei breeding variety identification method based on characteristic SNP markers - Google Patents

Abstract

The invention provides a method for identifying a litopenaeus vannamei culture variety based on characteristic SNP markers, which is characterized in that a method for identifying genetic background and variety sources of litopenaeus vannamei is established on a molecular level on the basis of providing a characteristic SNP marker set between litopenaeus vannamei varieties and groups; the SNP locus is positioned at the 36 th position of any sequence of SEQ ID NO. 1-14974. The SNP markers used in the invention are full genome scale SNP data sets, and are different from the traditional molecular markers in specific areas of the genome, so that the identification of the sample sources is more accurate. According to the invention, principal component analysis is applied to sample clustering, and the difference before the characteristic vector is used for representing the sample is used, so that the data quantization of the fuzzy result of the sample clustering is realized. According to the invention, the R script is utilized to visualize the sample clustering result, and the 95% confidence interval is utilized to divide the varieties to which the samples belong, so that the classification result is more visual.

Description

Litopenaeus vannamei breeding variety identification method based on characteristic SNP markers

Technical Field

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

Background

Litopenaeus vannamei (Litopenaeus vannamei or Penaeus vannamei), also known as Penaeus vannamei, belongs to the phylum Arthropoda, class Soft-shelled, family Penaeidae, genus Penaeus animals. The body color of the litopenaeus vannamei is blue or light grey, and the litopenaeus vannamei is naturally distributed in Sonola of Mexico to Tongbei TongPacific sea area in the North of Peruvian.

Litopenaeus vannamei is artificially cultured in America since the 70 s of the last century, and is initially hatched by artificially capturing wild mated parent shrimps, and nauplii are produced for artificial culture. With the expansion of the cultivation scale, shrimp larvae technology free of Specific Pathogen (SPF) has rapidly developed in Hawaii, florida, etc. The litopenaeus vannamei is introduced into various places of Asia in the last 80 th century due to the high growth speed and strong stress resistance, and gradually develops into one of the most important aquaculture species at present, so that the litopenaeus vannamei 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 perennial fishing, the germplasm resources of the wild litopenaeus vannamei are destroyed worldwide; under such circumstances, the prawn farming industry began to develop genetic improvement and fine breed breeding studies. Compared with wild seedlings, the post-representation of the artificially bred excellent seedlings shows better growth speed and disease resistance.

Litopenaeus vannamei was introduced into China since 1988, and localized culture has been carried out for decades. At present, 50 ten thousand pairs of parent shrimps are imported from the world every year in China. The imported litopenaeus vannamei seed shrimps are high in price, and have the problems of good quality, poor quality, unclear genetic background and the like. Therefore, the identification of the variety and source of parent shrimps is of great importance for the sustainable development of the litopenaeus vannamei breeding industry.

However, the difference of the appearance of the prawns from different sources is small, and the sources cannot be judged only by the culture experience of technicians. Therefore, it is desirable to provide a more efficient method for identifying the variety of Litopenaeus vannamei.

Disclosure of Invention

The invention aims to provide a method for identifying a litopenaeus vannamei culture variety based on characteristic SNP markers, namely a method for identifying genetic background and variety sources of litopenaeus vannamei on the molecular level on the basis of providing characteristic SNP marker sets between the litopenaeus vannamei variety and the litopenaeus crowd.

The invention firstly provides a SNP marker set, wherein SNP loci in the SNP marker set are positioned at 36 th position of any sequence with the sequence of SEQ ID NO. 1-14974;

the SNP marker set provided by the invention is used for identifying the variety source of the 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 concrete description of the embodiment, is a principal component cluster analysis method, comprising the following steps:

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

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

step 3) principal component analysis to determine feature vectors;

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

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

1. the SNP markers used in the invention are derived from SNP data sets with full genome scale, are different from the traditional molecular markers in specific areas of the genome (such as mitochondria, microsatellite markers and the like), and are more accurate in identifying the sources of samples.

2. According to the invention, principal component analysis is applied to sample clustering, and the difference before the characteristic vector is used for representing the sample is used, so that the data quantization of the fuzzy result of the sample clustering is realized.

3. According to the invention, the R script is utilized to visualize the sample clustering result, and the 95% confidence interval is utilized to divide the varieties to which the samples belong, so that the classification result is more visual.

Drawings

Fig. 1: and 18 clustering analysis result graphs of samples of the litopenaeus vannamei 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 the litopenaeus vannamei, and black circular points represent target samples to be identified. RH, KH, two domestic breeds of NonHai No. 1 and Kehai No. 1, TA, CP, BMK, SIS, four foreign companies of Dingfeng, zhengda, BMK and SIS;

fig. 2: and 5 clustering analysis result graphs of samples of the Litopenaeus vannamei with unknown sources.

Detailed Description

Single nucleotide polymorphism SNP is one of the most common molecular markers in heritable variation, and refers to DNA sequence polymorphism caused by variation of a single nucleotide at a genomic level between individuals or populations. SNP molecular markers have the characteristics of large quantity, high density and simple types. The identification of the source of prawns by utilizing the difference of genetic components requires a large number of molecular markers on genome scale, so SNP molecular markers are the most effective tools for source identification.

Currently, methods for obtaining genomic SNP markers include whole genome re-sequencing, genome chip, simplified genome sequencing, sequencing Genotyping (GBS) of restriction enzymes, PCR-based fluorescent labeling high-throughput methods, high-resolution melting curve analysis (HRM), taqMan probe method fluorescent quantitative PCR, and the like. Wherein the SNP marker density obtained by whole genome resequencing is highest,

the invention collects two domestic breeds of Nonghai No. 1 (yellow sea aquatic institute of China, sea-nong aquatic specie science and technology Co., ltd., RH) and Kehai No. 1 (China academy of sciences, northwest agroforestry science and technology university, hainan Oriental marine organism breeding Co., ltd., KH), the parent shrimp group of top-abundant (Top-high-water aquaculture Co., ltd., TA) and front-large (front-large-lobster group, CP) two Thailand companies, and the breeding group of BMK (Benchmark Genetics Shrimp breeding center, BMK) and SIS (Shrimp Improvement Systems, SIS) two American specie shrimp companies. The invention is used for screening the obtained characteristic Single Nucleotide Polymorphism (SNP) marker set between the Litopenaeus vannamei variety and the colony, and identifying the genetic background and variety source of the Litopenaeus vannamei at the molecular level.

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

Example 1: screening and identifying characteristic SNP (single nucleotide polymorphism) marker set of prawn variety sources

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

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

b. Genome alignment: after removing low-quality reads from all individual sequencing data, comparing the low-quality reads with a litopenaeus vannamei reference genome, and sequencing the samples according to genome coordinates, so that the genotype detection of the individual is facilitated.

c. Genotype detection: after PCR repeat reads removal, each individual mutation site was detected. After all individuals were pooled, SNP site filtration was performed (the filtration site mainly comprises sequencing depth less than 4, alignment quality less than 20, minor allele frequency less than 0.05, and individuals were typed with deletions). After filtering out the low quality sequencing data and the outlier samples, a total of 151 samples were used for subsequent analysis, including 25 nonhai No. 1, 35 kehai No. 1, 23 top-plumes, 25 positive, 16 SIS,27 BMKs. The whole genome SNP dataset is then quality checked and screened to obtain a high quality SNP set.

d. Obtaining a characteristic SNP set among Litopenaeus vannamei varieties and populations: data compression and redundant noise cancellation were performed using principal component analysis, and feature values (eigenevalue) for each site on each principal component were calculated. Screening 14974 SNP markers with highest characteristic values in the directions of the first five main components, and taking the SNP markers as characteristic SNP sets for identifying variety sources; wherein the SNP locus is positioned at the 36 th position of any sequence of 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 uses the SNP marker set as a molecular marker to carry out main component cluster analysis method detection.

Example 2: identification of samples of known origin

In 2021, from the yellow river, the county, the sea non-shrimp breeding company, obtained 18 Litopenaeus vannamei of known variety sources as test sets, 3 of the 6 varieties, and in laboratory, performing biopsy to obtain gill tissue for DNA extraction, then performing DNA re-sequencing library construction and Illumina second generation sequencing. And after genome comparison and genotype detection, obtaining all SNP locus sets of the test set.

Step 1): through sample collection, whole genome re-sequencing and genotyping, genotype information of a Test sample is obtained, a sequence table of a SNP set obtained through screening is used as a target reference for extracting an anchoring SNP locus, a SNP corresponding locus gene table of the sample to be tested is obtained, a Test file Test. Txt is taken as an example, and the sample names are named as letters and numbers, such as 'Test 1, test2, … and TestN', and the file format requirement is referred to in the form of the following table 1.

Table 1: 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, including a reference set and a test set: the genotype table of the sample to be tested is combined with a reference set (reference. Txt) and converted to a Plink format (comprising two files: analysis. Ped and analysis. Map) using the commands provided by the present method.

a. Preparing a genotype part of a ped file in a Plink format, transversely combining a file of a sample to be tested with a reference set provided by the method, reserving the genotype part and performing column transposition to obtain a genotype 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. preparation of the Plink format ped file sample information section (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. combining to obtain ped file:

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

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

d. preparing a Plink format map file:

$$$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. Eigenec) for each sample: the first two columns are the IDs of the samples, the latter column is the value of the feature vector on each principal component.

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

Step 4) visualizing the feature vectors, and determining variety sources:

a. adding a variety name and a data table header into an input file merge.

$$$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

The merge.PCA.txt first column (TYPE) is populated in the text editor with the reference sample being the variety name and the Test sample being "Test".

Starting the R program

From the sample feature vector distribution map, the source of the test sample variety is determined (FIG. 1).

And visualizing the principal component sample clustering result in a scatter diagram distribution mode, and distinguishing sample sources 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 with different characters represent different reference samples, and a dashed oval is a 95% confidence interval of the feature vector distribution of the corresponding variety. According to the distribution of the characteristic vectors of each sample, six varieties in the reference sample are layered clearly on the first four main components. The spatial distribution of the characteristic vectors on the first main component (1 a) and the second main component (1 b) of 18 test samples from 6 varieties is similar to the corresponding reference samples, and the vector distribution falls in the 95% confidence interval respectively, which indicates that the reliability of the sample identification result is over 95%.

Example 3: variety identification of unknown samples:

5 Litopenaeus vannamei of unknown origin were obtained from the market for example analysis and display. The characteristic SNP loci of the sample to be detected are extracted through sample collection, whole gene re-sequencing and genotype detection, and the data of the two groups of samples and the reference sample are respectively combined for principal component cluster analysis (the specific steps are the same as in example 2).

As shown in fig. 2, 5 black solid dots represent 5 test samples, gray symbols of different traits represent different reference samples, and dashed ellipses are 95% confidence intervals of the corresponding variety feature vector distribution. According to the distribution of each characteristic vector in the drawing, six varieties in the reference sample are clearly layered on the first four main components. The feature vectors of 2 test samples in 5 unknown test samples are spatially distributed in a 95% confidence interval of a nonhai (RH) variety, and 3 test samples are distributed in a 95% confidence interval of a Kehai (KH), so that the test samples are from the nonhai variety and the Kehai variety, and the reliability of an identification result is over 95%.

Claims (1)

1. Application of a reagent for detecting SNP marker sets in identifying sources of Litopenaeus vannamei varieties, wherein the nucleotide sequence of the SNP marker is SEQ ID NO. 1-7, and the SNP marker sets are SNP loci as follows:

1) The 36 th bit of the sequence SEQ ID NO. 1 is A/C;

2) The 36 th bit of the sequence SEQ ID NO. 2 is A/G;

3) The 36 th bit of the sequence SEQ ID NO. 3 is C/T;

4) The 36 th bit of the sequence SEQ ID NO. 4 is A/T;

5) The G/C is positioned at the 36 th position of the SEQ ID NO. 5;

6) The 36 th bit of the sequence SEQ ID NO. 6 is T/C;

7) At position 36 of SEQ ID NO. 7, T/C.