CN106480189A - A kind of disease-resistant prevalent variety cultivation method of Fish based on full-length genome selection - Google Patents
A kind of disease-resistant prevalent variety cultivation method of Fish based on full-length genome selection Download PDFInfo
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Abstract
The purpose of the present invention is to set up a kind of disease-resistant prevalent variety cultivation method of Fish based on full-length genome selection, make up the deficiency of traditional breeding technology, there is provided molecular breeding method for Fish disease-resistant high yield high-quality prevalent variety cultivation, solve the problems, such as to lack full-length genome system of selection in fish farming industry, the technological means of disease-resistant good variety selection are provided for fish farming industry, realize the update of fish breeding technology, promote Fish kind industry fast-developing.The premunition of the disease-resistant seed that the method for the present invention obtains significantly improves.Test result indicate that the infection survival rate of the disease-resistant seed cultivated by full-length genome system of selection and breeding survival rate exceed 20% 30% than matched group；By this method can quickly, high effect culture go out the disease-resistant improved seeds of Fish, improve resistances against diseases and the cultivation survival rate of Fish, there is in fish farming industry great using value and wide promotion prospect.
Description
Technical field
The invention belongs to Aquatic product Biotechnology in Genetic Breeding field is and in particular to a kind of Fish based on full-length genome selection are disease-resistant
Prevalent variety cultivation method.
Background technology
Fish farming industry is the pillar industry of China's culture fishery, 2721.94 ten thousand tons of fish culture yield in 2014, accounts for
The 57.3% of whole aquaculture production.Cultured fishes are the important sources of China's food proteins.
However, developing rapidly with fish farming industry, lack improved seeds, cultivation species germplasm degradation phenomena projects；
The deterioration of the expansion of cultivation scale, the raising of intensive degree and breeding environment and there is frequency in the aquiculture disease that leads to
Numerous, seriously govern the sustainable development of fish farming industry the problems such as cultured product medicine is residual prominent.Only for Fish, due to height
The immunodepression that density culture is formed, leads to the premunition of cultured fishes to decline；Due to the immune disease-resistance mechanism of Fish and anti-
The Molecular and genetic basis of sick power are short in understanding it is difficult to propose, from molecular level, the technology way that preventing and treating fish diseases occur and develop
Footpath；Due to lacking disease-resistant functional gene and disease-resistant molecular marker it is difficult to carry out disease-resistant improved seeds cultivation, breeding production can only be according to
Wild or artificial propagation many generations seed by premunition difference is carried out, thus popular disease frequently occurs in fish culture.
According to incompletely statistics, China is every year because the direct economic loss that disease causes to fish farming industry reaches 10,000,000,000 yuan.Disease is
Become the bottleneck of restriction China fish farming industry sustainable development.The Major Diseases of harm cultured fishes at present are bacillary and sick
Viral disease and parasite type disease.Wherein endangering larger 3 kind bacterial disease is tarda disease, streptococcus respectively
Disease and vibriosises.Although the prophylactic measures such as Antibiotics medicine or vaccine have certain effect, cannot fundamentally solve water
Produce the disease problem in cultivation.And Antibiotics medicine has easily in fish cylinder accumulation, reduce cultured fishes commercial quality, right
The health of consumer has potential hazard, so that pathogen is developed immunity to drugs and the problems such as severe contamination breeding environment, because
And the application in culture fishery is increasingly restricted.Simultaneously antibiotics using nor to meet people growing
Demand to the green aquatic product of drug residue free.Therefore, the disease-resistant fine-variety breeding of Fish is that China's aquatic products field is badly in need of capturing
One of key subjects.
So far, Fish fine-variety breeding is mainly based upon the selection-breeding of phenotypic character, including unexpected mass incident, family selective breeding,
Selection cross and BLUP selection-breeding etc., are all to be carried out according to the breeding value that the phenotypic number that body length, body weight etc. easily measure calculates
Select.After molecular marker occurs, then it is by positioning the molecular marker that is associated with important economical trait thus to economy
Shape carries out genotype selection, but the molecular marker quantity used by traditional molecular marking supplementary breeding is very limited, to single-gene
The Selection effect of character or qualitative trait is pretty good, but the Selection effect of the quantitative trait determining for polygenes is then not so good.By
It is by the quantitative trait of multiple Gene Handling it is difficult to measurement in disease resistance trait, select accuracy at a fairly low, so to disease-resistant breeding
Selection-breeding make slow progress always, limit the cultivation of Fish disease-resistant varieties.In the urgent need to a kind of new breeding technique or means
To capture this difficult problem.Therefore, the present invention establishes a kind of disease-resistant prevalent variety cultivation method of Fish based on full-length genome selection, purport
There is provided a kind of new molecular breeding technology for the disease-resistant prevalent variety cultivation of Fish.
Content of the invention
The purpose of the present invention is to set up a kind of disease-resistant prevalent variety cultivation method of Fish based on full-length genome selection, makes up tradition
The deficiency of breeding technique, provides molecular breeding method for Fish disease-resistant high yield high-quality prevalent variety cultivation, solves to lack in fish farming industry
The problem of weary full-length genome system of selection, provides the technological means of disease-resistant good variety selection, realizes Fish for fish farming industry
The update of breeding technique, promotes Fish kind industry fast-developing.
The Fish disease-resistant prevalent variety cultivation method being selected based on full-length genome of the present invention, is mainly comprised the steps：
1) foundation of the disease-resistant reference group of Fish and phenotypic character measure
When the fish family fry growth set up reaches total length 8-15 centimetre, using pathogenic microorganism to fish family fish
Seedling carries out pathogen artificial challenge, and after pathogenic bacterial infection morbidity, different time collects the dead fry of each family, records fry
Death time and total length, body weight, body width isophenous data, the individuality survived after collection infection simultaneously simultaneously records total length, body weight, body
Wide data, using the data of the living individuals of collection and dead individuals as the phenotypic parameter evaluating fish body premunition, and according to dead
Rate of dying and death time, choose experimental subjects in each family, obtain one group of generation that can react whole experimental population disease resistance trait
The individual combination of table is as reference group；The individual estimated breeding value (EBV) of reference group and group are calculated using animal model
Physical culture kind average, and it is converted into relative breeding value (RBV), the phenotype of reference group in calculating as gene group selection；
Described be converted into relative breeding value, be using under R language environment R-package asreml calculate obtain；
2) full-length genome of reference group resurvey sequence and genotype collection with analyzing and processing
The genome DNA sample extracting the reference group fry of collection builds storehouse sequencing, and building storehouse type is DNA-350bp, surveys
Sequence strategy is HiSeqPE150, to sequencing result first according to sequencing quality, filters out containing joint sequence and low quality base
More sequencing gained reads, compares reference gene group, and reference gene group is the genome sequencing knot of Cynoglossus semilaevis and Paralichthys olivaceuss
Really (GenBank ID：PRJNA73987；PRJNA73673), corresponding SNP site calling in detection reference gene group,
Arrange and generate .vcf file；Using plink .vcf file is read out, merges individual data items, subsequently carry out according to chromosome
Segmentation and quality control, carry out data filling using beagle, then the SNP site data on all chromosomes used is closed
And, conversion generates the .csv form genotype file in units of individuality, for the calculating of gene group selection；
3) full-length genome of reference group selects to calculate and SNP site effect analysis
With the relative breeding value (RBV) of reference group as phenotype, with reference group's sequencing gained SNP site data as gene
Type, carries out gene group selection calculating, the computational methods of use are Bayes C π, and the calculating instrument being used is R-package
BGLR, obtains the correlation values of each SNP site and disease-resistant phenotype after calculating, arrange output .txt file, as each SNP
The genetic effect value in site, the estimation of the genomic breeding value (GEBV) in selecting for full-length genome；
4) foundation of candidate population and full-length genome are resurveyed sequence
The fin ray of collection candidate population fish, extracts fin ray genomic DNA, carries out genomic library construction and full-length genome weight
Sequencing, Treatment Analysis SNP site data, in the case of disappearance phenotypic number, carry out gene group selection calculating；In reference group's base
After the completion of calculating because of group selection, the effect value of the SNP site by calculating, in conjunction with the individual each SNP site gene of candidate population
Type, obtains candidate population genome estimated breeding value；
The fish family that described candidate population refers to not carry out pathogenic bacterial infection, do not have a disease resistance trait phenotype is individual,
Never do not know the fish family individuality of disease-resistant phenotypic character or in gene group selection practical application with a selection part in family
During with Fish prevalent variety cultivation, choose for the parent fish breeding as candidate population；
5) genes of individuals group estimated breeding value (GEBV) analysis of reference group and candidate population
The hereditary effect of each SNP site according to the reference group obtaining and the gene of each SNP site of Different Individual
Type, calculates genes of individuals group estimated breeding value (GEBV) of reference group；Generate and export .txt note after calculating terminates
The individual GEBV of record reference group；According to the individual GEBV of reference group, it is stronger that preliminary screening goes out premunition in reference group
Individuality, and the family that these individualities are located；SNP gene according to the individual SNP site effect of reference group and candidate population
Type, calculates further and obtains the individual genome estimated breeding value of candidate population；According to the GEBV result of calculation of candidate population, count
Calculate the correlation coefficient of GEBV value and actual infection survival rate, and then verify the accuracy of gene group selection result of calculation.
6) cultivation of the disease-resistant seed of Fish
The size of the candidate population genome estimated breeding value being calculated according to full-length genome selection, in phenotype disappearance
In the case of, the premunition of candidate population is estimated and sorts；Select the high candidate population of GEBV individual as parent fish, carry out
The breeding of disease-resistant seed；The premunition breeding the filial generation seed of gained significantly improves；
In the infection experiment subsequently carrying out, survival rate is significantly higher than comparison to the filial generation seed that the method for the present invention obtains
Group, result shows that the infection survival rate of the offspring seed of gene group selection candidate population exceeds 20%-30% than matched group；Pass through
This method can quickly, high effect culture go out the disease-resistant improved seeds of Fish, improve resistances against diseases and the cultivation survival rate of Fish,
It is with a wide range of applications in fish farming industry and promotion prospect.
Brief description
Fig. 1：Paralichthys olivaceuss full-length genome is resurveyed distribution on each chromosome for the sequence sample SNP site.Abscissa chr1-chr24
Represent 24 chromosomes, vertical coordinate represents the number of SNP site on chromosome, minima is the 5223 of chr16, maximum is
The 20971 of chr1.
Fig. 2：Cynoglossus semilaevis full-length genome is resurveyed distribution on each chromosome for the sequence sample SNP site.Abscissa chr1-
Chr20 represents 20 autosomes, and vertical coordinate represents the number of SNP site on chromosome, and minima is the 51595 of chr16,
It is worth greatly 200654 for chr1.
Fig. 3：The effect value of 397215 SNP site that Paralichthys olivaceuss gene group selection sample calculates.Abscissa represents each SNP
Site arranges according to position on genome, and vertical coordinate represents SNP site effect value, size 1.18E-16 to 3.73E-4 it
Between.
Fig. 4：The effect value of 17529015 SNP site that Cynoglossus semilaevis gene group selection sample calculates.Abscissa table
Show that each SNP site arranges according to position on genome, vertical coordinate represents SNP site effect value, size is in 3.37E-19 extremely
3.83E-5 between.
Fig. 5：Paralichthys olivaceuss reference group GEBV, with Paralichthys olivaceuss reference group's relative breeding value (RBV) as phenotype, the reference that calculates
Colony GEBV, size is between 64.2-130.1.Abscissa represents that Paralichthys olivaceuss reference group is individual, and vertical coordinate is GEBV.
Fig. 6：The comparison of maternal filial generation survival rate and GEBV in Paralichthys olivaceuss candidate population, by filial generation survival rate with GEBV respectively
Sequence, compares the two dependency, and correlation coefficient is 0.81.Abscissa is that candidate population is individual, and vertical coordinate is sequence ranking.
Fig. 7：The comparison of the filial generation survival rate of male parent and GEBV in Paralichthys olivaceuss candidate population, by filial generation survival rate with GEBV respectively
Sequence, compares the two dependency, and correlation coefficient is 0.89.Abscissa is that candidate population is individual, and vertical coordinate is sequence ranking.
Fig. 8：Cynoglossus semilaevis reference group GEBV, with Cynoglossus semilaevis reference group's relative breeding value (RBV) as phenotype, calculates
The reference group GEBV going out, size is between 18.3-156.9.Abscissa represents that Cynoglossus semilaevis reference group is individual, and vertical coordinate is
GEBV.
Fig. 9：After the infection of Paralichthys olivaceuss seed Edwardsiella tarda, survival rate compares.Tooth is cultivated by gene group selection method
The disease-resistant seed of Flounder (is named as excellent No. 2 of Flounder), infects Flounder excellent No. 2 and matched group with Edwardsiella tarda, survives after calculating infection
Rate, excellent No. 2 survival rates of Flounder are 74%, and matched group survival rate is 44%.
Figure 10：Paralichthys olivaceuss seed breeding survival rate compares.(the name of the disease-resistant seed of Paralichthys olivaceuss is cultivated by gene group selection method
For excellent No. 2 of Flounder), its breeding survival rate is 64%；And matched group breeding survival rate is 39%.
Figure 11：Paralichthys olivaceuss seed cultivation daily gain compares.(the name of the disease-resistant seed of Paralichthys olivaceuss is cultivated by gene group selection method
For excellent No. 2 of Flounder), carry out the growth contrast experiments of Flounder excellent No. 2 and matched group, calculate cultivation daily gain, the excellent No. 2 group 0.82g/ of Flounder
My god, matched group is 0.66g/ days.
Figure 12：Tongue sole families Vibro harveyi infects survival rate within 2015, and black cylindricality represents gene group selection and waits
Select the individual family for male parent of colony, the common tongue sole that white bar represents without gene group selection be male parent family it is seen that
The survival rate of gene group selection family seed is mostly higher.
Figure 13：Cynoglossus semilaevis colony Vibro harveyi infection survival rate in 2015, tongue sole families according to male parent are
Gene group selection candidate population is individual or average individual is divided into Liang Ge colony, calculates survival rate, the seed that genome is selected
Survival rate is 67.6%, and ordinary group survival rate is 43%.
Specific embodiment
Below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example, in conjunction with accompanying drawing to the Fish being selected based on full-length genome disease-resistant prevalent variety cultivation
Method is described in detail：
First, the foundation of the disease-resistant reference group of Fish and phenotypic character measure
When the fish family fry growth set up reaches total length 8-15 centimetre, using pathogenic microorganism to fish family fish
Seedling carries out pathogen artificial challenge, and after pathogenic bacterial infection morbidity, different time collects the dead fry of each family, records fry
Death time and total length, body weight, body width isophenous data, the individuality survived after collection infection simultaneously simultaneously records total length, body weight, body
Wide data, using the living individuals of collection and dead individuals data as the phenotypic parameter evaluating fish body premunition, and according to death
Rate and death time, choose experimental subjects in each family, obtain one group of representative that can react whole experimental population disease resistance trait
Property individual combination as reference group；The individual estimated breeding value (EBV) of reference group and colony are calculated using animal model
Breeding average, and it is converted into relative breeding value (RBV), the phenotype of reference group in calculating as gene group selection；Concrete calculating
Use the R-package asreml under R language environment.It is described in detail below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example.
(1), the foundation of Paralichthys olivaceuss anti-Edwardsiella tarda disease reference group and phenotypic character measure
1. Paralichthys olivaceuss Edwardsiella tarda infection experiment
Begin to Paralichthys olivaceuss selection and use from 2003, first pass through natural selection and artificial challenge's approach and difference
The collection of geographical population and selection-breeding, obtain the Paralichthys olivaceuss fry with various trait, cultivate to sexual maturity, obtain Paralichthys olivaceuss selection-breeding parent
The fish first generation, including：Disease-resistance population (RS), day in-group (JS), colony of Korea S (KS), Huanghai Sea colony (YS).With these selection-breeding
Parent fish, as parent, sets up Paralichthys olivaceuss family.
Colony based on RS, JS, YS etc. in 2007, establishes 63 Paralichthys olivaceuss familys of F1 generation, by wherein 59 families
System carries out Vibrio anguillarum infection experiment, filters out 4 disease-resistant familys, after Vibrio anguillarum infection, survival rate reaches more than 50%.
According to the qualification result of 63 family descendants in 2007, the Parents selecting different groups in 2008, according to double
Row hybrid method sets up 30 familys, by carrying out Vibrio anguillarum infection experiment to 30 familys in 63 F1 familys, filters out 5
Anti-vibrio anguillarum family.
Using RS, JS, YS and the superior families selected and remain for 2007 are parent, build together within 2009 family 43, choose
Wherein 33 familys carry out Vibrio anguillarum infection experiment, wherein F1 family 26, are returned family 1, gynogenesiss generation family 2
Individual, F2 family 4, filter out anti-vibrio anguillarum family 6.
2010, after being introduced with Japan with the Paralichthys olivaceuss (RS) of anti-vibrio anguillarum infection in 2007, the Paralichthys olivaceuss colony (JS) of selection-breeding entered
Cultivation survival rate that row copulation obtains is high, the raun of growth hybrid Population faster as female parent, introduce selection-breeding Paralichthys olivaceuss with Korea S
Colony (KS) is hybridized as male parent, and the triple-crossing offspring obtaining is Paralichthys olivaceuss " excellent No. 1 of Flounder ".
2012, with RS, JS, YS and the superior families selected and remain for 2007,2009 are as parent, build up family 65, choosing
43 familys are taken to carry out Vibrio anguillarum infection experiment, wherein 31 F1 familys, 5 F2 familys, 6 F3 familys and 1 gynogenesis
Secondary family, filters out anti-vibrio anguillarum family 8.
2013, with the superior families that growth is fast, survival rate is high of foundation in 2007, after colony of Korea S (KS) and its selfing
In generation, 2009 is parent in the excellent family of anti-vibrio anguillarum infection, anti-LCDV, cultivation survival rate and Daily gain performance, sets up
Paralichthys olivaceuss family 56, wherein 32 familys are used for Edwardsiella tarda artificial liver support, screen anti-slow Ai Dehuashi
Bacterium family 6.Bacterium infection experiment is carried out according to the method for report.
2014, with colony of Korea S, superior families were parent within 2007,2009,2010, set up Paralichthys olivaceuss family 47, right
39 familys therein have carried out infection experiment, obtain anti-Edwardsiella tarda family 7.
In April, 2015, the excellent Paralichthys olivaceuss being gone out by growth and disease-resistant performance Analysis and Screening over the years with this laboratory
As parent, set up Paralichthys olivaceuss family 56.Choose 46 familys and carry out Edwardsiella tarda infection experiment, including 10 F3 families
System, 36 F4 familys, wherein family full-sibs 9, family half sibs 31, other combine family 6.Screen 5 to resist late
Slow tarda Paralichthys olivaceuss family.
2013-2015 years, reach 8-15cm in Paralichthys olivaceuss total length within continuous 3 years, when meeting experiment specification, carry out Paralichthys olivaceuss slow
Tarda infection experiment, have collected infected individuals time-to-live after infection, total length, body width, body weight isophenous data,
Acquire dead fry and the fin ray sample (table 1) of survival fry, for the extraction of genomic DNA, gene group selection is real simultaneously
Test reference group and be selected from these infection samples.
The each time Edwardsiella tarda of table 1 infects Paralichthys olivaceuss
2. the analysis of Paralichthys olivaceuss anti-Edwardsiella tarda reference group's phenotypic character
Using analysis software ASReml-R, reference group's phenotype is analyzed, is calculated from the animal model in software
2013rd, the heritability of infection experiment in 2014,2015 and estimated breeding value (EBV) that each is individual.For making the data one of 3 years
Cause, be easy to unified Analysis, the estimated breeding value that annual infection experiment individuality is calculated is converted to relative breeding value (RBV),
Merge the calculating that infection data phenotype carries out next step 3 years.
2.1 structure pedigrees
The Paralichthys olivaceuss family information being recorded since two thousand seven according to laboratory and parent's filial generation corresponding relation, build Paralichthys olivaceuss man
Be total pedigree, cover lefteye flounder colony of Korea S (KS) as parent, Japanese lefteye flounder colony (JS), disease-resistant Paralichthys olivaceuss colony (RS),
The family of,,, all Paralichthys olivaceuss parent fishs in 2015 and foundation in 2007 in 2009 in 2010 in 2012 in 2013 in 2014.
2.2 select model
The computation model using is the animal model of ASReml software, and this model can realize fixed effect breeding value simultaneously
BLUP with stochastic effect breeding value.Estimate each individual breeding value when, can make full use of offspring and
Sibship data knowable to all between parent, builds sibship matrix and inverse matrix, improves the accurate of breeding value estimation
Property.
Y=u+b+a+e；
Wherein y is the phenotype death time, and u is meansigma methodss, and b is fixed effect, and a is stochastic effect, and e is residual error item.A is
Family genetic correlation between thing individuality,It is additive variance covariance matrix between animal individual.The equation group of hereditary mixed model
For：
Wherein,The computing formula of heritability is：
The calculating of 2.3 infection experiment individuality breeding values
The software for calculation using is, R-3.22, installs R-package asreml and AAfun, calculate used under R environment
Script be：
library(asreml)
library(AAfun)
Mm<- asreml.read.table (" data.csv ", header=T, sep=", ")
Mm.ped<- asreml.read.table (" pedigree.csv ", header=T, sep=", ")
Mm.ainv<-asreml.Ainverse(Mm.ped)$ginv
ope<- asreml (fixed=time～1+Batch,
Random=～ped (Animal),
Rcov=～units,
Data=Mm,
Ginverse=list (Animal=Mm.ainv),
Maxiter=40)
summary(ope)$varcomp
Pin (ope, h2a～V1/ (V1+V2))
coef(ope)$fixed
random.effect<-coef(ope)$random
Write.csv (random.effect, file=" EBV.CSV ")
By above-mentioned script, calculate 2013-2015, the heritability of 3 groups of infection experiments and individual estimated breeding value respectively,
Select the individual death time (hour) that the character calculating is infection experiment, living individuals are designated as 396 according to the infection end time
Hour.Infection batch is set to fixed effect, the total pedigree of Paralichthys olivaceuss family in conjunction with 2.1 structures is calculated, preserves individual breeding
Individual breeding value is converted into relative breeding value, change type according to breeding average by value file：
The selection of the reference group that 2.4 full-length genome select
From the sample of infection experiment, pick out 96 familys (2013 32,2014 10,2015 48),
Each family chooses equal proportion death according to mortality rate and living individuals 10-15 is individual, the reference group of constitutivegene group selection, will
Choose individual relative breeding value as the phenotype (table 2) of gene group selection.
Table 1 for genome resurvey sequence Paralichthys olivaceuss individual
(2), the mensure of the foundation of Cynoglossus semilaevis anti-Vibro harveyi reference group and phenotype
1. Cynoglossus semilaevis Vibro harveyi infection
2014, set up tongue sole families totally 103, when tongue sole families fry growth is to 8-15cm, adopt
Intraperitoneal inoculation method carries out Vibro harveyi infection to each family fry.Infection experiment is carried out according to previously established method.Often
Individual family randomly selects 80-150 tail fry and carries out formal infection experiment, and infection cultivates the fiberglass in 2-3 cubic meter with fry
In tank.After infection, daily observed and recorded is carried out to the state and The dead quantity of each family fry, after pathogenic bacterial infection morbidity
Collect the experimental subjects of death, record death time and total length, body weight, the data such as body width, after collection infection simultaneously, survival is individual
Body simultaneously records its total length, body weight, the data such as body width, using these data as fish body premunition phenotypic parameter.Cynoglossus semilaevis resist
The reference group of Vibro harveyi genome Selecting research derives from the infected individuals (table 3) that have collected phenotype and fin ray.
Table 3：Vibro harveyi infects Cynoglossus semilaevis within 2014
2. Cynoglossus semilaevis Vibro harveyi infection reference group's phenotypic character analysis
Using analysis software ASReml-R, reference group's phenotype is analyzed, is calculated from the animal model in software
The heritability of infection experiment and each individual estimated breeding value, and the estimated breeding value that infection experiment individuality is calculated turns
It is changed to relative breeding value, carry out the calculating of next step.
2.1 select model
The computation model using is the animal model of ASReml software, and this model can realize fixed effect breeding value simultaneously
BLUP with stochastic effect breeding value.Estimate each individual breeding value when, can make full use of offspring and
Sibship data knowable to all between parent, builds sibship matrix and inverse matrix, improves the accurate of breeding value estimation
Property.
Y=u+b+a+e；
Wherein y is the phenotype death time, and u is meansigma methodss, and b is fixed effect, and a is stochastic effect, and e is residual error item.A is
Family genetic correlation between thing individuality,It is additive variance covariance matrix between animal individual.The equation group of hereditary mixed model
For：
Wherein,The computing formula of heritability is：
The calculating of 2.2 infection experiment individuality breeding values
The software for calculation using is, R-3.22, installs R-package asreml and AAfun, calculate foot used in R
Originally it is：
library(asreml)
library(AAfun)
Mm<- asreml.read.table (" data.csv ", header=T, sep=", ")
Mm.ped<- asreml.read.table (" pedigree.csv ", header=T, sep=", ")
Mm.ainv<-asreml.Ainverse(Mm.ped)$ginv
ope<- asreml (fixed=time～1+Batch,
Random=～ped (Animal),
Rcov=～units,
Data=Mm,
Ginverse=list (Animal=Mm.ainv),
Maxiter=40)
summary(ope)$varcomp
Pin (ope, h2a～V1/ (V1+V2))
coef(ope)$fixed
random.effect<-coef(ope)$random
Write.csv (random.effect, file=" EBV.CSV ")
By above-mentioned script, calculate the heritability of Cynoglossus semilaevis Vibro harveyi infection experiment colony in 2014 and individuality is estimated
Meter breeding value, selects the individual death time that the character calculating is infection experiment, and living individuals were designated as according to the infection end time
360 hours.Infection batch is set to fixed effect, preserves individual breeding value file, according to breeding average, individual breeding value is turned
Turn to relative breeding value, change type：
2.3 Cynoglossus semilaevis full-length genome select the selection of reference group
From the sample of infection experiment, pick out 107 familys, each family according to mortality rate choose equal proportion dead and
Living individuals 10, the reference group of constitutivegene group selection, will choose individual relative breeding value as gene group selection
Phenotype (table 4).
Table 4 for genome resurvey sequence Paralichthys olivaceuss individual
2nd, the full-length genome of reference group resurvey sequence and genotype collection with analyzing and processing
Fin ray using the reference group fry of collection extracts genomic DNA, detects integrity degree, extracts up-to-standard sample
Product are used for building storehouse sequencing, and building storehouse type is DNA-350bp, and sequencing strategy is HiSeqPE150, to sequencing result first according to survey
Sequence quality, filters out the reads obtaining containing the more sequencing of joint sequence and low quality base, compares reference gene group, ginseng
Examine genome sequencing result (the GenBank ID of the Cynoglossus semilaevis that genomic source completes in inventor and Paralichthys olivaceuss：
PRJNA73987；PRJNA73673), corresponding SNP site calling in detection reference gene group, arrange and generate .vcf literary composition
Part；Using Plink .vcf file is read out, merges individual data items, subsequently according to chromosome carry out segmentation and quality control,
Carry out data filling using beagle, then the SNP site data on all chromosomes used is merged, conversion generates with individuality
Gene group selection by unit calculates used .csv form genotype file.
It is described in detail below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example.
(1), Paralichthys olivaceuss full-length genome resurvey sequence and genotype collection and Treatment Analysis
1. Paralichthys olivaceuss full-length genome is resurveyed sequence and SNPCalling
The reference group selecting and candidate population fin ray are sent the extraction that sequencing company carries out genomic DNA and sequencing.Base
Because group extracts qualified totally 931, the sample (table 5) of detection, these samples are built storehouse sequencing, carries out SNPcalling.
Table 5 Paralichthys olivaceuss gene group selection reference group is counted with candidate population
The storehouse type of building of all samples is DNA-350bp for sequence of resurveying, after the completion of building storehouse, carries out full genome and resurveys
Sequence, sequencing strategy is HiSeqPE150, and data volume is 2G.According to sequencing batch, sequencing result is carried out be grouped SNPcalling,
Every group of the individual upper limit is set to 100, and the reference gene group that SNPcalling uses derives from the Paralichthys olivaceuss full genome of this laboratory
Group sequencing (GenBank ID：PRJNA73673).The method of SNPcalling is：
1. filter initial data and carry out Quality Control
1.1 filter out the reads containing joint sequence.
1.2, when the content of N in single-ended sequencing read exceedes the 10% of this read length ratio, remove this to paired
reads.
1.3 work as contain in single-ended sequencing read low quality (<=5) base number exceedes the 50% of this read length ratio
When, remove this to paired reads.
2. reference gene group compares
2.1 use software BWA
2.2 alignment parameters：mem -t 4 -k 32 -M
2.3 filter command：samtools view -bS
samtools rmdup
The detection in 3.SNP site
3.1 use software：samtools
3.2 filtration parameter：
2. the arrangement of Paralichthys olivaceuss gene group selection sequencing data genotype
Sequencing obtains the vcf file that the result of SNPcalling generates, and in uploading onto the server, by linux system, enters
The extraction of row SNP site and process, obtain the genotype file of gene group selection calculating.Processing method is:
Read：SNP sequence in vcf file is extracted by PLINK, obtains .ped .map file：
./plink --vcf ParalichthysOlivaceus.vcf.gz --recode 12 --allow-extra-
chr --out plink_1
By .ped and .map Piece file mergence
nohup ./plink --allow-extra-chr --file plink_1 --merge plink_1.ped
plink_1.map --merge-equal-pos --recode 12 --out merge_1
Data is split according to chromosome, and carries out quality control, quality control threshold value is gene type rate 0.1；Minimum
Gene frequency 0.05；Breathe out warm balanced ratio 0.000001.Because Paralichthys olivaceuss have 24 pairs of chromosomes, plink default treatment is people
Genome, therefore in cutting procedure, need to define data as chromosome be no less than 24 pairs common animals (as Canis familiaris L. --
Dog), code is as follows：
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 1 --dog --out result_qc_chr-1 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 2 --dog --out result_qc_chr-2 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 3 --dog --out result_qc_chr-3 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 4 --dog --out result_qc_chr-4 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 5 --dog --out result_qc_chr-5 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 6 --dog --out result_qc_chr-6 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 7 --dog --out result_qc_chr-7 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 8 --dog --out result_qc_chr-8 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 9 --dog --out result_qc_chr-9 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 10 --dog --out result_qc_chr-10 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 11 --dog --out result_qc_chr-11 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 12 --dog --out result_qc_chr-12 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 13 --dog --out result_qc_chr-13 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 14 --dog --out result_qc_chr-14 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 15 --dog --out result_qc_chr-15 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 16 --dog --out result_qc_chr-16 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 17 --dog --out result_qc_chr-17 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 18 --dog --out result_qc_chr-18 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 19 --dog --out result_qc_chr-19 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 20 --dog --out result_qc_chr-20 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 21 --dog --out result_qc_chr-21 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 22 --dog --out result_qc_chr-22 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 23 --dog --out result_qc_chr-23 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 24 --dog --out result_qc_chr-24 &
Data filling carries out data filling using software beagle, first the data of upper step gained is converted to beagle
The file format of identification：
nohup ./fcgene --ped result_qc_chr-1.ped --map result_qc_chr-1.map --
oformat beagle --out plink_beagle_chr-1 &
nohup ./fcgene --ped result_qc_chr-2.ped --map result_qc_chr-2.map --
oformat beagle --out plink_beagle_chr-2 &
nohup ./fcgene --ped result_qc_chr-3.ped --map result_qc_chr-3.map --
oformat beagle --out plink_beagle_chr-3 &
nohup ./fcgene --ped result_qc_chr-4.ped --map result_qc_chr-4.map --
oformat beagle --out plink_beagle_chr-4 &
nohup ./fcgene --ped result_qc_chr-5.ped --map result_qc_chr-5.map --
oformat beagle --out plink_beagle_chr-5 &
nohup ./fcgene --ped result_qc_chr-6.ped --map result_qc_chr-6.map --
oformat beagle --out plink_beagle_chr-6 &
nohup ./fcgene --ped result_qc_chr-7.ped --map result_qc_chr-7.map --
oformat beagle --out plink_beagle_chr-7 &
nohup ./fcgene --ped result_qc_chr-8.ped --map result_qc_chr-8.map --
oformat beagle --out plink_beagle_chr-8 &
nohup ./fcgene --ped result_qc_chr-9.ped --map result_qc_chr-9.map --
oformat beagle --out plink_beagle_chr-9 &
nohup ./fcgene --ped result_qc_chr-10.ped --map result_qc_chr-10.map
--oformat beagle --out plink_beagle_chr-10 &
nohup ./fcgene --ped result_qc_chr-11.ped --map result_qc_chr-11.map
--oformat beagle --out plink_beagle_chr-11 &
nohup ./fcgene --ped result_qc_chr-12.ped --map result_qc_chr-12.map
--oformat beagle --out plink_beagle_chr-12 &
nohup ./fcgene --ped result_qc_chr-13.ped --map result_qc_chr-13.map
--oformat beagle --out plink_beagle_chr-13 &
nohup ./fcgene --ped result_qc_chr-14.ped --map result_qc_chr-14.map
--oformat beagle --out plink_beagle_chr-14 &
nohup ./fcgene --ped result_qc_chr-15.ped --map result_qc_chr-15.map
--oformat beagle --out plink_beagle_chr-15 &
nohup ./fcgene --ped result_qc_chr-16.ped --map result_qc_chr-16.map
--oformat beagle --out plink_beagle_chr-16 &
nohup ./fcgene --ped result_qc_chr-17.ped --map result_qc_chr-17.map
--oformat beagle --out plink_beagle_chr-17 &
nohup ./fcgene --ped result_qc_chr-18.ped --map result_qc_chr-18.map
--oformat beagle --out plink_beagle_chr-18 &
nohup ./fcgene --ped result_qc_chr-19.ped --map result_qc_chr-19.map
--oformat beagle --out plink_beagle_chr-19 &
nohup ./fcgene --ped result_qc_chr-20.ped --map result_qc_chr-20.map
--oformat beagle --out plink_beagle_chr-20 &
nohup ./fcgene --ped result_qc_chr-21.ped --map result_qc_chr-21.map
--oformat beagle --out plink_beagle_chr-21 &
nohup ./fcgene --ped result_qc_chr-22.ped --map result_qc_chr-22.map
--oformat beagle --out plink_beagle_chr-22 &
nohup ./fcgene --ped result_qc_chr-23.ped --map result_qc_chr-23.map
--oformat beagle --out plink_beagle_chr-23 &
nohup ./fcgene --ped result_qc_chr-24.ped --map result_qc_chr-24.map
--oformat beagle --out plink_beagle_chr-24 &
Carry out data filling using beagle software, missing values are calculated as 0：
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-1.bgl
Missing=0 out=imputed_beagle_chr-1 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-2.bgl
Missing=0 out=imputed_beagle_chr-2 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-3.bgl
Missing=0 out=imputed_beagle_chr-3 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-4.bgl
Missing=0 out=imputed_beagle_chr-4 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-5.bgl
Missing=0 out=imputed_beagle_chr-5 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-6.bgl
Missing=0 out=imputed_beagle_chr-6 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-7.bgl
Missing=0 out=imputed_beagle_chr-7 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-8.bgl
Missing=0 out=imputed_beagle_chr-8 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-9.bgl
Missing=0 out=imputed_beagle_chr-9 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
10.bgl missing=0 out=imputed_beagle_chr-10 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
11.bgl missing=0 out=imputed_beagle_chr-11 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
12.bgl missing=0 out=imputed_beagle_chr-12 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
13.bgl missing=0 out=imputed_beagle_chr-13 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
14.bgl missing=0 out=imputed_beagle_chr-14 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
15.bgl missing=0 out=imputed_beagle_chr-15 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
16.bgl missing=0 out=imputed_beagle_chr-16 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
17.bgl missing=0 out=imputed_beagle_chr-17 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
18.bgl missing=0 out=imputed_beagle_chr-18 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
19.bgl missing=0 out=imputed_beagle_chr-19 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
20.bgl missing=0 out=imputed_beagle_chr-20 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
21.bgl missing=0 out=imputed_beagle_chr-21 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
22.bgl missing=0 out=imputed_beagle_chr-22 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
23.bgl missing=0 out=imputed_beagle_chr-23 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
24.bgl missing=0 out=imputed_beagle_chr-24 ＆
By populated data decompression：
gunzip -d imputed_beagle_chr-1.plink_beagle_chr-1.bgl.phased.gz
gunzip -d imputed_beagle_chr-2.plink_beagle_chr-2.bgl.phased.gz
gunzip -d imputed_beagle_chr-3.plink_beagle_chr-3.bgl.phased.gz
gunzip -d imputed_beagle_chr-4.plink_beagle_chr-4.bgl.phased.gz
gunzip -d imputed_beagle_chr-5.plink_beagle_chr-5.bgl.phased.gz
gunzip -d imputed_beagle_chr-6.plink_beagle_chr-6.bgl.phased.gz
gunzip -d imputed_beagle_chr-7.plink_beagle_chr-7.bgl.phased.gz
gunzip -d imputed_beagle_chr-8.plink_beagle_chr-8.bgl.phased.gz
gunzip -d imputed_beagle_chr-9.plink_beagle_chr-9.bgl.phased.gz
gunzip -d imputed_beagle_chr-10.plink_beagle_chr-10.bgl.phased.gz
gunzip -d imputed_beagle_chr-11.plink_beagle_chr-11.bgl.phased.gz
gunzip -d imputed_beagle_chr-12.plink_beagle_chr-12.bgl.phased.gz
gunzip -d imputed_beagle_chr-13.plink_beagle_chr-13.bgl.phased.gz
gunzip -d imputed_beagle_chr-14.plink_beagle_chr-14.bgl.phased.gz
gunzip -d imputed_beagle_chr-15.plink_beagle_chr-15.bgl.phased.gz
gunzip -d imputed_beagle_chr-16.plink_beagle_chr-16.bgl.phased.gz
gunzip -d imputed_beagle_chr-17.plink_beagle_chr-17.bgl.phased.gz
gunzip -d imputed_beagle_chr-18.plink_beagle_chr-18.bgl.phased.gz
gunzip -d imputed_beagle_chr-19.plink_beagle_chr-19.bgl.phased.gz
gunzip -d imputed_beagle_chr-20.plink_beagle_chr-20.bgl.phased.gz
gunzip -d imputed_beagle_chr-21.plink_beagle_chr-21.bgl.phased.gz
gunzip -d imputed_beagle_chr-22.plink_beagle_chr-22.bgl.phased.gz
gunzip -d imputed_beagle_chr-23.plink_beagle_chr-23.bgl.phased.gz
gunzip -d imputed_beagle_chr-24.plink_beagle_chr-24.bgl.phased.gz
To decompressing files renaming：
mv imputed_beagle_chr-1.plink_beagle_chr-1.bgl.phased imputed_beagle_
chr-1.bgl
mv imputed_beagle_chr-2.plink_beagle_chr-2.bgl.phased imputed_beagle_
chr-2.bgl
mv imputed_beagle_chr-3.plink_beagle_chr-3.bgl.phased imputed_beagle_
chr-3.bgl
mv imputed_beagle_chr-4.plink_beagle_chr-4.bgl.phased imputed_beagle_
chr-4.bgl
mv imputed_beagle_chr-5.plink_beagle_chr-5.bgl.phased imputed_beagle_
chr-5.bgl
mv imputed_beagle_chr-6.plink_beagle_chr-6.bgl.phased imputed_beagle_
chr-6.bgl
mv imputed_beagle_chr-7.plink_beagle_chr-7.bgl.phased imputed_beagle_
chr-7.bgl
mv imputed_beagle_chr-8.plink_beagle_chr-8.bgl.phased imputed_beagle_
chr-8.bgl
mv imputed_beagle_chr-9.plink_beagle_chr-9.bgl.phased imputed_beagle_
chr-9.bgl
mv imputed_beagle_chr-10.plink_beagle_chr-10.bgl.phased imputed_
beagle_chr-10.bgl
mv imputed_beagle_chr-11.plink_beagle_chr-11.bgl.phased imputed_
beagle_chr-11.bgl
mv imputed_beagle_chr-12.plink_beagle_chr-12.bgl.phased imputed_
beagle_chr-12.bgl
mv imputed_beagle_chr-13.plink_beagle_chr-13.bgl.phased imputed_
beagle_chr-13.bgl
mv imputed_beagle_chr-14.plink_beagle_chr-14.bgl.phased imputed_
beagle_chr-14.bgl
mv imputed_beagle_chr-15.plink_beagle_chr-15.bgl.phased imputed_
beagle_chr-15.bgl
mv imputed_beagle_chr-16.plink_beagle_chr-16.bgl.phased imputed_
beagle_chr-16.bgl
mv imputed_beagle_chr-17.plink_beagle_chr-17.bgl.phased imputed_
beagle_chr-17.bgl
mv imputed_beagle_chr-18.plink_beagle_chr-18.bgl.phased imputed_
beagle_chr-18.bgl
mv imputed_beagle_chr-19.plink_beagle_chr-19.bgl.phased imputed_
beagle_chr-19.bgl
mv imputed_beagle_chr-20.plink_beagle_chr-20.bgl.phased imputed_
beagle_chr-20.bgl
mv imputed_beagle_chr-21.plink_beagle_chr-21.bgl.phased imputed_
beagle_chr-21.bgl
mv imputed_beagle_chr-22.plink_beagle_chr-22.bgl.phased imputed_
beagle_chr-22.bgl
mv imputed_beagle_chr-23.plink_beagle_chr-23.bgl.phased imputed_
beagle_chr-23.bgl
mv imputed_beagle_chr-24.plink_beagle_chr-24.bgl.phased imputed_
beagle_chr-24.bgl
Using R, all data on 24 pairs of chromosomes are merged, code is：
The each loci gene type merging file is converted to 012：
./fcgene --bgl imputed_rbind_all.bgl --oformat plink --out beagle_
convert_plink
./plink --file beagle_convert_plink --geno 0.1 --maf 0.05 --hwe
0.000001 --recode A --allow-extra-chr --out final_result
Delete the first six row of file, and be converted to csv form, generate the genotype file that gene group selection calculates
Awk'{ for (i=7；i<=NF；i++)printf$i""FS；print""}'final_result.raw>
genotype.txt
nohup cat genotype.txt|sed's/[[:space:]]/,/g'>genotype.csv
3. the statistics of Paralichthys olivaceuss SNP site
Arrange the genotype file genotype.csv obtaining, comprise SNP site 397215 altogether, be distributed in 24 chromosomes
On, on each chromosome, SNP site number does not wait (Fig. 1) by 5223-20971.
(2), Cynoglossus semilaevis reference group full-length genome is resurveyed sequence and genotype Treatment Analysis
1. Cynoglossus semilaevis full-length genome is resurveyed sequence and SNPCalling
The reference group selecting and candidate population fin ray are sent the extraction that sequencing company carries out genomic DNA and sequencing.Base
Because group extracts qualified totally 863, the sample (table 6) of detection, these samples are built storehouse sequencing, carries out SNPcalling.
Table 6：Paralichthys olivaceuss gene group selection reference group is counted with candidate population
The storehouse type of building of all samples is DNA-350bp for sequence of resurveying, after the completion of building storehouse, carries out full genome and resurveys
Sequence, sequencing strategy is HiSeqPE150, and data volume is 1G.According to sequencing batch, sequencing result is carried out be grouped SNPcalling,
Every group of the individual upper limit is set to the full base of Cynoglossus semilaevis from this laboratory for the reference gene group of 100 SNPcalling uses
Because of group sequencing (GenBank ID：PRJNA73987).The method of SNPcalling is：
1. filter initial data and carry out Quality Control
1.1 filter out the reads containing joint sequence.
1.2, when the content of N in single-ended sequencing read exceedes the 10% of this read length ratio, remove this to paired
reads.
1.3 work as contain in single-ended sequencing read low quality (<=5) base number exceedes the 50% of this read length ratio
When, remove this to paired reads.
2. reference gene group compares
2.1 use software BWA
2.2 alignment parameters：mem -t 4 -k 32 -M
2.3 filter command：samtools view -bS
samtools rmdup
The detection in 3.SNP site
3.1 use software：samtools
3.2 filtration parameter：
2. the process of Cynoglossus semilaevis gene group selection sequencing data genotype
The vcf file that the result obtaining SNPcalling from sequencing company generates, in uploading onto the server, by Linux
System, carries out extraction and the process of SNP site, obtains the genotype file of gene group selection calculating.Processing method is:
Read：SNP sequence in vcf file is extracted by PLINK, obtains .ped .map file：
./plink --vcf sole.vcf.gz --recode 12 --allow-extra-chr --out plink_1
By .ped and .map Piece file mergence：
nohup ./plink --allow-extra-chr --file plink_1 --merge plink_1.ped
plink_1.map --merge-equal-pos --recode 12 --out merge_1
Data is split according to chromosome, and carries out quality control, quality control threshold value is gene type rate 0.1；Minimum
Gene frequency 0.05；Breathe out warm balanced ratio 0.000001.20 pairs of autosomes and a pair of property is contained in Cynoglossus semilaevis genome
Chromosome (ZZ/ZW type) due to gene group selection calculate in it is impossible to the site not having allele occurs, therefore at early stage
In reason, need to reject the SNP site on two sex chromosomies of Z, W, only retain the site on autosome, code is as follows：
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 1 --out result_qc_chr-1 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 2 --out result_qc_chr-2 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 3 --out result_qc_chr-3 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 4 --out result_qc_chr-4 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 5 --out result_qc_chr-5 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 6 --out result_qc_chr-6 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 7 --out result_qc_chr-7 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 8 --out result_qc_chr-8 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 9 --out result_qc_chr-9 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 10 --out result_qc_chr-10 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 11 --out result_qc_chr-11 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 12 --out result_qc_chr-12 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 13 --out result_qc_chr-13 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 14 --out result_qc_chr-14 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 15 --out result_qc_chr-15 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 16 --out result_qc_chr-16 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 17 --out result_qc_chr-17 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 18 --out result_qc_chr-18 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 19 --out result_qc_chr-19 &
nohup ./plink --file merge_1 --geno 0.1 --maf 0.05 --hwe 0.000001 --
recode 12 --allow-extra-chr --chr 20 --out result_qc_chr-20 &
Data filling carries out data filling using software beagle, first the data of upper step gained is converted to beagle
The file format of identification：
nohup ./fcgene --ped result_qc_chr-1.ped --map result_qc_chr-1.map --
oformat beagle --out plink_beagle_chr-1 &
nohup ./fcgene --ped result_qc_chr-2.ped --map result_qc_chr-2.map --
oformat beagle --out plink_beagle_chr-2 &
nohup ./fcgene --ped result_qc_chr-3.ped --map result_qc_chr-3.map --
oformat beagle --out plink_beagle_chr-3 &
nohup ./fcgene --ped result_qc_chr-4.ped --map result_qc_chr-4.map --
oformat beagle --out plink_beagle_chr-4 &
nohup ./fcgene --ped result_qc_chr-5.ped --map result_qc_chr-5.map --
oformat beagle --out plink_beagle_chr-5 &
nohup ./fcgene --ped result_qc_chr-6.ped --map result_qc_chr-6.map --
oformat beagle --out plink_beagle_chr-6 &
nohup ./fcgene --ped result_qc_chr-7.ped --map result_qc_chr-7.map --
oformat beagle --out plink_beagle_chr-7 &
nohup ./fcgene --ped result_qc_chr-8.ped --map result_qc_chr-8.map --
oformat beagle --out plink_beagle_chr-8 &
nohup ./fcgene --ped result_qc_chr-9.ped --map result_qc_chr-9.map --
oformat beagle --out plink_beagle_chr-9 &
nohup ./fcgene --ped result_qc_chr-10.ped --map result_qc_chr-10.map
--oformat beagle --out plink_beagle_chr-10 &
nohup ./fcgene --ped result_qc_chr-11.ped --map result_qc_chr-11.map
--oformat beagle --out plink_beagle_chr-11 &
nohup ./fcgene --ped result_qc_chr-12.ped --map result_qc_chr-12.map
--oformat beagle --out plink_beagle_chr-12 &
nohup ./fcgene --ped result_qc_chr-13.ped --map result_qc_chr-13.map
--oformat beagle --out plink_beagle_chr-13 &
nohup ./fcgene --ped result_qc_chr-14.ped --map result_qc_chr-14.map
--oformat beagle --out plink_beagle_chr-14 &
nohup ./fcgene --ped result_qc_chr-15.ped --map result_qc_chr-15.map
--oformat beagle --out plink_beagle_chr-15 &
nohup ./fcgene --ped result_qc_chr-16.ped --map result_qc_chr-16.map
--oformat beagle --out plink_beagle_chr-16 &
nohup ./fcgene --ped result_qc_chr-17.ped --map result_qc_chr-17.map
--oformat beagle --out plink_beagle_chr-17 &
nohup ./fcgene --ped result_qc_chr-18.ped --map result_qc_chr-18.map
--oformat beagle --out plink_beagle_chr-18 &
nohup ./fcgene --ped result_qc_chr-19.ped --map result_qc_chr-19.map
--oformat beagle --out plink_beagle_chr-19 &
nohup ./fcgene --ped result_qc_chr-20.ped --map result_qc_chr-20.map
--oformat beagle --out plink_beagle_chr-20 &
Carry out data filling using beagle software, missing values are calculated as 0：
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-1.bgl
Missing=0 out=imputed_beagle_chr-1 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-2.bgl
Missing=0 out=imputed_beagle_chr-2 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-3.bgl
Missing=0 out=imputed_beagle_chr-3 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-4.bgl
Missing=0 out=imputed_beagle_chr-4 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-5.bgl
Missing=0 out=imputed_beagle_chr-5 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-6.bgl
Missing=0 out=imputed_beagle_chr-6 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-7.bgl
Missing=0 out=imputed_beagle_chr-7 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-8.bgl
Missing=0 out=imputed_beagle_chr-8 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-9.bgl
Missing=0 out=imputed_beagle_chr-9 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
10.bgl missing=0 out=imputed_beagle_chr-10 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
11.bgl missing=0 out=imputed_beagle_chr-11 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
12.bgl missing=0 out=imputed_beagle_chr-12 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
13.bgl missing=0 out=imputed_beagle_chr-13 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
14.bgl missing=0 out=imputed_beagle_chr-14 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
15.bgl missing=0 out=imputed_beagle_chr-15 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
16.bgl missing=0 out=imputed_beagle_chr-16 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
17.bgl missing=0 out=imputed_beagle_chr-17 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
18.bgl missing=0 out=imputed_beagle_chr-18 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
19.bgl missing=0 out=imputed_beagle_chr-19 ＆
Nohup java-Xmx1000m-jar beagle.jar unphased=plink_beagle_chr-
20.bgl missing=0 out=imputed_beagle_chr-20 ＆
By populated data decompression：
gunzip -d imputed_beagle_chr-1.plink_beagle_chr-1.bgl.phased.gz
gunzip -d imputed_beagle_chr-2.plink_beagle_chr-2.bgl.phased.gz
gunzip -d imputed_beagle_chr-3.plink_beagle_chr-3.bgl.phased.gz
gunzip -d imputed_beagle_chr-4.plink_beagle_chr-4.bgl.phased.gz
gunzip -d imputed_beagle_chr-5.plink_beagle_chr-5.bgl.phased.gz
gunzip -d imputed_beagle_chr-6.plink_beagle_chr-6.bgl.phased.gz
gunzip -d imputed_beagle_chr-7.plink_beagle_chr-7.bgl.phased.gz
gunzip -d imputed_beagle_chr-8.plink_beagle_chr-8.bgl.phased.gz
gunzip -d imputed_beagle_chr-9.plink_beagle_chr-9.bgl.phased.gz
gunzip -d imputed_beagle_chr-10.plink_beagle_chr-10.bgl.phased.gz
gunzip -d imputed_beagle_chr-11.plink_beagle_chr-11.bgl.phased.gz
gunzip -d imputed_beagle_chr-12.plink_beagle_chr-12.bgl.phased.gz
gunzip -d imputed_beagle_chr-13.plink_beagle_chr-13.bgl.phased.gz
gunzip -d imputed_beagle_chr-14.plink_beagle_chr-14.bgl.phased.gz
gunzip -d imputed_beagle_chr-15.plink_beagle_chr-15.bgl.phased.gz
gunzip -d imputed_beagle_chr-16.plink_beagle_chr-16.bgl.phased.gz
gunzip -d imputed_beagle_chr-17.plink_beagle_chr-17.bgl.phased.gz
gunzip -d imputed_beagle_chr-18.plink_beagle_chr-18.bgl.phased.gz
gunzip -d imputed_beagle_chr-19.plink_beagle_chr-19.bgl.phased.gz
gunzip -d imputed_beagle_chr-20.plink_beagle_chr-20.bgl.phased.gz
To decompressing files renaming：
mv imputed_beagle_chr-1.plink_beagle_chr-1.bgl.phased imputed_beagle_
chr-1.bgl
mv imputed_beagle_chr-2.plink_beagle_chr-2.bgl.phased imputed_beagle_
chr-2.bgl
mv imputed_beagle_chr-3.plink_beagle_chr-3.bgl.phased imputed_beagle_
chr-3.bgl
mv imputed_beagle_chr-4.plink_beagle_chr-4.bgl.phased imputed_beagle_
chr-4.bgl
mv imputed_beagle_chr-5.plink_beagle_chr-5.bgl.phased imputed_beagle_
chr-5.bgl
mv imputed_beagle_chr-6.plink_beagle_chr-6.bgl.phased imputed_beagle_
chr-6.bgl
mv imputed_beagle_chr-7.plink_beagle_chr-7.bgl.phased imputed_beagle_
chr-7.bgl
mv imputed_beagle_chr-8.plink_beagle_chr-8.bgl.phased imputed_beagle_
chr-8.bgl
mv imputed_beagle_chr-9.plink_beagle_chr-9.bgl.phased imputed_beagle_
chr-9.bgl
mv imputed_beagle_chr-10.plink_beagle_chr-10.bgl.phased imputed_
beagle_chr-10.bgl
mv imputed_beagle_chr-11.plink_beagle_chr-11.bgl.phased imputed_
beagle_chr-11.bgl
mv imputed_beagle_chr-12.plink_beagle_chr-12.bgl.phased imputed_
beagle_chr-12.bgl
mv imputed_beagle_chr-13.plink_beagle_chr-13.bgl.phased imputed_
beagle_chr-13.bgl
mv imputed_beagle_chr-14.plink_beagle_chr-14.bgl.phased imputed_
beagle_chr-14.bgl
mv imputed_beagle_chr-15.plink_beagle_chr-15.bgl.phased imputed_
beagle_chr-15.bgl
mv imputed_beagle_chr-16.plink_beagle_chr-16.bgl.phased imputed_
beagle_chr-16.bgl
mv imputed_beagle_chr-17.plink_beagle_chr-17.bgl.phased imputed_
beagle_chr-17.bgl
mv imputed_beagle_chr-18.plink_beagle_chr-18.bgl.phased imputed_
beagle_chr-18.bgl
mv imputed_beagle_chr-19.plink_beagle_chr-19.bgl.phased imputed_
beagle_chr-19.bgl
mv imputed_beagle_chr-20.plink_beagle_chr-20.bgl.phased imputed_
beagle_chr-20.bgl
Using R, all data on 24 pairs of chromosomes are merged, code is：
Data1=read.table (" imputed_beagle_chr-1.bgl ", header=F)
Data2=read.table (" imputed_beagle_chr-2.bgl ", header=F)
Data3=read.table (" imputed_beagle_chr-3.bgl ", header=F)
Data4=read.table (" imputed_beagle_chr-4.bgl ", header=F)
Data5=read.table (" imputed_beagle_chr-5.bgl ", header=F)
Data6=read.table (" imputed_beagle_chr-6.bgl ", header=F)
Data7=read.table (" imputed_beagle_chr-7.bgl ", header=F)
Data8=read.table (" imputed_beagle_chr-8.bgl ", header=F)
Data9=read.table (" imputed_beagle_chr-9.bgl ", header=F)
Data10=read.table (" imputed_beagle_chr-10.bgl ", header=F)
Data11=read.table (" imputed_beagle_chr-11.bgl ", header=F)
Data12=read.table (" imputed_beagle_chr-12.bgl ", header=F)
Data13=read.table (" imputed_beagle_chr-13.bgl ", header=F)
Data14=read.table (" imputed_beagle_chr-14.bgl ", header=F)
Data15=read.table (" imputed_beagle_chr-15.bgl ", header=F)
Data16=read.table (" imputed_beagle_chr-16.bgl ", header=F)
Data17=read.table (" imputed_beagle_chr-17.bgl ", header=F)
Data18=read.table (" imputed_beagle_chr-18.bgl ", header=F)
Data19=read.table (" imputed_beagle_chr-19.bgl ", header=F)
Data20=read.table (" imputed_beagle_chr-20.bgl ", header=F)
Data2_12=data2 [- c (1,2) ,]
Data3_12=data3 [- c (1,2) ,]
Data4_12=data4 [- c (1,2) ,]
Data5_12=data5 [- c (1,2) ,]
Data6_12=data6 [- c (1,2) ,]
Data7_12=data7 [- c (1,2) ,]
Data8_12=data8 [- c (1,2) ,]
Data9_12=data9 [- c (1,2) ,]
Data10_12=data10 [- c (1,2) ,]
Data11_12=data11 [- c (1,2) ,]
Data12_12=data12 [- c (1,2) ,]
Data13_12=data13 [- c (1,2) ,]
Data14_12=data14 [- c (1,2) ,]
Data15_12=data15 [- c (1,2) ,]
Data16_12=data16 [- c (1,2) ,]
Data17_12=data17 [- c (1,2) ,]
Data18_12=data18 [- c (1,2) ,]
Data19_12=data19 [- c (1,2) ,]
Data20_12=data20 [- c (1,2) ,]
Data_rbind=rbind (data1, data2_12, data3_12, data4_12, data5_12, data6_12,
data7_12,data8_12,data9_12,data10_12,data11_12,data12_12,data13_12,data14_12,
data15_12,data16_12,data17_12,data18_12,data19_12,data20_12)；
Write.table (data_rbind, quote=F, row.names=F, col.name=F, file="
IMPUTED_RBIND_ALL.BGL")
q()
The each loci gene type merging file is converted to 012：
./fcgene --bgl imputed_rbind_all.bgl --oformat plink --out beagle_
convert_plink
./plink --file beagle_convert_plink --geno 0.1 --maf 0.05 --hwe
0.000001 --recode A --allow-extra-chr --out final_result
Delete the first six row of file, and be converted to csv form, generate the genotype file that gene group selection calculates
Awk'{ for (i=7；i<=NF；i++)printf$i""FS；print""}'final_result.raw>
genotype.txt
nohup cat genotype.txt|sed's/[[:space:]]/,/g'>genotype.csv
3. the statistics of Cynoglossus semilaevis SNP site
Arrange the genotype file genotype.csv obtaining, comprise SNP site 1752901 altogether, be distributed in 20 and often contaminate
On colour solid, on each chromosome, SNP site number does not wait (Fig. 2) by 51595-200654.
3rd, the full-length genome of reference group selects to calculate and SNP site effect analysis
With the relative breeding value (RBV) of reference group as phenotype, with reference group's sequencing gained SNP site data as gene
Type, carries out gene group selection calculating, the computational methods of use are Bayes C π, and the calculating instrument being used is R-package
BGLR, obtains the correlation values of each SNP site and disease-resistant phenotype after calculating, arrange output .txt file, as each SNP
The genetic effect value in site, the estimation of the genomic breeding value (GEBV) in selecting for full-length genome.
It is described in detail below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example.
(1) calculating and SNP site effect analysis that, Paralichthys olivaceuss full-length genome selects
1. Paralichthys olivaceuss full-length genome selects computing formula
Paralichthys olivaceuss gene group selection calculates selects Bayes algorithm, this algorithm effect value with each SNP site of calculating emphatically,
Then all SNP site effect value are added, obtain GEBV.
The prior variance distribution of SNP marker effect value is as follows：
The analysis model equation of Bayes C π is：
In model, y be phenotypic number, u be community average, qi be marker effect Normal Distribution qi～N (0,), m
It is the sum of labelling, X is incidence matrix corresponding with qi, and e is residual error.
2. Paralichthys olivaceuss gene group selection computational methods
The BayesC π algorithm being provided using R language pack BGLR, in conjunction with the genotype data genotype.csv putting in order
With phenotypic data phonetype.csv, totally 988 Paralichthys olivaceuss individualities enter for the reference group of sequence that full-length genome is resurveyed and candidate population
Row gene group selection calculates, and code is：
sink("outofanalysis.txt")
library(bigmemory)
library(biganalytics)
library(BGLR)
Pheno_y=as.matrix (read.table ("/phonetype.txt ", header=T))
Y=pheno_y [, 1]
X=as.matrix (read.big.matrix ("/genotype.csv ", type=" integer ", header=
T))
NIter=22000；BurnIn=2000；Thin=100
ETA<- list (MRK=list (X=x, model=" BayesC "))
fmBC<- BGLR (y=y, ETA=ETA, nIter=nIter, burnIn=burnIn, saveAt='BC_')
Save (fmBC, file="/FMBC.RDA ")
RV=c (fmBC $ varE, fmBC $ SD.varE)；RV
DIC=c (fmBC $ fit)；DIC
PBC=fmBC $ yhat；PBCSD=fmBC $ SD.yhat
Write.table (PBC, quote=F, row.names=T, file="/PBC.TXT ")
ghatBC<- x%*%fmBC $ ETA $ MRK $ b
Write.table (ghatBC, quote=F, row.names=T, file="/GHATBC.TXT ")
bhatBC<-fmBC$ETA$MRK$b
SD.bhatBC<-fmBC$ETA$MRK$SD.b
Write.table (bhatBC, quote=F, row.names=T, file="/SNPBC.TXT ")
Write.table (SD.bhatBC, quote=F, row.names=T, file="/SNPBC.SD.TXT ")
Ryhat=c (cor (y, fmBC $ yhat))
ryhat
Rghat=c (cor (y, ghatBC))
Rghat
ghatBC<- x%*%fmBC $ ETA $ MRK $ b
Lm.reg=lm (fmBC $ y～ghatBC)
summary(lm.reg)
sink()
q()
3. Paralichthys olivaceuss SNP site effect analysis
Carry out, in gene group selection calculating process, 397215 SNP site being obtained in 988 sequence Paralichthys olivaceuss individualities of resurveying
Effect value, ceiling effect value is 3.73E-4, and smallest effect value is 1.18E-16 (Fig. 3).
(2) calculating and SNP site effect analysis that, Cynoglossus semilaevis full-length genome selects
1. Cynoglossus semilaevis gene group selection computing formula
Cynoglossus semilaevis gene group selection calculates equally selects Bayes algorithm, and the prior variance of SNP marker effect value is distributed such as
Under：
The analysis model equation of Bayes C π is：
In model, y be phenotypic number, u be community average, qi be marker effect Normal Distribution qi～N (0,), m
It is the sum of labelling, X is incidence matrix corresponding with qi, and e is residual error.
2. Cynoglossus semilaevis gene group selection computational methods
The same BayesC π algorithm being provided using R language pack BGLR, in conjunction with the genotype data put in order
Genotype.csv and phenotypic data phonetype.csv, the reference group of sequence that full-length genome is resurveyed and candidate population totally 886
Individual Cynoglossus semilaevis individuality carries out gene group selection calculating, and code is：
sink("outofanalysis.txt")
library(bigmemory)
library(biganalytics)
library(BGLR)
Pheno_y=as.matrix (read.table ("/phonetype.txt ", header=T))
Y=pheno_y [, 1]
X=as.matrix (read.big.matrix ("/genotype.csv ", type=" integer ", header=
T))
NIter=22000；BurnIn=2000；Thin=100
ETA<- list (MRK=list (X=x, model=" BayesC "))
fmBC<- BGLR (y=y, ETA=ETA, nIter=nIter, burnIn=burnIn, saveAt='BC_')
Save (fmBC, file="/FMBC.RDA ")
RV=c (fmBC $ varE, fmBC $ SD.varE)；RV
DIC=c (fmBC $ fit)；DIC
PBC=fmBC $ yhat；PBCSD=fmBC $ SD.yhat
Write.table (PBC, quote=F, row.names=T, file="/PBC.TXT ")
ghatBC<- x%*%fmBC $ ETA $ MRK $ b
Write.table (ghatBC, quote=F, row.names=T, file="/GHATBC.TXT ")
bhatBC<-fmBC$ETA$MRK$b
SD.bhatBC<-fmBC$ETA$MRK$SD.b
Write.table (bhatBC, quote=F, row.names=T, file="/SNPBC.TXT ")
Write.table (SD.bhatBC, quote=F, row.names=T, file="/SNPBC.SD.TXT ")
Ryhat=c (cor (y, fmBC $ yhat))
ryhat
Rghat=c (cor (y, ghatBC))
Rghat
ghatBC<- x%*%fmBC $ ETA $ MRK $ b
Lm.reg=lm (fmBC $ y～ghatBC)
summary(lm.reg)
sink()
q()
3. Cynoglossus semilaevis SNP site effect analysis
Carry out gene group selection calculating using BGLR 886 are resurveyed sequence Cynoglossus semilaevis individuality, be obtained 1752901
The effect value of SNP site, ceiling effect value is 3.83E-5, and smallest effect value is 3.37E-19 (Fig. 4).
4th, the foundation of candidate population and full-length genome are resurveyed sequence
The fish family that candidate population refers to not carry out pathogenic bacterial infection, do not have a disease resistance trait phenotype is individual, from difference
A part is selected not know the fish family individuality of disease-resistant phenotypic character or in gene group selection practical application and Fish in family
During prevalent variety cultivation, choose for the parent fish breeding as candidate population, gather the fin ray of candidate population fish, extract fin ray genome
DNA, carries out genomic library construction and full-length genome is resurveyed sequence, and Treatment Analysis SNP site data, in the situation of disappearance phenotypic number
Under, carry out gene group selection calculating；After the completion of reference group's gene group selection calculates, the effect of the SNP site by calculating
Should be worth, in conjunction with the individual each SNP site genotype of candidate population, obtain candidate population genome estimated breeding value.Below with Paralichthys olivaceuss
It is described in detail with as a example Cynoglossus semilaevis.
(1) foundation of Paralichthys olivaceuss anti-Edwardsiella tarda ospc gene group selection candidate population
The candidate population of Paralichthys olivaceuss anti-Edwardsiella tarda gene group selection is selected from the father of the Paralichthys olivaceuss family set up 2015
Maternal.Comprise 57 Parents individual composition candidate population of wherein 38 familys, carry out gene group selection calculating, obtain each
Individual genome estimated breeding value (GEBV), is applied in the disease-resistant prevalent variety cultivation of Paralichthys olivaceuss.
(2) foundation of Cynoglossus semilaevis anti-Vibro harveyi ospc gene group selection candidate population
Cynoglossus semilaevis anti-Vibro harveyi gene group selection candidate population is selected from the tongue sole families set up 2015
Parent.Comprise 23 Parents individual composition candidate population of wherein 15 familys, carry out gene group selection calculating, obtain every
Individual genome estimated breeding value, is applied in the disease-resistant prevalent variety cultivation of Cynoglossus semilaevis.
5th, genes of individuals group estimated breeding value (GEBV) analysis of reference group and candidate population
The hereditary effect of each SNP site according to the reference group obtaining and the gene of each SNP site of Different Individual
Type, calculates genes of individuals group estimated breeding value (GEBV) of reference group；Generate and export .txt note after calculating terminates
The individual GEBV of record reference group.
According to the individual GEBV of reference group, preliminary screening goes out the stronger individuality of premunition in reference group, and these
The individual family being located.
According to the SNP genotype of the individual SNP site effect of reference group and candidate population, calculate further and obtain candidate
The individual genome estimated breeding value of colony；According to the GEBV result of calculation of candidate population, calculate GEBV value and deposit with actual infection
The correlation coefficient of motility rate, and then verify the accuracy of gene group selection result of calculation.Enter below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example
Row describes in detail.
(1), Paralichthys olivaceuss gene group selection genes of individuals group estimated breeding value (GEBV) analysis
1. reference group's genome estimated breeding value
It is calculated 988 individual genome estimated breeding values (GEBV), wherein reference group's individuality totally 931, will
The relative breeding value (RBV) that the individual GEBV and ASReml of reference group calculates carries out correlation analysiss, and correlation coefficient is：
0.97 (Fig. 5).
2. candidate population genome estimated breeding value
Calculate the genotype of gained SNP site effect value and candidate population SNP site according to reference group, calculate simultaneously
Go out the genome estimated breeding value (table 7) of 57 candidate population samples, according to the Edwardsiella tarda infection experiment of filial generation
Survival rate is ranked up, and its GEBV is ranked up, the dependency of analysis survival rate and GEBV, and correlation coefficient is 0.82.Will
57 candidate population according to sex be divided into male parent, maternal two groups, contrast respectively its filial generation infection survival rate and GEBV sort related
Coefficient, female parent group is 0.81, and male parent group is 0.89 (Fig. 6, Fig. 7).It is higher than 80% tooth through the survival rate that infection experiment selects
Flounder family F1551, F1503, F1501, F1550, its Parent GEBV is above meansigma methodss, and before calculating GEBV
20%.
The genome estimated breeding value of the parent of 7 2015 years Paralichthys olivaceuss familys of table
(2), Cynoglossus semilaevis gene group selection genes of individuals group estimated breeding value (GEBV) analysis
1. reference group's genome estimated breeding value
It is calculated 886 individual genome estimated breeding values (GEBV), wherein reference group's individuality totally 963, will
The relative breeding value (RBV) that the individual GEBV and ASReml of reference group calculates carries out correlation analysiss, and correlation coefficient is：
0.99 (Fig. 8).
2. candidate population genome estimated breeding value
Calculate the genotype of gained SNP site effect value and each SNP site of candidate population according to reference group, calculate simultaneously
Go out the genome estimated breeding value (table 8) of 23 candidate population samples.
The genome estimated breeding value of the parent of 8 2015 years tongue sole families of table
Six. the cultivation of the disease-resistant seed of Fish
The size of the candidate population genome estimated breeding value being calculated according to full-length genome selection, in phenotype disappearance
In the case of, the premunition of candidate population is estimated and sorts；Select the high candidate population of GEBV individual as parent fish, carry out
The breeding of disease-resistant seed.The premunition breeding the filial generation seed of gained significantly improves, in the infection experiment subsequently carrying out, survival
Rate is higher than matched group, and through statistics, the infection survival rate of gene group selection candidate population offspring and breeding survival rate are higher than matched group
Go out 20%-30%.Popularization and application can be carried out in breeding production.
It is described in detail below taking Paralichthys olivaceuss and Cynoglossus semilaevis as a example.
(1), the cultivation of the disease-resistant seed of Paralichthys olivaceuss
The Paralichthys olivaceuss family that 2015 are set up, is many results for selection-breeding, through the family selective breeding starting from 2007, choosing
Take defect individual in Japanese lefteye flounder colony, lefteye flounder colony of Korea S and Chinese Paralichthys olivaceuss Kang Man vibrio colony as parent, by generation with
In conjunction with growth fast, breeding survival rate is high, the characteristic such as strong to Vibrio anguillarum disease premunition and strong to Edwardsiella tarda premunition is
Target, the Paralichthys olivaceuss family that final selection-breeding obtains.
After Paralichthys olivaceuss family in 2015 is set up, contrast cultivation, in November, 2015 are carried out, each family chooses fry
About 200 tails, carry out fluorescent labeling and raise together with same pond.After cultivation 7 months, carry out measurement and the living individuals of growth traitss
Statistics, calculate each family daily gain and breeding survival rate.And choose each family Paralichthys olivaceuss and carry out Edwardsiella tarda infection
Experiment, collection sample is used for gene group selection and calculates.
With Edwardsiella tarda infection Paralichthys olivaceuss family fry as reference group, carry out SNP site effect analysis, gained is tied
Fruit is used for the calculating of candidate population GEBV, comprises each family parent of above-mentioned infection in candidate population, according to GEBV result of calculation,
Very high or higher from the individual GEBV of the candidate population of the familys such as F1251, F1256, F1334, it breeds gained Paralichthys olivaceuss filial generation
(being named as excellent No. 2 of Paralichthys olivaceuss Flounder), is 74% in the metainfective survival rate of Edwardsiella tarda, more metainfective survival than matched group
Rate (44%) high by 30% (Fig. 9).The breeding survival rate (64%) of excellent No. 2 of Flounder is higher by 25% (Figure 10) than matched group (39%) simultaneously.
Growth contrast experiment shows that the growth (daily gain) of excellent No. 2 of Flounder is also fast than matched group (high) (Figure 11).As can be seen here, base of the present invention
Have in excellent No. 2 breedings of Paralichthys olivaceuss Flounder that full-length genome system of selection is cultivated that premunition is strong, breeding survival rate is high and it is very fast to grow
Advantage.
The parent of " excellent No. 2 of Flounder ", is the candidate population during Paralichthys olivaceuss anti-Edwardsiella tarda gene group selection calculates
Body, calculates gained GEBV and the actual infection survival rate of family is very high or higher.As can be seen here, " excellent No. 2 of Flounder " is genome choosing
The technology of selecting is applied to a successful example of the disease-resistant prevalent variety cultivation of Fish.
(2), the cultivation of the disease-resistant seed of Cynoglossus semilaevis
2014, in the sequencing of the reference group completing all Cynoglossus semilaevis anti-Vibro harveyi genome choice experiment
After the evaluation work of GEBV.The result being calculated according to reference group's gene group selection, selects GEBV highest individual affiliated
10 familys milter, as the candidate parent fish of tongue sole families in 2015, individually cultivated and fortification.
The 9-11 month in 2015, when setting up tongue sole families, choose above-mentioned milter, set up tongue sole families 15,
By the male parent of these familys and supporting maternal clip fin ray, carry out extracting genome DNA, build storehouse sequencing, be in this experiment
Candidate population.
30 familys that 2015 set up are carried out Vibro harveyi infection experiment, wherein have 13 familys by August, 2016
Male parent be gene group selection candidate population.From result of infection as can be seen that the candidate population selected using genome is as father
This family, infection survival rate is mostly higher (Figure 12).30 familys participating in infection are divided into gene group selection male parent and general
Logical male parent Liang Ge colony, its infection survival rate is respectively 67.6% and 43%, and gene group selection seed is than the survival of common seed
Rate improves 24.6% (Figure 13).
The result of infection experiment is it was demonstrated that calculate filtered out candidate population as parent, Ke Yiyou through gene group selection
Effect improves premunition and the survival rate that cynoglossus semilaevis cultivation colony infects to Vibro harveyi, is the disease-resistant prevalent variety cultivation of Cynoglossus semilaevis
Provide new, effective molecular breeding technology means.
Claims (6)
1. a kind of disease-resistant prevalent variety cultivation method of the Fish based on full-length genome selection it is characterised in that described method include as
Lower step：
1) foundation of the disease-resistant reference group of Fish and phenotypic character measure
When the fish family fry growth set up reaches total length 8-15 centimetre, using pathogenic microorganism, fish family fry is entered
Row pathogen artificial challenge, after pathogenic bacterial infection morbidity, different time collects the dead fry of each family, and record fry is dead
Time and total length, body weight, body width isophenous data, the individuality survived after collection infection simultaneously simultaneously records total length, body weight, body width number
According to using the data of the living individuals of collection and dead individuals as the phenotypic parameter evaluating fish body premunition, and according to mortality rate
And the death time, choose experimental subjects in each family, obtain one group of representativeness that can react whole experimental population disease resistance trait
Individual combination is as reference group；The individual estimated breeding value (EBV) of reference group is calculated using animal model and colony educates
Plant average, and be converted into relative breeding value (RBV), the phenotype of reference group in calculating as gene group selection；
2) full-length genome of reference group resurvey sequence and genotype collection with analyzing and processing
The genome DNA sample extracting the reference group fry of collection builds storehouse sequencing, and building storehouse type is DNA-350bp, and be sequenced plan
Slightly HiSeqPE150, to sequencing result first according to sequencing quality, filters out more containing joint sequence and low quality base
Sequencing gained reads, compare reference gene group, detection reference gene group in corresponding SNP site calling, arrange life
Become .vcf file；Using plink .vcf file is read out, merges individual data items, subsequently according to chromosome carry out segmentation and
Quality control, carry out data filling using beagle, then the SNP site data on all chromosomes used is merged, conversion
Generate the .csv form genotype file in units of individuality, for the calculating of gene group selection；
3) full-length genome of reference group selects to calculate and SNP site effect analysis
With the relative breeding value (RBV) of reference group as phenotype, with reference group's sequencing gained SNP site data as genotype,
Carry out gene group selection calculating, the computational methods of use are Bayes C π, and the calculating instrument being used is R-package BGLR,
Obtain the correlation values of each SNP site and disease-resistant phenotype after calculating, arrange output .txt file, as each SNP site
Genetic effect value, for full-length genome select in genomic breeding value (GEBV) estimation；
4) foundation of candidate population and full-length genome are resurveyed sequence
The fin ray of collection candidate population fish, extracts fin ray genomic DNA, carries out genomic library construction and full-length genome is resurveyed
Sequence, Treatment Analysis SNP site data, in the case of disappearance phenotypic number, carry out gene group selection calculating；In reference group's gene
After the completion of group selection calculates, the effect value of the SNP site by calculating, in conjunction with the individual each SNP site genotype of candidate population,
Obtain candidate population genome estimated breeding value；
5) genes of individuals group estimated breeding value (GEBV) analysis of reference group and candidate population
The hereditary effect of each SNP site according to the reference group obtaining and the genotype of each SNP site of Different Individual, meter
Calculate genes of individuals group estimated breeding value (GEBV) drawing reference group；Generate and export .txt record reference after calculating terminates
The individual GEBV of colony；According to the individual GEBV of reference group, preliminary screening goes out the stronger individuality of premunition in reference group, with
And the family that these individualities are located；According to the SNP genotype of the individual SNP site effect of reference group and candidate population, enter one
Step calculates and obtains the individual genome estimated breeding value of candidate population；According to the GEBV result of calculation of candidate population, calculate GEBV
Value and the actual correlation coefficient infecting survival rate, and then verify the accuracy of gene group selection result of calculation；
6) cultivation of the disease-resistant seed of Fish
The size of the candidate population genome estimated breeding value being calculated according to full-length genome selection, in the situation of phenotype disappearance
Under, the premunition of candidate population is estimated and sorts；Select the high candidate population of GEBV individual as parent fish, carry out disease-resistant
The breeding of seed；The premunition breeding the filial generation seed of gained significantly improves.
2. the method for claim 1 is it is characterised in that described step 1) in be converted into relative breeding value, be using R language
R-package asreml under speech environment calculates acquisition.
3. the method for claim 1 is it is characterised in that described step 2) in reference gene group be Cynoglossus semilaevis and tooth
The genome sequencing result of Flounder.
4. method as claimed in claim 3 is it is characterised in that described Cynoglossus semilaevis genome sequencing result, its
ID in GenBank is PRJNA73987.
5. method as claimed in claim 3 is it is characterised in that the genome sequencing result of described Paralichthys olivaceuss is in GenBank
In ID be PRJNA73673.
6. the method for claim 1 is it is characterised in that described step 4) described in candidate population refer to not carry out
Pathogenic bacterial infection, the fish family not having a disease resistance trait phenotype are individual.
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