A whole-genome association analysis of noncompensatory fertility in Holstein bulls.

Increasing fertility in dairy cattle is an important goal. Male infertility represents a part of the overall infertility in dairy cattle and can be partitioned into compensatory and noncompensatory components, where compensatory refers to infertility that can be overcome by increasing sperm number and noncompensatory infertility represents the remainder, presumably due to molecular and genomic defects. Through estimation of single nucleotide polymorphism (SNP) association with noncompensatory bull fertility, it is possible to identify regions of the genome influential to this trait. Use of this information in selection can allow for an increase in cattle fertility, resulting in economic benefits. In this study, high-density SNP genotypes and noncompensatory fertility data from 795 Holstein sires were used to examine SNP associations with fertility. A Bayes B analysis was performed to develop information for genomic selection and to identify genomic regions associated with noncompensatory fertility. A cross-validation approach was used to assess the effectiveness of the models within the original set of 795 bulls. Correlations of predicted and observed fertility values were approximately 0.145 in cross-validation.

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds.

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance.

Relationship between individual herd-heritability estimates and sire misidentification rate.

The objectives of this study were to estimate heritabilities within herds participating in Dairy Herd Improvement and determine the relationship of the individual herd heritability with sire misidentification rate. Individual herd heritabilities for milk, fat, and protein yield and somatic cell score (SCS) were calculated with daughter-dam regression and daughter-sire predicted transmitting ability (PTA) regression using 4,712,166 records from 16,336 herds available for August 2000 evaluations and 7,084,953 records from 20,920 herds available for August 2006 evaluations. Herd heritabilities were estimated using regression models that included fixed breed, age within parity, herd-year-season of calving, dam records nested within state, sire PTA within state, and an interaction between sire PTA and herd variance; random regression coefficients were dam records within herd and sire PTA within herd. Average daughter-dam herd heritability estimates ranged from 0.21 (SCS in 2000) to 0.73 (protein percentage in 2006), whereas daughter-sire herd heritability ranged from 0.10 (SCS in 2000) to 0.42 (protein percentage in 2006). Verification of sire identification with DNA marker analysis was provided by Accelerated Genetics and Alta Genetics Inc. Daughter-sire herd heritability was more strongly correlated with sire misidentification rate than daughter-dam herd heritability. The correlation between the first principal component for all measures of herd heritability and sire misidentification rate was -0.38 (176 herds) and -0.50 (230 herds) in 2000 and 2006, respectively. Herd heritability can be estimated with simple regression techniques for several thousand herds simultaneously. The herd heritability estimates were correlated negatively with sire misidentification rates and could be used to identify herds that provide inaccurate data for progeny testing.

Survey of management practices on reproductive performance of dairy cattle on large US commercial farms.

A survey regarding general management, sire selection, reproductive management, inseminator training and technique, heat abatement, body condition scoring, facility design and grouping, nutrition, employee training and management, and animal health and bio-security was carried out from March to September of 2004 in 153 herds in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program. A total of 103 herds (67.3%) completed the survey. Herd size was 613 +/- 46 cows, with herds located in Wisconsin (26), California (12), New York (11), Minnesota (10), Michigan (7), Washington (6), Pennsylvania (6), Iowa (5), Idaho (5), Texas (4), Ohio (4), and other states (7). These farms sold 34.5 +/- 0.3 kg of milk/d per cow, with an annual culling rate of 34 +/- 1% and a calving interval of 13.8 +/- 0.1 mo. Cows were observed for estrus 2.8 +/- 0.3 times/d, for a duration of 27 +/- 4 min, but 78% of the respondents admitted that detection of estrus was not the employee's sole responsibility at that time. Managers tried to achieve pregnancy until 8.8 +/- 0.9 failed inseminations, 300 +/- 26 d postpartum, or milk yield <17.7 +/- 0.5 kg/d. Nonpregnant cows were culled at 326 +/- 36 d postpartum or milk yield <16.4 +/- 0.3 kg/ d. Mean durations of the voluntary waiting period were 52 +/- 1.3 and 53 +/- 1.4 d for primiparous and multiparous cows, respectively. Hormonal synchronization or timed artificial insemination programs were used in 87% of the herds, with 86% synchronizing first services, 77% resynchronizing repeat services, and 59% treating cystic, anestrous, or anovular cows. Finding good employees was identified as the greatest labor challenge, followed by training and supervising employees. Mastitis and hairy heel warts were noted as the greatest animal health concerns, followed by lameness, abortions, and death losses, whereas the greatest reproductive challenges were artificial insemination service rate, conception rate, twinning, and retained placenta or metritis. Results of this study can provide a useful benchmark or reference with regard to commonly used management practices on large commercial US dairy farms at the present time.

Genetic analysis of clinical mastitis data from on-farm management software using threshold models.

Producer-recorded clinical mastitis data from 77,791 cows in 418 herds were used to determine the potential for genetic improvement of mastitis resistance using data from on-farm management software programs. The following threshold sire models were applied: 1) a single-trait lactation model, where mastitis was recorded as 0 or 1 in first lactation only; 2) a 3-trait lactation model, where mastitis was recorded as 0 or 1 in each of the first 3 lactations, and 3) a 12-trait, lactation-segment model, where mastitis was recorded as 0 or 1 in each of 4 segments (0 to 50, 51 to 155, 156 to 260, and 261 to 365 d postpartum) in each of the first 3 lactations. Lactation incidence rates were 0.16, 0.20, and 0.24 in first, second, and third lactation, respectively, and incidence rates within various segments of these lactations ranged from 0.036 in late first lactation to 0.093 in early third lactation. Estimated heritability of liability to clinical mastitis ranged from 0.07 to 0.15, depending on the model and stage of lactation. Heritability estimates were higher in first lactation than in subsequent lactations, but estimates were generally similar for different segments of the same lactation. Genetic correlations between lactations from the 3-trait model ranged from 0.42 to 0.49, while correlations between segments within lactation from the 12-trait model ranged from 0.26 to 0.64. Based on the results presented herein, it appears that at least 2 segments are needed per lactation, because mastitis in early lactation is lowly correlated with mastitis in mid or late lactation. Predicted transmitting abilities of sires ranged from 0.77 to 0.89 for probability of no mastitis during the first lactation and from 0.36 to 0.59 for probability of no mastitis during the first 3 lactations. Overall, this study shows that farmer-recorded clinical mastitis data can make a valuable contribution to genetic selection programs, but additional systems for gathering and storing this information must be developed, and more extensive data recording in progeny test herds should be encouraged.

The effect of synchronization on genetic parameters of reproductive traits in dairy cattle.

Genetic evaluation and selection is one strategy for improving female reproductive performance. Many producers use synchronization of ovulation or estrus to manage reproduction. The objective of this study was to examine the effects of reproductive synchronization on genetic parameter estimates of days to first breeding (DFB), days open (DO), and pregnancy rate at 120 d postpartum (PR120). Data were collected from 64 producers participating in an artificial insemination progeny testing program and using Dairy Comp 305 herd management software to record reproductive treatments and events. Data included 18,359 records for DFB and 16,379 records for DO and PR120. Synchronization was classified by breeding codes at time of insemination. The traits DFB and DO were analyzed using a linear model with age at calving, herd-year-season, and parity as fixed effects and sire and residual as random effects. For PR120, a threshold sire model was used with fixed effects as in the DFB and DO models. Three models were applied to the complete data sets of all traits; a base model with no synchronization effect, an expanded model with a fixed synchronization effect, and an interaction model with a random sire by herd management interaction. Herd management categories were based on an individual herd's use of synchronization protocols. Also, data subsets were analyzed separately based on cow synchronization treatment and herd management categories. Synchronized records for DFB had on average 40% higher sire variance and 60% lower residual variance than nonsynchronized records. Heritability for DFB ranged from 0.01 to 0.09. Sire variance was 40% lower for DO and 25% lower for PR120 in first synchronized records than either later-synchronized or nonsynchronized records. Residual variances for DO varied by 3% among cow treatment categories and 14% for herd management categories. Heritabilities ranged from 0.03 to 0.07 for DO and 0.10 to 0.26 for PR120. Including a fixed effect for synchronization in the DO model reduced sire variance by 33% and residual variance by 10%. Sire by herd management interactions were less than 2% of the total variance for all traits. Accounting for synchronization, especially for DFB, may improve accuracy of genetic parameter estimates and animal evaluations.

Genetic evaluation of dairy sires for milking duration using electronically recorded milking times of their daughters.

The potential of using electronically recorded data from on-farm milking parlor and herd management software programs for genetic evaluation of dairy sires for milking duration of their daughters was assessed in the present study. Single measurements of milking duration were collected weekly from 29 herds between June 1, 2003 and April 1, 2004. These included 73,547 observations corresponding to 10,152 Holstein cows from 1551 sires. Average milking duration for a single milking in our data set was 4.5 min. Estimated heritability of milking duration was 0.17, and predicted transmitting abilities (PTA) of individual sires ranged from -0.48 min for sires with the fastest milking daughters to 0.59 min for sires with the slowest milking daughters. The correlation between PTA for milking duration and PTA for somatic cell score (SCS) was -0.15, indicating that sires whose daughters milk most quickly also tend to transmit higher SCS to their progeny. Correlations between PTA milking duration and PTA for teat placement and teat length were -0.14 and 0.20, respectively, indicating that sires that transmit wide teat placement and long teats tend to have daughters that milk slowly. Based on the results presented herein, it appears that genetic selection based on objective, electronically recorded milking times is possible. This approach would greatly improve the quality and efficiency of data collection relative to conventional evaluations of milking speed, which are based on farmer surveys. The number of herds currently equipped to routinely capture milking times is limited, but this number is increasing very rapidly. Future research should focus on refinement of data reporting and validation systems, as well as estimation of the economic value of milking duration. This trait may have an intermediate optimum, because cows that milk too slowly will disrupt parlor flow and reduce milking efficiency, but cows that milk too quickly may be at greater risk for mastitis.

Genetic parameters for conformation traits in herds that differ in mean final score and completeness of pedigree and performance data.

The objectives of this study were to assess differences in the heritability of type (conformation) traits between herds that differ in mean final score and completeness of pedigree and performance data and to estimate genetic correlations among these environments. Measurement of subjective characteristics, such as conformation traits, may be more difficult in herds with poor management conditions, and genetic evaluation of sires using data from such herds could lead to inaccurate selection decisions. Furthermore, missing pedigree data is a significant problem in many herds, and a lack of phenotypic data from maternal relatives may reduce the effectiveness of animal model evaluation systems. These hypotheses were examined using type classification scores of 1,728,836 first-parity Holstein cows (from 54,223 sires) that calved from 1993 to 2002 in 24,207 US dairy herds. These data included 480,927 records from progeny test daughters, but only 254,891 (47%) were from dams that had valid sire identification, and only 132,953 (28%) were from dams that had also been classified. Herds were grouped into quartiles by mean classification score, percentage of known maternal grandsires, and percentage of classified dams. Estimated heritability of final score was 0.20 in herds with mean score <74.5, 0.17 in herds with <25% known maternal grandsires, and 0.19 in herds with <18% classified dams. Conversely, estimates were 0.39 in herds with mean score >78.7, 0.35 in herds with 100% known maternal grandsires, and 0.37 in herds with >71% classified dams. Estimated genetic correlations between quartiles ranged from 0.86 to 0.95. Based on this study, it appears that improvements in animal identification and data collection in progeny test herds would lead to greater accuracy and stability of genetic evaluations for conformation traits in US Holstein cattle.

Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits.

The objectives of this study were to calculate genetic correlations between health traits that were recorded in on-farm herd management software programs and to assess relationships between these traits and other traits that are routinely evaluated in US dairy sires. Data consisted of 272,576 lactation incidence records for displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) from 161,622 cows in 646 herds. These data were collected between January 1, 2001 and December 31, 2003 in herds using the Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. Binary incidence data for all disorders were analyzed simultaneously using a multiple-trait threshold sire model that included random sire and herd-year-season of calving effects. Although data from multiple lactations were available for some animals, our genetic analysis included only first parity records due to concerns about selection bias and improper modeling of the covariance structure. Heritability estimates for the presence or absence of each disorder during first lactation were 0.14 for DA, 0.06 for KET, 0.09 for MAST, 0.03 for LAME, 0.04 for CYST, and 0.06 for MET. Estimated genetic correlations were 0.45 between DA and KET, 0.42 between KET and CYST, 0.20 between MAST and LAME, 0.19 between KET and LAME, 0.17 between DA and CYST, 0.17 between KET and LAME, 0.17 between KET and MET, and 0.16 between LAME and CYST. All other correlations were negligible. Correlations between predicted transmitting abilities for the aforementioned health traits and existing production, type, and fitness traits were low, though it must be noted that these estimates may have been biased by low reliability of the health trait evaluations. Based on results of this study, it appears that genetic selection for health disorders recorded in on-farm software programs can be effective. These traits can be incorporated into selection indices directly, or they can be combined into composite traits, such as "reproductive disorders", "metabolic disorders", or "early lactation disorders".

Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values.

The objective of this study was to determine the feasibility of genetic selection for health traits in dairy cattle using data recorded in on-farm herd management software programs. Data regarding displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) were collected between January 1, 2001 and December 31, 2003 in herds using Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. All herds in this study were either participants in the Alta Genetics (Watertown, WI) Advantage progeny testing program or customers of the Dairy Records Management Systems (Raleigh, NC) processing center. Minimum lactation incidence rates were applied to ensure adequate reporting of these disorders within individual herds. After editing, DA, KET, MAST, LAME, CYST, and MET data from 75,252 (313), 52,898 (250), 105,029 (429), 50,611 (212), 65,080 (340), and 97,318 (418) cows (herds) remained for analysis. Average lactation incidence rates were 0.03, 0.10, 0.20, 0.10, 0.08, and 0.21 for DA, KET, MAST, LAME, CYST, and MET (including retained placenta), respectively. Data for each disorder were analyzed separately using a threshold sire model that included a fixed parity effect and random sire and herd-year-season of calving effects; both first lactation and all lactation analyses were carried out. Heritability estimates from first lactation (all lactation) analyses were 0.18 (0.15) for DA, 0.11 (0.06) for KET, 0.10 (0.09) for MAST, 0.07 (0.06) for LAME, 0.08 (0.05) for CYST, and 0.08 (0.07) for MET. Corresponding heritability estimates for the pooled incidence rate of all diseases between calving and 50 d postpartum were 0.12 and 0.10 for the first and all lactation analyses, respectively. Mean differences in PTA for probability of disease between the 10 best and 10 worst sires were 0.034 for DA, 0.069 for KET, 0.130 for MAST, 0.054 for LAME, 0.039 for CYST, and 0.120 for MET. Based on the results of this study, it appears that genetic selection against common health disorders using data from on-farm recording systems is possible.

Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries.

Currently, the International Bull Evaluation Service calculates international dairy sire evaluations using the multiple-trait across country evaluation procedure. This method depends implicitly on political boundaries between countries, because the input data are national evaluations from each participating country. Therefore, different countries are treated as different production environments. The goal of this study was to identify factors that describe the production system on each farm. Such factors could be used to group herds across countries for borderless genetic evaluations. First lactation milk records of Holstein cows calving between January 1, 1990 and December 31, 1997 in Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the USA were used in this study. Thirteen genetic, management, and climatic variables were considered as potential indicators of production environments: peak milk yield, persistency, herd size, age at first calving, seasonality of calving, standard deviation of milk yield, culling rate, days to peak yield, fat to protein ratio, sire PTA milk, percentage of North American Holstein genes, maximum monthly temperature, and annual rainfall. Herds were grouped into quintiles based on herd averages for each of these variables. Genetic correlations for lactation milk yield between quintiles were significantly less than one for maximum monthly temperature, sire PTA milk, percent North American Holstein genes, herd size, and peak milk yield. The variables can be used to group herds into similar production environments, regardless of country borders, for the purpose of accounting for genotype by environment interaction in international dairy sire evaluation.

Application of a multiple-trait herd cluster model for genetic evaluation of dairy sires from seventeen countries.

The multiple-trait across country evaluation method is currently used for international genetic evaluation of dairy sires. This method simultaneously combines national estimated breeding value (EBV) of sires in all countries and produces a separate breeding value to be used in each of the 24 countries that participate in the service. The major drawbacks to this method are the large number of genetic parameters that must be estimated and the large number of EBV produced for each sire. In the current method, each sire receives an EBV for each separate environment, and environments change at the country borders. It is unreasonable to assume that each country contains only one homogeneous environment and that every country has a distinctly different environment from all others. In the present study, an alternative method for international sire evaluation was utilized. Herds were grouped according to important management, climatic, and genetic factors rather than country borders. Data consisted of 16,403,413 first lactation cows in Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the United States. Herds were grouped according to 13 descriptive herd variables, including temperature, rainfall, peak yield, persistency, herd-size, age at calving, seasonality of calving, standard deviation of milk yield, culling percentage, fat-to-protein ratio, days to peak yield, percent of North American Holstein genes, and average PTA milk of sires. Variables were weighted by their relative importance in explaining genotype by environment interactions between herds. Herds were grouped into seven clusters; clusters ranged in size from 4805 to 59,272 herds and 1,414,966 to 3,966,431 cows. The proposed model predicts EBV for dairy sires based on the production environment in which their progeny wili perform, rather than the country where they will be located.

Extent and benefits of multi-country progeny testing of young dairy sires.

One of the current trends within the artificial insemination industry is to progeny test young dairy bulls in multiple countries. The objectives of this study were to assess the extent of multi-country progeny testing and to measure the corresponding gains in reliability of international breeding value estimates. Data of Holstein bulls that were born between July 1, 1992, and December 31, 1994, and progeny tested in countries that participate in the International Bull Evaluation Service were used in the present study, because these were the youngest bulls that had completed multi-country progeny testing before the study. Based on August 1999 international sire evaluation data, a total of 562 bulls from 10 countries were multi-country sampled for production traits during this 2.5-yr period, and 233 bulls from seven countries were multi-country sampled for type traits. The United States, Canada, The Netherlands, France, and Germany were most active in multicountry progeny testing, and Germany, New Zealand, Australia, France, and The Netherlands were the most common countries of foreign sampling. Mean reliabilities of international breeding values were calculated within each country. Means for milk yield were 0.89 for single-country sampled bulls with local progeny (i.e., progeny in the home country), 0.71 for single-country sampled bulls with no local progeny, 0.90 for multicountry sampled bulls with local progeny, and 0.78 for multi-country sampled bulls with no local progeny. Mean reliabilities for teat placement for these groups of bulls were 0.80, 0.71, 0.88, and 0.83, respectively, and means for rear udder width were 0.79, 0.60, 0.85, and 0.68, respectively. Gains in reliability in the country of foreign sampling were greatest when foreign progeny were located in countries that had low genetic correlations with the home country.

International genetic evaluation of dairy sires using a multiple-trait model with individual animal performance records.

The objectives of this study were to estimate variance components and predict sire breeding values for milk, fat, and protein yield by using a multiple-trait model in which lactation yield in each country was considered as a different trait. Data included first lactation records of 16,145,832 Holstein-sired cows that calved between January 1, 1990, and December 31, 1997, in 243,466 herds in Australia, Austria, Belgium, Canada, the Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the USA. Milk, fat, and protein were analyzed separately by using a 17-trait sire model; in this case, "traits" refer to measurements of the same biological parameter in different production systems. Our genetic model included the systematic effects of herd-year-season of calving, age at calving, milking frequency, and heterosis class (i.e., breed composition). Heritability estimates ranged from 0.24 in Australia (protein) to 0.34 in Israel (milk) and The Netherlands (fat). Genetic correlations between countries ranged from 0.77 for Austria-Czech Republic (protein), Estonia-Finland (fat), Estonia-Ireland (milk), Estonia-Israel (milk), and Hungary-New Zealand (fat), to 0.96 for Australia-Ireland (milk), Australia-New Zealand (milk), Belgium-Netherlands (milk), and Belgium-USA (fat). Correlations differed markedly from parameters used currently in international sire evaluations. In particular, genetic correlations were 0.91 to 0.96 between Australia, Ireland, and New Zealand; all of these countries rely heavily on rotational grazing. Correlations were also 0.91 to 0.96 between Belgium, Canada, Italy, The Netherlands, and the USA; all of these countries use intensive management systems. Correlations between these two groups of countries were 0.80 to 0.90. The percentage of elite bulls (top 1% for milk yield) selected in common by each pair of countries ranged from 0.42 for Germany-Estonia and Germany-Israel to 0.78 for Belgium-Netherlands.

Characterization of dairy production systems in countries that participate in the International Bull Evaluation Service.

The International Bull Evaluation Service Centre has routinely calculated international dairy sire evaluations since 1994. Production systems vary between countries and between herds within a country, and these differences can cause significant genotype x environment interactions. First-lactation records of Holstein cows calving from January 1, 1990 through December 31, 1997, were used in this study. Countries that provided data for this study included Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Hungary, Ireland, Israel, Italy, The Netherlands, New Zealand, South Africa, Switzerland, and the United States. Country means were calculated for 13 variables related to climate, herd management, and genetic background. These variables were considered as possible causes of genotype by environment interaction. Highest peak yields were found in Israel and the United States at 31.4 and 30.5 kg, respectively. New Zealand and Estonia had the lowest daily peak yields at 17.1 and 18.9 kg, respectively. This was consistent with genetic differences between these countries, because Israel had the highest average predicted transmitting abilities (PTA) milk among sires, while Estonia had the lowest PTA milk. Persistency of lactation, defined as milk yield at 240 d postpartum divided by milk yield at 60 d postpartum, was highest in the Czech Republic and Estonia at 1.34, and lowest in Israel at 1.05. Herd size also varied substantially between countries, ranging from 2.3 first-lactation cows per herd-year in Finland to 62 per herd-year in Hungary. In summary, tremendous variation exists between the leading dairy countries in management, genetic, and climatic factors.