CN113337578B

CN113337578B - Method for efficiently screening positive SNP (Single nucleotide polymorphism) of aquatic animal based on transcriptome data

Info

Publication number: CN113337578B
Application number: CN202110672814.XA
Authority: CN
Inventors: 王国栋; 黄永裕; 张丽莉; 黄世玉
Original assignee: Jimei University
Current assignee: Jimei University
Priority date: 2021-06-17
Filing date: 2021-06-17
Publication date: 2022-11-08
Anticipated expiration: 2041-06-17
Also published as: CN113337578A

Abstract

The invention discloses a method for efficiently screening positive SNP of aquatic animals based on transcriptome data. The method selects a target character dipolar group as a transcriptome sequencing sample, utilizes a bioinformatics approach to discover SNP according to transcriptome data, and performs SNP screening by taking sequencing depth, an allele frequency difference significance p value, a Minimum Allele Frequency (MAF) and an allele frequency imbalance number AFI as parameters, thereby establishing a set of flow for accurately screening positive SNP. The method has the advantages of high accuracy, low cost and high efficiency in screening the positive SNP of the aquatic animals, and can provide a basis for the development of the SNP marker and the breeding of the germplasm resources of the aquatic products.

Description

Method for efficiently screening positive SNP of aquatic animals based on transcriptome data

Technical Field

The invention belongs to the technical field of aquatic animal heredity and molecular marker assisted selective breeding, and particularly relates to a method for efficiently screening aquatic animal positive SNP (Single nucleotide polymorphism) based on transcriptome data.

Background

The aquatic germplasm resources are important material bases for fishery production in China and important food protein sources for human beings, however, with the development of culture technology and the increase of market demands, the culture density is continuously increased, the habitat environment is worsened, seedlings are mixed, and the germplasm of the existing variety is degraded, grows slowly and has reduced stress resistance. Meanwhile, because aquatic animals have various varieties and habits, the living body genetic resources are difficult to store, the existing genetic resources have few living body materials and low utilization rate of the genetic resources, and the development of the aquatic germplasm resources with excellent properties is restricted. Therefore, germplasm creation has become one of the key points of research in the aquaculture industry in China.

Genetic markers refer to materials or traits that are used to distinguish between different individuals or populations and are stably inherited. Single Nucleotide Polymorphism (SNP) refers to sequence polymorphism caused by variation of a Single nucleotide at a specific site in a genomic DNA sequence, and includes Single base transitions, transversions, insertions, deletions, and the like. SNP as a third-generation molecular marker is widely applied to molecular marker-assisted breeding due to the characteristics of large quantity, wide distribution, high polymorphism, high mutation rate, easiness in automatic high-throughput detection and the like.

There are many methods for screening SNPs, such as allele specific oligonucleotide fragment Analysis (ASO), gene chip technology (Gene chips), probe technology (TaqMan), AFLP, denaturing Gradient Gel Electrophoresis (DGGE), single Strand Conformation Polymorphism (SSCP), etc., and various methods are long, among which direct sequencing is the most rapid, direct, and the highest accuracy. By directly sequencing the same gene or DNA fragment of different individuals and then simply comparing the sequences, SNP can be intuitively obtained, and information such as the type of a mutated base, accurate position and the like can also be obtained. However, the cost of DNA sequencing is high, and a large amount of sequencing work is more expensive. Currently, methods for screening SNPs based on transcriptome data have been developed, but since the transcriptome reflects genetic information under certain physiological conditions, there may be a high error rate, and further, since inaccurate repetition and RNA editing may also result in low efficiency of screening positive SNPs.

Therefore, providing a method for efficiently screening positive SNPs of aquatic animals based on transcriptome data is a technical problem to be solved urgently by those skilled in the art.

Disclosure of Invention

The invention aims to develop a method for efficiently screening positive SNP of aquatic animals based on transcriptome data. The invention establishes a set of accurate screening program and screening standard taking reading depth, p value, MAF and AFI as screening indexes by measuring transcriptome data of aquatic animals and combining bioinformatics. The method has the advantages of high accuracy, low cost and high efficiency in screening the positive SNP of the aquatic animals, and can provide a basis for the development of the SNP marker and the breeding of the germplasm resources of the aquatic products.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

s1, biological sampling: selecting two extreme aquatic animal individuals with target characters, respectively collecting tissues or organs with the number of 200 or more than the individuals, and extracting RNA.

S2, library construction and sequencing: and (3) determining the RNA sequence in the step S1 by using a second generation high-throughput sequencing platform to construct a library. Fastp software removes low quality sequences as well as sequences including sequencing adapters, poly-N. Alignment of the Fastp software-treated sequences to the rRNA database with Bowtie2 software removed ribosomal RNA. And (3) comparing the sequence after removing the ribosomal RNA with the reference genome of the aquatic animal by TopHat2 software, and confirming the SNP site.

S3, SNP identification: SNPs were called with GATK default parameters and filtered. Screening the mass fraction with quality more than 30 to obtain x1 assumed SNPs; screening the reading depth to be more than or equal to 10 to obtain x2 assumed SNPs; finally, SNPs with MAF larger than 0.05 are screened to obtain x3 assumed SNPs. The two-tailed Fisher was then used to precisely test the significance of the allelic frequency difference between the different traits for each SNP in x 3. Calculating the p-value and AFI of each SNP in x3, and screening SNPs with a p-value of <0.05 divided by x3 and an AFI value of >4or <0.25 as high-quality SNPs after Bonferroni correction.

S4, SNP verification: and designing primers and amplifying flanking sequences of SNPs according to the reference genome sequence of the aquatic animal to perform DNA mixed pool sequencing.

X1 in step S3 represents the number of putative SNPs with the mass fraction of more than 30 screened from the transcriptome; x2 represents the number of the putative SNPs with the screening reading depth of more than or equal to 10 from the x 1; x3 represents the number of putative SNPs screened for MAF greater than 0.05 from x 2. SNPs in steps S3 and S4 are a complex form of SNP.

Compared with the prior art, the invention has the following outstanding advantages:

1. the positive rate of SNP screening is high. The positive rate of screening SNP in the transcriptome can reach 72 percent by adopting the method, and is obviously improved compared with the existing scheme.

2. The invention mainly adopts two indexes to screen candidate SNP. The P value of Fisher exact test can determine whether the allele frequency of the SNP is different in different traits, and then further use AFI to emphasize the difference multiples. By using transcriptomes of two extreme traits, this method can efficiently identify SNP sites having a high degree of association with a target trait.

3. The operability is strong. The RNA extraction, sequencing and SNP calling programs of the invention have universal applicability and have no special requirements. And the P value and the AFI are calculated simply, so that the method is theoretically suitable for different species and different properties, and has wide adaptability.

4. The method is convenient for rapidly finding the candidate sites with high reliability in thousands of SNP, can promote the breeding efficiency of aquatic animals, further breed fast-growing varieties and improve the growth speed of the population.

Drawings

The invention is further described with reference to the following figures and specific examples.

FIG. 1 is a schematic diagram of the workflow of screening single nucleotide polymorphisms in a transcriptome of Litopenaeus vannamei.

Detailed Description

The present invention is further described below in conjunction with embodiments, it being understood that these examples are for illustrative purposes only and do not limit the scope of the present invention.

Example 1

S1, biological sampling

The study adopted 16 different genetic background prawn strains. 3 ponds and 48 net cages are arranged in total and are divided into three repeated groups, and each group has 16 net cages in each pond. The three ponds are in a breeding building and are connected through pipelines. The seawater is circulated by pumps in three tanks, each having four aeration zones. Each net cage is provided with 5 ventilation points; one in the center and the other in the four corners. In 16 cages per pond, 16 strains were randomly placed, with only 1000 larvae from the same strain per cage. The number of individuals is adjusted every month, so that the individual density in each net cage is kept consistent. After 120 days of rearing, a total of 80 samples RG1, 16 net cages were collected from each of the 5 heaviest individuals in each net cage of the first pond. Since the molting effect is directly related to the muscle expansion and distension of crustaceans, and the eye stalk can secrete molting-regulating hormones. Canthus elimination appears to be the most successful method of affecting molting and growth (Chen et al, 1995. The hepatopancreas and intestines contain digestive enzymes, and studies have shown a positive correlation between high growth rate, final body weight, and digestive capacity (Brito et al, 2000. Therefore, the eyestalk, the hepatopancreas and the intestinal tract tissue are taken to extract RNA and are evenly mixed. Similarly, we selected the lightest 5 individuals per net box in the first pond as sample SG1. Also in the second and third ponds, samples RG2, SG2 and RG3, SG3 were obtained.

S2, library construction and sequencing

After total RNA was obtained, mRNA was enriched with Oligo (dT) beads, and rRNA-enriched mRNA was removed with Ribo-ZeroTM Magnetic Kit (Epicentre). The enriched mRNA is then reverse transcribed into cDNA using random primers. cDNA was synthesized using DNA polymerase I, RNase H, dNTP and buffer. The cDNA fragments were purified using the QiaQuick PCR extraction kit, ends repaired, poly (A) added, and ligated into the Illumina sequencing adapter. Using HiSeq ^TM 2500 construction of 6 pools and sequencing. To obtain high quality clean reads, linker sequences, poly-N and low quality sequences were further filtered with software fastp (Chen et al, 2018). Bowtie2 (Langmead and Salzberg, 2012) was used to remove rRNA by mapping to rRNA databases. The unmapped sequences were then aligned with TopHat2 to a reference genome (ncbi _ GCA _ 003789085.1) (Kim et al, 2013). All raw data are stored in NCBI (BioProject PRJAN664224, accession number: SRR12664621-SRR 12664626).

S3.SNP identification

SNPs were called and filtered using unmapped sequence to reference genome alignment and GATK (der-auweera et al, 2013) software default parameters. SNPs are assumed if the mass fraction of SNPs is greater than 30, the read depth is greater than or equal to 10, and MAF (minimum allele frequency) > 0.05. The significance of the difference in allele frequency of each SNP between RG and SG was determined using a two-tailed Fisher's exact test (Fisher, 1922). After Bonferroni correction (Bland and Altman, 1995), we defined the significance of the difference in RG and SG allele frequencies with p-value <4.97e-7 (0.05/100633, FIG. 1). In addition, AFI (number of imbalances in allele frequency, ratio of RG allele frequency to SG allele frequency) was defined and calculated (Salem et al, 2012).

S4.SNP verification

To verify the reliability of the SNP identification procedure, 35 SNPs were screened out of 100633 SNPs, 18 of which were randomly selected out of 104 high quality SNPs (fig. 1), and 17 SNPs were additionally selected for comparison according to p-value >4.97e-7,0.25< = AFI < =4. Then extracting the sequence of SNP site + -200 bp, designing primer3 with the product size mostly between 100-150bp, all the primers are synthesized by Scenario Biotech Limited. Table 1 shows SNP location information for 35 amplifiable target sequences.

DNA was extracted from 240 fast growing individuals (RG) and 240 slow growing individuals (SG) respectively, and two pools of DNA were established, one as RG template (mixing DNA from all RG individuals) and the other as SG template (mixing DNA from all SG individuals). PCR products were recovered from agarose gel electrophoresis using a HiPure gel pure DNA minikit (Magen, guangzhou, china) and then these products were mixed in equal amounts to ensure that each product contributed an equal amount of DNA to the pool (Cutler and Jensen,2010

2010; gautier et al, 2013), two samples (RG and SG) were sent to deo biotechnology limited, guangzhou for DNA pool sequencing.

As shown in table 1, when relaxed screening conditions are applied (p-value > =4.97e-7, and 0.25< = AFI < = 4), only 9 pairs of pool sequencing validation 17 can be detected accurately, with an accuracy rate of 52.94%. After strict filtration (p-value <4.97e-7, and AFI >4or less than 0.25), 13 pairs of 18 pairs can be accurately verified according to a re-sequencing result, and the detection rate is up to 72.22%. The efficiency is improved by 36 percent compared with the prior art.

TABLE 1.35 information on the SNPs of interest

Note: the first 17 SNPs were randomly selected from 96819 low-quality SNPs. After filtration, it means that the 18 SNPs were randomly selected from 104 high-quality SNPs. NA indicates that no SNP is detected at the target position.

While specific embodiments of the invention have been described, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, as equivalent modifications and variations as will be made by those skilled in the art in light of the spirit of the invention are intended to be included within the scope of the appended claims.

Claims

1. A method for screening high-quality SNP of Litopenaeus vannamei based on transcriptome data is characterized by comprising the following steps:

s1, biological sampling: selecting two types of Litopenaeus vannamei individuals with rapid growth and slow growth, respectively collecting tissues or organs with the number of 200 or more, and extracting RNA;

s2, library construction and sequencing: determining the RNA sequence in the step S1 by using a second generation high-throughput sequencing platform to construct a library;

removing low-quality sequences and sequences comprising sequencing connectors and poly-N by Fastp software;

aligning the Fastp software-treated sequences to the rRNA database with Bowtie2 software to remove ribosomal RNA;

comparing the sequence with the reference genome of the litopenaeus vannamei through TopHat2 software after the ribosome RNA is removed, and confirming the SNP locus;

s3, SNP identification: calling SNPs by using GATK default parameters and filtering;

screening the mass fraction with quality more than 30 to obtain x1 assumed SNPs;

screening the reading depth to be more than or equal to 10 to obtain x2 assumed SNPs;

finally, screening SNPs with MAF larger than 0.05 to obtain x3 assumed SNPs, wherein the MAF is the minimum allele frequency;

the two-tailed Fisher exact test was then used to confirm the significance of the minimal allelic frequency difference for each SNP in x3 between fast-growing and slow-growing individuals;

calculating the p value and the AFI of each SNP in x3, screening SNPs with a p value of less than 0.05/x3 and an AFI value of greater than 4or an AFI value of less than 0.25 after correction by Bonferroni as high-quality SNPs, wherein the AFI is the unbalanced number of allele frequencies, namely the ratio of the allele frequencies of fast-growing individuals to those of slow-growing individuals;

s4, SNP verification: designing a primer and amplifying a flanking sequence of the SNPs according to the reference genome sequence of the litopenaeus vannamei to perform DNA mixed pool sequencing to verify the authenticity of the SNP;

x1 in step S3 represents the number of putative SNPs with the mass fraction of more than 30 screened from the transcriptome; x2 represents the number of the putative SNPs with the screening reading depth of more than or equal to 10 from the x 1; x3 represents the number of putative SNPs screened for MAF greater than 0.05 from x 2;

SNPs in steps S3 and S4 are a plurality of SNPs.