LU101241B1 - Breeding method for simplifying selection of high-yield a2a2 homozygous genotype dairy cows based on pedigree data - Google Patents
Breeding method for simplifying selection of high-yield a2a2 homozygous genotype dairy cows based on pedigree data Download PDFInfo
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Abstract
The present invention discloses a breeding method for simplifying selection of high-yield A2A2 homozygous genotype dairy cows based on pedigree data. Through carrying out the dairy herd improvement (DHI), the pedigree data and phenotype data of individual cows are collected, and the genetic evaluation is conducted combined with the results of paternal P casein genotype to obtain the breeding values of milk yield, milk fat yield, milk protein yield and milk protein rate. Finally, the high production indexes (HPIs) of individuals are calculated to obtain the high-yield A2A2 homozygous individuals. The present invention performs the herb screening by applying the daily DHI data, which greatly reduces the workload during the actual production process of the dairy farm; The present invention can screen the homozygous A2A2 individuals from the herd quickly, simply and accurately.
Description
BREEDING METHOD FOR SIMPLIFYING SELECTION OF HIGH-YIELD A2A2 HOMOZYGOUS GENOTYPE DAIRY COWS BASED ON PEDIGREE DATA
TECHNICAL FIELD
The present invention relates to the field of dairy cattle breeding, and specifically to a breeding method for simplifying selection of high-yield A2A2 homozygous genotype dairy cows based on pedigree data.
BACKGROUND TECHNOLOGY
The protein in milk, as the main material basis for the quality of milk, is mainly composed of casein and whey protein. Casein accounts for about 80% of milk protein, and is divided into four types: alpha si, alpha s2, beta, and kappa. Beta (ß) casein accounts for about 30% of the total protein. Studies have reported that ß-casein has 13 types, namely, Al, A2, A3, A4, B, C, D, E, F, Hl, H2,1, and G. ß-casein Al and A2 are the two most common types in the dairy cows. The difference is that the ß- casein gene undergoes a base change, causing that the amino acid at the corresponding position changes from proline to histidine. It is precisely the above changes in one amino acid that makes milk different in the digestion process. Certain enzymes of A1 milk can be specifically hydrolyzed at their histidine during digestion or fresh milk processing, thus forming a peptide fragment consisting of seven amino acids, called ß-Cytomorphin (BCM-7). BCM-7 may be absorbed into blood through penetrating the gastrointestinal wall, thereby affecting the digestive system and immune cells. It may be related to diseases such as diabetes type I, digestive diseases, and immune disorders; However, the amino acid in the ß casein A2 is changed into proline, which cannot be specifically hydrolyzed and does not form BCM-7. Consumers prefer A2 milk for food safety reasons. Therefore, the market value of A2 milk is very high. Breeding A2 homozygous dairy cows is a prerequisite for the production of A2 milk.
Molecular genetics is an effective means of breeding dairy cows. By testing different variants of ß-casein, A2 homozygous bulls and cows can be screened out to achieve the selection of A2 homozygous dairy cows. For example, CN105925717A, CN107287292A, CN105219839A, CN105018582A, and CN105018581A disclose test methods and corresponding kits for distinguishing different variants of ß-casein, and provide an effective means for breeding of A2 homozygous dairy cows. However, the molecular testing and screening one by one increases the amount of work in the preliminary stage, or further hybridization and breeding are required, and it is often impossible to take all factors into consideration for the high-yield performance of dairy cows. Thus, it is of important production and economic significance to invent a breeding method for simplifying selection of high-yield A2A2 dairy cows.
SUMMARY
In view of the problems existing in prior art, the present invention mainly aims to provide a breeding method for simplifying selection of high-yield A2A2 dairy cows and producing A2 raw milk with high market values. By screening the dairy cows by the present invention, the A2A2 genotype cows with high yield can be bred efficiently.
Specifically, the present invention adopts the technical solutions as follows to achieve the above objectives:
The present invention discloses a breeding method for simplifying selection of high-yield A2A2 homozygous genotype cows based on pedigree data. The method includes the following steps: (1) carrying out the dairy herd improvement (DHI), and collecting pedigree data and phenotype data of individual cows; (2) examining or obtaining the paternal ß casein genotype results in the pedigree, and determining the paternal genotypes as AlAl, A1A2 or A2A2; (3) classifying the individuals with performance records with paternal genotype as Al A2 or A2A2 by the calving season and the calving age; (4) carrying out genetic evaluation to obtain the breeding values of milk yield, milk fat yield, milk protein yield and milk protein rate, calculating the average value of each breeding value, and classify the individuals by the mean value; marking the individual with the breeding value above the average as 1, and the others as 0; (5) calculating the high production index (HPI) of individual
wherein, m is the breeding value classification of milk yield, fy is the breeding value classification of milk fat yield, py is the breeding value classification of milk protein yield, and pp is the breeding value classification of milk protein rate.
When the HPI is greater than 0.85, the dairy cow has a higher probability of being a high-yield A2A2 homozygous cow.
The dairy cows with a probability above 80% are considered as the potential
A2A2 homozygous cows. The cows with a probability above 85% and 90% are preferred, and that above 95% are more preferred.
In the preferred embodiment of the present invention, in step (1), pedigree data and phenotypic data of the individual cows are collected. The pedigree data include the individual's date of birth, paternal number, maternal number, and phenotypic data. The phenotypic data include information closely related to production performance, such as the calving date, parity, determined daily milk yield, milk fat percentage, milk protein rate, and somatic cell score.
Further, the milk fat yield and the milk protein yield are obtained through calculation. Milk fat yield = milk yield * milk fat percentage; milk protein yield = milk yield * milk protein rate.
In a specific embodiment, dairy cows of the present invention participate in the dairy herd improvement (DHI). The phenotypic values such as milk yield, milk fat percentage and milk protein rate, as well as the records of parity and the number of days of milk secretion, are obtained; The herd come from a large-scale dairy farm, and are fed by total mixed rations (TMR) feeding. Samples are taken in the morning, at noon, and in the evening, respectively, with a ratio of 4:3:3.
In preferred options in present invention in step (3), individuals with performance records with paternal genotype as A1A2 or A2A2 are classified by the calving season and the calving age. By the calving season, the individuals can be classified into three types: the dairy cows calving from November to February of the next year, the dairy cows calving from March to May and from September to October, and the dairy cows calving from June to August; By the calving age, the dairy cows can be classified into three types: the dairy cows calving in the 22th month through the 25th month, the dairy cows calving in the 26th month through the 29th month, and the dairy cows calving in the 30th month through the 33rd month. The records of 305 days of milk secretion from the fifth day are kept.
In the preferred embodiment of the present invention, in step (4), there are different kinds of software for calculating the breeding value through the genetic evaluation, such as DFREML, MTDFREML, VCE, ASREML, DMU, GBS, and Herdsman. Preferably, the present invention uses the DMU software for genetic evaluation to obtain breeding values of milk yield, milk fat yield, milk protein yield, and milk protein rate.
In a more preferred embodiment, the present invention performs data compilation and establishes a mathematical model as follows:
wherein, yj;k2 is the phenotypic record of the ith calving season, the jth calving age, the lâh test day, and the 7th individual; St is the fixed effect of the ith calving season effect; Age}- is the fixed effect of the yth calving age, and Tdk is the fixed effect of the kth test day;am„ is the random regression coefficient of the nth genetic effect of the mth individual; pmn is the random regression coefficient of the nth permanent environmental effect of the mth individual; zmnlis a Legendre multiplier calculated based on the different number of days of milk secretion corresponding to the nth genetic or permanent environmental effects of m individuals; naJnp is a Legendre polynomial for the genetic and permanent environmental effects on different test days; eiJklm is a random residual; The present invention uses the DMU software for genetic evaluation to obtain breeding values of milk yield, milk fat yield, milk protein yield, and milk protein rate.
The preferred embodiment of the present invention also includes step (6), in which the ß-casein genotype is identified for an individual dairy cow having an HPI greater than 0.85 to further determine the A2A2 homozygous genotype dairy cows.
The ß-casein genotype identification method can be carried out by methods or kits which have been reported in the prior art, such as CN105925717A, CN107287292A, CN105219839A, CN105018582A, CN105018581A, and CN105861671 A, which are also incorporated in this application.
The present invention achieves the following beneficial effects: (1) Through statistical analysis in the present invention, it is found that different genotypes of ß-casein have an effect on the breeding value of bulls' the breeding traits; the ß-casein gene locus has an extremely significant effect on the milk yield and milk protein yield (P<0.01) and a significant effect on the breeding value of the milk fat yield (P<0.05), but a non-significant effect on milk fat percentage (P>0.05), and the effect on milk protein rate was close to the significant level (P=0.0739). The A2A2 genotype has the highest least square means for milk yield, milk fat yield, and milk protein yield, and the lowest least square mean for milk protein rate. The screening method described in the present invention was obtained by fitting and regression analysis.
(2) The difference between the screening method (HPI method) described in this invention and the molecular detection screening results is not significant (P>0.05). The HPI method of the present invention achieves a determination accuracy of more than 95%, which is absolutely acceptable in production. The invention performs the herb screening by applying the daily DHI data, which greatly reduces the workload during the actual production process of the dairy farm. The present invention can screen the homozygous A2A2 individuals from the herd quickly, simply and accurately. The dairy farm administrators can milk the A2A2 homozygous individuals separately or expand the propagation of the A2A2 homozygous individuals to produce high-value A2 raw milk.
DETAILED DESCRIPTION
It is important to note that the following detailed description is illustrative and aims to provide a further description of the present invention. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by ordinary technical personnel in the field of technology to which present invention belongs.
It should be noted that the terminologies used herein is for the purpose of describing particular mode of execution, but not for limiting the exemplary implementation mode of the present invention. It is understood that the term "including" and / or "comprising" used in the specification indicates the presence of features, steps, operations and/or combinations thereof.
As described in the background of the present invention, the existing molecular breeding has a large workload in the preliminary stage, and further operations such as hybridization and breeding are required, and it is often impossible to take all factors into consideration for the high-yield performance of dairy cows. Thus, the present invention provides a breeding method for simplifying selection of high-yield A2A2 homozygous genotype dairy cows based on pedigree data. The method is described specifically as follows:
In the first step, dairy cows participate in the dairy herd improvement (DHI). The phenotypic values such as milk yield, milk fat percentage and milk protein rate, as well as the records of parity and the number of days of milk secretion, are obtained. The herd come from a large-scale dairy farm, and are fed by total mixed rations (TMR) feeding. Samples are taken in the morning, at noon, and in the evening, respectively, with a ratio of 4:3:3.
Pedigree data and phenotypic data of the individual cows are collected. The pedigree data include the individual's date of birth, paternal number, maternal number, and phenotypic data. The phenotypic data include information closely related to production performance, such as the calving date, parity, determined daily milk yield (kg), milk fat percentage (%), milk protein rate (%), and somatic cell score; the milk fat yield (kg) and the milk protein yield (kg) are obtained through calculation. Milk fat yield = milk yield * milk fat percentage; milk protein yield = milk yield * milk protein rate;
In the second step, the paternal ß casein genotype identification results in the phenotype data are checked, ß-casein Al and A2 are the two most common types in the dairy cows. The paternal genotypes are generally A1A1, A1A2 or A2A2. If the paternal genotype is A1A2 or A2A2, the dairy cow is possible to be a A2A2 homozygous dairy cow.
In the third step, the individuals with performance records with paternal genotype as A1A2 or A2A2 are classified by the calving season and the calving age. By the calving season, the individuals can be classified into three types: the dairy cows calving from November to February of the next year, the dairy cows calving from March to May and from September to October, and the dairy cows calving from June to August. By the calving age, the dairy cows can be classified into three types: the dairy cows calving in the 22th month through the 25th month, the dairy cows calving in the 26th month through the 29th month, and the dairy cows calving in the 30th month through the 33rd month. The records of 305 days of milk secretion from the fifth day are kept;
In the fourth step, the above information is integrated to establish a mathematical model as follows:
(1), wherein, is the phenotypic record of the zth calving season, the yth calving age, the Vh test day, and the Ith individual; is the fixed effect of the ith calving season effect; Agej is the fixed effect of the /h calving age, and Tdk is the fixed effect of the kth test day; amn is the random regression coefficient of the nth genetic effect of the mth individual; pmn is the random regression coefficient of the nth permanent environmental effect of the mth individual; zmnl is a Legendre multiplier calculated
based on the different number of days of milk secretion corresponding to the nth genetic or permanent environmental effects of m individuals; is a Legendre polynomial for the genetic and permanent environmental effects on different test days; ei}kim. is a random residual.
The present invention uses the DMU software for genetic evaluation to obtain breeding values of milk yield, milk fat yield, milk protein yield, and milk protein rate. The average value of each breeding value is calculated, and the individuals are classified by the mean value; the individual with the breeding value above the average is marked as 1, and the others as 0.
In the fifth step, the high production index (HPI) is calculated.
wherein, m is the breeding value classification of milk yield, fy is the breeding value classification of milk fat yield, py is the breeding value classification of milk protein yield, and pp is the breeding value classification of milk protein rate. When the HPI is greater than 0.85, the dairy cow can be determined as a high-yield A2A2 homozygous cow.
The design idea of the present invention and the source of the HPI formula:
Studies have found that the production capacity of dairy cows is not only determined by their milk yield, but also related to the production traits such as milk fat percentage, milk protein rate and somatic cell score. The molecular biology method for identifying the A2 genotype is not a technical problem anymore. The problem is the cost and the ease of operation of identification. The present invention firstly investigates the A2 allele and genotype frequency in the herd, analyzes the feasibility of rapidly breeding A2A2 homozygous dairy cows, and then designs a breeding method for simplifying selection of high-yield A2A2 dairy cows. In order to reduce costs, the method first screens out the high-yield A2A2 dairy cows by the daily laboratory technology, and then breeds the high-yield A2A2 herd by the breeding and hybridization technology. The samples are collected and dairy cows participated in the DHI determination in the early stage. The data are collected and sorted in the middle stage. The PROC LOGISTIC program of the SAS8.2 system is used for the regression analysis of the breeding values, and the independent variables are selected to establish the optimal regression equation, i.e. the A2A2 high production index (HPI) formula. The blood samples from some cows in the same dairy farm are simultaneously
collected to extract DNA. The genotyping identification is carried out by the method of the patent "testing the primer composition of dairy cow's ß-casein gene SNP and its use (CN201610260677.8)", and the SAS8.2 FREQ program is used to perform a suitability test analysis. The significance of difference of the two screening results is compared.
According to the Mendelian concept, the possibility of descendant genotypes can be analyzed if the parental genotypes are known. In the present invention (see Table 1): when the paternal genotype is Al Al, the progeny genotype can only be Al A1 or Al A2, and the probability of being the homozygous genotype A2A2 is zero.
When the paternal genotype is A1A2, and the maternal genotype is A1A1, the progeny genotype can only be A1A1 (50%), A1A2 (50%), that is no homozygous genotype A2A2 will happen; If the maternal genotype is A1A2, the progeny genotype can be A1A1 (25%), A1A2 (50%), and A2A2 (25%), which means the progeny has a probability of 25% to be the homozygous genotype A2A2; If the maternal genotype is A2A2, the progeny genotype can be A1A2 (50%) and A2A2 (50%), which means the progeny has a probability of 25% to be the homozygous genotype A2A2. In this case, if the maternal genotype is unknown, the probability of the progeny genotype being A2A2 is 25%.
When the paternal genotype is A2A2, and the maternal genotype is A1A1, the progeny genotype can only be A1A2 (100%), and it is impossible to be the homozygous genotype A2A2. If the maternal genotype is A1A2, the progeny genotype can be A1A2 (50%) and A2A2 (50%), which means the progeny has a probability of 50% to be the homozygous genotype A2A2. If the maternal genotype is A2A2, the progeny genotype can only be A2A2 (100%). In this case, if the maternal genotype is unknown, the probability of the progeny genotype being A2A2 is 50%.
Table 1 Proportion of Progeny Genotypes with Known Genotypes of Parents
Paternal genotype Paternal genotype Paternal Parental genotype and allele (A1A1) (A1A2) genotype(AlA2) _Al_Al A2 A2_
Paternal Al A1A1 A1A1 A1A2 A1A2 genotype Proportion of 50%AlA2 s F 100%AlAl 100%AlA2 (A1A1)_progeny_5O%A1A1_
Paternal Al A1A1 A1A1 A1A2 A1A2 genotype A2 A1A2 A1A2 A2A2 A2A2 (A1A2) 25%A1A1
Proportion of 50%AlA2 50%AlA2 F 50%AlA2 progeny 5O%A1A1 5O%A1A1 _PSJ_25%A2A2_
Paternal A2 A1A2 A1A2 A2A2 A2A2 genotype Proportion of 50%AlA2 ë P 100%AlA2 100%A2A2 (A2A2)_progeny_50%A2A2_
In summary, the inventors analyzed that it is possible to know an individual's genotype if the paternal genotype is known combined with the information of other aspects. However, at present, all breeding companies have carried out the genotype identification and labeling of bulls, which provides conditions for the implementation and verification of the technical scheme of the present invention.
Embodiment 1 Analysis of ß-Casein Different Genotypes and Breeding Values of A2 Homozygous Bull's Production Traits I. Data Collection
The genetic evaluation and pedigree information of more than 600,000 cows are downloaded from the Canadian CDN website. The ß-casein genotype was identified for all 3881 bulls that are currently in breeding. The trait breeding values such as bull numbers, pedigrees, genotypes and milk yield, milk fat yield, milk protein yield, milk fat percentage, milk protein rate and other traits directly related to production performance are recorded, and a database is set up. II. Data Statistics
The SAS software GLM process is used to analyze the ß-casein allele and genotype frequencies in the bull population and their effects on production performance. It is found that the frequencies of A1 and A2 alleles are 39.69% and 60.31%, and that of A1A1, A1A2 and A2A2 are 15.95%, 47.49% and 36.56%. The A2 allele frequency and the Al A2 genotype frequency are higher. See Table 2 for details.
Table 2 Detection of ß-casein Alleles and Genotype Frequencies in the Population
Genotype_Allele_ _A1A1 A1A2 A2A2 A1_A2_
Number of individuals (cows) 619 1843 1419
Frequency (%)_15.95_47.49_36.56_39.69_60.31_
Through statistical analysis, it is found that in the present invention, different genotypes of ß-casein have an effect on the breeding value of bulls' the breeding traits; the ß-casein gene locus has an extremely significant effect on the milk yield and milk protein yield (P<0.01), as well as the breeding value of the milk fat yield (P<0.05), but a non-significant effect on milk fat percentage (P>0.05), and the effect on milk protein rate was close to the significant level (P=0.0739). See Table 3 for details.
Table 3 Effect of Gene Locus on Breeding Values of 5 Traits
Character Freedom degree F value P value
Milk yield 2 9.69 <.0001
Milk fat yield 2 4.38 0.0126
Milk protein yield 2 9.76 <.0001
Butter-fat percentage 2 0.64 0.5263
Milk protein rate 2 2.61 0.0739
The breeding values of milk yields of different genotypes of bulls are further compared. The results show that the least squares mean of milk yield of A2A2 genotype is the highest, and significantly higher than A1A1 (P=0.0002) and A1A2 (P=0.0003). See Table 4 for details.
Table 4 Comparison of Different Genotypes on the Breeding Value of Milk Yield t Standard . . . , . . ..
Genotype Least squares mean Al Al A1A2 A2A2 _error_ A1A1 527.9790 46.9314 - -1.1451 -3.7680 A1A2 590.0939 27.1986 0.2522 - -3.6329 A2A2 739.9049_30.9968 0.0002 0.0003 _
Note: The three columns on the right show the t and p values, where the upper triangle is the t value, and the lower triangle is the P value.
The breeding values of milk fat yield of different genotypes of bulls are further compared. The results show that the least squares mean of milk fat yield of A2A2 genotype is the highest, and significantly higher than A1A1 (P=0.0200) and A1A2 (P=0.0086). See Table 5 for details.
Table 5 Comparison of Different Genotypes on the Breeding Value of Milk Fat Yield T Standard ,,,, ,,,-, , , „
Genotype Least squares mean AIAI AIA2 A2A2 error
AlAl 34.1405 l.8850 - -0.4149 -2.3270 A1A2 35.0445 1.0924 0.6782 - -2.6281 A2A2 39.3975_1.2450 0,0200 0.0086 _
Note: The three columns on the right show the t and p values, where the upper triangle is the t value, and the lower triangle is the P value.
The breeding values of milk protein yield of different genotypes of bulls are further compared. The results show that the least squares mean of milk protein yield of A2A2 genotype is the highest, and significantly higher than AlAl (P=0.002) and Al A2 (P=0.002). See Table 6 for details.
Table 6 Comparison of Different Genotypes on the Breeding Value of Milk Protein Yield . Standard .... ., .- . _ , _
Genotype Least squares mean AlAl A1A2 A2A2 _____error_
AlAl 25.0921 1.6632 - -1.0537 -3.7298 A1A2 27.1177 0.9639 0.2921 - -3.7009 A2A2 32.5264_1.0985 0.0002 0.0002 _
Note: The three columns on the right show the t and p values, where the upper triangle is the t value, and the lower triangle is the P value.
The breeding values of milk protein rate of different genotypes of bulls are further compared. The results show that the least squares mean of milk protein rate of A2A2 genotype is the lowest, with significant difference with A1A2 (P=0.0274) and A1A2 (P=0.1475). See Table 7 for details.
Table 7 Comparison of Different Genotypes on the Breeding Value of Milk Protein Rate____ T Standard ,,,, ., . _ ,-,,-,
Genotype Least squares mean AlAl A1A2 A2A2 ___'_error_
AlAl 0.1387 0.0105 - 1.1863 2.2063 A1A2 0.1244 0.0061 0.2356 - 1.4488 A2A2 0.1110_0.0069 0.0274 0,1475 _
Note: The three columns on the right show the t and p values, where the upper triangle is the t value, and the lower triangle is the P value.
Through the above analysis, the traits affecting milking performance are determined to be milk yield, milk fat yield, milk protein yield and milk protein rate, while the milk fat percentage is not significantly affected. The traits with significant effects are further analyzed. The A2A2 genotype has the highest least square mean for milk yield, milk fat yield, and milk protein yield, and the lowest least squares mean for milk protein rate. Then, the SAS software PROC LOGISTIC process is used to fit and obtain the regression equation:
wherein, m is the classification of the breeding values of milk yield; fy is the classification of the breeding values of milk fat yield; py is the classification of the breeding values of milk protein yield; pp is the classification of the breeding values of milk protein rate. The herd HPIs is calculated by the above formula. When the HPI is >0.85, the individual is determined to be a high-yield A2A2 homozygous genotype.
Embodiment 2 Comparison between HPI Method and PCR Molecular Detection Method
The inventors select a total of 151 cows with the paternal genotypes as A1A2/A2A2 among about 500 adult cows in a DHI-scaled cattle farm around Jinan, Shandong Province. Relevant data are collected, and milk samples are taken to measure the milk components. The cow's HPI is calculated using the method of the present invention, and the cows are screened according to the principle of HPI > 0.85. At the same time, blood samples are taken to extract DNA, and genotype is determined by referring to the patent CN201610260677.8 method to obtain individual genotypes.
Specific steps: (1) Milk samples are taken from the test herd (151 heads) are collected to record the calving date, sampling date, and milk yield of individual cow. The sampling method refers to the Technical Specification of Chinese Holstein Cattle Performance Test (NY/T 1450-2007). At the time of sampling, the milk yield per cow is recorded, and the milk is sampled three times a day. The samples taken in the morning, at noon
and in the evening are mixed at a ratio of 4:3:3, totaling about 40-45 ml. Before the sampling, 0.03 g of potassium dichromate is added as a preservative, and the milk sample is stored at room temperature. The milk component is measured within 24 hours. At the same time, blood samples are taken and DNA is extracted. (2) The DHI Laboratory of the Dairy Research Center of Shandong Academy of Agricultural Sciences uses the FOSS FC and FT+ instruments to measure the milk fat percentage (F%), milk protein rate (P%) and other components and calculate the milk fat yield and milk protein yield according to the method specified in the Technical Specification of Chinese Holstein Cattle Performance Test. (3) The individuals are classified by the calving season and the calving age according to the date of birth and the calving date. By the calving season, the individuals can be classified into three types: the dairy cows calving from November to February of the next year, the dairy cows calving from March to May and from September to October, and the dairy cows calving from June to August. By the calving age, the dairy cows can be classified into three types: the dairy cows calving in the 22th month through the 25th month, the dairy cows calving in the 26th month through the 29th month, and the dairy cows calving in the 30th month through the 33rd month. The records of 305 days of milk secretion from the fifth day are kept. (4) On the basis of model, the present invention uses the DMU software for genetic evaluation to obtain breeding values of milk yield, milk fat yield, milk protein yield, and milk protein rate. The average value of each breeding value is calculated, and the individuals are classified the mean value; the individual with the breeding value above the average is marked as 1, and the others as 0;
The model is shown as follows:
(1), wherein, yijkl is the phenotypic record of the ith calving season, the jth calving age, the kth test day, and the 1th individual; 5É is the fixed effect of the ith calving season effect; Age^ is the fixed effect of the jth calving age, and T dk is the fixed effect of the kth test day; amn is the random regression coefficient of the nth permanent environmental effect of the mth individual; pmn is the random regression coefficient of the nth permanent environmental effect of the mth individual; zmnl is a Legendre multiplier calculated based on the different number of days of milk secretion
corresponding to the nth genetic or permanent environmental effects of m individuals; nanp is a Legendre polynomial for the genetic and permanent environmental effects on different test days; is a random residual. (5) The high production index (HPI) of individual is calculated:
Wherein, m is the classification of the breeding values of milk yield; fy is the classification of the breeding values of milk fat yield; py is the classification of the breeding values of milk protein yield; pp is the classification of the breeding values of milk protein rate. When the HPI is greater than 0.85, the individual is determined to be a high-yield A2A2 homozygous individual. (6) Screening of dairy cows: The HPI values of the cows are further compared. If the HPI>0.85, the cow is an A2A2 homozygous high-yield individual, otherwise it is the non-A2A2 individual (A1A2 or A1A1 individuals). In the experiment, a total of 43 A2A2 individuals with an HPI > 0.85 are screened, accounting for 28.48%. (7) The genotype is determined by referring to the method referred in the patent CN201610260677.8 so as to obtain the individual genotype. In the experiment, 39A2A2 individuals are screened out by this method, accounting for 25.83%. See Table 8 for details.
Table 8 Comparison of the Results of the Two Methods Determination method
Number of samples and A2A2 A1A2 or A1A1 proportion_
Number of . TTT,T individuals 43 108 151
AHPI method <C°WS> .
Proport,on 2g4g% J2% ,θθ (/o)
Number of individuals 39 112 151 PCR z t method <C°WS) .
Proportron gJ% ,θθ _(%)_ (8) The SAS8.2 FREQ program is used to perform the suitability test analysis, and the differences between the two screening results are compared. The differences
between the HPI method and the method referred in the patent CN201610260677.8 are not significant (χ2=0.2678, P= 0.6048 , P>0.05).
Conclusion: The chi-square test is performed using the PROC FREQ program of SAS 8.2. The result differences between the AHPI method and the method referred in the patent CN201610260677.8 are determined to be insignificant (P>0.05). The HPI screening results completely cover the results of the PCR method (the PCR results are included in the 43 cows screened out by the HPI screening). The HPI method can achieve a determination accuracy of more than 95%. This is perfectly acceptable in production. The method of the present invention greatly reduces the workload in the actual production process of the cattle farm by applying daily DHI data for large group screening.
Although the present invention is based on the research results obtained from a cattle farm in Shandong Province, it can be widely used on a large scale, because Holstein cows have a more consistent genetic basis worldwide. The invention can be widely used on a large scale to achieve the screening effects described in the present invention.
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