CN109101786B - Genome breeding value estimation method integrating dominant effect - Google Patents
Genome breeding value estimation method integrating dominant effect Download PDFInfo
- Publication number
- CN109101786B CN109101786B CN201810997830.4A CN201810997830A CN109101786B CN 109101786 B CN109101786 B CN 109101786B CN 201810997830 A CN201810997830 A CN 201810997830A CN 109101786 B CN109101786 B CN 109101786B
- Authority
- CN
- China
- Prior art keywords
- marker
- genome
- genomic
- breeding value
- breeding
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a genome breeding value estimation method integrating dominant effect, and relates to the technical field of livestock and poultry genetic breeding. The method comprises the steps of determining a reference group and a candidate group, determining and obtaining a target character phenotype of the reference group, marking the whole genome of the reference group, controlling the quality of the gene marker of the reference group, counting the deviation amplitude of the heterozygous marker of the reference group, establishing a genome marker recoding rule, marking the whole genome of the candidate group, controlling the quality of the gene marker of the candidate group, recoding the genome marker, estimating the genome breeding value and the like. The present invention formulates coding rules based on the degree of deviation of heterozygous and homozygous genotype phenotypes, proceeds from the genomic marker end, recodes heterozygous genotypes, makes the genetic marker codes to contain dominant effects, and then estimates genomic breeding values. The method adapts to the requirements of livestock and poultry genetic breeding, and can greatly improve the accuracy of the genome estimated breeding value without increasing the complexity of the model.
Description
Technical Field
The invention relates to the technical field of livestock and poultry genetic breeding, in particular to a genome breeding value estimation method integrating dominant effect.
Background
The hybridization technique is widely applied to the genetic breeding of animals and plants such as pigs, chickens, cattle, rice, corn and the like. Its application has two main aspects: on one hand, the method is used for breeding new varieties, and the excellent characteristics of parents are integrated by a hybridization technology to form new varieties. On the other hand, the hybrid vigor is utilized to improve the production efficiency, and the hybrid vigor refers to the phenomenon that heterozygous filial generation is superior to the parent mean value. Dominant effects are one of the most prominent causes of heterosis, and refer to effects resulting from interactions between alleles within the same genetic locus. With the continuous development of biotechnology, the whole genome marker can be classified, and an opportunity is provided for the application of dominant effect in the genetic breeding of animals and plants.
Genome selection is a new generation of animal and plant genetic breeding technology, which estimates the marker effect based on molecular markers distributed in the whole genome by utilizing the close linkage relationship between the markers and genes, and then calculates the individual breeding value in an accumulation manner. The method is firstly proposed by Meuwissen et al in 2001, has the characteristics of capability of shortening generation intervals, early seed selection, high selection accuracy and the like, and is widely applied to breeding of livestock and poultry such as dairy cows, pigs, chickens and the like. Although the dominant effect is an important genetic effect, the commonly used methods of estimating the genomic breeding value do not take into account the dominant effect, but only the additive effect. The dominant effect is also researched by scholars, but the model assumes that the additive effect and the dominant effect are mutually independent effects, which is inconsistent with the fact that a certain relation exists between the two effects and is one of the reasons for reducing the accuracy of the genome estimated breeding value.
The invention introduces dominant effect from gene marker end integration to estimate genome breeding value, and has not been reported in animal and plant genetic breeding at home and abroad at present.
Disclosure of Invention
In order to overcome the shortcomings and drawbacks of the prior art, the present invention aims to provide a method for estimating a genomic breeding value by integrating dominant effects.
According to the characteristics of dominant inheritance, the invention firstly recodes the heterozygous genotype based on the phenotype bias of heterozygote, and then estimates the individual breeding value of each individual by a genome optimal linear unbiased estimation (GBLUP) method, thereby achieving the purpose of integrating dominant effect and improving the accuracy of genome estimated breeding value.
The purpose of the invention is realized by the following technical scheme:
a method for estimating a genome breeding value integrating a dominant effect comprises the following steps:
(1) determining a reference population and a candidate population;
(2) acquiring target character phenotype determination data of a reference population;
(3) typing the whole genome marker of the reference population;
(4) controlling the quality of the reference population gene marker;
(5) reference population heterozygous marker deviation amplitude statistics;
respectively counting and calculating the phenotype mean values of three genotypes of AA, AA and AA
And
and calculating the deviation amplitude between the heterozygous genotype and the homozygous genotype according to the following formula:
(6) establishing a genome marker recoding rule;
the heterozygous gene was determined according to the magnitude of deviation between the heterozygous genotype and the homozygous genotype as followsType recoding rule: (a) if d isAA>daaAa is encoded as Aa; (b) if d isAA<daaAa encodes AA; (c) if d isAA=daaAa does not need to be re-encoded;
(7) typing the whole genome marker of the candidate population;
(8) controlling the quality of the candidate group gene markers;
adopting the same gene marker quality control standard as the reference population in the step (4);
(9) genomic marker recoding: recoding the reference population and candidate population genomic markers by adopting the coding rule established in the step (6);
(10) estimation of genomic breeding values: based on the recoded genome marker information, an inter-individual relationship matrix is constructed, a fixed effect is determined, a linear model is constructed, and the target trait individual breeding value is estimated by using methods such as a genome optimal linear unbiased estimation method (GBLUP).
In the step (10), based on the recoded genome marker information, an inter-individual relationship matrix is constructed, target trait data is integrated according to the following model, and a genome optimal linear unbiased estimation method (GBLUP) is used for estimating the breeding value of the target trait individual:
y=Xb+Zhh+e，
wherein y represents an observed value of the target trait, b represents a fixed effect vector, X is a correlation matrix of b, h represents an additive and dominant complex genetic effect vector based on genomic marker information, and ZhIs the correlation matrix of h;
let h follow the following normal distribution: h to N (0) in the presence of a catalyst,
) Wherein H is an inter-individual relationship matrix constructed by using genome marker information,
is the genomic genetic variance, e is the residual effect vector, obeying a normal distribution: e to N (0) are selected from,
) Wherein I is an identity matrix,
is the residual variance.
Preferably, the fixation effect is gender, lot size, etc.
The mechanism of the invention is as follows:
the phenomenon of dominant inheritance exists in nature, namely the phenotype of the heterozygous genotype Aa is generally the same as that of one of the homozygous genotypes AA and Aa. Using this biological feature, we recode the heterozygous genotype of the genomic marker for integration of the dominant genetic effect, such that the recoded genomic marker comprises the dominant genetic effect.
Compared with the prior art, the invention has the following advantages and effects:
(1) the present invention is different from the conventional genome breeding value estimation model. The conventional breeding value estimation model considers the dominant effect by putting the dominant effect into the breeding value estimation model as an influence factor. The invention starts from a brand-new angle, and recodes the heterozygous marker according to the characteristic of the dominant effect so as to achieve the aim of integrating the dominant effect and estimating the breeding value. The method does not need to add too many factors in the model, does not increase the difficulty of estimating the breeding value, and has the advantages of easier convergence of model iteration and high calculation speed. The invention overcomes the defect that the mutual influence relation between the additive effect and the dominant effect is not considered in the model hypothesis, introduces the dominant effect more reasonably, and can effectively improve the accuracy of the genome estimated breeding value.
(2) The present invention formulates coding rules based on the degree of deviation of heterozygous and homozygous genotype phenotypes, proceeds from the genomic marker end, recodes heterozygous genotypes, makes the genetic marker codes to contain dominant effects, and then estimates genomic breeding values. The method adapts to the requirements of livestock and poultry genetic breeding, and can greatly improve the accuracy of the genome estimated breeding value without increasing the complexity of the model.
Drawings
FIG. 1 is a flow chart of the method for estimating the breeding value of a genome integrating a dominant effect according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
The flow chart of the method for estimating the weight genome breeding value of the integrated dominant effect is shown in figure 1.
The High Quality broiler A Line (HQLA) and Huiyang bearded chicken (Huiyang Beard chicken, HB) used in the examples are disclosed in the literature "Genetic separation of growth traits in a Chinese index x commercial chicken heart cross. BMC Genomics 2013,14(1): 151".
Example 1 estimation method of genome weight breeding value of AH population of yellow-feathered broilers
In order to verify the effect of the method, a cross-validation method is adopted to divide a reference population and a candidate population, and the accuracy of the genome estimated breeding value of the method is compared with that of the other two common methods.
The yellow-feather broiler AH group is constructed by taking a high-quality broiler A line and a Huiyang beard chicken as parents and adopting distant hybridization F2 design. In this example, a total of 582 genotype individuals were used, including 20 in the F0 generation, 51 in the F1 generation, and 511 in the F2 generation. The F2 generations were from 8 half sib families, all with 12 week weight records.
In this example, the target trait was 12 weeks body weight. The participation groups are randomly divided by adopting a 10-time cross-validation method, namely F2 generation 511 chickens are randomly divided into even 10 groups, wherein 9 groups comprise 51 chickens, and 1 group comprises 52 chickens. Of the 10 uniform groups, 1 group masked the phenotype values of the body weight as candidate group and the rest of the chickens as reference group. The method was carried out as follows.
(1) Determining a reference population and a candidate population;
10 even groups divided by a 10-time cross-validation method are adopted, 1 group of 51 chickens is selected as a candidate group, and the other 9 groups are 460 chickens in total as a reference group.
(2) Acquiring target character phenotype determination data of a reference population;
the phenotype of the trait of interest was determined, and in this example the trait of interest was 12 weeks body weight, and the statistical data is shown in table 1.
Table 1 reference population 12 week body weight statistics description
| Number of individuals | Mean value | Standard deviation of | Maximum value | Minimum value |
| 460 of only | 2028.5 g | 362.4 g | 3250 g | 927 g |
(3) Typing the whole genome marker of the reference population;
in this example, a chicken 60K SNP chip was used for genomic marker typing.
(4) Controlling the quality of the reference population gene marker;
the following standard quality controls were used in this example: markers with Call rate less than 95%, Gentrain score less than 0.6 and MAF less than 0.01 were deleted in sequence.
(5) Reference population heterozygous marker effect deviation amplitude statistics;
in this example, all heterozygous marker loci in the whole genome need to be counted, and as shown in table 2, 3 heterozygous marker loci such as M1, M2, M3 are selected as examples to illustrate the statistical method of the deviation amplitude of heterozygous marker effect.
TABLE 2 statistical example of deviation amplitude of heterozygous marker Effect
(6) Establishing a genome marker recoding rule;
the rules for shuffling and recoding heterozygous markers are determined according to the deviation of heterozygous marker effect, and are also illustrated by taking 3 heterozygous marker sites such as M1, M2 and M3 as examples, as shown in Table 3.
TABLE 3 example of rules for hybrid marker recoding
| Marking | Deviation amplitude dAA(gram) | Deviation amplitude daa(gram) | Recoding rules |
| M1 | 132.2 | 17.6 | Aa→aa |
| M2 | 18.6 | 201.8 | Aa→AA |
| M3 | 93.4 | 1.5 | Aa→aa |
(7) Typing the whole genome marker of the candidate population;
the candidate group genotyping method is identical to the reference group genotyping method of step (3).
(8) Controlling the quality of the candidate group gene markers;
the quality control method of the candidate group gene markers is consistent with the genotyping method of the reference group gene markers in the step (4).
(9) Genomic marker recoding: recoding the reference population and candidate population genomic markers by adopting the coding rule established in the step (6);
(10) estimation of genomic breeding values: based on the recoded genomic marker information, Gmantrix software (http:// www.dmu.agrsci.dk/Gmantrix /) was used to construct an inter-individual relationship matrix. The weight data were integrated using DMU software (http:// www.dmu.agrsci.dk/DMU /), and the breeding values were estimated using the genome-optimal linear unbiased estimation method (GBLUP) for the 12-week weight genome.
y=Xb+Zhh+e，
Wherein y represents the phenotype of the target trait, in this example a 12 week weight observation, b represents a fixed effect vector, in this example gender and batch, X is a correlation matrix for b, h represents an additive and dominant complex genetic effect vector based on genomic marker information, and Z represents a complex genetic effect vectorhIs the correlation matrix of h; let h follow the following normal distribution: h to N (0) in the presence of a catalyst,
) Wherein H is an inter-individual relationship matrix constructed by using genome marker information,
is the genomic genetic variance, e is the residual effect vector, obeying a normal distribution: e to N (0) are selected from,
) Wherein I is an identity matrix,
is the residual variance.
To verify the efficacy of the method of the invention, we compared the accuracy of the genomic breeding values of the method of the invention with the other two commonly used methods using the same data.
Method one (M)Add): the difference from the method of the invention is that the method does not recode the gene marker, and the influence of additive effect factors is considered in the use model, and the specific use model is as follows:
y=Xb+Zaa+e，
wherein y, b, e and X are defined in accordance with the model used in the method of the invention, a represents an additive genetic effect vector, and ZaIs the correlation matrix of a. Suppose a follows the following normal distribution: a to N (0) are selected from,
) Wherein G is a genetic relationship matrix between individuals constructed by using genome marker information,
is the genomic genetic variance.
Method two (M)A+D): the difference with the method of the invention is that the method does not recode the gene marker, and the influence of additive and dominant effect factors is considered in the use model, and the specific model is as follows:
y=Xb+Zaa+Zdd+e
wherein y, b, a, e, X and ZaDefinition and method of (M)Add) Model identity, d represents dominant genetic Effect vector, ZdIs the correlation matrix for d. Suppose d obeys the following normal distribution: the ratio of d to N (0,
) Wherein D is a relationship matrix constructed using genomic heterozygous marker information,
is the genomic dominant effect genetic variance.
The results of the comparison of the three methods are shown in Table 4, and it can be seen from the results of this example that the calculation time consumption and the accuracy of the breeding value estimation of the present invention are superior to those of the other two methods, namely, the comparison method one (M)Add) The method reduces the calculation time by 18.6 percent and improves the accuracy by 15.5 percent; method two (M)A+D) Although the calculation time is consumed the most, the influence of the dominant effect on the target character is not captured, and compared with the method, the calculation time is reduced by 56.1%, and the accuracy is improved by 15.5%.
TABLE 4 time consumption and accuracy of estimated breeding value of genome for three methods
| The method of the invention | MAdd | MA+D | |
| Time consuming | 61s | 75s | 139s |
| Accuracy of | 0.595 | 0.515 | 0.515 |
Example 2 estimation method of pig herd genome breeding value
Data for pigs used in the examples are disclosed in the document "A Common data set for Genomic Analysis of Livestock publications. G3: Genes | Genetics 2012,2(4): 429-.
The swine herd was from a core line of PIC swine improvement international group, the swine herd data set did not disclose specific names of the core line and detailed names of the measured traits, the measured trait names were replaced with the symbols T1, T2, T3, T4 and T5, and all phenotypic values had been fixed effect culled. In this example, "T1" was selected as the target trait, which was a low heritability trait, which was 0.07, for a total of 2804 phenotypic records. And randomly dividing the group into 10 groups by adopting a 10-time cross-validation method, wherein the groups are divided into 10 uniform groups, wherein the groups comprise 6 groups of 280 pigs, and the groups comprise 4 groups of 281 pigs. Of the 10 uniform cohorts, 1 of them was chosen as the candidate cohort, the remaining pigs as the reference cohort. The method was carried out as follows.
(1) Determining a reference population and a candidate population;
10 even groups divided by a 10-fold cross-validation method are adopted, 1 group of 280 pigs is selected as a candidate group, and the other 9 groups are used as a reference group for 2524 pigs.
(2) Acquiring target character phenotype determination data of a reference population;
the phenotype of the target trait was determined, in this example the target trait is "T1" and the statistical data is shown in Table 5.
Table 5 statistical description of the reference population trait "T1
| Number of individuals | Mean value | Standard deviation of | Maximum value | Minimum value |
| 2524 head | -0.054 | 1.174 | 10.139 | -3.897 |
(3) Typing the whole genome marker of the reference population;
in this example, the Illumina PorcineSNP60 chip was used for genomic marker typing.
(4) Controlling the quality of the reference population gene marker;
the herd data has been quality controlled, and in this example, a flag is used that only deletes MAF less than 0.01.
(5) Reference population heterozygous marker effect deviation amplitude statistics;
in this example, all heterozygous marker loci in the whole genome need to be counted, and as shown in table 6, 3 heterozygous marker loci, such as m1, m2, m3, are selected as examples to illustrate the statistical method of the deviation amplitude of heterozygous marker effect.
TABLE 6 statistical examples of hybrid marker Effect deviation amplitude
(6) Establishing a genome marker recoding rule;
the rules for shuffling of heterozygous markers based on the bias of heterozygous marker effects are shown in Table 7, which is also illustrated by the example of 3 heterozygous marker sites m1, m2 and m 3.
TABLE 7 example of rules for hybrid marker recoding
| Marking | Deviation amplitude dAA | Deviation amplitude daa | Recoding rules |
| m1 | 0.0545 | 0.1285 | Aa→AA |
| m2 | 0.1841 | 0.0427 | Aa→aa |
| m3 | 0.0063 | 0.0501 | Aa→AA |
(7) Typing the whole genome marker of the candidate population;
the candidate group genotyping method is identical to the reference group genotyping method of step (3).
(8) Controlling the quality of the candidate group gene markers;
the quality control method of the candidate group gene markers is consistent with the genotyping method of the reference group gene markers in the step (4).
(9) Genomic marker recoding: recoding the reference population and candidate population genomic markers by adopting the coding rule established in the step (6);
(10) estimation of genomic breeding values: based on the recoded genome marker information, Gmantrix software (http:// www.dmu.agrsci.dk/Gmantrix /) is used to construct the inter-individual relationship matrix, and the fixed effect is not considered in the model because the original data in the example has been eliminated. Using DMU software (http:// www.dmu.agrsci.dk/DMU /), breeding values were estimated using the genome-optimal Linear unbiased estimation (GBLUP) "T1" phenotype genome according to the following model integration data.
y=ZHH+E，
Wherein y represents the phenotype of the trait of interest, in this example the observation of the "T1" phenotype, h represents the additive and dominant complex genetic effect vectors based on genomic marker information, ZhIs the correlation matrix of h; let h follow the following normal distribution: h to N (0) in the presence of a catalyst,
) Wherein H is an inter-individual relationship matrix constructed by using genome marker information,
is the genomic genetic variance, e is the residual effect vector, obeying a normal distribution: e to N (0) are selected from,
) Wherein I is an identity matrix,
is the residual variance.
To verify the efficacy of the method of the invention, we compared the accuracy of the genomic breeding values of the method of the invention with the other two commonly used methods using the same data.
Method one (M)Add): the difference from the method of the invention is that the method does not recode the gene marker, and the influence of additive effect factors is considered in the use model, and the specific use model is as follows:
Y=Zaa+e，
wherein y and e are defined in accordance with the model used in the method of the invention, a represents an additive genetic effect vector, ZaIs the correlation matrix of a. Suppose a follows the following normal distribution: a to N (0) are selected from,
) Wherein G is a genetic relationship matrix between individuals constructed by using genome marker information,
is the genomic genetic variance.
Method two (M)A+D): the difference from the method of the invention is that the method does not recode the gene marker, and the influence of additive and dominant effect factors is considered in the use model, and the specific model is as follows.
y=Zaa+Zdd+e
Wherein y, a, e and ZaDefinition and method of (M)Add) Model identity, d represents dominant genetic Effect vector, ZdIs the correlation matrix for d. Suppose d obeys the following normal distribution: the ratio of d to N (0,
) Wherein D is a relationship matrix constructed using genomic heterozygous marker information,
is the genomic dominant effect genetic variance.
The results of the comparison of the three methods are shown in Table 8, and it can be seen from the results of this example that the calculation time consumption and the accuracy of the breeding value estimation of the present invention are superior to those of the other two methods, namely, the comparison method one (M)Add) The inventionThe calculation time of the method is reduced by 44.4%, and the accuracy is improved by 482.7%; method two (M)A+D) Although the calculation time is consumed most, the influence of the dominant effect on the target character is not captured, but the accuracy is worst due to the increase of the complexity of the model, and compared with the method, the calculation time is reduced by 73.4%, and the accuracy is improved by 550.0%.
TABLE 8 time consumption and accuracy of estimated breeding value of genome for three methods
| The method of the invention | MAdd | MA+D | |
| Time consuming | 99s | 178s | 372s |
| Accuracy of | 0.169 | 0.029 | 0.026 |
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Therefore, it is intended that all such modifications and improvements within the spirit and scope of the invention be considered as within the scope and spirit of the invention.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (4)
1. A method for estimating a genome breeding value integrating a dominant effect, comprising the steps of:
(1) determining a reference population and a candidate population;
(2) acquiring target character phenotype determination data of a reference population;
(3) typing the whole genome marker of the reference population;
(4) controlling the quality of the reference population gene marker;
(5) reference population heterozygous marker deviation amplitude statistics;
respectively counting and calculating the phenotype mean values of three genotypes of AA, AA and AA
And
and calculating the deviation amplitude between the heterozygous genotype and the homozygous genotype according to the following formula:
(6) establishing a genome marker recoding rule;
according to heterozygous genotype and homozygous baseThe rules for heterozygous genotype recoding were determined by the following rules, due to the magnitude of the deviation between genotypes: (a) if d isAA＞daaAa is encoded as Aa; (b) if d isAA＜daaAa encodes AA; (c) if d isAA＝daaAa does not need to be re-encoded;
(7) typing the whole genome marker of the candidate population;
(8) controlling the quality of the candidate group gene markers;
adopting the same gene marker quality control standard as the reference population in the step (4);
(9) genomic marker recoding: recoding the reference population and candidate population genomic markers by adopting the coding rule established in the step (6);
(10) estimation of genomic breeding values: and constructing a relation matrix between individuals on the basis of the recoded genome marker information, determining a fixed effect, constructing a linear model, and estimating the individual breeding value of the target character.
2. The method for estimating a genomic breeding value for an integrative dominant effect according to claim 1, wherein:
in the step (10), the individual breeding value of the target character is estimated by using a genome optimal linear unbiased estimation method.
3. The method for estimating a genomic breeding value for an integrative dominant effect according to claim 1 or 2, wherein:
in the step (10), based on the recoded genome marker information, constructing an inter-individual relationship matrix, integrating target character data according to the following model, and estimating the individual breeding value of the target character by using a genome optimal linear unbiased estimation method:
y＝Xb+Zhh+e，
wherein y represents an observed value of the target trait, b represents a fixed effect vector, X is a correlation matrix of b, h represents an additive and dominant complex genetic effect vector based on genomic marker information, and ZhIs the correlation matrix of h; let h follow the following normal distribution:
wherein H is an inter-individual relationship matrix constructed by using genome marker information,
is the genomic genetic variance, e is the residual effect vector, obeying a normal distribution:
wherein I is a matrix of units and I is a matrix of units,
is the residual variance.
4. The method of estimating a genomic breeding value for an integrative dominant effect according to claim 3, wherein:
the fixed effect is gender and batch.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810997830.4A CN109101786B (en) | 2018-08-29 | 2018-08-29 | Genome breeding value estimation method integrating dominant effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810997830.4A CN109101786B (en) | 2018-08-29 | 2018-08-29 | Genome breeding value estimation method integrating dominant effect |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109101786A CN109101786A (en) | 2018-12-28 |
| CN109101786B true CN109101786B (en) | 2021-02-09 |
Family
ID=64864195
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810997830.4A Active CN109101786B (en) | 2018-08-29 | 2018-08-29 | Genome breeding value estimation method integrating dominant effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109101786B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109727641B (en) * | 2019-01-22 | 2021-03-23 | 隆平农业发展股份有限公司 | Whole genome prediction method and device |
| CN110459265B (en) * | 2019-08-14 | 2022-07-05 | 中国农业科学院作物科学研究所 | Method for improving prediction accuracy of whole genome |
| CN110564832B (en) * | 2019-09-12 | 2023-06-23 | 广东省农业科学院动物科学研究所 | Genome breeding value estimation method based on high-throughput sequencing platform and application |
| CN111613271B (en) * | 2020-04-26 | 2023-02-14 | 西南大学 | Method for predicting dominant genetic effect of quantitative characters of livestock and poultry and application |
| CN112735519B (en) * | 2021-01-11 | 2022-08-30 | 华中农业大学 | Method, device and storage medium for positioning segregation character |
| CN112712852A (en) * | 2021-01-12 | 2021-04-27 | 深圳市华大农业应用研究院 | Method, device and system for estimating genome breeding value |
| CN112837750A (en) * | 2021-02-07 | 2021-05-25 | 深圳市华大农业应用研究院 | Animal and plant breeding value prediction method and device |
| CN113223606B (en) * | 2021-05-13 | 2022-05-27 | 浙江大学 | Genome selection method for genetic improvement of complex traits |
| CN113940313B (en) * | 2021-11-17 | 2022-10-28 | 北京市华都峪口禽业有限责任公司 | Meat poultry character selection method |
| CN115316266B (en) * | 2022-09-05 | 2023-09-08 | 新疆农垦科学院 | Crop population improvement and variety breeding method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1857061A (en) * | 2006-05-18 | 2006-11-08 | 西北农林科技大学 | Method for large scale cultivating common wheat near isogenic line |
| CN101260435A (en) * | 2008-04-18 | 2008-09-10 | 华中农业大学 | Molecule mark TIAF1 correlated with pig production character and application thereof |
| CN101854797A (en) * | 2007-06-22 | 2010-10-06 | 孟山都技术公司 | Method and composition at The Characters and expression selection locus |
| CN101970688A (en) * | 2007-09-12 | 2011-02-09 | 美国辉瑞有限公司 | Methods of using genetic markers and related epistatic interactions |
| 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 |
| CN103914632A (en) * | 2014-02-26 | 2014-07-09 | 中国农业大学 | Method for rapidly evaluating genome breeding value and application |
| CN106779076A (en) * | 2016-11-18 | 2017-05-31 | 栾图 | Breeding variety system and its algorithm based on biological information |
| CN106755441A (en) * | 2016-12-29 | 2017-05-31 | 华南农业大学 | A kind of method that gene group selection based on multiple characters carries out forest multiple characters pyramiding breeding |
| CN107105624A (en) * | 2014-10-27 | 2017-08-29 | 先锋国际良种公司 | improved molecular breeding method |
| CN107338321A (en) * | 2017-08-29 | 2017-11-10 | 集美大学 | A kind of method for determining optimal SNP quantity and its carrying out genome selection and use to large yellow croaker production performance by selection markers |
| CN107590364A (en) * | 2017-08-29 | 2018-01-16 | 集美大学 | A kind of quick bayes method of new estimation genomic breeding value |
-
2018
- 2018-08-29 CN CN201810997830.4A patent/CN109101786B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1857061A (en) * | 2006-05-18 | 2006-11-08 | 西北农林科技大学 | Method for large scale cultivating common wheat near isogenic line |
| CN101854797A (en) * | 2007-06-22 | 2010-10-06 | 孟山都技术公司 | Method and composition at The Characters and expression selection locus |
| CN101970688A (en) * | 2007-09-12 | 2011-02-09 | 美国辉瑞有限公司 | Methods of using genetic markers and related epistatic interactions |
| CN101260435A (en) * | 2008-04-18 | 2008-09-10 | 华中农业大学 | Molecule mark TIAF1 correlated with pig production character and application thereof |
| 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 |
| CN103914632A (en) * | 2014-02-26 | 2014-07-09 | 中国农业大学 | Method for rapidly evaluating genome breeding value and application |
| CN107105624A (en) * | 2014-10-27 | 2017-08-29 | 先锋国际良种公司 | improved molecular breeding method |
| CN106779076A (en) * | 2016-11-18 | 2017-05-31 | 栾图 | Breeding variety system and its algorithm based on biological information |
| CN106755441A (en) * | 2016-12-29 | 2017-05-31 | 华南农业大学 | A kind of method that gene group selection based on multiple characters carries out forest multiple characters pyramiding breeding |
| CN107338321A (en) * | 2017-08-29 | 2017-11-10 | 集美大学 | A kind of method for determining optimal SNP quantity and its carrying out genome selection and use to large yellow croaker production performance by selection markers |
| CN107590364A (en) * | 2017-08-29 | 2018-01-16 | 集美大学 | A kind of quick bayes method of new estimation genomic breeding value |
Non-Patent Citations (4)
| Title |
|---|
| Bayesian models with dominance effects for genomic evaluation of quantitative traits;ROBIN WELLMANN;《Genet. Res.》;20121231;论文全文 * |
| Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers;Guosheng Su;《PLOS ONE》;20120930;第7卷(第9期);论文全文 * |
| Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulate;Guoqing Tang;《ELSEVIER》;20141231;论文全文 * |
| 优质肉鸡基因组选择方案优化研究;刘天飞;《中国博士学位论文全文数据库 农业科技辑》;20161015(第10期);论文全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109101786A (en) | 2018-12-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109101786B (en) | Genome breeding value estimation method integrating dominant effect | |
| Knol et al. | Genomic selection in commercial pig breeding | |
| Aliloo et al. | Including nonadditive genetic effects in mating programs to maximize dairy farm profitability | |
| Wolc et al. | Persistence of accuracy of genomic estimated breeding values over generations in layer chickens | |
| Song et al. | Using different single-step strategies to improve the efficiency of genomic prediction on body measurement traits in pig | |
| CN107967409B (en) | Pig whole genome low-density SNP chip and manufacturing method and application thereof | |
| Lopes et al. | A genome-wide association study reveals dominance effects on number of teats in pigs | |
| Kelleher et al. | Inference of population structure of purebred dairy and beef cattle using high-density genotype data | |
| Schierenbeck et al. | Controlling inbreeding and maximizing genetic gain using semi-definite programming with pedigree-based and genomic relationships | |
| CN110176274A (en) | A method of boar blood lineage is divided based on full-length genome SNP information | |
| CN112266965A (en) | Genome selection method for improving genetic progress of residual feed intake of yellow-feathered broilers | |
| CN110870473B (en) | Method for selecting and retaining yellow-feathered broilers with high uniformity | |
| CN111370058B (en) | Method for tracing buffalo blood line source and carrying out genome matching based on whole genome SNP information | |
| Kaseja et al. | Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values | |
| CN110564832B (en) | Genome breeding value estimation method based on high-throughput sequencing platform and application | |
| Van der Werf | An overview of animal breeding programs | |
| CN113549699B (en) | Genome selection method for egg yield of white-feather broilers | |
| Marjanovic et al. | Relatedness between numerically small Dutch Red dairy cattle populations and possibilities for multibreed genomic prediction | |
| CN115261486A (en) | Huaxi cattle whole genome selective breeding chip and application thereof | |
| Calus et al. | Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset | |
| RU2728376C1 (en) | Method of selection of maternal and paternal groups of pigs during selection | |
| Norman et al. | 0324 Breed base representation in dairy animals of five breeds | |
| Sudrajad et al. | Genome-wide association study of birth weight in Bali cattle (Bos javanicus). | |
| Nwogwugwu et al. | Review on the genetic improvement and application of genomic selection in Korean Hanwoo cattle | |
| Topolski et al. | Inbreeding in a population of Polish Holstein-Friesian young bulls before and after genomic selection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20181228 Assignee: Luo Siquan Assignor: INSTITUTE OF ANIMAL SCIENCE, GUANGDONG ACADEMY OF AGRICULTURAL SCIENCES Contract record no.: X2022980017043 Denomination of invention: A Method for Estimating Genomic Breeding Values by Integrating Dominant Effects Granted publication date: 20210209 License type: Common License Record date: 20220929 |
|
| EE01 | Entry into force of recordation of patent licensing contract |