CN113555063A - Threshold character genome breeding value estimation method based on SNP chip and application - Google Patents

Threshold character genome breeding value estimation method based on SNP chip and application Download PDF

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CN113555063A
CN113555063A CN202110859235.6A CN202110859235A CN113555063A CN 113555063 A CN113555063 A CN 113555063A CN 202110859235 A CN202110859235 A CN 202110859235A CN 113555063 A CN113555063 A CN 113555063A
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李秀金
黄运茂
田允波
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Zhongkai University of Agriculture and Engineering
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Abstract

The threshold character genome breeding value estimation method based on the SNP chip comprises the following steps: step S1, acquiring a data file, and preprocessing the data file to obtain reliable preprocessed data; s2, carrying out SNP effect value estimation on the preprocessed data obtained in the S1, selecting a Bayesian method LD-Bayes T to estimate the SNP effect, and obtaining all SNP effect values of a single trait; step S3, obtaining individual genome breeding value of single threshold character according to SNP effect value and candidate group SNP genotype. The invention integrates SNP chip information, SNP position information and phenotype information, can carry out genome breeding estimation on threshold traits with economic value of animals according to the SNP information of the animals, and is used for precise seed selection of the animals.

Description

Threshold character genome breeding value estimation method based on SNP chip and application
Technical Field
The invention relates to the technical field of genome breeding values, in particular to a threshold character genome breeding value estimation method based on an SNP chip and application thereof.
Background
The extensive application of genetic theory and computers to animal breeding is an essential feature of modern animal breeding. Since the 80 s of the 20 th century, the breeding value-based livestock and poultry breeding selection and matching becomes a main method for livestock and poultry breeding, and the breeding value is estimated to become the core content of animal genetic breeding. The essence of the breeding value estimation method is to use the character records of the individual and/or relatives to carry out appropriate weighting to improve the accuracy of selection.
With the deep development of biotechnology, the genomes of livestock and poultry are deeply researched, and high-density whole genome SNP chips of most livestock and poultry species are also continuously made. To effectively apply the high-density whole genome marker to the genetic improvement of livestock and poultry, Meuwissen et al first proposed the concept of Genome Selection (GS) in 2001. An important basic assumption for genome selection is: each gene or Quantitative Trait Locus (QTL) affecting a trait of interest is in linkage disequilibrium with at least one marker of the high-density whole genome markers. Genome selection enables the estimation of genomic breeding values for each individual using high density whole genome SNPs and selection based thereon. Compared with the traditional genetic evaluation method utilizing pedigree information, genome selection can more effectively reflect the genetic relationship between individuals by utilizing whole genome genetic marker information, so that the breeding value of the individuals can be more accurately estimated. In addition, the genome selection can realize early seed selection through early genotype determination, thereby greatly shortening the generation interval and reducing the breeding cost. Schaeffer indicated in 2006 that in a Canadian-like cow breeding system, compared with the traditional descendant determination, the genome selection scheme can improve the genetic progress by 2 times, reduce the breeding cost by 92%, and has obvious advantages. At present, genome selection has been widely applied to molecular breeding of livestock and poultry in various countries, especially in the aspects of breeding of dairy cows, pigs and chickens.
At present, theoretical research and practical application of most genome selection calculation methods are directed at quantitative traits, but the research on the genome breeding value estimation method is less for threshold traits (calving difficulty of cows, death of piglets before weaning, and the like). The threshold trait is simple in phenotype (similar to quality trait), and complex in genetic basis, and is influenced by multiple genes (similar to quantitative trait), and the phenotypic value and the gene effect value are not in linear relation.
Therefore, the analysis of the threshold trait by the quantitative trait-specific calculation method is not efficient and even error occurs. Wang et al proposed in 2012 a threshold trait genome selection estimation method based on the threshold model, Bayes t, namely Bayes TA, Bayes tb and Bayes tcpi. They found that the Bayes t method significantly improved the accuracy of the estimation of the genomic breeding value over the conventional corresponding Bayes method. With the widespread use of high-density SNP chips (cow 700K, pig and chicken 600K, etc.), the degree of Linkage Disequilibrium (LD) between SNPs has become higher and higher, and LD information has also become more and more important. However, the threshold trait genomic breeding value estimation method does not take LD information between SNPs into consideration, and thus, it is urgently required to take account of calculation models. Therefore, for the threshold trait, it is urgently needed to improve the threshold trait genomic breeding value estimation method by considering the LD information between adjacent SNPs on the basis of the BayesT method study, and further improve the accuracy of threshold trait genomic breeding value estimation.
Disclosure of Invention
The invention aims to provide a threshold trait genome breeding value estimation method based on an SNP chip and application thereof, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
threshold behavior: the phenotype shows discontinuous variation, is similar to quality traits, is controlled by a micro-effective polygene on the basis of heredity, is similar to quantitative traits, and is easily influenced by the environment.
Breeding value: the breeding value of breeding stock refers to the sum of additive effects of genes determining a quantitative trait in quantitative genetics as the individual breeding value of the trait.
Genome breeding value: and accumulating the SNP effects of the whole genome of the individual to obtain a breeding value.
Reference population: individuals in the group have SNP chip genotype information and phenotype data information, and the whole genome SNP marker effect can be estimated according to the reference group, so that the genome breeding value of the candidate group individuals can be predicted.
Candidate population: consisting of individuals having only SNP chip genotype information.
The threshold trait genome breeding value estimation method based on the SNP chip comprises the following steps:
step S1, acquiring a data file, and preprocessing the data file to obtain reliable preprocessed data;
s2, carrying out SNP effect value estimation on the preprocessed data obtained in the S1, selecting a Bayesian method LD-Bayes T to estimate the SNP effect, and obtaining all SNP effect values of a single trait;
step S3, obtaining individual genome breeding value of single threshold character according to SNP effect value and candidate group SNP genotype.
Preferably, the Bayesian method LD-Bayesian T is one of LD-Bayesian A, LD-Bayesian B and LD-Bayesian Cpi.
Preferably, the step S1 includes:
step S11, obtaining SNP chip data and preprocessing, including reading the SNP chip data and filling missing genotypes;
step S12, obtaining SNP map files and preprocessing, wherein the SNP map files necessarily comprise chromosome numbers and SNP sequencing;
and step S13, acquiring phenotype data, preprocessing, and screening individual phenotype values in the SNP file in the step S11.
Preferably, the step S1 specifically includes:
step S11, acquiring SNP chip data, and storing files in a compressed format to save hard disk space; filling the SNP markers or individuals with deletion in the genotype of the chip by using a Beagle program, so that the detection quality of the genotype of the chip is improved;
in step S12, the deletion genotype filling further includes genotype quality control, and the quality control parameters of the genotype quality control include the detection rate of each SNP marker, the minimum allele frequency, the hayes-weinberg equilibrium test, and the individual detection rate.
Preferably, in the step S2, the method for estimating the genome breeding value by using the bayesian method LD-BayesT, and the data selection only requires genome information and phenotype information, the method estimates the effect of each SNP marker in the SNP chip by using the markov chain monte carlo algorithm, and the pi value used by LD-bayesian tb needs to be manually set to 0.95.
Preferably, in step S3, there are two methods for synthesizing genome breeding values:
(1) taking the economic weighted value of each character as a weight without considering the pedigree index, and generating a comprehensive genome breeding value by weighting for individual selection;
(2) considering the pedigree index, firstly weighting and combining the individual pedigree index and the genome breeding value of the individual trait into a new value as the final genome breeding value of the trait, wherein the combined weight is the reliability of the individual pedigree index and the genome breeding value respectively, and after obtaining the new genome breeding values of all traits, calculating the comprehensive genome breeding value according to the step (1).
An application of a threshold character genome breeding value estimation method based on an SNP chip in animal breeding.
Compared with the prior art, the invention has the beneficial effects that:
1. the method integrates SNP chip information, SNP position information and phenotype information, can carry out genome breeding estimation on threshold traits with economic values of animals according to the SNP information of the animals, and is used for accurate seed selection of the animals;
2. the method is compiled by using the scientific language Fortran, can accelerate the calculation and shorten the calculation time, and is suitable for operation under Linux and Windows systems;
3. the method can promote the application of genome selection in the field of domestic animal breeding and can better exert the advantages of genome selection in the field of animal breeding.
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FIG. 1 is a flow chart of the method of the present invention in an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The threshold trait genome breeding value estimation method based on the SNP chip comprises the following steps:
step S1, acquiring a data file, and preprocessing the data file to obtain reliable preprocessed data;
s2, carrying out SNP effect value estimation on the preprocessed data obtained in the S1, selecting a Bayesian method LD-Bayes T to estimate the SNP effect, and obtaining all SNP effect values of a single trait;
step S3, obtaining individual genome breeding value of single threshold character according to SNP effect value and candidate group SNP genotype.
The Bayesian method LD-Bayes T of the present embodiment is one of LD-Bayes A, LD-Bayes B and LD-Bayes Cpi.
Step S1 of the present embodiment includes:
step S11, obtaining SNP chip data and preprocessing, including reading the SNP chip data and filling missing genotypes;
step S12, obtaining SNP map files and preprocessing, wherein the SNP map files necessarily comprise chromosome numbers and SNP sequencing;
and step S13, acquiring phenotype data, preprocessing, and screening individual phenotype values in the SNP file in the step S11.
Step S1 of this embodiment specifically includes:
step S11, acquiring SNP chip data, and storing files in a compressed format to save hard disk space; filling the SNP markers or individuals with deletion in the genotype of the chip by using a Beagle program, so that the detection quality of the genotype of the chip is improved;
in step S12, the deletion genotype filling further includes genotype quality control, and the quality control parameters of the genotype quality control include the detection rate of each SNP marker, the minimum allele frequency, the hayes-weinberg equilibrium test, and the individual detection rate.
In step S2 of this example, the method for estimating the genomic breeding value by using the bayesian method LD-BayesT, wherein the data selection only requires genomic information and phenotypic information, estimates the effect of each SNP marker in the SNP chip by the markov chain monte carlo algorithm, and the pi value used by LD-bayesian tb needs to be manually set to 0.95.
In step S3 of this example, there are two methods for synthesizing genome breeding values:
(1) taking the economic weighted value of each character as a weight without considering the pedigree index, and generating a comprehensive genome breeding value by weighting for individual selection;
(2) considering the pedigree index, firstly weighting and combining the individual pedigree index and the genome breeding value of the individual trait into a new value as the final genome breeding value of the trait, wherein the combined weight is the reliability of the individual pedigree index and the genome breeding value respectively, and after obtaining the new genome breeding values of all traits, calculating the comprehensive genome breeding value according to the step (1).
Example 1
The simulation data was an outcrossing population modeled by the 2010 QTL-MAS Workshop, pedigree consisting of 3226 individuals of 5 generations (F0-F4). The F0 generation had 5 male animals and 15 female animals. Each dam was mated 1 time to produce 30 offspring. This data mimics two traits, trait Q being a quantitative trait and trait B being a binary trait. 10031 SNP markers were uniformly distributed on 5 chromosomes, each chromosome was 100million bps in length, and there were no deletion data and genotype judgment errors.
The specific data information provided by the simulation data is as follows: pedigree files, SNP marker files, and phenotype files, generations 3226 individuals from F0-F1 all had genotypes and true breeding values, with generations 2326 individuals from F0-F3 having phenotype records as reference populations. The F4 generation 900 individuals had no phenotypic record as candidate populations.
For this simulation data, SNP effect value estimation was performed on the preprocessed data obtained in step S1 using step S2 using LD-Bayes T (LD-Bayes TA, LD-Bayes TB, and LD-Bayes TCpi) and Bayes T (Bayes TA, Bayes TB, and Bayes TCpi) as the estimation methods. The individual genomic breeding value for a single trait is obtained by estimating the threshold trait genomic breeding value using step S3. The LD-Bayes T method is further divided into 3 methods:
1. threshold model
Let liIs the value of the latent variable of the ith individual, and the model is as follows
li＝x′iβ+z′ig+ei
Wherein β is a fixed effect vector, g is a SNP effect vector, eiIs a random error, obedience
Figure BDA0003185135930000061
x′iIs line vector, z'iIs a row vector for the genotype. We assume that β and g are known, then 1 has a conditional probability distribution of
Figure BDA0003185135930000071
Wherein latent variables are not observable
Figure BDA0003185135930000072
Fixed to 1.
Assuming y as the classification phenotype for the threshold trait, is { yiJ (i ═ 1, 2, …, k), there were k classifications of phenotype, and t was the threshold1To tk-1. Let tmin＝-∞，tmax＝+∞，t1＝0，t＝(tmin，t1，t2，…，tk-1，tmax) Then, then
Figure BDA0003185135930000073
Wherein
Figure BDA0003185135930000074
A cumulative distribution function of a standard normal distribution. And the model joint conditional probability distribution is
Figure BDA0003185135930000075
2. Estimation of genomic breeding values using the LD-Bayesian method
Prior distribution assumption for the LD-BayesTA method:
①
Figure BDA0003185135930000076
that is, each SNP effect has a linear regression relationship with the previous SNP effect except for the first and last SNP of each chromosome.
②
Figure BDA0003185135930000077
Residual Effect of Each SNP δjWith respective different effect variances
Figure BDA0003185135930000078
③
Figure BDA0003185135930000081
I.e., the variance of the SNP residual effect obeys a degree of freedom vδWith a scale parameter of
Figure BDA0003185135930000082
The inverse chi-square distribution of (c). v. ofδ～(vδ+1)-2，
Figure BDA0003185135930000083
④
Figure BDA0003185135930000084
I.e. each ld value has a respective different mean value of effect muldSum variance
Figure BDA0003185135930000085
Figure BDA0003185135930000086
⑤
Figure BDA0003185135930000087
Wherein T { (T)1，…，tk-1)|tmin≤t1≤...≤tk-1≤tmax}
Sixthly, P (b) constant, namely, the fixed effect and the population mean value are uniformly distributed.
And acquiring the complete condition posterior distribution of each variable, and then performing Gibbs sampling through the complete condition posterior distribution of each variable to acquire an estimated value of each variable.
Procedure for Gibbs sampling:
step 1, initialization
Giving initial values to all unknown parameters
Figure BDA0003185135930000088
Step 2, update lj
From ljFull condition posterior distribution
Figure BDA0003185135930000089
Truncating the normal distribution (critical range: [ t ]i，ti+1]) And (6) sampling.
Step 3 update tj
From tjFull condition posterior distribution uniform distribution U (max (1| y ═ j), min (1| y ═ j +1)) samples
Step 4, update bj
From bjFull condition posterior distribution of
Figure BDA0003185135930000091
And (6) sampling.
Step 5, update deltak 2(k＝1，2，…，q)
From deltak 2Full condition posterior distribution of
Figure BDA0003185135930000092
And (6) sampling.
Step 6, update ldj，j-1(k＝1，2，…，q)
From ldj，j-1Full conditional posterior normal distribution of
Figure BDA0003185135930000093
And (6) sampling.
Step 7, update gk(k＝1，2，…，q)
From gkThe complete condition posterior distribution of (2) is normally distributed sampled.
Step 8, repeating steps 2-7
Until convergence reaches a plateau and enough samples are obtained.
3. Estimation of genomic breeding values using the LD-Bayesian TB method
An important difference between LD-Bayesian TB and LD-Bayesian TA is the SNP residual effect deltakIn contrast, LD-Bayesian hypothesis assumes a residual Effect δ of all SNPskAll have effects, and the variance of the effects is different; while the assumption of LD-Bayesian TB is that most SNPs have a residual effect of δkNo effect value, only a few SNPs remaining effect deltakThere is an effect and there is a respective variance of the effect. A priori assumptions on pairs are: under the probability of pi,
Figure BDA0003185135930000094
under the probability of 1-pi,
Figure BDA0003185135930000095
and pi is artificially set, and indicates the SNP marker ratio with or without an effect of 0, and is usually set to 0.95. MCMC sampling using Bayes B except for
Figure BDA0003185135930000096
The Gibbs samples from Bayesian are used in the same manner as Metropolis-Hasting (MH) samples, but are simply sampled from the inverse Chi-chi-square distribution.
4. Estimation of genomic breeding values using the LD-Bayes TCpi method
LD-Bayes TCpi is proposed for the deficiency of LD-Bayes TA and LD-Bayes TB. Compared with LD-Bayesian TB, LD-Bayesian TCpi has two obvious differences, firstly, LD-Bayesian TB is the artificially set pi value, and LD-Bayesian TCpi regards the pi value as an unknown parameter and is obtained by inference through data information and prior information. Second, in the SNP effect variance prior hypothesis, the LD-Bayes TB hypothesis that the SNP residual effect is delta under the probability of 1-pikThe effects and the variance of the effects are different, respectively, while LD-Bayes TCpi assumes the residual effect delta of SNPkThere is an effect and the same variance of the effect. Theoretically, LD-Bayes TCpi is superior, but in different cases, LD-Bayes TA and LD-Bayes TB are close to the same.
The method comprises the steps of selecting the LD-Bayesian T method (LD-Bayesian TA, LD-Bayesian TB and LD-Bayesian TCpi) and the previous Bayesian T method (Bayesian TA, Bayesian TB and Bayesian TCpi) to predict the genome breeding value of an F4 generation 900 individual, and using the correlation coefficient of the genome breeding value and the real breeding value thereof as the accuracy, wherein the higher the correlation coefficient is, the more accurate the estimation of the genome breeding value is. And meanwhile, the real breeding pair genome breeding value is regressed, the regression coefficient measures the unbiased estimation of the genome breeding value, and the closer the regression coefficient is to 1.0, the more unbiased the estimation of the genome breeding value is.
Table 1900 progeny genome breeding value estimation accuracy and unbiasedness
Figure BDA0003185135930000101
Figure BDA0003185135930000111
Table 1 estimates the accuracy and unbiasedness of the genomic breeding values for the LD-Bayes T methods of the present invention (LD-Bayes TA, LD-Bayes TB, and LD-Bayes TCpi) and previous Bayes T methods (Bayes TA, Bayes TB, and Bayes TCpi). Compared with Bayesian TA, the improved LD-Bayesian TA improves 0.088 in the accuracy of breeding value estimation, and the unbiased property is also obviously improved. LD-Bayesian TB is equivalent to Bayesian TB in the accuracy and unbiased of breeding value estimation. LD-bayesian tcpi reduces breeding value estimation accuracy but improves prediction unbiased compared to bayesian tcpi. At the same time, the unbiased nature of the remaining methods, except for bayesian ta, was close to 1.0, indicating that the method hardly overestimates or underestimates genomic breeding values compared to the true genome. The reliability of the method is proved.

Claims (7)

1. The threshold trait genome breeding value estimation method based on the SNP chip is characterized by comprising the following steps of:
step S1, acquiring a data file, and preprocessing the data file to obtain reliable preprocessed data;
s2, carrying out SNP effect value estimation on the preprocessed data obtained in the S1, selecting a Bayesian method LD-Bayes T to estimate the SNP effect, and obtaining all SNP effect values of a single trait;
step S3, obtaining individual genome breeding value of single threshold character according to SNP effect value and candidate group SNP genotype.
2. The SNP chip-based threshold trait genomic breeding value estimation method as claimed in claim 1, wherein the Bayesian method LD-Bayes T is one of LD-BayesA, LD-Bayes B and LD-Bayes Cpi.
3. The method for estimating the genomic breeding value of the threshold trait based on the SNP chip according to claim 1, wherein the step S1 comprises:
step S11, obtaining SNP chip data and preprocessing, including reading the SNP chip data and filling missing genotypes;
step S12, obtaining SNP map files and preprocessing, wherein the SNP map files necessarily comprise chromosome numbers and SNP sequencing;
and step S13, acquiring phenotype data, preprocessing, and screening individual phenotype values in the SNP file in the step S11.
4. The method for estimating the threshold trait genomic breeding value based on the SNP chip as set forth in claim 3, wherein the step S1 specifically comprises:
step S11, acquiring SNP chip data, and storing files in a compressed format to save hard disk space; filling the SNP markers or individuals with deletion in the genotype of the chip by using a Beagle program, so that the detection quality of the genotype of the chip is improved;
in step S12, the deletion genotype filling further includes genotype quality control, and the quality control parameters of the genotype quality control include the detection rate of each SNP marker, the minimum allele frequency, the hayes-weinberg equilibrium test, and the individual detection rate.
5. The method for estimating the genomic breeding value of the threshold trait based on the SNP chip as claimed in claim 1, wherein the genomic breeding value is estimated by using a Bayesian method LD-Bayesian T in step S2, the data selection only requires genomic information and phenotypic information, the method estimates the effect of each SNP marker in the SNP chip by a Markov chain Monte Carlo algorithm, and the pi value used by LD-Bayesian TB needs to be artificially set to 0.95.
6. The method for estimating the threshold trait genomic breeding value based on the SNP chip as claimed in claim 1, wherein there are two methods for synthesizing the genomic breeding value in step S3:
(1) taking the economic weighted value of each character as a weight without considering the pedigree index, and generating a comprehensive genome breeding value by weighting for individual selection;
(2) considering the pedigree index, firstly weighting and combining the individual pedigree index and the genome breeding value of the individual trait into a new value as the final genome breeding value of the trait, wherein the combined weight is the reliability of the individual pedigree index and the genome breeding value respectively, and after obtaining the new genome breeding values of all traits, calculating the comprehensive genome breeding value according to the step (1).
7. An application of a threshold character genome breeding value estimation method based on an SNP chip in animal breeding.
CN202110859235.6A 2021-07-28 2021-07-28 Threshold character genome breeding value estimation method based on SNP chip and application Pending CN113555063A (en)

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WO2020133588A1 (en) * 2018-12-28 2020-07-02 华中农业大学 Rapid and stable method for evaluating individual animal genome breeding values
CN111477272A (en) * 2020-04-08 2020-07-31 山西省农业科学院畜牧兽医研究所 Method for assisting in selecting high-yield-litter-size rex rabbits by using SNPs (single nucleotide polymorphisms)
WO2021011990A1 (en) * 2019-07-25 2021-01-28 Agriculture Victoria Services Pty Ltd An iterative regression method for genomic prediction

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914631A (en) * 2014-02-26 2014-07-09 中国农业大学 Comprehensive genomic estimated breeding value (GEBV) method and application on the basis of single nucleotide polymorphism (SNP) chip
CN106022005A (en) * 2016-05-21 2016-10-12 安徽省农业科学院畜牧兽医研究所 Bayes method for joint estimation of continuous traits and threshold traits based on genomic estimated breeding value
CN107590364A (en) * 2017-08-29 2018-01-16 集美大学 A kind of quick bayes method of new estimation genomic breeding value
WO2020133588A1 (en) * 2018-12-28 2020-07-02 华中农业大学 Rapid and stable method for evaluating individual animal genome breeding values
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