WO2023245401A1 - Characteristic snp marker-based method for identifying litopenaeus vannamei breeds - Google Patents

Abstract

The present invention provides a characteristic SNP marker-based method for identifying litopenaeus vannamei breeds. On the basis of providing a characteristic SNP marker set of different litopenaeus vannamei breeds and populations, a method for identifying the genetic background and breed source of litopenaeus vannamei is established on the molecular level. The SNP site in the SNP marker set is position 36 in any of the sequences set forth in SEQ ID NOs: 1-14974.

Description

A method for identification of cultured species of Litopenaeus vannamei based on characteristic SNP markers Technical field

The invention belongs to the technical field of aquaculture molecular markers, and specifically relates to a method for identifying cultured species of Litopenaeus vannamei based on characteristic SNP markers.

Background technique

Litopenaeus vannamei (Litopenaeus vannamei or Penaeus vannamei), also known as South American white shrimp, belongs to the phylum Arthropoda, the class Softshell, the family Penaeus, and the genus Litopenaeus. Litopenaeus vannamei has a bluish-blue or light bluish-gray body color and is naturally distributed in the eastern Pacific Ocean from Sonora, Mexico to Tumbes in northern Peru.

Litopenaeus vannamei has been artificially cultured in the Americas since the 1970s. Initially, wild mated broodstock were artificially captured and hatched to produce nauplii for artificial breeding. With the expansion of breeding scale, its specific-pathogen-free (SPF) shrimp seedling technology is developing rapidly in Hawaii, Florida and other places. Due to its fast growth rate and strong stress resistance, Litopenaeus vannamei was introduced to various parts of Asia in the late 1980s and has gradually developed into one of the most important aquaculture species today, with great economic value.

With the rapid development of the Litopenaeus vannamei industry, the demand for high-quality seedlings is increasing. With years of fishing, the germplasm resources of wild Litopenaeus vannamei have been destroyed worldwide. Under this situation, the shrimp farming industry has begun to develop research on genetic improvement and breeding of improved species. Compared with wild seedlings, the offspring of artificially selected excellent seedlings show better growth speed and disease resistance.

Litopenaeus vannamei was introduced to China in 1988 and has been cultivated locally for decades. At present, my country still needs to import more than 500,000 pairs of broodstock from the international market every year. However, imported Litopenaeus vannamei seed shrimp are not only expensive, but also have problems such as uneven quality and unclear genetic background. Therefore, identifying the species and origin of broodstock is crucial for the sustainable development of the Litopenaeus vannamei aquaculture industry.

However, the differences in the appearance of shrimps from different sources are small, and the source cannot be judged based only on the breeding experience of technicians. Therefore, it is necessary to provide a more effective method for identification of Litopenaeus vannamei species.

Contents of the invention

The purpose of the present invention is to provide a method for identification of Litopenaeus vannamei culture species based on characteristic SNP markers, that is, on the basis of providing characteristic SNP marker sets among Litopenaeus vannamei species and groups, an identification method is established at the molecular level. Methods of genetic background and species origin of Litopenaeus vannamei.

The present invention first provides a SNP marker set. The SNP site in the SNP marker set is located at position 36 of any sequence of SEQ ID NO: 1-14974;

One use of the SNP marker set provided by the present invention is to identify the species source of Litopenaeus vannamei;

The present invention also provides a method for identifying the source of Litopenaeus vannamei species, which uses the above-mentioned SNP marker set as a molecular marker for detection;

The described method, as a specific description of the embodiment, is based on the principal component cluster analysis method and includes the following steps:

Step 1) Use the above SNP marker set to obtain the gene table corresponding to each SNP site of the sample to be tested;

Step 2) Prepare the input file for principal component analysis, including reference set and test set;

Step 3) Principal component analysis determines the feature vector;

Step 4) Visualize the feature vector and determine the source of the variety.

Compared with the existing technology, the method of the present invention has the following advantages:

1. The SNP markers used in this invention are derived from whole-genome scale SNP data sets. They are different from previous molecular markers in specific regions of the genome (such as mitochondria, microsatellite markers, etc.), and can more accurately identify the source of samples.

2. The present invention applies principal component analysis to sample clustering, uses feature vectors to characterize the differences before samples, and realizes data quantification of fuzzy results of sample clustering.

3. The present invention uses R scripts to visualize the sample clustering results, and uses 95% confidence intervals to classify the varieties to which the samples belong, making the classification results more intuitive.

Description of the drawings

Figure 1: Cluster analysis results of 18 samples of Litopenaeus vannamei from known sources, where x and y represent the characteristic values of the sample on the first four principal component vectors, and the points of different shapes represent the main species of Litopenaeus vannamei. and the reference sample of the population, and the black round dots represent the target samples to be identified. RH and KH represent the two domestically selected varieties, Renhai No. 1 and Kehai No. 1, while TA, CP, BMK and SIS represent the selected varieties from four foreign companies: Dingfeng, Zhengda, BMK and SIS respectively;

Figure 2: Cluster analysis results of 5 samples of Litopenaeus vannamei from unknown sources.

Detailed ways

Single nucleotide polymorphism (SNP) is the most common molecular marker among heritable variations. It refers to the DNA sequence polymorphism caused by the variation of a single nucleotide at the genome level between individuals or groups. SNP molecular markers have the characteristics of large number, high density and simple type. Using differences in genetic components to identify the origin of shrimp requires a large number of genome-scale molecular markers, so SNP molecular markers are the most effective tool for source identification.

Currently, methods for obtaining genomic SNP markers include whole-genome resequencing, genome microarrays, simplified genome sequencing, restriction enzyme sequencing genotyping (GBS), PCR-based fluorescent labeling high-throughput methods, and high-resolution melting curves Analysis (HRM), TaqMan probe method fluorescence quantitative PCR, etc. Among them, the density of SNP markers obtained by whole-genome resequencing is the highest.

The invention brings together Renhai No. 1 (Yellow Sea Fisheries Research Institute of the Chinese Academy of Fisheries Sciences, Hairen Aquatic Seed Industry Technology Co., Ltd., RH) and Kehai No. 1 (Oceanic Research Institute of the Chinese Academy of Sciences, Northwest A&F University, Hainan Oriental Middle School Ke Marine Biological Breeding Co., Ltd., KH) two domestically selected varieties, Dingfeng (Dingfeng Aquaculture Co., Ltd., TA) and Zhengda (CP Charoen Pokphand Group, CP) broodstock groups of two Thai companies, as well as BMK ( Benchmark Genetics Shrimp Breeding Center, BMK) and SIS (Shrimp Improvement Systems, SIS) are the breeding groups of two American shrimp companies. The characteristic single nucleotide polymorphism (SNP) marker set among Litopenaeus vannamei species and populations obtained through screening in the present invention is used to identify the genetic background and species origin of the shrimp at the molecular level.

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

Example 1: Screening and identifying characteristic SNP marker collections from which shrimp species originate

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

a. Sample and data collection: First, all collected shrimp samples were vivisected, and DNA was extracted using SDS cleavage phenol chloroform extraction method to construct a 250-350bp genome resequencing library, and then Illumina paired-end sequencing was performed.

b. Genome comparison: After removing low-quality reads from all individual sequencing data, they are compared to the Litopenaeus vannamei reference genome, and then sorted according to genome coordinates to facilitate individual genotype detection.

c. Genotype detection: After PCR duplicate reads are removed, each individual mutation site is detected. After merging all individuals, perform SNP site filtering (filtering sites mainly include sequencing depth less than 4, alignment quality less than 20, minor allele frequency less than 0.05, and individual typing missing). After filtering out low-quality sequencing data and outlier samples, a total of 151 samples were used for subsequent analysis, including 25 Renhai 1, 35 Kehai 1, 23 Dingfeng, 25 Zhengda, and 16 SIS , 27 BMK. The whole-genome SNP data set is then quality tested and filtered to obtain high-quality SNP sets.

d. Acquisition of characteristic SNP sets among Litopenaeus vannamei species and populations: Principal component analysis was used for data compression and redundant noise elimination, and the eigenvalues (eigenvalues) of each site on each principal component were calculated. The 14974 SNP markers with the highest characteristic values in the first five principal component directions were screened and used as a characteristic SNP set to identify the source of the variety; the SNP site is located at the 36th position of any sequence with the sequence SEQ ID NO: 1-14974 Bit.

The SNP marker set provided by the present invention is used to identify the species source of Litopenaeus vannamei, and can also be used in the genetic selection of Litopenaeus vannamei.

The present invention also provides a method for identifying the source of Litopenaeus vannamei species, which is to use the above-mentioned SNP marker set as a molecular marker to perform principal component cluster analysis method detection.

Example 2: Identification of samples from known sources

In July 2021, 18 Litopenaeus vannamei from known species were obtained from the Penaeus vannamei breeding company in Huanghua County, Hebei Province as a test set. Three of each of the above-mentioned six species were vivisected in the laboratory. Gill tissue was obtained for DNA extraction, followed by DNA resequencing library construction and Illumina second-generation sequencing. After genome comparison and genotype detection, the entire SNP site set of the test set was obtained.

Step 1): Obtain the genotype information of the test sample through sample collection, whole-genome resequencing, and genotyping. Use the sequence list of the SNP set obtained through screening as a target reference for anchoring SNP site extraction to obtain the sample to be tested. SNP corresponding site gene table, (take the test file Test.txt as an example, the sample names are letters plus numbers, such as "Test1, Test2,...,TestN") The file format requirements are as shown in Table 1 below.

Table 1: Example table of test sample genotype file format

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): Prepare input files for principal component analysis, including reference set and test set: merge the genotype table of the test sample with the reference set (reference.txt), and use the command provided by this method to convert it into Plink format (including two files: analysis.ped and analysis.map).

a. Prepare the genotype part of the ped file in Plink format, merge the file of the sample to be tested horizontally with the reference set provided by this method, retain the genotype part and transpose the rows and columns as the 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. Prepare the sample information part of the Plink format ped file (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. Merge to obtain the ped file:

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

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

d. Prepare 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 to determine the feature vector: Use Plink software under the Linux system to perform principal component analysis and obtain the feature vector file (merge.eigenvec) of each sample: the first two columns are the ID of the sample, and the following columns are features. Vector value on each principal component.

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

Step 4) Visualize the feature vector and determine the source of the variety:

a. Add the variety name and data header to the input file merge.eigenvec:

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

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

merge.PCA.txt

In the text editor, fill the first column (TYPE) of merge.PCA.txt with the reference sample as the variety name and the test sample as "Test".

Start R program

install.packages("ggplot2")

library(ggplot2)

setwd("/path/to/input/file/")

pdf("PCA.pdf",width＝6,height＝5)

all_vec<-read.table("val-pca.txt",header=T)

ref<-subset(all_vec,TYPE!='Test')

test<-subset(all_vec,TYPE=='Test')

myplot1_2<-ggplot(ref,aes(x＝PC1,y＝PC2,shape＝TYPE))+

geom_point(size＝4,color="grey30")+

scale_shape_manual(values=c(6,5,2,0,3,4))+

stat_ellipse(type="norm",linetype=2)+

geom_point(size=4,data=test,shape=19)

myplot1_2+theme(panel.background=element_blank())+

theme(axis.line=element_line(colour="black"))

myplot1_2<-ggplot(ref,aes(x＝PC3,y＝PC4,shape＝TYPE))+

geom_point(size＝4,color="grey30")+

scale_shape_manual(values=c(6,5,2,0,3,4))+

stat_ellipse(type="norm",linetype=2)+

geom_point(size=4,data=test,shape=19)

myplot1_2+theme(panel.background=element_blank())+

theme(axis.line=element_line(colour="black"))

dev.off()

According to the sample feature vector distribution diagram, the source of the test sample variety is determined (Figure 1).

Use the scatter plot distribution form to visualize the principal component sample clustering results, and identify the source of the samples based on the 95% confidence interval of each group. As shown in the eigenvector distribution in Figure 1, 18 solid black points correspond to 18 test samples, the gray symbols of different traits represent different reference samples, and the dotted ellipse is the 95% confidence interval of the eigenvector distribution of the corresponding variety. According to the distribution of each sample feature vector, the six varieties in the reference sample are clearly stratified on the first four principal components. The spatial distribution of feature vectors on the first and second principal components (1a) and the third and fourth principal components (1b) of 18 test samples from 6 varieties are similar to the corresponding reference samples, and the vector distribution falling within their respective 95% confidence intervals, indicating that the sample identification results are more than 95% reliable.

Example 3: Variety identification of unknown samples:

Five Litopenaeus vannamei from unknown sources were obtained from the market for case analysis and demonstration. Through sample collection, whole-gene resequencing, and genotype detection, the characteristic SNP set sites of the samples to be tested were extracted, and the data of the two groups of samples and the reference sample were merged to perform principal component cluster analysis (the specific steps are the same as Example 2 ).

As shown in Figure 2, 5 solid black dots represent 5 test samples, gray symbols with different traits represent different reference samples, and the dotted ellipse is the 95% confidence interval of the corresponding variety characteristic vector distribution. According to the distribution of each eigenvector shown in the figure, the six varieties in the reference sample are clearly stratified on the first four principal components. The eigenvectors of 2 test samples among the 5 unknown test samples are spatially distributed within the 95% confidence interval of the Renhai (RH) variety, and the 3 test samples are distributed within the 95% confidence interval of the Kehai (KH) variety, indicating that the test The samples come from two varieties, Renhai and Kehai, and the identification results are more than 95% reliable.

Claims (6)

1. A SNP marker set, characterized in that the SNP site in the SNP marker set is located at position 36 of any sequence in SEQ ID NO: 1-14974.
2. Application of the SNP marker set according to claim 1 in identifying the species origin of Litopenaeus vannamei.
3. Application of the SNP marker set according to claim 1 in genetic breeding of Litopenaeus vannamei.
4. A method for identifying the source of Litopenaeus vannamei species, characterized in that the method uses the SNP marker set described in claim 1 as a molecular marker to identify the source of Litopenaeus vannamei species.
5. The method according to claim 4, characterized in that the method is a principal component cluster analysis method.
6. The method of claim 5, characterized in that the method includes the following steps:

   Step 1): Using the SNP marker set according to claim 1, obtain the gene table corresponding to each SNP site of the sample to be tested;

   Step 2): Prepare the input file for principal component analysis. The file includes the reference set and the test set;

   Step 3): Principal component analysis determines the feature vector;

   Step 4): Visualize the feature vector and determine the source of the variety.

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