CN116064846A - Method for evaluating comprehensive breeding value of growth and resistance traits of jewfish and application - Google Patents
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Abstract
The invention relates to the field of animal genetic breeding, and provides a method for evaluating comprehensive breeding values of growth and resistance traits of jewfish and application thereof, wherein the method comprises the following steps: measuring the growth trait and resistance trait of a reference population; carrying out whole genome resequencing and SNP typing on the reference population; carrying out whole genome association analysis on the growth trait and the resistance trait, and identifying SNP loci related to the growth trait and the resistance trait; establishing a whole genome selection model based on different SNP marker numbers; calculating genome breeding values of the reference population and the verification population based on the optimal whole genome selection model; and calculating comprehensive breeding values according to genome breeding values of the reference population and the verification population. The invention establishes the method for evaluating the comprehensive breeding value of the synergistic breeding of the growth traits and the resistance traits of the jewfish, and the core breeding population with the advantages of both growth and resistance can be selected by evaluating the comprehensive breeding value of the candidate parent fish, so that the breeding period is greatly shortened, and the method has wide application prospect.
Description
Technical Field
The invention relates to the field of animal genetic breeding, in particular to a method for evaluating comprehensive breeding values of growth and resistance traits of jewfish and application thereof.
Background
Lateolabrax (Lateolabrax maculatus) belonging to the order Perciformes, the family Serratidae, and the genus Lateolabrax is distributed in the areas of Bohai sea, east sea, and south sea. In recent years, the annual output of the Chinese perch culture exceeds 15 ten thousand tons, and the first three of the output of the sea water culture fishes are already the sea water culture prop industry. However, the current jewfish breeding systems are facing unprecedented challenges: the lack of excellent jewfish varieties does not establish a scientific breeding system, the germplasm degeneration phenomenon caused by long-term inbreeding is already revealed, and the improvement process is greatly behind other main marine cultured fishes. Therefore, the genetic breeding work is urgently needed to be carried out aiming at the economic characters focused by the jewfish breeding industry.
The primary target characters of fish breeding are growth characters, so that the growth rate is improved, the rapid growth varieties are cultivated, the cultivation period and cost can be shortened, and the economic benefit is effectively improved; in addition, resistance traits are also the focus of attention of breeding work, for example, hypoxia tolerance, temperature tolerance, saline-alkali tolerance, disease resistance and the like are important economic traits which significantly affect the high-quality development of the aquaculture industry. At present, the conventional breeding mode is often adopted to carry out genetic improvement on single characters of fishes, and the genetic breeding requirement of the jewfish is difficult to meet.
Disclosure of Invention
In view of the above, the invention provides a method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish and application thereof, aiming at solving the problems mentioned in the background art.
The invention applies the whole genome selection technology (GS) to the genetic breeding of the jewfish, and establishes a method for evaluating the comprehensive breeding value of the synergistic breeding of the growth and the resistance characters for the first time. The method evaluates the comprehensive breeding value of the candidate parent fish, can select out core breeding groups with the advantages of growth and resistance, greatly shortens the breeding period, provides reference for multi-character compound breeding of other fish, and has wide application prospect.
The technical scheme of the invention is realized as follows:
a method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish, comprising the following steps:
s1, measuring the growth characters and resistance characters of a reference population;
s2, carrying out whole genome resequencing and SNP typing on a reference population;
s3, carrying out whole genome association analysis on the growth trait and the resistance trait, and identifying SNP loci related to the growth trait and the resistance trait;
s4, establishing a whole genome selection model based on different SNP marker numbers;
s5, calculating genome breeding values of a reference population and a verification population based on an optimal whole genome selection model;
s6, calculating a comprehensive breeding value according to genome breeding values of the reference population and the verification population.
Further, the method further comprises:
and evaluating the prediction accuracy of the whole genome selection model according to the correlation between the genome breeding value and the actual phenotype, and selecting the optimal whole genome selection model.
Further, in the step S1, the specific step of measuring the growth trait and the resistance trait of the reference population includes: firstly, temporary culture and domestication are carried out on a reference population, and normal feeding is carried out during the temporary culture period; and then selecting individuals with strong constitution and good ingestion condition, giving stress factor treatment, and recording the growth index and death time of the individuals after death due to environmental stress, wherein the individuals are respectively used as phenotypes of growth and resistance traits. Furthermore, the temporary culture domestication time is more than 2 weeks, and the stress factor is alkalinity stress by using carbonate water (26.16+/-0.5 mmol/L).
Further, in step S5, genome breeding values of the reference population and the validation population are calculated based on the selected optimal whole genome selection model.
Further, in the step S4 and the step S5, the prediction accuracy of the whole genome selection model is evaluated by adopting five-fold cross validation.
Further, in the step S2, the specific step of performing whole genome resequencing on the reference population includes:
extracting total genomic DNA of each individual in a reference population;
detecting the total genomic DNA;
randomly breaking the total genome DNA which is qualified by detection, purifying and screening the DNA fragments which meet the requirements, connecting the DNA fragments with a sequencing joint, preparing DNB (DNA nanospheres) by rolling circle amplification, and sequencing the prepared DNB.
Further, in the step S2, the specific step of SNP typing includes:
quality control and filtering are carried out on the whole genome resequencing data;
comparing the quality-controlled and filtered whole genome re-sequencing data to reference genome data, and eliminating the influence of PCR preference in the whole genome re-sequencing data;
and (3) detecting single nucleotide polymorphism of the whole genome resequencing data after PCR preference is eliminated, and realizing genotyping.
Further, in the step S3, the specific step of performing genome-wide association analysis for the growth trait and the resistance trait includes:
filtering whole genome resequencing data;
filling the missing SNPs in sequencing;
and carrying out whole genome association analysis on the growth trait and the resistance trait respectively.
Further, in the step S6, the step of calculating the comprehensive breeding value according to the genome breeding values of the reference population and the verification population includes:
normalizing genome breeding values of a reference population and a verification population;
and respectively giving corresponding weights to the growth characters and the resistance characters in the normalization processing results, and calculating comprehensive breeding values.
Further, the method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish is applied to the breeding of the jewfish.
Compared with the prior art, the invention has the beneficial effects that:
the method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish establishes the method for evaluating the comprehensive breeding value of the cooperative breeding of the growth traits and the resistance traits, and the method evaluates the comprehensive breeding value of the candidate parent fish to select a core breeding population with the advantages of both growth and resistance, so that the breeding period is greatly shortened, and the method has a wide application prospect.
Drawings
FIG. 1 is a flow chart of the steps of a method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish according to an embodiment of the present invention;
FIG. 2 is a flow chart of steps of a method for evaluating comprehensive breeding values of the body length and alkali resistance of the jewfish according to an embodiment of the present invention;
FIG. 3 is a phenotype distribution diagram of a reference population length trait in an embodiment of the invention;
FIG. 4 is a graph showing the alkali-resistant character phenotype profile of a reference population according to an embodiment of the present invention;
FIG. 5 is a graph showing the result of judging the optimal marker of the whole genome selection model for body length trait according to the embodiment of the present invention;
FIG. 6 is a graph of the result of determining the best marker of the alkali-resistant trait whole genome selection model according to the embodiment of the invention;
FIG. 7 is a graph showing the results of evaluating the body length and alkali resistance of a reference population in accordance with an embodiment of the present invention;
FIG. 8 is a graph showing the results of evaluating the length traits and alkali resistance traits of a verified population in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The experimental methods used in the embodiment of the invention are conventional methods unless otherwise specified.
Materials, reagents, and the like used in the examples of the present invention are commercially available unless otherwise specified.
The embodiment of the invention uses carbonate water (26.16+/-0.5 mmol/L) which is sodium bicarbonate and sodium carbonate according to the molar ratio of 16:5, the reagent is analytically pure, and the manufacturer is national medicine group chemical reagent limited company.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1, a method for evaluating comprehensive breeding values of growth and resistance traits of jewfish according to an embodiment of the present invention includes the following steps:
s100, measuring the growth trait and the resistance trait of the reference population.
In this example, a reference population is first established, and then the established reference population is measured for growth traits and resistance traits. The specific measurement flow is as follows:
firstly, temporary culture and domestication are carried out on a reference population, and normal feeding is carried out during the temporary culture period; and then selecting an individual with strong constitution and good ingestion condition, and giving stress factor treatment to the individual, wherein the characteristic that the individual loses physical balance and cannot recover to a normal swimming state within 1 minute is taken as a death standard of environmental stress. Recording the growth index (body length, weight, etc.) and death time as the phenotype of growth and resistance traits respectively; then, the pectoral fin of each fish is put into absolute ethyl alcohol and preserved at the temperature of minus 20 ℃ for extracting genome DNA.
S200, carrying out whole genome resequencing and SNP typing on the reference population.
In this example, DNA of the reference population was extracted from the above-stored pectoral fins, and whole genome resequencing was performed, followed by SNP (single nucleotide polymorphism ) typing of the whole genome resequencing. SNP typing is to identify the SNP of each fish individual.
S300, carrying out whole genome association analysis on the growth trait and the resistance trait, and identifying SNP loci related to the growth trait and the resistance trait.
In this example, SNP sites associated with growth traits and resistance traits were identified by whole genome association analysis of the growth traits and resistance traits.
S400, establishing a whole genome selection model based on different SNP marker numbers.
In this example, different marker densities were selected, genotype data of SNP markers were generated in the linux environment, and a whole genome selection model was established in the R language environment using rrBLUP analysis methods in rrBLUP package. Specifically, a model was built by ridge regression best linear unbiased prediction using the mixed.solve function of R-packet rrBLUP.
S500, calculating genome breeding values of a reference population and a verification population based on the whole genome selection model.
In this embodiment, the genome breeding value (GEBV) of the reference population is calculated using the whole genome selection model, and the genome breeding value of the verified population may be repeatedly calculated a plurality of times, and the individuals of the verified population are kept identical while the genome breeding values (GEBV) of the verified population growth trait and the resistance trait are calculated, respectively.
S600, calculating comprehensive breeding values according to genome breeding values of the reference population and the verification population.
In the embodiment, a method for evaluating the comprehensive breeding value of the cooperative breeding of the growth trait and the resistance trait is established, and the core breeding population with the advantages of both growth and resistance can be selected by evaluating the comprehensive breeding value of the candidate parent fish by the method, so that the breeding period is greatly shortened, and the method has a wide application prospect.
In one case of this embodiment, the method further includes:
evaluating the prediction accuracy of the whole genome selection model according to the correlation between the genome breeding value and the actual phenotype, and selecting the optimal whole genome selection model;
and calculating genome breeding values of the reference population and the verification population based on the selected optimal whole genome selection model.
In this embodiment, the breeding effect is improved by selecting the optimal whole genome selection model.
In one instance of this embodiment, five-fold cross-validation was used to assess the predictive accuracy of the whole genome selection model.
Five-fold cross validation divides the population into five parts randomly, four parts are used as training sets for model construction, the remaining one part is used as validation sets for verification, and the validation sets are used as validation populations.
In one aspect of this embodiment, the step of performing whole genome resequencing on the reference population comprises:
extracting total genomic DNA of each individual in a reference population;
detecting the total genomic DNA;
randomly breaking the total genome DNA which is qualified by detection, purifying and screening the DNA fragments which meet the requirements, connecting the DNA fragments with a sequencing joint, preparing DNB through rolling circle amplification, and sequencing the prepared DNB.
In this example, reference population DNA was extracted using TIANamp Marine Animals DNA Kit kit reference instructions to obtain total genomic DNA for each individual; detecting the quality and concentration of sample DNA by using a Biodropsis BD-1000 nucleic acid analyzer, detecting the integrity by using 1% agarose gel electrophoresis, and storing at-20 ℃ for standby; randomly breaking the DNA sample which is qualified by detection, purifying and screening the DNA fragment which meets the requirements, connecting the DNA fragment with a sequencing joint, preparing DNB (DNA nanospheres) by rolling circle amplification, and sequencing by using a BGISEQ-500 sequencer.
In one case of the present embodiment, the step of SNP typing includes:
quality control and filtering are carried out on the whole genome resequencing data;
comparing the quality-controlled and filtered whole genome re-sequencing data to reference genome data, and eliminating the influence of PCR preference in the whole genome re-sequencing data;
and (3) detecting single nucleotide polymorphism of the whole genome resequencing data after PCR preference is eliminated, and realizing genotyping.
In this embodiment, fastQC v0.11.9 is used to control the quality of the original data (Raw data), and the sequence (clean data) is obtained after filtering by the SOAPnuke software; comparing clean data to a jewfish reference genome by using BWA v0.7.17, generating ordered bam files by using Samtools v1.14 software, and then eliminating the influence of PCR preference in sequencing by using Picard v2.27.1 software; the g.vcf file for each sample was obtained using GATK4 v4.2.6.1 software, and the g.vcf files for all samples were combined to obtain the total vcf file for all sequenced individuals. Filtering the mixture based on GATK to obtain SNP meeting basic conditions.
In one aspect of this embodiment, the step of performing a whole genome association analysis for the growth trait and the resistance trait comprises:
filtering whole genome resequencing data;
filling the missing SNPs in sequencing;
and carrying out whole genome association analysis on the growth trait and the resistance trait respectively.
In this example, the genotype data was further filtered using Plink v1.90 software; filling part of the missing SNP by using Begale v4.12 software to obtain high-quality genotype data; genome-wide association analysis was performed on growth trait and alkali-resistant trait, respectively, using GEMMA v0.98.3 software.
In one aspect of this embodiment, the step of calculating the integrated breeding value from the genomic breeding values of the reference population and the validation population comprises:
normalizing genome breeding values of a reference population and a verification population;
and respectively giving corresponding weights to the growth characters and the resistance characters in the normalization processing results, and calculating comprehensive breeding values.
In this example, the genomic breeding values (GEBV) for the growth trait and the resistance trait were normalized using dispersion normalization; giving different weights to the growth characters and the resistance characters, and calculating comprehensive breeding values; calculating the correlation between the comprehensive breeding value of the reference population and the growth trait and resistance trait phenotype, so as to evaluate the accuracy of the invention in predicting the growth trait and resistance trait of fish; and calculating the correlation between the comprehensive breeding value of the population and the growth trait and resistance trait phenotype under 10 random sampling, so as to evaluate the accuracy of the invention in predicting the growth trait and resistance trait of the fish.
As shown in fig. 2, in one embodiment, the method of evaluating the overall breeding value for the growth and resistance traits of jewfish is applied to the breeding of jewfish body length and alkali-resistant traits.
At present, the system of the perch seed industry in China is facing unprecedented challenges: the lack of excellent jewfish varieties does not establish a scientific breeding system, the germplasm degeneration phenomenon caused by long-term inbreeding is already revealed, and the improvement process is greatly behind other main marine cultured fishes. Therefore, the genetic breeding work is urgently needed to be carried out aiming at the economic characters focused by the jewfish breeding industry. The primary task of breeding is to improve the growth rate of the jewfish, cultivate the fast-growing variety, shorten the cultivation period and the cost and effectively improve the economic benefit; in addition, the jewfish belongs to the wild-salt fish, can normally grow in a saline-alkali water area within a certain range, can select and breed saline-alkali resistant varieties according to water quality conditions of different areas, and can adapt to medium saline-alkali water area cultivation after selecting and breeding. Therefore, the improvement of the growth speed and the saline-alkali tolerance of the jewfish through genetic breeding has important significance for the sustainable development of the jewfish breeding industry and the efficient utilization of the domestic saline-alkali water area.
The method for evaluating the comprehensive breeding value of the body length and the alkali resistance of the jewfish comprises the following steps:
s10, establishing a reference population, and measuring the body length character and alkali resistance character of the reference population.
In the embodiment, more than 500 tails of wild one-year-old Lateolabrax japonicus in Bohai sea are obtained in 2021 in 11 months, so that genetic diversity is ensured, the weight specification of the fish is 73.62 +/-37.99 g, and the body length specification is 18.50+/-2.69 cm. The cement was acclimatized and temporarily cultured in an indoor cement pond for two weeks for evaluation of alkalinity resistance. And normally feeding during temporary rearing.
And selecting 327 Lateolabrax japonicus with strong constitution and good ingestion condition, transferring the Lateolabrax japonicus into a culture pond filled with carbonate water (26.16+/-0.5 mmol/L), and performing an alkalinity stress experiment for 72 hours to evaluate the alkalinity tolerance of each Lateolabrax japonicus. Starting from the alkalinity stress, the health status of each fish is continuously tracked. Taking the characteristic that the swimming state cannot be recovered to a normal swimming state within 1 minute as a death standard of the jewfish alkalinity stress, recording death time as an alkali-resistant character index, taking the jewfish from an experimental barrel, recording the body length of the jewfish as a growth character index, and taking the pectoral fin to store in absolute ethyl alcohol. As shown in fig. 3 and 4, is a phenotype profile of the body length trait and the alkali resistance trait of the reference population.
S20, carrying out whole genome resequencing and SNP typing on the reference population.
In this example, to eliminate mortality due to handling and individual health, 301 individuals were finally selected for pooling sequencing, and the perch fin DNA was extracted using TIANamp Marine Animals DNA Kit kit reference instructions to obtain total genomic DNA for each individual; the DNA sample is used for library construction sequencing after the integrity and the quality of the DNA sample are checked by agarose gel electrophoresis and Qubit; randomly breaking a DNA sample which is qualified in detection, purifying and screening a DNA fragment which meets the requirements, connecting the DNA fragment with a sequencing joint, preparing DNB through rolling circle amplification, and sequencing by using a BGISEQ-500 sequencer to obtain the original data (Raw data).
In this embodiment, the FastQC v0.11.9 software is used to control the quality of the original data (Raw data), and then the sequence (clean data) is obtained after the filtering by the SOAPnuke software; using BWA v0.7.17 software, building a jewfish reference genome index (index), and aligning the filtered clean data to the jewfish reference genome; converting and ordering the generated sam file into a bam file by using Samtools v1.14 software; then, utilizing a MarDuplex module marker library of Picard v2.27.1 software to generate preferential repeated reads due to amplification deviation generated by PCR reaction in the construction process, and reducing the influence of the repeated sequences on genotyping accuracy; detecting single nucleotide polymorphism by using GATK4 v4.2.6.1 software, genotyping by using a biplotypeCaller module, firstly generating gvcf files of all samples, and then merging required samples by using combineGCFs to generate all sample vcf files for subsequent analysis. In order to ensure SNP accuracy, preliminary filtration was performed again by the VariantFilter Module of GATK4 software.
In this embodiment, this part of the work is mainly done under the linux system, and the following are specific command parameters of this part of the work:
# data filtering
a) Fastqc-t 12$i.fq ($i is the name of each sample) # this step is data quality control, check the original data quality of each sample;
b) SOAPnuke-1$ i_1.Fq-2$ i_1. Fq-T4-n 0.1-l 5-Q0.5-Q2-G-5 1-o$ i_c1.Fq-D$ i_c2.Fq# this step is data filtering, and each sample clean data is obtained.
# data alignment and format conversion
a) bwa-t 8-M genome $i_c1.Fq $i_c2.Fq|samtools view-bS- > $i.bam# this step is to align clean data to the reference genome and convert to a binary bam file;
b) samtools sort the bam files at a step of samtools sort @12-m 1G$i.bam-o$i_sort $bam#;
c) picard MarkDuplicates I = $i_sort.bam o= $i_picard.bam m=rm.bamremove_duplicaltes=wire# this step is to remove amplification bias of the PCR reaction.
Identification of # and filtration of SNP
a) picard AddOrReplaceReadGroups I = $i_picard.bamo = $i.bamso=reorder ID = $ilb = $ilpl=illumina pu=run SM = $i# this step is to add labels to each sample for sample index establishment;
b) samtools index $i.bam# this step is to build a sample index;
c) samtools faidx genome # this step is to build a reference genome index;
d)picard CreateSequenceDictionary R＝genome.fa O＝genome.Dict；
e) gatk HaplotypeCaller-R genome. Fa-I$i.bam-ERC GVCF-O$i.g. vcf-genoyping-mode DISCOVERY-pcr-indel-model CONSERVATIVE-sample-plody 2-min-base-quality-score 10-kmer-size 25# this step is to obtain g.vcf for each sample, including snp for each sample;
f) gatk CombineGVCFs-R genome. Fa-O all. Raw. Vcf-V$i.g.vcf# this step is to merge the g.vcf files for each sample, generating a vcf file containing the snp for all samples;
g) gatk VariantFiltration-R genome. Fa-O all. Filter. Vcf-V all. Raw. Vcf-filter-name Filter Qual-filter-expression "QUAL <30.0" - -filter-name Filter QD-filter-expression "QD <13.0" - -filter-name Filter MQ-filter-expression "MQ <20.0" - -filter-name Filter FS-filter-expression "FS >20.0" - -filter-name FilterMQRankSum- -filter-expression "MQRankSum < -3.0" - -filter-name FilterReadPosRankSum- -filter-excompression "Filter ReadPosRankSum < -3.0" # this step is a preliminary screening of the identified SNPs.
S30, carrying out whole genome association analysis on the body length character and the alkali resistance character, and identifying SNP loci related to the body length character and the alkali resistance character.
In this example, the genotyping data was further filtered using Plink v1.90 software. The main filtering parameters are as follows: minimum allele frequency (minor allele frequencies, MAF) greater than 0.05; a genotype deletion rate (missing rate) of less than 0.05; individual loss rate (missing rate) is less than 0.02; and does not conform to Hardy-Weinberg Law (p < 0.05); filling the filtered genotype data by using Beagle v5.4 software to predict the partial SNP genotypes lost by sequencing, and finally obtaining 301 individuals and 4660345 SNP loci; and respectively carrying out whole genome association analysis on the length and survival time of the reference population and the SNP typing data after screening, wherein the used software is GEMMA v0.98.3 software in a linux environment, a population structure (PCA) is used as a fixed effect, a genetic relationship is used as a random effect, and the obtained significant P values of all SNP loci are added into the analysis of a mixed linear model and are sorted from small to large.
In this embodiment, this part of the work is mainly done under the linux system, and the following are specific command parameters of this part of the work:
# filtration of genotyping data using Plink v1.90 software
plink--vcf all.filter.vcf--maf 0.05--geno 0.05--mind 0.02–hwe 0.05--recode vcf-iid--out gwas.filtered；
# filling in filtered genotype data using Beagle v5.4 software
java-Xmx300m-jar beagle.28Jun21.220.jar gt＝gwas.filtered.vcf out＝gwas.imputed.vcf ne＝301；
# converting VCF File to Plink Format File (ped, map) using Plink v1.90
plink--vcf gwas.imputed.vcf--make-bed--out gwas.imputed；
# Add the phenotype value of the trait to ped File using Shell script
sh phe. Sh# this step is an execution script;
the phe. Sh content is as follows:
awk'{print$1,$2,$3,$4,$5}'gwas.imputed.ped>ID
awk'{$1＝null；$2＝null；$3＝null；$4＝null；$5＝null；$6＝null；print$0}'gwas.imputed.ped>new.ped
paste-d""ID tl>tl.phe
paste-d""ID time>time.phe
paste-d""tl.phe new.ped>gwas.tl.ped
paste-d""time.phe new.ped>gwas.time.ped
cp gwas.imputed.map>gwas.tl.map
cp gwas.imputed.map>gwas.time.map；
# PCA analysis of genotype data using Plink v1.90 software
plink--file gwas.imputed--pca 10--out pca；
# # uses the first three principal components of PCA results as covariates
awk'{print$1,$2,$3,$4}'pca.eigenvec>c.txt；
# computing affinity G matrix using GEMMA v0.98.3 software
plink-file gwas. Inputted-make-bed-out gwas. Inputted# this step is to generate a binary file;
the step of gemma-0.98.1-linux-static-bfile gwas-gk 2-o kinshift # is to calculate the relationship;
# GWAS correlation analysis Using GEMMA v0.98.3 software
plink--file gwas.tl--make-bed--out gwas.tl
plink-file gwas. Time-make-bed-out gwas. Time# this step is to convert the file into a binary file;
gemma-0.98.1-linux-static-bfile gwas.tl-lmm-k./output/kinship.sXX.txt
txt-o GWAS. Tl# this step is GWAS analysis of body length traits;
gemma-0.98.1-linux-static-bfile gwas.time-lmm-k
output/kinshift. SXX. Txt-c.txt-o GWAS. Time# this step is a GWAS analysis of alkali resistance traits;
# # rank SNP loci according to their P value from small to large
awk '{ print $2, $12}' gwas. Tl. Gemma. Assoc. Txt > sed-i '1d' > sort-k2-g > awk '{ print $1}' tl. Sorted# body length trait;
awk '{ print $2, $12}' gwas.time.gemma.assoc.txt > sed-i '1d' > sort-k2-g > awk '{ print $1}' time.sort# alkali resistant property.
S40, establishing a whole genome selection model based on different SNP marker numbers, evaluating the prediction accuracy of the whole genome selection model according to the correlation between the genome breeding value and the actual phenotype, and selecting the optimal whole genome selection model.
In this embodiment, 50, 100, 150, 200, 400, 800, 1000, 1200, 1400, 1600, 3200, 6400 and 12800 SNP markers are selected as selection markers according to the already ordered SNP markers, respectively, and genotype data of the SNP markers are generated in a linux environment; establishing a whole genome selection model by using a rrBLUP analysis method in a rrBLUP package under an R language environment, calculating the prediction accuracy of the model under different marking amounts by using five-fold cross validation, wherein the five-fold cross validation is to randomly divide a reference group into 5 parts, 4 parts are training sets, 1 part are test sets, predicting the GEBV of the test sets by using the model established by the training sets, and the prediction accuracy is the square of the correlation coefficient between the GEBV of the test sets and an actual phenotype value. The results show that: the prediction accuracy of the body length trait has been substantially maximized at 1000 markers. The prediction accuracy of the alkali resistance property is basically highest under 1400 marks; when the prediction accuracy reaches the highest, the model established under the corresponding marker density is the optimal whole genome selection model. As shown in FIG. 5, the graph is a result graph of determining the optimal markers of the whole genome selection model for the body length trait, the abscissa indicates the number of markers, the ordinate in the left graph indicates the model fitness, and the ordinate in the right graph indicates the genome breeding value (GEBV) prediction accuracy. As shown in FIG. 6, the graph is a result graph of determining the optimal markers of the alkali-tolerant trait whole genome selection model, the abscissa represents the number of markers, the ordinate in the left graph represents the model fitness, and the ordinate in the right graph represents the genome breeding value (GEBV) prediction accuracy.
This part of the work is mainly completed in the linux system and the R language environment, and the following specific command parameters of this part of the work are as follows:
# # genotype data was generated based on the selected SNP loci (demonstrated by the body length trait)
head-N N tl.dissolved > N.ID# this step is to choose N tag numbers;
plink-bfile gwas. Tl-extract n.id-make-bed-out n# this step is to obtain N tagged genotype binary data;
plink-bfile N-recoodeA-out test# this step is to convert binary data into 012 type genotype data;
head test.raw|cut-d""-f 1-9
cat test. Raw|cut-d "" -f1,7- |sed's/- [ A-Z ]// g' > genetype. Txt# this step is converted to genotype data available for rrBLUP packages;
# construction of full genome selection model Using rrBLUP package and calculation of prediction accuracy
myGD < -read.table ("genetype. Txt", sep= ", head=t) # this step is to read in genotype data;
myY < -read.table ("p.txt", head=t) # this step is to read in phenotype data;
trais=1# this step is to set the trait number to 1;
cycle = 30# this step is 30 cycles;
get. Accuracy=matrix (nrow=circles, ncol=tracks) # this step is to construct a get data matrix;
for(r in 1:cycles){
n＝287
testing＝sample(n,round(n/5),replace＝F)
tracking= -testing # this step is the partitioning of training and validation sets;
myGD_mat < -myGD < -1 > deletes the first column;
mygd_train < -as matrix (mygd_mat [ training ]), # this step is a matrix transformation of training set genotype data;
mygd_test < -as. Matrix (mygd_mat [ testing ]) this step is a matrix transformation of the validation set genotype data;
myY _train < -myY [ training,2] # this step is to read in the phenotype data of the training set;
myY _test < -myY [ testing,2] # this step is to read in the phenotype data of the validation set;
rrblup_model < -mixed. Solution (y= myY _train, z=mygd_train) # this step is to build a model;
pred_effects_testing < -mygd_test% rrblup_model $u# this step is to calculate the breeding value;
GEBV<-c(pred_effects_testing)
GEBV < -as.data.frame (GEBV) # this step is to convert the GEBV to a data frame format;
rownames (GEBV) < -myY [ testing,1] # this step is to assign the verification set GEBV row name;
GEBV. Accuracy [ r,1] < -cor (GEBV, myY _test) # this step is to calculate the correlation of GEBV with the phenotypic data;
}
mean (gebv. Accuracy) # this step is to calculate the average correlation;
std.error (gebv. Accuracy) # this step is the standard deviation of the calculated correlation.
S50, calculating genome breeding values of a reference population and a verified fish population based on the optimal whole genome selection model.
In the embodiment, the GEBV of the reference population body length trait and the alkali resistance trait are calculated by respectively using the optimal whole genome models of the body length trait and the alkali resistance trait; respectively using an optimal whole genome model of the body length property and the alkali resistance property, randomly extracting and verifying a group by utilizing five-fold cross verification, calculating GEBV of the body length property and the alkali resistance property, and repeating for 10 times; when GEBV for verifying the body length character and the alkali resistance character of the population is calculated respectively, individuals of the verification population are consistent.
This part of the work is mainly done in the R language environment, the following are specific command parameters of this part of the work:
calculation of genomic breeding values for reference populations (demonstrated by body length traits)
myGM<-read.table("snp.txt",sep＝"",head＝T)
myGD<-read.table("genotype.txt",sep＝"",head＝T)
myY<-read.table("TL.txt",head＝T)
myX<-read.table("cv",sep＝"\t",head＝F)
myGD_mat<-myGD[,-1]
myGD_TOTAL<-as.matrix(myGD_mat[,])
myY_TOTAL<-myY[,2]
rrBLUP_model<-mixed.solve(y＝myY_TOTAL,myGD_TOTAL)pred_effect<-myGD_TOTAL％*％rrBLUP_model$upred_effect<-as.data.frame(pred_effect)
rownames(pred_effect)<-myY[,1]
Calculation of genome breeding values for validated populations (demonstrated by body length traits)
myGD<-read.table("genotype.txt",sep＝"",head＝T)
myY<-read.table("P.txt",head＝T)
traits＝1
cycles＝10
GEBV.accuracy＝matrix(nrow＝cycles,ncol＝traits)
for(r in 1:cycles){
n＝287
testing＝sample(n,round(n/5),replace＝F)
training＝-testingmyGD_mat<-myGD[,-1]
myGD_train<-as.matrix(myGD_mat[training,])
myGD_test<-as.matrix(myGD_mat[testing,])
myY_train<-myY[training,2]
myY_test<-myY[testing,2]
rrBLUP_model<-mixed.solve(y＝myY_train,Z＝myGD_train)
pred_effects_testing<-myGD_test％*％rrBLUP_model$u
GEBV<-c(pred_effects_testing)
GEBV<-as.data.frame(GEBV)
rownames(GEBV)<-myY[testing,1]
The specific parameter of } # is the same as above.
S60, calculating comprehensive breeding values according to genome breeding values of the reference population and the verification population, and evaluating the body length characters and the alkali resistance characters of the comprehensive breeding values.
In this example, the genomic breeding values for the body length trait and the alkali-resistant trait were normalized using dispersion normalization. Dispersion normalization formula: x= (x-min)/(max-min); giving a weight of 0.6 to the body length character and a weight of 0.4 to the alkali-resistant character, and calculating a comprehensive breeding value; the formula is: GEBV complex = 0.6 x GEBV body length +0.4 x GEBV alkali resistance; calculating the correlation of the comprehensive breeding value of the reference population with the body length character and the alkali-resistant character phenotype, and the result shows that the correlation coefficients of the comprehensive breeding value and the body length character and alkali-resistant character phenotype are respectively 0.70 (P < 0.05) and 0.63 (P < 0.05) and are obviously correlated; and (3) under 10 times of random sampling, verifying the correlation between the comprehensive breeding value of the population and the phenotype of the body length character and the alkali resistance character, wherein the result shows that the correlation coefficient between the comprehensive breeding value and the phenotype of the body length character and the phenotype of the alkali resistance character is between 0.5 and 0.8 (P < 0.05), and the accuracy of the prediction of the phenotype of the body length character and the phenotype of the alkali resistance character is shown. As shown in FIG. 7, the results of evaluation of the body length trait and the alkali resistance trait of the reference population are shown, the abscissa represents the comprehensive breeding value of individuals, and the ordinate represents the phenotype value of individuals. As shown in fig. 8, the result graph of evaluating the body length trait and the alkali resistance trait of the verified population, the abscissa represents the number of cycles, and the ordinate represents the correlation coefficient of the comprehensive breeding value and the phenotype value of the verified fish population.
In the embodiment, the comprehensive breeding value of the body length character and the alkali resistance character of the jewfish is evaluated by using a whole genome selective breeding technology (GS), and the feasibility of the GS in the multi-character synergistic improvement of the jewfish is theoretically proved. Comprising the following steps: establishing a reference population, and performing phenotype measurement of the body length character and the alkali resistance character on the reference population; constructing a DNA library for whole genome re-sequencing and genotyping; carrying out whole genome association analysis on the body length characters and the alkali resistance characters of the reference population, and respectively identifying SNP loci related to the body length characters and the alkali resistance characters; selecting different marker densities, establishing a whole genome selection model by using a rrBLUP analysis method, and selecting an optimal whole genome selection model through the prediction accuracy of a correlation evaluation model of a genome breeding value (GEBV) and an actual phenotype; under the optimal model, GEBV of the reference population and the verified population body length character and alkali-resistant character is calculated, the GEBV is normalized, and meanwhile, the weight of 0.6 is given to the body length character and the weight of 0.4 is given to the alkali-resistant character, and the comprehensive breeding value is calculated. In the reference population, the phenotype correlation of the comprehensive breeding value and the body length character and alkali resistance character is respectively 0.70 (P < 0.05) and 0.63 (P < 0.05); in the verification population, the phenotype correlation of the comprehensive breeding value and the body length character and alkali resistance character is between 0.5 and 0.8 (P < 0.05). The result shows that the invention is feasible for the synergistic improvement of the body length character and the alkali resistance character of the jewfish. The method evaluates the comprehensive breeding value of the parent fish, can select the breeding population with specific length and saline-alkali tolerance potential, greatly shortens the breeding period, provides reference and foundation for the multi-character synergistic improvement of other fishes, and has wide application prospect.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish, comprising the following steps:
s1, measuring the growth characters and resistance characters of a reference population;
s2, carrying out whole genome resequencing and SNP typing on a reference population;
s3, carrying out whole genome association analysis on the growth trait and the resistance trait, and identifying SNP loci related to the growth trait and the resistance trait;
s4, establishing a whole genome selection model based on different SNP marker numbers;
s5, calculating genome breeding values of a reference population and a verification population based on an optimal whole genome selection model;
s6, calculating a comprehensive breeding value according to genome breeding values of the reference population and the verification population.
2. The method for assessing the integrated breeding value of the growth and resistance traits of jewfish according to claim 1, further comprising:
and evaluating the prediction accuracy of the whole genome selection model according to the correlation between the genome breeding value and the actual phenotype, and selecting the optimal whole genome selection model.
3. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 1, wherein in step S1, the specific step of measuring the growth traits and resistance traits of the reference population comprises: firstly, temporary culture and domestication are carried out on a reference population, and normal feeding is carried out during the temporary culture period; and then selecting individuals with strong constitution and good ingestion condition, giving stress factor treatment, and recording the growth index and death time of the individuals after death due to environmental stress, wherein the individuals are respectively used as phenotypes of growth and resistance traits.
4. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 2, wherein in step S5, the genome breeding values of the reference population and the verification population are calculated based on the selected optimal whole genome selection model.
5. The method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish according to claim 2, wherein in the steps S4 and S5, the prediction accuracy of the whole genome selection model is evaluated by adopting five-fold cross validation.
6. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 1, wherein in step S2, the specific step of performing whole genome resequencing on the reference population comprises:
extracting total genomic DNA of each individual in a reference population;
detecting the total genomic DNA;
randomly breaking the total genome DNA which is qualified in detection, purifying and screening the DNA fragments which meet the requirements, connecting the DNA fragments with a sequencing joint, preparing the DNA nanospheres through rolling circle amplification, and sequencing the prepared DNA nanospheres.
7. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 1, wherein in the step S2, the specific step of SNP typing comprises:
quality control and filtering are carried out on the whole genome resequencing data;
comparing the quality-controlled and filtered whole genome re-sequencing data to reference genome data, and eliminating the influence of PCR preference in the whole genome re-sequencing data;
and (3) detecting single nucleotide polymorphism of the whole genome resequencing data after PCR preference is eliminated, and realizing genotyping.
8. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 1, wherein in the step S3, the specific step of performing the whole genome association analysis for the growth traits and the resistance traits comprises:
filtering whole genome resequencing data;
filling the missing SNPs in sequencing;
and carrying out whole genome association analysis on the growth trait and the resistance trait respectively.
9. The method for evaluating the comprehensive breeding value of the growth and resistance traits of jewfish according to claim 1, wherein in the step S6, the specific step of calculating the comprehensive breeding value according to the genome breeding values of the reference population and the verification population comprises:
normalizing genome breeding values of a reference population and a verification population;
and respectively giving corresponding weights to the growth characters and the resistance characters in the normalization processing results, and calculating comprehensive breeding values.
10. The method for evaluating the comprehensive breeding value of the growth and resistance traits of the jewfish according to any one of claims 1 to 9, applied to the breeding of the jewfish.
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