CN110295236B - SNP molecular genetic marker for pig feed conversion rate - Google Patents
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Abstract
The disclosure provides SNP molecular genetic markers for pig feed conversion rate, by identifying a molecular marker WU_10.2_3_116703757 which affects the feed weight-increasing ratio of boars, the feed weight-increasing ratio of boars with different genotypes is obviously different, and successfully screening a large effector molecule genetic marker which affects the feed weight-increasing ratio of the boars through the correlation analysis of the feed weight-increasing ratio of the boars and the whole genome molecular genetic markers, wherein the feed weight-increasing ratio of the boars with different genotypes is obviously different; through detecting the molecular marker, the method can be applied to breeding of breeding pigs, select and leave homozygous pigs with low feed weight-increasing ratio, effectively reduce feed consumption in the production process, reduce pig raising production cost, improve economic benefit and competitiveness of enterprises, accelerate breeding progress of strains with high feed utilization efficiency, and effectively reduce feed consumption and raising cost.
Description
Technical Field
The disclosure relates to the technical field of pig genetic genes, in particular to SNP molecular genetic markers for pig feed conversion rate.
Background
Feed efficiency is an important economic property, and is always focused on domestic and foreign pig raising enterprises and pig raising improvement companies. Feed efficiency was studied at mid-20 th century using Feed Gain ratio (F/G) at home and abroad, and the trait was a medium genetic quantitative trait. 13 QTL' S related to feed conversion were detected in chickens by Mignon et al (Mignon G S, rideau N, gabriel I, et al detection of QTL controlling feed efficiency and excretion in chickens fed a wheat-based diet Selection Evolution,47 (1): 74 (2015)). It has been reported that a single nucleotide polymorphism (single nucleotide polymorphism, SNP) site significantly correlated with feed conversion rate exists in a pig cell signal transduction inhibitor 2 (Suppressor of Cytokine Signalling, CRADD) gene and a Melanocortin receptor 4 (MC4R) gene. Studies by Ma et al (Ma Y, yu C, mohamed E M, et al A causal link from ALK to hexokinase II overexpression and hyperactive glycolysis in EML4-ALK-positive cancer oncogene,35 (47), 6132-6142 (2016), doi: 10.1038/onc.2016.150) have shown that the ALK gene (ALK Receptor Tyrosine Kinase) is involved in mediating glucose metabolism in the body. Therefore, the feed conversion rate is a good selective response as a moderate genetic trait for evaluating the feed utilization efficiency.
The utilization efficiency related characters of the auxiliary breeding feed of SNP markers have important influence on pig raising production management and enterprise economic benefit. (1) The low feed weight-increasing ratio of the feed is higher than that of the pig (Vigors S, sweeney T, oshea C J, et al, pins that are divergent in feed efficiency, differ in intestinal enzyme and nutrient transporter gene expression, nutrient digestibility and microbial activity. Animal,10 (11): 1848-1855 (2016)), so that the feed use amount and the production cost in production can be reduced, further the feed resource is saved, the discharge of the pig can be reduced to a certain extent, and the pressure of competing grain resources between the pig and human and the environmental protection problem of the pig industry is relieved. (2) The effective molecular marker is developed for feed efficiency related character breeding, so that the breeding period is greatly shortened, the breeding cost is reduced, the seed selection accuracy is improved, the genetic progress is accelerated, and the phenomena of seed introduction, degradation and re-seed introduction can be avoided.
Therefore, the excavation and utilization of the novel genes related to the feed gain ratio have great significance for the genetic breeding of pigs. Based on high-density SNP data covering the whole genome and trait phenotype recordings of large populations, candidate genes controlling traits can be accurately located by whole genome association analysis techniques (GWAS) (Hirschhorn, J.N & Daly, m.j. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet.6,95-108 (2005)). Although this technology still has some drawbacks (De, r., bus, W.S. & Moore, j.h. bioengineering challenges in genome-wide association studies (GWAS) & Methods mol. Biol.1168,63-81 (2014)), it has been widely used for human complex disease candidate gene excavation and localization of important economic trait key genes of livestock and poultry. Classical GWAS are generally Based on single-marker regression analysis of all markers one by one, followed by setting a significant threshold to screen for significant sites, based on software such as Plink (Purcell, s.et al Plink: A Tool Set for Whole-Genome Association and Population-Based Linkage analysis.am.j.hum.genet.813, 559-575 (2007)). The method is often faced with the problems of high calculation intensity, overestimation marking effect, unreasonable significance threshold setting and the like. To further increase the efficiency of GWAS, new methods and software are continually being proposed. The genetic marker is suitable for the condition that a large number of individuals possess phenotype records and only a small number of individuals possess genotype data, in particular for the condition that the genetic marker is suitable for the whole genome correlation analysis of important economic traits of livestock and poultry, based on GBPf 90 software (Misztal, I.et al.BLUPF90 (BGF 90) in Proc.7th Woold Wog.Prod.21-22: 9782738010520), the genetic marker can be easily selected for the genetic marker of the pig, and the genetic marker has the significance of the genetic marker is easy to realize by using the genetic marker.
Disclosure of Invention
In view of the above technical problems, the present disclosure provides a SNP molecular genetic marker for pig feed conversion rate, by identifying a molecular marker wu_10.2_3_116703757 affecting boar feed weight gain ratio, the marker has a significant difference in feed weight gain ratio of boars of different genotypes, and successfully screening a large effector molecule genetic marker affecting pig feed weight gain ratio by correlation analysis of boar feed weight gain ratio and whole genome molecular genetic marker, wherein the related wu_10.2_3_116703757 genetic marker is a mutation site with SNP number wu_10.2_3_116703757, see the swine genome database (sscrofa 11.1) in NCBI.
The SNP molecular genetic marker (WU_10.2_3_116703757) disclosed is referred to Ensembl database (http:// asia. Ensembl. Org/Sus_scrofa/Search/newdb=core) to obtain a gene fragment (or RS number RS 343880869) with accession number WU_10.2_3_116703757, the SNP molecular genetic marker is positioned at 109882181bp position of pig chromosome 3, WU_10.2_3_116703757 belongs to an intron sequence of ALK gene, the position is a C > T mutation (mutation site), C > T is C is allele with large frequency, T is allele with small frequency, symbol > is allele frequency size, and the SNP molecular genetic marker is as follows:
5'-ATACAATTCAACCCAAAACAGAACCTGAGGAAGAGCAATCTGTGCCCTATTTGTCCTGTAAGCGGTGACATTGTTTCAGTAAACAGCACTTCCATCACAAR (T/C) GAATTGTCGCTTAATTTGAATTGCATGGTTTTGAACATTTGTATTTATATGCAAATAATTATTGAATTATTTTGTTTGGTTTTGATAGTCATGCAAGAGC-3'; r is a mutation site, and when R at nucleotide 101 of the above sequence is C or T, that is, R (T/C), the above sequence polymorphism is caused; when nucleotide 101 of the above nucleotide sequence is T, pigs have a lower feed gain ratio, and 5 '-and-3' represent the 5 'and 3' ends of the nucleotide sequence, respectively.
The weight gain ratio of the WU_10.2_3_116703757 marker genotype TT genotype individual to CC genotype boar individual is 0.18, the weight gain ratio of the TT genotype individual to CC genotype boar individual is reduced by 8.41%, so that T is an allele which is favorable for remarkably reducing the weight gain ratio, the feed consumption in the production process of pigs can be effectively reduced by selecting TT genotype homozygous pigs with low weight gain ratio (as the DNA of the pigs is of a reverse helical double-stranded structure, the nucleotide of the mutation site of each chain is the TT genotype homozygous pigs when the nucleotide of each chain is T, wherein each chain has a nucleotide sequence, T represents homozygous pigs with one mutation site of T, the mutation site of each TT genotype is double-stranded, the mutation site of each CC genotype is C, and the mutation site of each chain of the CT genotype is T and the mutation site of each chain is C.
The method for screening SNP molecular genetic markers of pig feed conversion rate specifically comprises the following steps:
1. flow step of obtaining molecular markers
1.1, collecting an ear tissue sample or a blood sample of a boar, extracting total DNA, and detecting the quality of the DNA. Genotyping was performed using GGP 50k SNP (GeneSeek, US) chip to obtain SNP marker genotypes covering the whole genome.
1.2 the physical positions of all SNP markers were updated according to the latest edition of the porcine reference genome (Sscofa 11.1) using the NCBI genome alignment program (https:// www.ncbi.nlm.nih.gov /). SNPs with unknown genomic positions are not used for association analysis.
1.3, quality control was performed on SNP markers on all autosomes using Plink software, standard: the individual detection rate is more than or equal to 90 percent; SNP detection rate is more than or equal to 90%; the minor allele frequency is greater than or equal to 0.01; the Hardy-Winberg equilibrium p value is more than or equal to 10 -6 . For the deletion genotypes, the padding was performed using Beagle software (version 4.1).
2. Procedure for verification of molecular markers
2.1, sorting the breeding pig genealogy, which mainly comprises information such as individual number, father, mother, date of birth and the like of the boar. By using
The formula analyzes the growth data recorded by the fully automated breeding pig production performance measurement system (FIRE, usa) to obtain feed gain ratio phenotype data for phenotype-genotype correlation analysis. Wherein FCR is feed weight-increasing ratio; w (W) a Weight is increased for living body; w (W) f Is the consumption of feed.
2.2, statistical model, and adopting weighted one-step whole genome association analysis (weighted single step genome-wide association study, wsGWAS) to carry out whole genome association analysis. The method comprises the steps of firstly estimating individual breeding values based on a mixed model equation set, and then converting the breeding values into marking effects based on the equivalence relation between a breeding value model and a marking effect model. The whole genome association analysis model adopted by the invention is as follows:
y＝Xb+Za+Wp+e，
wherein y is a feed weight gain ratio observation value vector; x, Z and W are design matrices; b is a fixed effector vector (environment, age of day);
is a breeding value vector; />
Permanent environmental effects for the individual; />
Is the residual. H is an affinity matrix for integrating the pedigree and the SNP marker simultaneously, and the calculation formula of the inverse matrix is as follows:
wherein A is a family-based affinity matrix; a is that 22 The method comprises the steps of A, dividing a block matrix corresponding to genotype individuals; g ω ＝0.9G+0.1A 22 ，
Z is a genotype matrix corrected for minor allele frequency (minor allele frequency, MAF) for the genome-wide SNP marker-based genetic relationship moment, wherein 0-2p,1-2p and 2-2p represent the three genotypes AA, AA and AA, respectively, and p is the minor allele frequency; d is a diagonal matrix, representing the weight of the SNP; p is p i Minor allele frequencies for the ith marker; m is the number of marks.
For the above mixed model, the AI-REML (average information restricted maximum likelihood) method is used to estimate the variance component and the breeding value is obtained by solving the mixed model equation set. The marking weight is obtained in an iterative manner, and the main steps are as follows:
step 1: initialization (t=1), D (t) ＝I，G (t) ＝λZD (t) Z′，
Step 2: calculating individual breeding values through ssGBLUP;
step 3: by the formula
Transforming individual breeding values into SNP effect, wherein +.>
A breeding value for individuals with genotype;
step 4: using the formula
Calculating SNP weights for the next iteration;
step 5: using the formula
Standardizing SNP weights to ensure consistent variances;
step 6: using formula G (t+1) ＝λZD (t+1) Z' calculates the genetic relationship matrix for the next iteration;
step 7: let t=t+1 and start the next iteration from step 2.
The steps are iterated for three times, and finally the SNP marker effect is obtained. The marking effect of the third iteration output is taken as a final result. The calculation process is mainly implemented by programming and calling BLUPF90 software on an R statistical analysis platform, wherein AIREMLF90 program is used for variance component estimation, BLUPF90 program is used for calculating breeding values, and postGSf90 program is used for calculating marking effect.
2.3, screening markers, namely, taking absolute values of effect values of all the markers to draw a Manhattan diagram, and displaying and screening SNP markers with large effects. And analyzing the feed gain ratio difference conditions of the boars of the populations with different genotypes by adopting analysis of variance and multiple comparison (R statistical analysis platform).
The beneficial effects of the present disclosure are: the disclosure provides SNP molecular genetic markers of pig feed conversion rate, which have significant difference in feed weight ratio of boars with different genotypes; through detecting the molecular marker, the method can be applied to breeding of breeding pigs, select and leave homozygous pigs with low feed weight-increasing ratio, effectively reduce feed consumption in the production process, reduce pig raising production cost, improve economic benefit and competitiveness of enterprises, accelerate breeding progress of strains with high feed utilization efficiency, and effectively reduce feed consumption and raising cost.
Drawings
The above and other features of the present disclosure will become more apparent from the detailed description of the embodiments illustrated in the accompanying drawings, in which like reference numerals designate like or similar elements, and which, as will be apparent to those of ordinary skill in the art, are merely some examples of the present disclosure, from which other drawings may be made without inventive effort, wherein:
FIG. 1 is a flowchart showing the method of screening SNP molecular genetic markers of pig feed conversion rate according to the present disclosure;
FIG. 2 shows WU_10.2_3_116703757 marker genome positions and feed gain ratio whole genome SNP effect distribution of the present disclosure.
Detailed Description
The conception, specific structure, and technical effects produced by the present disclosure will be clearly and completely described below in connection with the embodiments and the drawings to fully understand the objects, aspects, and effects of the present disclosure. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
As shown in fig. 1, which is a workflow diagram of a method for screening a SNP molecular genetic marker for pig feed conversion rate according to the present disclosure, a method for screening a SNP molecular genetic marker for pig feed conversion rate according to the present disclosure is described below in conjunction with fig. 1.
The disclosed method for screening SNP molecular genetic markers of pig feed conversion rate specifically comprises the following steps:
(1) Phenotype-pedigree data acquisition
The basic research community of the present disclosure was Duroc boars, all from Guangxi Yangxiang farm, inc. pig farm. The complete pedigree contains 735 pigs of 4 generations, wherein the feed gain ratio character phenotype data of 370 Duroc boars are recorded in 2015-2018. The weight-increasing ratio of the feed is adopted
Formula (VI)Growth data recorded by the fully automated breeding pig production performance measurement system (FIRE, usa) was analyzed for phenotype-genotype association analysis. Wherein FCR is feed weight-increasing ratio; w (W) a Weight is increased for living body; w (W) f Is the consumption of feed.
(2) Genotyping and quality control
Ear tissue samples or blood samples of 1733-head boars were collected, total DNA was extracted, and genotyping was performed using GGP 50k SNP (GeneSeek, US) chips, obtaining 50705 SNP markers covering the whole genome. The physical location of all SNP markers was updated using NCBI genome alignment program (https:// www.ncbi.nlm.nih.gov /) according to the latest edition of porcine reference genome (Srcrofa 11.1). SNPs with unknown genomic positions are not used for association analysis. For all SNP markers on autosomes, quality control was performed using Plink software, standard: the individual detection rate is more than or equal to 90 percent; SNP detection rate is more than or equal to 90%; the minor allele frequency is greater than or equal to 0.01; the Hardy-Winberg equilibrium p value is more than or equal to 10 -6 . For the deletion genotypes, the padding was performed using Beagle software (version 4.1). Based on the above quality control criteria, 1623 pigs and 28289 SNP markers remained for association analysis.
(3) Statistical model
To fully utilize all phenotype data and genotype data, the present invention discloses a weighted one-step whole genome association analysis (weighted single step genome-wide association study, wsgwas) for whole genome association analysis. The method comprises the steps of firstly estimating individual breeding values based on a mixed model equation set, and then converting the breeding values into marking effects based on the equivalence relation between a breeding value model and a marking effect model. The whole genome association analysis model adopted by the invention is as follows:
y＝Xb+Za+Wp+e，
wherein y is a feed weight gain ratio observation value vector; x, Z and W are design matrices; b is a fixed effector vector (environment, age of day);
is a breeding value vector; />
Permanent environmental effects for the individual; />
Is the residual. H is an affinity matrix for integrating the pedigree and the SNP marker simultaneously, and the calculation formula of the inverse matrix is as follows:
wherein A is a family-based affinity matrix; a is that 22 The method comprises the steps of A, dividing a block matrix corresponding to genotype individuals; g ω ＝0.9G+0.1A 22 ，
Z is a genotype matrix corrected for minor allele frequency (minor allele frequency, MAF) for the genome-wide SNP marker-based genetic relationship moment, wherein 0-2p,1-2p and 2-2p represent the three genotypes AA, AA and AA, respectively, and p is the minor allele frequency; d is a diagonal matrix, representing the weight of the SNP; p is p i Minor allele frequencies for the ith marker; m is the number of marks.
Corresponding to the mixed model, the AI-REML (average information restricted maximum likelihood) method is adopted to estimate the variance component, and the breeding value is obtained by solving the mixed model equation set. The marking weight is obtained in an iterative manner, and the main steps are as follows:
step 1: initialization (t=1), D (t) ＝I，G (t) ＝λZD (t) Z′，
Step 2: calculating individual breeding values through ssGBLUP;
step 3: by the formula
Conversion of individual breeding values to SNP effect, wherein->
A breeding value for individuals with genotype;
step 4: using the formula
Calculating SNP weights for the next iteration;
step 5: using the formula
Standardizing SNP weights to ensure consistent variances;
step 6: using formula G (t+1) ＝λZD (t+1) Z' calculates the genetic relationship matrix for the next iteration;
step 7: let t=t+1 and start the next iteration from step 2.
And iterating the steps for three times, and finally obtaining the SNP marker effect, namely obtaining the SNP marker effect. The marking effect of the third iteration output is taken as a final result. The calculation process is mainly implemented by programming and calling BLUPF90 software on an R statistical analysis platform, wherein AIREMLF90 program is used for variance component estimation, BLUPF90 program is used for calculating breeding values, and postGSf90 program is used for calculating marking effect.
(4) Marker screening
And (3) taking absolute values of all the effect values of the markers to draw Manhattan diagrams, and displaying and screening SNP markers with large effects. And analyzing the feed gain ratio difference conditions of the boars of the populations with different genotypes by adopting analysis of variance and multiple comparison (R statistical analysis platform).
Analyzing the feed weight-increasing ratio of boars with different genotypes
For the effect values of all markers, the absolute value of the marker is drawn into a Manhattan chart, and SNP markers with large effects are displayed and screened (shown in FIG. 2, and the genomic positions of the WU_10.2_3_116703757 markers and the distribution of the SNP effect of the whole genome of the feed weight-increasing ratio are shown in FIG. 2). And analyzing the feed gain ratio difference conditions of the boars of the populations with different genotypes by adopting analysis of variance and multiple comparison (R statistical analysis platform) (table 1).
Use of a SNP molecular genetic marker for pig feed conversion rate (wu_10.2_3_116703757 marker gene sequence) in pig feed conversion rate assisted selection:
the present disclosure identifies a molecular marker wu_10.2_3_116703757 that affects the feed gain ratio of boars, as can be seen from table 1, the feed gain ratio of boars with different genotypes is significantly different (table 1 indicates wu_10.2_3_116703757 marks the feed gain ratio of boars with different genotypes);
table 1 WU_10.2_3_116703757 marks weight gain ratio of boar feeds with different genotypes
The WU_10.2_3_116703757 marker genotype is detected to assist breeding of the breeding pigs, and TT homozygous breeding pigs can be selected and remained to enter a core group, so that the feed weight ratio is reduced, and the feed consumption and the breeding cost are effectively reduced;
the SNP molecular genetic marker (WU_10.2_3_116703757) is positioned at 109882181bp position of pig chromosome 3, belongs to an intron sequence of ALK gene, is a C > T mutation (Sscofa 11.1), and has the following sequence of 100bp at the upstream and downstream of a mutation site:
5'-ATACAATTCAACCCAAAACAGAACCTGAGGAAGAGCAATCTGTGCCCTATTTGTCCTGTAAGCGGTGACATTGTTTCAGTAAACAGCACTTCCATCACAAR (T/C) GAATTGTCGCTTAATTTGAATTGCATGGTTTTGAACATTTGTATTTATATGCAAATAATTATTGAATTATTTTGTTTGGTTTTGATAGTCATGCAAGAGC-3'; r is a mutation site, and when R at nucleotide 101 of the above sequence is C or T, that is, R (T/C), the above sequence polymorphism is caused; when nucleotide 101 of the above nucleotide sequence is T, pigs have a lower feed gain ratio, and 5 '-and-3' represent the 5 'and 3' ends of the nucleotide sequence, respectively.
(the nucleotide sequence of the sequence when the mutation point is T is shown as a nucleotide sequence shown in a sequence table SEQ ID No. 1), wherein the sequence table SEQ ID No.1 is a nucleotide sequence 100bp upstream and downstream of the mutation point of the genetic marker (namely SNP No. WU_10.2_3_116703757 and RS No. rs 343880869) obtained by screening.
The weight gain ratio of the WU_10.2_3_116703757 marker genotype TT and the feed gain ratio of the CC boar individuals are different by 0.18, and the weight gain ratio of the TT individuals to the CC individuals is reduced by 8.41%, so that T is an allele which is favorable for obviously reducing the weight gain ratio of the feed.
Main references:
1.Mignon G S,rideau N,Gabriel I,et al.Detection of QTL controlling feed efficiency and excretion in chickens fed a wheat-based diet.Genetics Selection Evolution,47(1):74(2015)。
2.Ma Y,Yu C,Mohamed E M,et al.A causal link from ALK to hexokinase II overexpression and hyperactive glycolysis in EML4-ALK-positive lung cancer.Oncogene,35(47),6132–6142(2016).doi:10.1038/onc.2016.150。
3.Vigors S,Sweeney T,Oshea C J,et al.Pigs that are divergent in feed efficiency,differ in intestinal enzyme and nutrient transporter gene expression,nutrientdigestibility and microbial activity.Animal,10(11):1848-1855(2016)。
4.Hirschhorn,J.N.&Daly,M.J.Genome-wide association studies for common diseases and complex traits.Nat.Rev.Genet.6,95–108(2005)。
5.De,R.,Bush,W.S.&Moore,J.H.Bioinformatics challenges in genome-wide association studies(GWAS).Methods Mol.Biol.1168,63–81(2014)。
6.Purcell,S.et al.PLINK:A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses.Am.J.Hum.Genet.813,559–575(2007)。
7.WANG,H.,MISZTAL,I.,AGUILAR,I.,LEGARRA,A.&MUIR,W.M.Genome-wide association mapping including phenotypes from relatives without genotypes.Genet Res 94,73–83(2012)。
8.Wang,H.et al.Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step(ssGWAS)for 6-week body weight in broiler chickens.Front.Genet.5,1–10(2014)。
9.Misztal,I.et al.BLUPF90 and related programs(BGF90).in Proc.7th World Congr.Genet.Appl.Livest.Prod.21–22(2002).doi:9782738010520。
sequence listing
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Claims (1)
1. The application of the reagent for typing and detecting SNP molecular genetic markers related to the pig feed weight gain ratio in auxiliary selection of Duroc boar feed weight gain ratio is characterized in that the SNP molecular genetic markers are positioned at 109882181bp positions of a pig chromosome 3, belong to an intron sequence of an ALK gene, are C > T mutation, and the pig reference genome is Srcrofa 11.1; and selecting and reserving Duroc boar individuals with low feed weight gain ratio and the SNP molecular genetic marker genotype of which is TT genotype.
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| CN113699246B (en) * | 2021-07-26 | 2023-07-11 | 华南农业大学 | SNP molecular marker affecting pig feed conversion efficiency character and application thereof |
| CN115341045A (en) * | 2022-10-19 | 2022-11-15 | 佛山科学技术学院 | Method for predicting pig feed conversion rate by using microorganisms and related SNP sites thereof |
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