CN110289048B - QTL related to milk production traits of buffalo as well as screening method and application thereof - Google Patents
QTL related to milk production traits of buffalo as well as screening method and application thereof Download PDFInfo
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Abstract
The invention discloses a QTL related to the milk production character of buffalo as well as a screening method and application thereof. The screening method of the QTL related to the milk production traits of the buffalo comprises the following specific steps: sample collection, genotyping, population genetic analysis, selection signal identification and Quantitative Trait Locus (QTL) identification related to the milk production traits of the buffalo. Screening 4 QTLs containing the highest significance SNPs, such as 11, 12, 16 and 19\\ u Block2, 1 and 5; the method is applied to the judgment and breeding of buffalos with excellent milk production characteristics by using H1H3 and H3H4 in 11-Block2 or H1H2, H2H2 in 12_Block 2, H2H2 in 16_Block 1, H1H4 in 16_Block 1 and H2H4 in 19 _Block5. The invention provides technical support for breeding the buffalo with excellent milk production performance, and is a novel method for auxiliary detection of the milk production character of the buffalo.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of biology, in particular to a Quantitative Trait Locus (QTL) related to milk production traits of buffalos and a screening method and application thereof.
[ background of the invention ]
The buffalo is an important characteristic dairy livestock species in southern areas of China, has excellent milk quality and rich nutrition, has a value of king in milk, and is deeply loved by consumers. However, lower milk yields have become a significant scientific problem affecting the development of the buffalo industry. As is well known, the milk production character is an important economic character of the milk buffalo, belongs to quantitative character and is controlled by a micro-effective polygene. Genetic analysis of the milk production traits of the buffalo has important scientific significance for improving the yield per unit level of the species.
With the rapid development of modern biotechnology, breeding theory and related technologies have been changed significantly, wherein molecular breeding has become the mainstream means of livestock breeding. Quantitative Trait Loci (QTL) represent the position of a Quantitative trait gene in the genome, and their location can be used as an aid in molecular breeding. The SNP marking technology is used for positioning the QTL region, the close relation between the QTL and the continuously changed quantitative trait phenotypes is dynamically tracked, the accuracy and predictability of quantitative trait excellent genotype selection can be improved, and the genetic progress is promoted. It is obvious that QTL has become a hotspot and difficulty in research in the field of molecular breeding. However, the QTL analysis related to the milk producing trait of buffalo was less studied than for other animal species. The only Liu et al, (2018) reported 2 QTL regions that affect the milk production traits of Italy Mediterranean buffalo (Liu JJ, et al, genome-wide association students to identify the same cow feeding practice in water buffalo. J Dairy Sci.2018,101 (1): 433-444.). Therefore, the number of QTL related to the milk-producing traits of milk buffalos screened at present is still small, which is far from meeting the requirements of molecular breeding.
The invention relates to a QTL related to the milk production character of a buffalo as well as a screening method and application thereof, increases the quantity of the QTL related to the milk production character of the buffalo, and can lay a foundation for the molecular breeding research of the milk buffalo in the future.
[ summary of the invention ]
The invention aims to: aiming at the existing problems, the QTL related to the milk production character of the buffalo and the screening method and the application thereof are provided, and the technical support is provided for the breeding of the milk buffalo breed with excellent characters by screening the QTL related to the milk production character of the buffalo and applying the QTL to the judgment and breeding of the buffalo with specific characters.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the screening method of the QTL related to the milk production traits of the buffalo comprises the following specific screening steps:
(1) Sample collection and genotyping: blood sample collection was performed on 122 buffalos, while genotype data from 74 buffalos reported in Colli et al (2018) was collected; blood sample sampling for 122 buffalos
Carrying out Genotyping on Buffalo individuals by using Buffalo Genotyping Array; genotyping data for all individuals, using plink softwareSNP quality control, where individuals meeting the following selection thresholds were to be excluded: the Minor Alloy Frequency (MAF) is less than or equal to 0.05, the SNP call rate is less than or equal to 95 percent, and the exponential call rate is less than or equal to 95 percent;
(2) Population genetic analysis:
1) And (3) analyzing a population structure: performing buffalo population principal component analysis by using EIGENSOFT software, constructing a phylogenetic evolutionary tree by using MEGA7 software, and performing population structure analysis by combining ADMIXTURE software to judge the layering condition among research populations;
2) Linkage disequilibrium analysis: according to the group structure analysis result in the last step, the research buffalo group can be divided into two groups; aiming at each group, performing linkage disequilibrium attenuation analysis by using PopLDdecay software, and exploring the genetic diversity and variation condition of buffalo population;
3) Continuous homozygous fragment (ROH) analysis: ROH analysis was performed between river-type and swamp-type buffalo group samples using detectRUNS R package, the main parameters of the analysis were as follows: windowSize =15, threshold =0.05, minsnp =20, and mindensity =1/50000.F ROH The calculation of the values is as follows:
(3) Identification of the selection signal: selecting signal analysis is carried out on two groups of high-yield milk and low-yield milk by hapFLK software, and an FDR (frequency domain ratio) threshold value of less than 0.01 is used as a standard for judging that the selecting signal between comparison groups is obvious;
(4) Identifying QTL related to the milk production traits of the buffalo:
a. collecting 489 genotype and phenotype data of Mediterranean buffalo;
b. visualizing linked molecular markers by HaploView software, and constructing individual haplotypes by utilizing PHASE software;
c. analyzing the relevance between the QTL and the milk production traits by using SAS9.4 software;
the model of the correlation analysis is as follows:
Y ijklm ＝μ+H l +Y i +S k +P j +B m +e ijklm ，
wherein, Y ijklm = observed value of trait,. Mu = mean, H l = cattle field stationary effect (4 cattle field), Y i = year fixation effect, sk = season fixation effect (season 2), P j = fetal fixed effect (8 fetal levels), B m = fixed effect of haplotype, e ijklm = residual error.
Further explaining, the milk production traits of the buffalo comprise peak milk yield, milk fat content, milk protein content, milk fat rate and milk protein rate;
performing haplotype analysis around a 0.5-Mb window containing the most significant SNP in the correlated haplotypes obtained by the step (4):
analysis of haplotype combinations:
in chromosome 11, block2, buffalos with doubled H1H3 showed higher milk, milk and milk protein yields compared to other doubled buffalos, whereas doubled H3H4 buffalos and H1H2 buffalos had the highest peak milk and milk or milk protein rates;
buffalo with doubled H2 in chromosome 12 Block2 was highly significantly associated with milk fat and milk protein rates;
at chromosome 16, block1, buffalos with doubled H2 had the highest levels significantly associated with the highest peak milk yield, milk fat and milk protein, while those with doubled H1H4 had the highest genetic effect on milk yield;
buffalos with doubled H2H4 in chromosome 19 Block5 had the highest levels significantly associated with higher peak milk yield, milk fat content and milk protein content.
Further, in the double type of chromosome 11 Block2, the base sequence of the H1 haplotype is ATTTTCGT; the base sequence of the H2 haplotype is GCCCCAGT; the base sequence of the H3 haplotype is ATCTTCAC; the base sequence of the H4 haplotype is GTCTCAGT;
in the double type of chromosome 12 Block2, the base sequence of the H2 haplotype is CC;
in the double type of chromosome 16 Block1, the base sequence of the H1 haplotype is CCGG; the base sequence of the H2 haplotype is CTAA; the base sequence of the H4 haplotype is TTAA;
in the double type of chromosome 19 Block5, the base sequence of the H2 haplotype is GGCGT; the base sequence of the H4 haplotype is ATTAG.
The method is applied, and double H1H3 in chromosome 11 Block2 is used for judging and breeding buffalo with high milk yield, high milk fat content and high milk protein content;
the double type H3H4 or H1H2 double type in chromosome 11 Block2 is applied to judge and breed the buffalo with the highest peak milk yield, high milk fat rate or milk protein rate;
double H2H2 in chromosome 12 Block2 is applied to judge and breed buffalo with high milk fat rate and milk protein rate;
the double H2H2 in the chromosome 16 Block1 is applied to judge and breed buffalo with the highest peak milk yield, high milk fat content and high milk protein content,
double H1H4 in chromosome 16 Block1 is used for judging and breeding buffalo with high milk yield;
the double H2H4 in chromosome 19 Block5 is used for judging and breeding buffalo with higher peak milk yield, high milk fat content and high milk protein content.
Further, the gene locus of the SNP with the highest significance in the chromosome 11 Block2 is AX-85056732;
the genetic locus of the SNP with the highest significance in the chromosome 12 Block2 is AX-85129983;
the genetic locus of the SNP with the highest significance in the chromosome 16 Block1 is AX-85069732;
the gene locus of the SNP with the highest significance in the chromosome 19 Block5 is AX-85108518
Compared with the prior art, the invention has the beneficial effects that:
1. providing a QTL related to the milk production character of the buffalo, and using the QTL as a method for judging and breeding the buffalo with the highest peak milk yield, high milk fat content, high milk protein content, high milk fat rate and/or high milk protein rate;
2. the screening method provides technical support for breeding the milk buffalo with the highest peak milk yield, high milk fat content, high milk protein content, high milk fat rate and/or high milk protein rate and excellent properties.
3. Opens up a new technology for breeding the milk buffalo with excellent characters, and is a new method for the auxiliary detection of the milk production characters of the buffalo.
[ description of the drawings ]
FIG. 1 is a diagram showing the analysis of the principal components of buffalo population;
FIG. 2-buffalo population evolutionary tree analysis diagram;
FIG. 3-structural analysis of buffalo population;
FIG. 4-buffalo linkage disequilibrium results plot;
FIG. 5 is a graph showing the results of analysis of buffalo continuous homozygous fragments;
FIG. 6 is a graph showing the results of buffalo selection signal analysis;
FIG. 7 is a block diagram of the haplotype of SNPs on chromosome 8 in the buffalo haplotype construction analysis;
FIG. 8 is a block diagram of the haplotype of the SNP on chromosome 11 in the buffalo haplotype construction analysis;
FIG. 9 is a block diagram of the haplotype of the SNP on chromosome 12 in the buffalo haplotype construction analysis;
FIG. 10 is a block diagram of the haplotype block for SNPs on chromosome 16 in the buffalo haplotype construction analysis;
FIG. 11 is a block diagram of the haplotype of the SNP on chromosome 19 in the buffalo haplotype construction analysis;
FIG. 12 is a block diagram of the haplotype block for SNPs on chromosome 24 in the buffalo haplotype construction analysis.
[ detailed description ] A
Example 1:
1. screening method
1. Sample collection and genotyping
1) 13 buffalo species, a total of 196 buffalo individuals were used in the study;
2) Blood samples were collected from 122 buffalo individuals, including 23 river buffalos, 20 hybrid buffalos, and 79 swamp buffalos (from five breeds), and used for subsequent genotyping studies. The remaining genotype data for 74 buffalos was reported from studies in Colli et al (2018);
3) Genotyping of 122-head buffalo
Buffalo Genotyping Array (Affymetrix, santa Clara, calif., USA);
4) And (3) data quality control: SNP quality control was done using plink software, where individuals meeting the following selection thresholds were excluded: the Minor Alloy Frequency (MAF) is less than or equal to 0.05, the SNP call rate is less than or equal to 95 percent, and the exponential call rate is less than or equal to 95 percent;
5) Finally, 35547SNPs pass quality control and can be used for subsequent analysis.
2. Population genetic analysis
1) And (3) analyzing a population structure: performing buffalo population principal component analysis by using EIGENSOFT software, constructing a phylogenetic evolutionary tree by using MEGA7 software, and performing research population structure analysis by combining ADMIXTURE software to judge the layering condition among research populations;
2) Linkage disequilibrium analysis: according to the group structure analysis result in the last step, the research buffalo group can be divided into two groups; aiming at each group, performing linkage disequilibrium attenuation analysis by using PopLDdecay software, and exploring the genetic diversity and variation condition of buffalo population;
3) Continuous homozygous fragment (ROH) analysis: ROH analysis was performed between river-type and swamp-type buffalo group samples using detectRUNS R package, the main parameters of the analysis were as follows: windowSize =15, threshold =0.05, minsnp =20, and mindensity =1/50000.F ROH The calculation of the values is as follows:
3. selection signal identification
Selection signal analysis was performed for both populations of high and low milk production using hapFLK software. The FDR <0.01 threshold serves as a criterion for determining that the selection signal between the comparison groups is significant.
4. QTL identification and application
1) Genotype and phenotype data were collected for 489 Mediterranean buffaloes.
2) The haploView software visualizes linked molecular markers, and the PHASE software is utilized to construct individual haplotypes.
3) The association between QTL and the milk production trait of buffalo was analyzed using SAS9.4 software. The model of the correlation analysis is as follows:
Y ijklm ＝μ+H l +Y i +S k +P j +B m +e ijklm ，
Y ijklm = observed value of trait,. Mu = mean, H l = cattle field stationary effect (4 cattle field), Y i = year fixation effect, sk = season fixation effect (season 2), P j = fetal-time fixation effect ((8 fetal-time levels), B m = fixed effect of haplotype, e ijklm = residual error.
2. Analysis results
1. Group structure analysis
As can be seen from FIG. 1, the study population can be divided into three categories of river type, swamp type and hybrid type, and the results are consistent with the results of the evolutionary tree analysis (FIG. 2). While the population structure analysis (figure 3) further shows that the study population of this trial can be divided into two groups, a river-type group and a swamp-type group, where the cross-type individuals have been incorporated into the river-type group. Therefore, the subsequent analysis was performed in both river type and swamp type
2. Linkage disequilibrium and continuity homozygous fragment analysis
In this study, the overall estimated LD was different between river and swamp populations (fig. 4). Compared to the river group (r 2= 0.61), swamp group buffalo possessed the largest average LD (r 2= 0.88). As expected, LD decreases as the physical distance between pairs of SNPs increases. The LD attenuation of the swamp group decreased significantly compared to the river group. When the LD decay decreased to 0.2, the decay distance was about 15kb for the swamp group and about 50kb for the river-type group.
To further evaluate the close relative reproduction between populations, we analyzed continuous homozygous segments between river and swamp populations. As shown in FIG. 5, buffalos in the swamp group had a higher F than the river-type group ROH Overall level.
3. Selection signal identification
As shown in fig. 6, the HapFLK analysis showed a total of 12 region selection signals in the comparative model. These regions are located on chromosome 1 (197939132-201746066 kb), chromosome 2 (84613461-91314070 kb), chromosome 8 (80317117-88124162 kb and 96080274-98588633 kb), chromosome 11 (25584886-27617098 kb), chromosome 12 (15979826-16215148 kb), chromosome 15 (7435513-13248436 kb), chromosome 16 (64367119-69995014 kb, 70024989-994535 kb and 80043731-24183865 kb), chromosome 19 (69537494-71631407 kb) and chromosome 24 (12608124-13746779 kb).
Table 1 summarizes annotation information for outlier SNPs in the comparative model.
The candidate selective scanning areas are 0.24Mb to 9.97Mb in chr12 and chr16, respectively.
The average length of the candidate regions is 4.24Mb. In addition, the largest number of SNPs (35) in the genomic region was found in chr15, and the length was 5.81Mb. Notably, a total of 12 significant SNPs were identified, corresponding to 8 candidate genes and 1 lncRNA (LOC 112579725) fragment.
TABLE 1 analysis of selection signal region identification analysis between river and swamp groups using hapFLK
4. QTL identification and application
To further identify QTLs associated with the milk production trait of buffalos, we performed haplotype analysis using a 0.5-Mb window around the top significant SNP, as shown in FIGS. 7-12. The results show that a total of 18 blocks were identified and located on chromosomes 8, 11, 12, 16, 19 and 24, respectively. For them, a total of 4 blocks (11, block2, 12, 16, 1 and 19, block5) were found to have the highest significant SNPs. The maximum length of the module is 19\ u Block5, 264kb.
TABLE 2 haplotype Association analysis of the milk production traits of buffalos
Remarking: chr _ block = buffalo chromosome and haplotype block region;
2 PM = maximum peak milk yield; MY =270-d total milk yield; FY =270-d amount of cream; FP =270-d creaminess rate; PY =270-d milk protein amount; PP =270-d milk protein rate.
* P <0.05; * P <0.01; * P <0.001: significant association.
From table 2, haplotype association analysis showed that a total of 12 blocks were identified as being associated with milk producing traits. Three blocks (11, u Block2, 19, u Block3 and 19, u Block 4) were found to have significant genetic effects on all cow milk traits (P < 0.05). The table also shows that 8, 12, and 19\\ u Block2, and 19_Block1 correlate with milk protein and milk fat rates in buffalo (P < 0.05). Furthermore, we found that a total of 6 blocks (8, 12, 16, 19 and 24) correlated with the milk, milk fat or milk protein production of buffalo (P < 0.05).
For the blocks containing the most significant SNPs, we further performed haplotype combination analysis in this study, with the results as follows:
TABLE 3 analysis of the genetic Effect of haplotype combinations on 4 QTL regions of the milk production traits of buffalos
From the Bonferroni analysis shown in Table 3, it was shown that buffalo with double H2H2 in 12_Block2 was significantly associated with milk fat and milk protein rates (P < 0.05).
In 11_Block2, individuals with double type H1H3 showed higher milk, milk and milk protein yields compared to the other double types, while double type H3H4 and H1H2 had the highest peak milk, milk fat or milk protein rate (P < 0.05).
For 16_Block1, buffalos with doubled H2H2 had the highest levels significantly associated with PM (highest peak milk yield), FY (milk fat content) and PY (milk protein content), while doubled H1H4 had the highest genetic effect on MY (total milk yield) (P < 0.05).
In addition, these buffalos with doubled H2H4 form in 19_block5 showed higher peak milk yield, milk fat and milk protein yield (P < 0.05) compared to the other doubled forms.
In conclusion, the applicant firstly discovers through research that:
in chromosome 11, block2, buffalos with doubled H1H3 showed higher milk, milk fat and milk protein yields compared to other doubled buffalos, whereas doubled H3H4 buffalos and H1H2 buffalos had the highest peak milk, milk fat or milk protein yield;
buffalo with doubled H2 in chromosome 12 Block2 was highly significantly associated with milk fat and milk protein rates;
at chromosome 16, block1, buffalos with doubled H2 had the highest levels significantly associated with the highest peak milk yield, milk fat and milk protein, while those with doubled H1H4 had the highest genetic effect on milk yield;
buffalos with doubled H2H4 in chromosome 19 Block5 had the highest levels significantly associated with higher peak milk yield, milk fat content and milk protein content.
Therefore, buffalo with high milk yield, high milk fat content and high milk protein content can be judged and bred through double H1H3 in chromosome 11 Block 2;
the buffalo with the highest peak milk yield, high milk fat rate or milk protein rate in the milk can be judged and bred through the double type H3H4 or H1H2 in the chromosome 11 Block 2;
the buffalo with high milk fat rate and milk protein rate can be judged and bred through double H2H2 in chromosome 12 Block 2;
can judge and breed the buffalo with the highest peak milk yield, high milk fat content and high milk protein content by the double H2H2 in the chromosome 16 Block1,
the buffalo with high milk yield can be judged and bred through double H1H4 in chromosome 16 Block 1;
the buffalo with higher peak milk yield, high milk fat content and high protein content can be judged and bred by the double H2H4 in the chromosome 19 Block 5.
The above examples are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.
Claims (5)
1. The screening method of the QTL related to the milk production traits of the buffalo is characterized by comprising the following specific screening steps:
(1) Sample collection and genotyping: blood sample collection was performed on 122 buffalos, while genotype data from 74 buffalos reported in Colli et al 2018 was collected; carrying out Genotyping on Buffalo individuals by adopting Buffalo Genotyping Array aiming at blood sample samples of 122 buffalos; SNP quality control was performed using plink software for genotype data of all individuals, where individuals meeting the following selection thresholds were excluded: the minor alloy frequency is less than or equal to 0.05, the SNP call rate is less than or equal to 95 percent, and the exponential call rate is less than or equal to 95 percent;
(2) Population genetic analysis:
1) And (3) analyzing a population structure: performing buffalo population principal component analysis by using EIGENSOFT software, constructing a phylogenetic evolutionary tree by using MEGA7 software, and performing population structure analysis by combining ADMIXTURE software to judge the layering condition among research populations;
2) Linkage disequilibrium analysis: according to the group structure analysis result in the last step, the research buffalo group can be divided into two groups; aiming at each group, performing linkage disequilibrium attenuation analysis by using PopLDdecay software, and exploring the genetic diversity and variation condition of buffalo groups;
3) Analysis of continuous homozygous fragments: ROH analysis was performed on samples from the river and swamp buffalo groups using the detectrns R package, the main parameters of the analysis being as follows: windows size =15, threshold =0.05, minstp =20, and mindensity =1/50000; f ROH The calculation of the values is as follows:
(3) Identification of the selection signal: selecting signal analysis is carried out on two groups of high-yield milk and low-yield milk by hapFLK software, and an FDR (frequency domain ratio) threshold value of less than 0.01 is used as a standard for judging that the selecting signal between comparison groups is obvious;
(4) QTL identification related to the milk production traits of buffalos:
a. collecting 489 genotype and phenotype data of Mediterranean buffalo;
b. visualizing linked molecular markers by HaploView software, and constructing individual haplotypes by utilizing the PHASE software;
c. analyzing the relevance between the QTL and the milk production traits of the buffalo by using SAS9.4 software;
the model of the correlation analysis is as follows:
Y ijklm ＝μ+H l +Y i +S k +P j +B m +e ijklm ，
wherein, Y ijklm = observed value of trait,. Mu = mean, H l Cow farm fixing effect of =4 cow farm, Y i = year fixation effect, S k Seasonal fixed effect of =2 seasons, P j Fix effect of fetal count of =8 fetal count levels, B m = fixing effect of haplotype, e ijklm = residual error.
2. The method of claim 1, wherein the dairy milk production trait comprises peak milk yield, milk fat content, milk protein content, milk fat rate, and milk protein rate;
performing haplotype analysis around a 0.5-Mb window containing the most significant SNP in the correlated haplotypes obtained by the step (4):
chromosome 8 Block2, chromosome 12 Block2 and chromosome 19 Block1 are significantly related to milk protein rate and milk fat rate in buffalo;
chromosome 8 Block3, chromosome 12 Block1, chromosome 16 Block1, chromosome 19 Block2, chromosome 19 Block5 and chromosome 24 Block1 are significantly related to the milk yield, milk fat content or milk protein content of buffalo;
analysis of haplotype combinations:
in chromosome 11, block2, buffalos with doubled H1H3 showed higher milk, milk and milk protein yields compared to other doubled buffalos, whereas doubled H3H4 buffalos and H1H2 buffalos had the highest peak milk and milk or milk protein rates;
buffalo with doubling type H2 in chromosome 12 Block2 is highly significantly associated with milk fat and milk egg rate;
at chromosome 16, block1, buffalos with doubled H2 had the highest levels significantly associated with the highest peak milk yield, milk fat and milk protein, while those with doubled H1H4 had the highest genetic effect on milk yield;
buffalos with doubled H2H4 in chromosome 19 Block5 had the highest levels significantly associated with higher peak milk yield, milk fat content and milk protein content.
3. The method of claim 2,
in the double type of chromosome 11 Block2, the base sequence of the H1 haplotype is ATTTTCGT; the base sequence of the H2 haplotype is GCCCCAGT; the base sequence of the H3 haplotype is ATCTTCAC; the base sequence of the H4 haplotype is GTCTCAGT;
in the double type of chromosome 12 Block2, the base sequence of the H2 haplotype is CC;
in the double type of chromosome 16 Block1, the base sequence of the H1 haplotype is CCGG; the base sequence of the H2 haplotype is CTAA; the base sequence of the H4 haplotype is TTAA;
in the double type of chromosome 19 Block5, the base sequence of the H2 haplotype is GGCGT; the base sequence of the H4 haplotype is ATTAG.
4. The method for screening the QTL related to the milk production traits of the buffalo as claimed in claim 2, is characterized in that double H1H3 in chromosome 11 Block2 is used for judging and breeding the buffalo with high milk yield, high milk fat content and high milk protein content;
double type H3H4 or H1H2 double type in chromosome 11 Block2 is applied to judge and breed buffalo with highest peak milk yield, high milk fat rate or milk protein rate;
double H2H2 in chromosome 12 Block2 is applied to judge and breed buffalo with high milk fat rate and milk protein rate;
the double H2H2 in the chromosome 16 Block1 is applied to judge and breed buffalo with the highest peak milk yield, high milk fat content and high milk protein content,
double H1H4 in chromosome 16 Block1 is used for judging and breeding buffalo with high milk yield;
the double H2H4 in chromosome 19 Block5 is used for judging and breeding buffalo with higher peak milk yield, high milk fat content and high milk protein content.
5. The method of claim 2, wherein the genetic locus for the most significant SNP in chromosome 11, block2, is AX-85056732;
the genetic locus of the SNP with the highest significance in the chromosome 12 Block2 is AX-85129983;
the genetic locus of the SNP with the highest significance in the chromosome 16 Block1 is AX-85069732;
the gene locus of the most significant SNP in chromosome 19 Block5 is AX-85108518.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104232632A (en) * | 2014-09-16 | 2014-12-24 | 中国农业大学 | Application of molecular marker in detection of milk production character of milk cow |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104232632A (en) * | 2014-09-16 | 2014-12-24 | 中国农业大学 | Application of molecular marker in detection of milk production character of milk cow |
| CN108157293A (en) * | 2018-02-07 | 2018-06-15 | 山东省农业科学院奶牛研究中心 | A kind of breeding method for simplifying selection high productivity energy A2A2 homozygous genotype milk cows based on pedigree information |
Non-Patent Citations (1)
| Title |
|---|
| Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo;J. J. Liu等;《American Dairy Science Association》;20180131;第101卷(第1期);第433-444页 * |
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