Biomarker and method for evaluating and predicting production life of dairy cow

Abstract

The invention provides a biomarker and a method for evaluating and predicting the production life of a dairy cow. The biomarker is anti-mullerian hormone AMH. The invention further provides a method for evaluating and predicting the production life of the dairy cow, and judgment is carried out by detecting the AMH concentration in serum. A set of unified and effective dairy cow production life evaluation technology is established by collecting standardized samples, establishing a phenotype database and using a unified cell factor level determination method and a correlation analysis method. By measuring the AMH level in serum and analyzing the relationship between the AMH level and the fetal number / age of the dairy cow, the production life of the dairy cow can be evaluated and predicted at the early age stage of the dairy cow, so that the dairy cow with the long production life can be screened and cultivated in the early stage, and the method has great significance in improving the available fetal number of the dairy cow, further improving the production benefit of a farmer and reducing the breeding cost. The method is of great significance in accelerating improved variety propagation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of dairy cattle breeding, and particularly relates to a biomarker for evaluating and predicting the production life of dairy cows and a method thereof. BACKGROUND

[0002] The existing evaluation method for the reproductive life, i.e. the production life, of dairy cows lacks a reliable biomarker for early and accurate evaluation of the reproductive potential and life of dairy cows, and mostly relies on statistical analysis of reproductive data after traditional selection and elimination. These methods may have subjectivity and hysteresis, and it is difficult to accurately predict the production life of dairy cows in a timely manner.

[0003] Fine regulation between oocytes and their closely connected follicular cells promotes oocyte maturation, fertilization and embryonic development. During follicular development, in addition to endocrine regulation by the hypothalamic-pituitary-gonadal axis, paracrine or autocrine factors from oocytes maintain the homeostasis of the microenvironment within developing follicles, regulating oocyte maturation and granulosa cell proliferation. It has been found that these key regulatory factors are mainly growth differentiation factor-9 (GDF-9) and bone morphogenetic protein-15 (BMP-15) in the transforming growth factor beta (TGFβ) family. GDF-9 / BMP-15 is mainly expressed in oocytes and is structurally and functionally similar, and is an essential cytokine for follicular development.

[0004] Anti-Müllerian hormone (AMH) belongs to the TGFβ family and is a dimeric glycoprotein hormone formed by the connection of two monomers by disulfide bonds, with a size of 140 kDa, mainly expressed in gonads, and plays an important role in the growth and development of follicles. It is only produced by healthy growing follicles, and the AMH concentration changes little in the estrus cycle of cows, and is closely related to the development of bovine oocytes or follicles. Studies have shown that culturing bovine oocytes in an in vitro culture medium containing 50-150 ng / mL AMH can improve the maturation quality of oocytes and increase the developmental potential of oocytes.

[0005] The existing evaluation method for the reproductive life, i.e. the production life, of dairy cows lacks a biomarker with high specificity and accuracy, and for the long production life trait, it is urgent to find a more direct fine selection index and establish a selection technique to improve the selection accuracy. SUMMARY

[0006] To solve the above technical problems, the present application provides a new use of the biomarker AMH for evaluating the reproductive potential and production life of dairy cows, so as to realize early prediction and screening of long production life dairy cows, and solve the problem that the prior art cannot early and accurately evaluate and predict the reproductive life, i.e. the production life, of dairy cows, thereby causing economic losses.

[0007] To achieve the above object, the present application adopts the following technical solutions:

[0008] A biomarker for evaluating and predicting the production life of a dairy cow, the biomarker being anti-Mullerian hormone (AMH).

[0009] A method for evaluating and predicting the production life of a dairy cow, comprising: collecting a serum sample of a cow, detecting the content level of AMH in the serum, and recording the production parity or age of the cow when the serum is collected, determining whether to keep the cow for further production or to cull the cow based on the content level of AMH.

[0010] Optionally, the detection of the content level of AMH in the serum can use an ELISA detection kit.

[0011] The method as described above, preferably, when the content level of AMH in the serum of the cow is ≥ 1400 pg / mL, and the cow is a reserve cow before first mating or a young cow from conception to parturition after first mating, it indicates that the reproductive potential is high, and the cow can be selected as a high-reproduction group with a final parity ≥ 3.

[0012] Further, when the content level of AMH in the serum of the cow is ≥ 1481.7 pg / mL, and the cow is a reserve cow before first mating or a young cow from conception to parturition after first mating, it indicates that the reproductive potential is high, and the cow can be selected as a high-reproduction group with a final parity ≥ 3.

[0013] The method as described above, preferably, when the content level of AMH in the serum of the cow is < 970 pg / mL, and the production parity of the cow is equal to or less than two, it indicates that the reproductive potential is low, and the cow should be culled.

[0014] Further, when the content level of AMH in the serum of the cow is < 843.09 pg / mL, and the production parity of the cow is equal to or less than two, it indicates that the reproductive potential is low, and the cow should be culled.

[0015] The method as described above, preferably, when the content level of AMH in the serum sample of the cow is between 970-1400 pg / mL, and the production parity of the cow is equal to or less than two, it has a certain reproductive potential, and can be selected according to actual production needs. Further, when the content level of AMH in the serum sample of the cow is between 843.09-1481.7 pg / mL, and the production parity of the cow is equal to or less than two, it has a certain reproductive potential

[0016] Application of the biomarker AMH in evaluating and predicting the production life of a dairy cow.

[0017] The application as described above, preferably when the AMH content in the serum sample of the cow is ≥ 1400 pg / mL, and is a replacement heifer and young cow, the cow is left to continue production.

[0018] Further, when the AMH content in the serum sample of the cow is measured to be ≥ 1481.7 pg / mL, and is a replacement heifer and young cow, the cow is left to continue production.

[0019] The application as described above, preferably when the AMH content in the serum sample of the cow is < 970 pg / mL, and the production parity of the dairy cow is equal to or less than two, the cow is culled.

[0020] Further, when the AMH content in the serum sample of the cow is < 843.09 pg / mL, and the production parity of the cow is equal to or less than two, the cow is not used for production and should be culled.

[0021] The application as described above, preferably when the AMH content in the serum sample of the cow is between 970-1400 pg / mL, and the production parity of the cow is equal to or less than two, has a certain reproductive potential, and can be selected and culled according to actual production needs.

[0022] Further, when the AMH content in the serum sample of the cow is between 843.09-1481.7 pg / mL, and the production parity of the cow is equal to or less than two, has a certain reproductive potential, and can be selected and culled according to actual production needs.

[0023] The application of the biomarker AMH in the preparation of a detection reagent for evaluating and predicting the production life of a dairy cow.

[0024] The application as described above, preferably when the AMH content in the serum sample of the cow is ≥ 1400 pg / mL, and is a replacement heifer and young cow, the cow is left to continue production.

[0025] The application as described above, preferably when the AMH content in the serum sample of the cow is < 970 pg / mL, and the production parity of the dairy cow is equal to or less than two, the cow is culled.

[0026] Further, when the AMH content in the serum sample of the cow is ≥ 1481.7 pg / mL, and is a replacement heifer and young cow, the cow is left to continue production;

[0027] When the AMH content in the serum sample of the cow is < 843.09 pg / mL, and the production parity of the cow is equal to or less than two, the cow is culled.

[0028] The application as described above, preferably when the AMH content in the serum sample of the cow is between 970-1400 pg / mL, and the production parity of the cow is equal to or less than two, has certain reproductive potential, and can be selected and removed according to actual production needs.

[0029] Further, preferably when the AMH content in the serum sample of the cow is between 843.09-1481.7 pg / mL, and the production parity of the cow is equal to or less than two, has certain reproductive potential, and can be selected and removed according to actual production needs.

[0030] The present application has the following beneficial effects:

[0031] The present application provides a milk cow production life prediction method based on AMH, which has significant advantages and effects compared with the traditional milk cow reproductive life, i.e., production life evaluation method.

[0032] The present application can predict the production life of the milk cow at an early age by determining the AMH level in the serum and analyzing the relationship between the AMH level and the parity / age of the milk cow, thereby realizing early screening and breeding of milk cows with long production life, improving the available parity of the cow, and improving the production efficiency of the breeder, reducing the breeding cost, and accelerating the expansion of good breeds.

[0033] The milk cow production life prediction method provided by the present application can accurately evaluate the production life of the milk cow, timely remove the cows without production capacity, reduce the blindness in the breeding process, and reduce the breeding cost. Through early screening and breeding of milk cows with long production life, the expansion speed of good breeds can be accelerated, and the overall efficiency of the milk cow breeding industry can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 Standard curve for ELISA determination of GDF-9 level in serum.

[0035] Figure 2 Relationship between serum GDF-9 concentration and parity of the milk cow.

[0036] Figure 3 Standard curve for ELISA determination of BMP-15 level in serum.

[0037] Figure 4 Relationship between serum BMP-15 concentration and parity of the milk cow.

[0038] Figure 5 BMP-15 concentration in the Holstein cow population for breeding / removal.

[0039] Figure 6 Standard curve for ELISA determination of AMH level in serum.

[0040] Figure 7 For serum AMH concentration and the relationship between the number of pregnancies in dairy cows.

[0041] Figure 8 For AMH concentration in the Holstein dairy cow population in the breeding / elimination.

[0042] Figure 9 For AMH concentration in the Holstein dairy cow population of different elimination pregnancy.

[0043] Figure 10 For AMH concentration in the Holstein dairy cow population of different calving intervals.

[0044] Figure 11 For AMH concentration in the Holstein dairy cow population of different first-service age.

[0045] Figure 12 For the prediction response curve based on the AMH concentration in the breeding cow population for the probability of ≥3 pregnancies.

[0046] Figure 13 For the ROC curve of the prediction model based on the number of pregnancies in the breeding cow population (AUC quantifies the ability of the model to distinguish).

[0047] Figure 14 For the prediction response curve based on the AMH concentration in the elimination cow population for the probability of ≥3 pregnancies.

[0048] Figure 15 For the ROC curve of the prediction model based on the number of pregnancies in the elimination cow population (AUC quantifies the ability of the model to distinguish). DETAILED DESCRIPTION

[0049] The present application provides the use of a biomarker AMH for evaluating and predicting the production life of dairy cows. The method of the present application is obtained by the following steps:

[0050] First, collect blood as a serum sample source at any time point during the estrus cycle of healthy Holstein cows without a history of reproductive diseases, covering cows of different pregnancies / different ages, and collect the basic data and related reproductive data of the corresponding cow individuals, establish a phenotype database; then use ELISA technology to determine the levels of multiple cytokines in the collected serum samples multiple times, optimize the ELISA experimental conditions such as temperature and time, to improve the accuracy and stability of the determination, take the average value as the final result, to reduce experimental error; finally, correlate the serum hormone levels of dairy cows with the phenotype data of dairy cow individuals, and screen out hormone level indicators significantly related to the production life of dairy cows through significance test, for evaluating and predicting the production life of dairy cows.

[0051] Among them, the individual basic data of the dairy cow includes birth date, age, parity, calving age, body condition score, etc.; the dairy cow reproduction data includes first service age, parity, open days, service number, breeding status, lactation days, etc.; the cytokine level determination includes growth differentiation factor 9 (GDF-9), bone morphogenetic protein 15 (BMP-15), AMH hormone, etc.

[0052] In the research, 1, an optimized ELISA determination method is adopted:

[0053] By optimizing the ELISA determination method, the ELISA technology with a proper measurement range is selected to determine the hormone level of the serum sample, so that the sensitivity and accuracy of detection are improved. This includes screening suitable antibodies, optimizing incubation time and temperature, improving the washing step, etc., so as to ensure that the standard curve R 2 ≥0.99 to ensure the stability and reliability of the determination results.

[0054] 2, the serum sample processing conditions are optimized:

[0055] Nearly 1000 Holstein dairy cows are collected for biological samples, and in the serum sample processing process, the sample processing method, centrifugation conditions and storage conditions are optimized to reduce the error and loss in the sample processing process and improve the accuracy of the determination results.

[0056] 3, the serum hormone determination is combined with the phenotype data such as the parity of the dairy cow:

[0057] The application not only determines the concentrations of cytokines or hormones such as GDF-9, BMP-15 and AMH in serum, establishes a phenotype database containing about 40,000 data in total, but also performs correlation analysis on these determination results and the parity data of the dairy cow and performs appropriate cluster analysis. This helps to reveal the change rule of the serum hormone level of the dairy cow with different parities and provides a new perspective for evaluating the reproductive performance of the dairy cow.

[0058] 4, the construction of the prediction model is optimized:

[0059] The AMH concentration data collected are subjected to outlier processing, and the outliers are identified and removed by the box plot method, so as to ensure the data quality; at the same time, the parity information is converted into a binary classification label with 3 parities as the dividing point. Stratified sampling is adopted to ensure that the proportion of different final parity categories in the training set and the test set is basically consistent with that of the original data set, and a logistic regression model is applied to construct the prediction model, so as to improve the accuracy of the model.

[0060] The following examples are intended to further illustrate the application but not to limit the application. Any modification and replacement within the spirit and principle of the application shall fall into the scope of the application.

[0061] If not specifically indicated, the technical means used in the examples are the conventional means well known to those skilled in the art. Unless otherwise specified, the reagents used in the method are of analytical purity or above.

[0062] Example 1

[0063] Collection of bovine serum samples:

[0064] The bovine was subjected to tail vein blood collection using dry vacuum tubes containing no pyrogen and endotoxin and no additives. Each blood collection tube collected 5 mL of fresh blood. After completion of collection, the blood in the collection tube was allowed to stand at room temperature for 2 h (to prevent sun exposure) until the blood coagulated. The blood was centrifuged at 3000 r / min for 10 min, and the upper clear supernatant, i.e., serum, was aspirated and subpackaged in sterile 2 mL EP tubes and stored in a -80°C refrigerator.

[0065] ELISA determination:

[0066] The frozen bovine serum samples were taken out of the refrigerator and allowed to stand at room temperature for 2 h, and then slowly shaken to completely dissolve and mix uniformly. At the same time, all components of the ELISA kit (available from Shanghai Enzyme-linked Biotechnology Co., Ltd.) were allowed to stand at room temperature for 2 h for rewarming.

[0067] 1) Before use, all reagents were thoroughly mixed. Do not generate a large amount of foam in the liquid to avoid adding a large amount of air bubbles during sample addition, which will cause errors in sample addition. Dilute the concentrated washing solution with distilled water at a ratio of 1:20, i.e., 1 part of concentrated washing solution and 19 parts of distilled water.

[0068] 2) Set up standard wells, blank wells and sample wells. Add 50 μL of different concentrations of standard discs to the standard wells, do not add to the blank wells, and add 50 μL of the sample to be tested to the sample wells. Set up 3 replicate wells for the standard wells, blank wells and sample wells, and take the average of the replicate wells for subsequent data analysis.

[0069] 3) Immediately add 100 μL of horseradish peroxidase (HRP)-labeled detection antibody to the wells except the blank wells. Cover the plate, gently shake to mix, and incubate at 37°C for 60 min in the dark.

[0070] 4) Remove the cover, shake off the liquid in the wells, and pat dry on a blotting paper. Add enough washing solution (350 μL) to each well, stand for 1 min, shake off the washing solution, and pat dry with a blotting paper. Repeat this operation 5 times.

[0071] 5) Mix the substrates A and B at 1:1 volume, add 100 μL of the substrate mixture to all wells, cover the reaction plate with sealing film, and incubate at 37°C for 15 min in the dark.

[0072] 6) Take out the enzyme-labeled plate, quickly add 50 μL of the termination solution, and measure the result within 10 min after adding the termination solution.

[0073] 7) Use the enzyme-labeled instrument to measure the absorbance (OD value) of each well at 450 nm.

[0074] Experiment I, determination of the serum GDF-9 cytokine level of dairy cows

[0075] According to the above sample collection method and ELISA determination method, the serum GDF-9 cytokine level was determined, and a total of 1094 serum samples were determined. After optimization of the ELISA method, the detection range of the GDF-9 cytokine ELISA kit (Shanghai Enzyme Link, item number ml966358V) was 2.5 pg / mL-80 pg / mL, the sensitivity was less than 0.1 pg / mL, the intra-plate coefficient of variation was less than 10%, and the inter-plate coefficient of variation was less than 15%. After subtracting the blank well reading from the well reading of the standard in the ELISA detection kit, the corresponding standard concentration (80, 40, 20, 10, 5, 2.5 pg / mL, respectively) and the corresponding OD value were used to establish a standard curve, and the standard curve was as follows Figure 1 , the X axis is the concentration (pg / mL), the Y axis is the OD value, and the R 2 is greater than 0.99, with excellent fitting degree.

[0076] Then, after subtracting the blank well reading from the serum sample well reading, the GDF-9 cytokine concentration of the sample was calculated by substituting the standard curve of the corresponding determination batch, and the correlation analysis with the parity of dairy cows was performed. The results are shown in Table 1 and Figure 2 , it was found that the serum GDF9 concentration of Holstein cows had large individual level variation within each parity group, and the change rule between groups was not obvious, which was difficult to be used as an evaluation and prediction index for the production life or available parity of dairy cows. All results are expressed as mean ± standard error (SEMs), and when there is a same letter mark, it is not significantly different (P>0.05), and "ns" represents not significantly different (P>0.05).

[0077] Table 1 Relationship between serum GDF-9 concentration and parity of dairy cows

[0078]

[0079] Experiment II, determination of the serum BMP-15 cytokine level of dairy cows

[0080] Serum BMP-15 cytokine levels were determined by the above sample collection method and ELISA assay method, a total of 1094 serum samples were determined. After optimization of ELISA method, the detection range of serum BMP-15 cytokine ELISA kit (Shanghai Enzyme Link, item number ml900254V) was 125 pg / mL-4000 pg / mL, the sensitivity was less than 10 pg / mL, the intra-plate coefficient of variation was less than 10%, and the inter-plate coefficient of variation was less than 15%. After subtracting the blank well reading from the well reading of the standard in the ELISA kit, the standard curve was established using the corresponding standard concentration (4000, 2000, 1000, 500, 250, 125 pg / mL respectively) and the corresponding OD value, and the standard curve was as follows Figure 3 , the X axis is the concentration (pg / mL), and the Y axis is the OD value, R 2 Both are greater than 0.99, with excellent fitting degree.

[0081] Then the sample well reading was subtracted from the blank well reading, and the sample concentration was calculated by substituting the standard curve of the corresponding determination batch, and the correlation analysis was performed with the parity of dairy cows. The results are shown in Table 2 and Figure 4 , it was found that compared with the 0 parity young cows which were in the growth and development stage, the serum BMP-15 concentration maintained a relatively higher level (756-850 pg / mL) in 1-3 parities, and the BMP-15 level decreased after 3 parities and was lower than that of young cows, indicating that the quality of oocytes of high parity dairy cows decreased. This is also consistent with the fact that 1-3 parity Holstein cows have higher reproductive performance, while the overall reproductive performance of dairy cows decreases after 3 parities. Therefore, it is believed that the BMP-15 level can better reflect the current reproductive performance of dairy cows, but it is difficult to evaluate and predict the production life or available parity. All results are expressed as mean ± SEMs, and when there is one same letter mark, the difference is not significant (P>0.05), and "ns" represents the difference is not significant (P>0.05).

[0082] Table 2 Relationship between BMP-15 concentration and parity of dairy cows

[0083]

[0084] Further analysis of the difference in BMP15 level between the cattle group eliminated due to reproductive problems and the breeding cattle group found that the average serum BMP-15 concentration of the breeding Holstein cattle group was 706.1 pg / mL, and that of the eliminated cattle group was 530.7 pg / mL (P<0.05) (as shown in Table 3 and Figure 5 ), which had a significant difference, and this result further confirmed that dairy cows with low BMP-15 level had lower overall reproductive performance. All results are expressed as mean ± SEMs, and "*" represents a significant difference (P<0.05), and any completely different letter mark represents a significant difference (P<0.05).

[0085] Table 3 BMP-15 concentration in Holstein dairy cow population

[0086]

[0087] Example Three, Determination of AMH hormone level in dairy cow serum

[0088] According to the above sample collection method and ELISA determination method, the determination of serum AMH hormone level was carried out, and a total of 1094 serum samples were determined. After optimization of ELISA method, the detection range of AMH ELISA detection kit (Shanghai Enzyme Link, item number ml944588V) was 156.25 pg / mL-5000 pg / mL, the sensitivity was less than 10 pg / mL, the intra-plate coefficient of variation was less than 10%, and the inter-plate coefficient of variation was less than 15%. After subtracting the blank well reading from the well reading of the standard in the ELISA detection kit, the standard curve was established using the corresponding AMH standard concentration (5000, 2500, 1250, 625, 312.5, 156.25 pg / mL respectively) and the corresponding OD value, and the standard curve was as follows Figure 6 , the X axis is the concentration (pg / mL), and the Y axis is the OD value, R 2 all greater than 0.99, with excellent fitting degree.

[0089] Then the sample well reading was subtracted from the blank well reading, and the sample concentration was calculated by substituting the standard curve of the corresponding determination batch, and the correlation analysis was carried out with the parity of dairy cows. The results are shown in Table 4 and Figure 7 , it was found that when the parity was ≤2, the AMH concentration decreased with the increase of parity, while at higher parity (≥3 parity), the AMH concentration increased significantly (P<0.05). This may be because in actual production, most dairy farms start to select and cull dairy cows at about 3 parity, and dairy cows with poor reproductive ability such as repeated breeding failure, abortion, and suffering from reproductive diseases are culled, and dairy cows with good reproductive performance are left for subsequent production. This indicates that Holstein cows with high parity (i.e. long reproductive life) often have higher AMH concentration. All results are expressed as mean ± SEM, and when there is one same letter mark, it means no significant difference (P>0.05), and when there is a completely different letter mark, it means significant difference (P<0.05).

[0090] Table 4 Relationship between AMH concentration and parity of dairy cows

[0091]

[0092]

[0093] The cows were divided into the breeding herd (covering all parity), high parity breeding herd (>3 parities) and culled cows, and their average AMH concentrations were analyzed, as shown in Table 5 and Figure 8 The average AMH concentration of the breeding herd was not significantly different from the culled cows, while the AMH concentration of the high parity breeding herd (>3 parities) was significantly higher than the culled cows (P<0.05). It was further suggested that high AMH concentration was associated with long productive life of Holstein cows, while cows with low AMH level were more likely to be culled, resulting in low productive life. All results were expressed as mean ± SEMs, "ns" represented no significant difference (P>0.05), "*" represented significant difference (P<0.05), when having one same letter mark was not significantly different (P>0.05), and having completely different letter marks was significantly different (P<0.05).

[0094] Table 5 AMH concentration of breeding / culled Holstein cow herd

[0095]

[0096] The culled cows were further divided according to parity, and the average AMH concentration of each parity of culled cows was calculated, as shown in Table 6 and Figure 8, low parity i.e. <3 parities, Holstein cows had significantly lower average AMH values than high parity i.e. >3 parities (P<0.05). This again confirms the potential of Holstein cows with high AMH levels for a longer productive life. Also, in line with this, the average AMH levels in Table 4 were 929 pg / mL for 2 parities and 1425 pg / mL for 3 parities, which indicates that after 2 parities, Holstein cows undergo a large-scale culling, removing cows with poor reproductive performance (generally with lower AMH values) from the herd, resulting in an increase in the average AMH concentration in 3 parities. Since AMH concentration reflects the current ovarian reserve, young cows have the most abundant ovarian reserve. Therefore, young cows have the highest AMH levels, and after that, AMH levels decrease with increasing number of ovulations or parities. Therefore, in Table 4, the average AMH concentration decreases with increasing parity in 0-2 parities. After culling, the average AMH level of the herd begins to rise and remains at a high level. In view of this, we believe that AMH has a certain function in evaluating and predicting the productive life of dairy cows and can be used as a biomarker for evaluating and predicting the productive life of dairy cows. That is, when selecting Holstein cows with long reproductive life early, the serum AMH level of heifers or young cows should be at least higher than the average AMH level of 1400 pg / mL of Holstein cows culled from high parity (≥3 parities), and young cows with AMH levels higher than the threshold of 1400 pg / mL have higher reproductive potential with higher parity or longer productive life. Young cows with AMH levels lower than the average AMH level of 970 pg / mL of Holstein cows culled from low parity (<3 parities) still have certain reproductive potential, but should be culled first in culling. All results are expressed as mean ± SEMs, and "*" represents significant differences (P<0.05), and any with completely different letter marks are significantly different (P<0.05).

[0097] Table 6 AMH concentration of Holstein cow groups culled at different parities

[0098]

[0099] The calving interval is an important reproductive indicator, and the shorter the calving interval, the higher the cow's reproductive ability. The ideal calving interval is 360-380 days, and for every month the calving interval is extended, the milk yield will decrease by 2.5%. In this statistics, the best calving interval is =365 days, and the average AMH concentration of two cow groups with calving interval ≤365 days and calving interval >365 days is calculated, and the results are shown in Table 7 and Figure 10The results show that the average AMH concentration of the group of cows with calving interval ≤365 days is 1077.829261 pg / mL, and the average AMH concentration of the group of cows with calving interval > 365 days is 891.6219191 pg / mL, with significant difference (P < 0.05), indicating that the cows with higher AMH concentration, i.e., more abundant ovarian reserve, have shorter calving interval and higher breeding conception rate. All results are expressed as mean ± SEms, and "*" represents significant difference (P < 0.05), and any group with completely different letter marks is significantly different (P < 0.05).

[0100] Table 7 AMH concentration of Holstein cow groups with different calving intervals

[0101]

[0102] The first mating age of Holstein cows is generally between 14-16 months. Before the cows mature and their body weight exceeds 350 kg (about 70% of adult body weight), early first mating age means potentially longer utilization period. As shown in Table 8 and Figure 11 It is found that the average AMH concentration of the group of cows with first mating age ≤14 months is 1109.28293 pg / mL, and the average AMH concentration of the group of cows with first mating age > 14 months is 920.8222326 pg / mL, with significant difference (P < 0.05), indicating that the Holstein cows with higher AMH concentration, i.e., more abundant ovarian reserve, may have certain precocity and greater utilization potential. All results are expressed as mean ± SEms, and "*" represents significant difference (P < 0.05), and any group with completely different letter marks is significantly different (P < 0.05).

[0103] Table 8 AMH concentration of Holstein cow groups with different first mating ages

[0104]

[0105] Meanwhile, in production, it is generally believed that the Holstein cow group with calving interval > 365 days or the cow group with first mating age > 14 months has lower reproductive ability, and the average AMH concentration of these two groups is lower than 970 pg / mL, which is consistent with the above conclusion.

[0106] Example 2

[0107] According to the above results, it is considered that AMH has the function of evaluating and predicting the production life of dairy cows, so first, the related data of the cow herd are targeted, and a model for predicting the culling parity, i.e., the production life of Holstein dairy cows, based on the AMH concentration is constructed. Specifically, by using the specific AMH concentration of dairy cows, a logistic regression model in the generalized linear model (GLM) is constructed for prediction, including the following steps:

[0108] (1) Independent variable (explanatory variable): serum AMH concentration (continuous variable); dependent variable (response variable): parity (binary variable, "0" for the final parity <3 parities, "1" for the final parity ≥3 parities). Model form: Logit(P(Y=1))=β0+β1*[AMH], where: "P(Y=1)" represents the probability that the dairy cow belongs to the final parity ≥3 parities; "[AMH]" represents the standardized / normalized AMH concentration value; "β0" is the intercept term, and β1 is the regression coefficient of the independent variable.

[0109] (2) To evaluate the generalization ability of the model, the preprocessed complete data set is randomly divided into: "training set": accounting for 80%, used to construct the logistic regression model. "Test set": accounting for 20%, only used to evaluate the prediction performance of the final model, simulating the new data prediction scenario. Preferably, stratified sampling is used to ensure that the proportion of different final parity categories in the training set and the test set is basically consistent with that of the original data set.

[0110] (3) Evaluation index: "The prediction performance of the final model is evaluated on the independent test set, mainly using the following indexes:

[0111] ① Accuracy: the proportion of correctly predicted samples in the total samples.

[0112] ② Precision: the proportion of actual final parity ≥3 parities among the dairy cows predicted as final parity ≥3 parities.

[0113] ③ Recall: the proportion of dairy cows correctly predicted as final parity ≥3 parities among the actual final parity ≥3 parities.

[0114] ④ F1 score: the harmonic mean of precision and sensitivity.

[0115] (4) Evaluation method: calculate the specific values of the above indexes on the test set. Preferably, report the ROC curve and AUC value: a comprehensive index for measuring the ability of the model to distinguish different categories, and the closer the value is to 1, the better.

[0116] A total of 1094 serum samples were collected from the healthy and non-reproductive disease cattle in Wuhan Guangming Ecological Demonstration Dairy Farm. The AMH concentration of each serum sample was determined, and the AMH concentration data was processed to identify and eliminate outliers. At the same time, the actual parity information was recorded, and the parity information was converted into a binary classification label: parity ≥ 3 was set to 1, and parity < 3 was set to 0. The pre-processed AMH concentration data was used as the input feature, and the parity binary classification label was used as the output. The training set (80%) and the test set (20%) were divided, and the binary classification logistic regression model was used to learn the association between AMH concentration and the probability of parity ≥ 3.

[0117] The model was verified using the test set data, and the AMH concentration and parity ≥ 3 probability association curve was drawn. By analyzing the association between AMH concentration and parity ≥ 3 probability, as shown in Figure 12 , with the increase of AMH concentration, the probability of parity ≥ 3 showed an increasing trend, which reflected the predictive value of AMH concentration on parity and provided a regular basis for predicting parity based on this indicator.

[0118] To verify the performance of the model, the ROC curve was drawn (as shown in Figure 13 ). The ROC curve takes the false positive rate (1-Specificity) as the horizontal axis and the true positive rate (Sensitivity) as the vertical axis. The AUC (area under the curve) is 0.717, indicating that the model has certain discrimination ability and can effectively distinguish different parity conditions to a certain extent (the closer the AUC is to 1, the stronger the discrimination ability; around 0.5, it is close to random guessing). It can be used as an important quantitative indicator of model prediction performance.

[0119] As shown in Table 9, about 80% of the sample number was taken as the training set (876 samples), and the remaining about 20% of the sample number was taken as the test set (218 samples). Further evaluation by accuracy (0.735), precision (0.748), recall (0.928), and F1 value (0.828) showed that the model predicted correctly about 73.5% of the samples, the actual parity ≥ 3 was about 74.8% of the samples predicted to be parity ≥ 3, and the actual parity ≥ 3 was correctly identified about 92.8% of the time. The comprehensive performance indicator F1 reached 0.828, proving that the model has good effect in parity prediction.

[0120] Table 9: Evaluation index table of in-lactating cow prediction model

[0121]

[0122] In view of the potential of low parity in the breeding herd to have high breeding or long production life, which may cause errors in the prediction model, we continuously tracked and selected 179 Holstein cows eliminated due to reproductive problems (repeated breeding failure, etc.) to accurately associate their AMH concentration with the relationship between the Holstein cow elimination parity, in order to optimize the logistic regression model for predicting the Holstein cow elimination parity, i.e. the production life, based on the AMH concentration.

[0123] As Figure 14 , as mentioned above, as the AMH concentration increases, the probability of high parity gradually increases. In order to accurately define the predictive value of AMH concentration on parity probability, the probability inflection point with confidence and production coverage needs to be selected. If the inflection point is set to be too high a threshold (such as 90%), due to the natural difference in AMH concentration distribution in the cow population, only 28.58% of the samples can meet this condition, resulting in a lack of effective sample size that the model can accurately predict, and it cannot form a universal guidance for large-scale breeding scenarios; if a too low threshold is set, although the coverage sample size increases, the prediction result has insufficient confidence, and the false positive rate is high, a large number of cows judged by the model as "high parity probability" may not have the expected actual parity, which is easy to cause misjudgment risk.

[0124] Therefore, the present application selects 75% probability as the key inflection point of high parity reproductive potential cows. Under this threshold, the coverage rate (parity = 1 of AMH≥1481.7) is 57.14%, and the false positive rate (FPR) is 4.17%, and the model prediction result has a certain confidence and can cover enough production scenarios. As Figure 14 shown, when the AMH concentration of the cow is 1481.7 ng / mL, the probability of the cow elimination parity being ≥3 is 75%. Under natural conditions, the AMH concentration of the cow will gradually decrease with the increase of the parity, therefore when selecting Holstein cows with long reproductive life early, the serum AMH level of the replacement heifer or young cow should be at least higher than 1481.7 ng / mL, and the replacement heifer or young cow higher than this threshold has higher reproductive potential of parity or longer production life. On the contrary, when the high parity probability (≥3) is 25% (i.e. the low parity probability is 75%), the AMH concentration is 843.09 ng / mL. It is explained that in the early selection process, the replacement heifer or young cow with AMH concentration lower than this threshold can utilize the probability of parity (75%) less than 3, and should be considered for elimination.

[0125] In order to verify the performance of the model, the ROC curve is drawn (as Figure 15). The AUC (Area Under Curve) is 0.863, which indicates that the model has strong discriminant ability for predicting the culling cow group parity, and can effectively identify the positive and negative examples related to parity. As shown in Table 10, about 80% of the sample number is taken as the training set (143 samples), and the remaining about 20% of the sample number is taken as the test set (36 samples). Further evaluation through accuracy (0.8), precision (0.7586207), recall (0.9166667) and F1 value (0.8301887) shows that the proportion of correctly predicted samples is about 80%, the proportion of samples actually having parity ≥ 3 among the samples predicted to have parity ≥ 3 is about 75.86%, the proportion of actually having parity ≥ 3 samples correctly identified is about 91.67%, and the comprehensive performance index F1 reaches 0.83. Compared with the prediction model based on the breeding cow group, this model has more accurate prediction effect in parity prediction, which can effectively assist in breeding decision-making, breeding management and other application scenarios related to parity of breeding cow group.

[0126] At the same time, the AMH concentration of the high-probability (75%) high-fertility potential dairy cow group predicted by the model is related to the relevant data in production. The AMH concentration of the high-fertility group predicted by the model (1481.7 ng / mL) is higher than the average AMH level of the 3-parity breeding group (1425 ng / mL), the average AMH level of the culling parity ≥ 3 cow group (1378 ng / mL), the average AMH level of the cow group with calving interval ≤ 365 days (1078 ng / mL), and the average AMH level of the cow group with first mating age ≤ 14 months (1109 ng / mL). While the AMH concentration of the low-probability (25%) high-fertility potential dairy cow group predicted by the model is related to the relevant data in production. The AMH concentration of the low-fertility group predicted by the model (843.09 ng / mL) is lower than the average AMH level of the 3-parity breeding group (1425 ng / mL), the average AMH level of the culling parity < 3 cow group (973.9 ng / mL), the average AMH level of the cow group with calving interval > 365 days (891.6 ng / mL), and the average AMH level of the cow group with first mating age > 14 months (920.8 ng / mL). This further indicates the accuracy and effectiveness of the model, which helps to more scientifically predict the culling parity of cows in actual production, and assists managers in decision-making whether to accelerate culling or targeted treatment (such as re-evaluation of reproductive value), thereby improving breeding economic benefits.

[0127] Table 10 Evaluation index table of culling cow group prediction model

[0128]

[0129] In summary, the present application studies find that Holstein cows with high AMH levels have higher reproductive potential, especially longer production life, and when assessing and predicting Holstein cow production life in the early stage, the heifers (i.e., cows before first mating) or young cows (i.e., cows from conception to parturition) with AMH levels higher than 1400 pg / mL, especially higher than 1481.7 ng / mL, have longer production life reproductive potential, and it is necessary to avoid selecting heifers or young cows with AMH levels lower than 970 pg / mL, especially lower than 843.09 ng / mL. Therefore, cows with AMH higher than 1400 pg / mL should be selected, and cows with AMH lower than 970 pg / mL should be culled as early as possible, especially cows with AMH higher than 1481.7 pg / mL should be selected, and cows with AMH lower than 843.09 pg / mL should be culled as early as possible. When the AMH content in the serum sample of the cow is between 843.09 and 1481.7 pg / mL, and the production parity of the cow is equal to or less than two, it has certain reproductive potential, and can be selected and culled according to actual production needs.

[0130] In a farm, it is crucial for the development of agriculture to be able to conceive and give birth to healthy offspring with the least number of services and the shortest time after calving, especially for dairy cows. By accurately and quickly detecting markers, predicting the reproductive life of Holstein cows helps relevant personnel to develop more accurate individual selection or culling programs, thereby improving production efficiency. The existing technology is still blank in predicting and assessing the production life of cows, and is mostly dependent on the statistics of final reproduction data. AMH is the latest clinically discovered marker with the highest predictive value for ovarian reserve function and ovarian response, and the serum AMH level is not affected by gonadotropin, changes little in a single estrus cycle or multiple estrus periods, is stable and repeatable, which has obvious advantages for establishing an AMH biomarker.

[0131] The method provided by the present application is simple and easy to operate, and only needs to collect the serum of the cow and use ELISA or other methods to determine the AMH concentration to realize the early prediction and selection of Holstein cows. At the same time, the initial AMH level of the cow is determined after birth and is only determined by itself, and has little to do with feeding and management conditions, regional differences and the like, so it is helpful to establish a unified and effective cow production life assessment technology.

[0132] The method provided by the present application is simple and easy to operate, and only needs to collect the serum of the cow and use ELISA or other methods to determine the AMH concentration to realize the early prediction and selection of Holstein cows. At the same time, the initial AMH level of the cow is determined after birth and is only determined by itself, and has little to do with feeding and management conditions, regional differences and the like, so it is helpful to establish a unified and effective cow production life assessment technology.

[0133] 1. A method for assessing and predicting the production life of cows is established:

[0134] For a long time, the statistical means of dairy cow production life mainly rely on the reproductive data record after the final selection and elimination of dairy cows, but cannot evaluate and predict the reproductive potential or production life of dairy cows. The present application innovatively introduces AMH into the evaluation and prediction of dairy cow production life, filling the long-existing gap in this field.

[0135] 2. Early prediction and screening is realized:

[0136] The traditional statistical method has obvious lag, and it is difficult to accurately predict the production life of dairy cows in time. The present application can predict the production life of dairy cows at an early age by determining the AMH level in serum and analyzing its relationship with the parity / age of dairy cows, so as to realize early screening and cultivation of dairy cows with long production life.

[0137] 3. The accuracy and specificity of evaluation are improved:

[0138] Although the research on AMH as a marker of ovarian reserve in dairy cows is relatively less, its wide application in the field of human reproduction has proved its specificity and accuracy as an evaluation index. Therefore, the present application collects serum samples of Holstein cows of different parity / different age, determines the hormone level of AMH and other hormones, carries out correlation analysis combined with individual basic data and reproductive data, and constructs a dairy cow production life prediction model (AUC: 0.863; accuracy: 0.8; precision: 0.7586207) based on the AMH level of eliminated dairy cows, which can more accurately reflect and predict the production life potential of dairy cows.

[0139] 4. Reduce the breeding cost and accelerate the expansion of good breeds:

[0140] An accurate evaluation method can reduce the blindness in the breeding process and reduce the breeding cost. Through early screening and cultivation of dairy cows with long production life, the expansion speed of good breeds can be accelerated, and the overall benefit of the dairy industry can be improved.

[0141] 5. Establish a unified and effective evaluation technology:

[0142] The present application can establish a unified and effective dairy cow production life evaluation technology by collecting standardized samples, establishing a phenotype database, using a unified cytokine level determination method and correlation analysis method. Moreover, the AMH hormone level is mainly related to ovarian reserve, which is determined after the birth of an individual and decreases with the increase of parity or age. The evaluation technology provided by the present application can reduce the influence of regional differences and different feeding management on the evaluation results, and improve the accuracy of the evaluation results and the popularization and applicability of the technology.

Claims

1. A biomarker for assessing and predicting the productive lifespan of dairy cows, characterized in that, The biomarker is AMH.

2. A method for assessing and predicting the productive lifespan of dairy cows, characterized in that, It includes: Collect cow serum samples, test the AMH level in the serum, and record the parity or age of the cow at the time of serum collection. The AMH level is used to determine whether to keep the cow for further production or to cull it.

3. The method as described in claim 2, characterized in that, When the serum AMH level of cows is ≥1400pg / mL, and the cows are either gilts before their first mating or young cows from their first mating to calving, it indicates high reproductive potential, and they can be selected as a high-breeding population with ≥3 parities.

4. The method as described in claim 2, characterized in that, When the AMH level in the serum of a cow is <970 pg / mL, and the cow has a parity of less than or equal to two parities, it indicates low reproductive potential and the cow should be culled.

5. Application of the biomarker AMH in assessing and predicting the productive lifespan of cows.

6. The application as described in claim 5, characterized in that, When the AMH level in the serum sample of a cow is ≥1400 pg / mL, and the cow is a gilt or young cow, the cow is allowed to continue producing milk.

7. The application as described in claim 5, characterized in that, When the AMH level in a cow's serum sample is <970 pg / mL, and the cow's parity is equal to or less than two parities, the cow should not be used for production and should be culled.

8. The application as described in claim 5, characterized in that, When the AMH level in the serum sample of a cow is measured to be between 970 and 1400 pg / mL, and the cow has a parity of less than or equal to two parities, it has certain reproductive potential and can be selected for breeding according to actual production needs.

9. Application of biomarker AMH in the preparation of reagents for assessing and predicting the productive lifespan of cows.

10. The application as described in claim 9, characterized in that, When the AMH level in a cow's blood sample is ≥1400 pg / mL, and the cow is a gilt or young heifer, the cow is allowed to continue producing. When the AMH level in a cow's blood sample is <970 pg / mL, and the cow has a parity of less than or equal to second parity, the cow is culled.

Images