Heritability of changes in genetic evaluations of dairy bulls from first to later records of daughters

Objectives of this study were to investigate changes in predicted transmitting abilities (PTA) of yields from evaluations based on first records to evaluations based on first and later records of daughters and determine whether these changes are heritable. Data were USDA sire evaluations of July 1989 through May 2000 on Holstein bulls in standard progeny testing programs. Changes in PTA for milk, fat, and protein from evaluations based on first records of daughters to evaluations on first and second were obtained on 2001 bulls. These were divided into two sets: subset 1 (n = 889) included bulls first evaluated before 1995 and subset 2 (n = 1112) included bulls first evaluated in 1995 and later. Changes in PTA from first-record evaluation to most recent evaluation (May 2000) were obtained on 2524 bulls first evaluated in 1995 or later. Mean changes in PTA for bulls first evaluated in 1995 and later were smaller than mean changes for bulls evaluated earlier but standard deviations were similar. Regressions of changes in PTA on changes in parent average showed that a change of 1.0 kg in parent average resulted in 1.1 to 1.2 kg change in PTA. Heritabilities estimated with animal model ranged from 0.14 to 0.23 for changes from first-record evaluation to evaluation on first and second, and 0.27 to 0.35 for changes from first-record evaluation to most recent evaluation. Heritabilities of this magnitude allow for identifying bulls that decrease in PTA.     

Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets

The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a dam probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (≥0.99) and in Holstein (≥0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction.     

Progeny testing and selection intensity for Holstein bulls in different countries

International Bull Evaluation Service (Interbull) Holstein evaluations from February 1995 through February 2003 were used to determine characteristics of progeny testing for Holstein bulls in Australia, Canada, Denmark, France, Germany, Italy, New Zealand, Sweden, The Netherlands, and the United States. The decision to graduate a bull from progeny test (PT) was assumed to have been made based on the second Interbull evaluation, and graduation was defined as the addition of 200 daughters in the period 2.5 to 4.5 yr later. Mean bull age at PT decision varied across countries by 12 mo. Mean numbers of herds and daughters ranged from 39 to 111 and 54 to 144, respectively. Countries with higher requirements for official evaluations generally had more herds and daughters but older bulls at PT decision. Mean estimated breeding values for yield traits of sires of tested bulls were most similar across countries for fat, differing by only 6.4 kg. The four countries highest for sire protein differed only by 1 kg; however, the range was 12 kg. Percentages of bulls graduated ranged from 4.4 to 14.7 across countries. Selection intensities (standardized selection differentials) tended to be about 1.0 for yield traits. Selection intensities for somatic cell score were generally unfavorable, reflecting selection for negatively correlated yield traits. Reflecting variation in national breeding goals, selection intensities for stature were positive for most countries and highly negative for New Zealand. Selection intensity for fore udder was generally the lowest among the traits examined. All but one country showed positive selection for udder support. These statistics permit comparison of the components of PT programs across country, illustrating possible opportunities for improvement.     

International genetic evaluations of Holstein sires for milk somatic cell and clinical mastitis

International genetic evaluations for milk somatic cell and clinical mastitis have been implemented on a routine basis by Interbull. This paper examines possible genetic consequences of such evaluations. Holstein data from 12 countries were used for this purpose. Trait definitions and national genetic evaluation procedures were first summarized and showed that differences between countries existed. Estimated genetic correlations among milk somatic cell in these countries ranged from 0.47 to 0.97, with a median of 0.88. Estimated genetic correlations among clinical mastitis in three Nordic countries ranged from 0.59 to 0.83, and estimated genetic correlations between clinical mastitis in the three Nordic countries and milk somatic cell in the non-Nordic countries ranged from 0.37 to 0.78 with a median of 0.55. Bulls without daughter information in the Nordic countries had low reliabilities on the Nordic clinical mastitis scales. International genetic evaluations for milk somatic cell and clinical mastitis enable a broader selection among foreign bulls, and higher selection differentials were found when using international evaluations compared with national evaluations.     

Invited Review: Reliability of genomic predictions for North American Holstein bulls

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R²) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R² values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R² values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.     

Assessment of the value of international genetic evaluations for yield in predicting domestic breeding values for foreign Holstein bulls

International genetic evaluations are a valuable source of information for decisions about the importation of (the semen of) foreign bulls. This study analyzed data from 6 countries (Australia, Canada, Italy, France, the Netherlands, and the United States) and compared international evaluations for production traits of foreign bulls (i.e., when no national daughter information was available) to their national breeding values in August 2009, which were based only on domestic daughters’ data. A total of 821 bulls with highly reliable estimated breeding values (EBV) for milk, fat, and protein yield were analyzed. No evidence of systematic over- or underestimation was found in most of the countries analyzed. Observed correlations between national and international evaluations were close to 0.9 and, for most countries, generally close to their expected values (calculated from national and international EBV reliabilities). In Italy, however, higher differences between observed and expected correlations and significant mean differences between EBV for more than one trait were observed in bulls progeny-tested in the United States and in other European countries (with differences up to 33.1% of the genetic standard deviation). These results were probably induced by a relatively recent change in the model for national evaluation. The findings in this study reflect a conservative estimate of the real value of international evaluations, as changes in methodologies in either the national or the international evaluations decreased the ability of past international evaluations to predict current national evaluations. Nevertheless, our results indicate that international evaluations based on foreign information for Holstein bulls were reasonably accurate predictors of the future national breeding values based only upon domestic daughters.     

A relationship matrix including full pedigree and genomic information

Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-derived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.     

Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2 h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.     

Rates of inbreeding and genetic diversity in Canadian Holstein and Jersey cattle

The accumulation of inbreeding and the loss of genetic diversity is a potential problem in the modern dairy cattle breeds. Therefore, the purpose of this study was to analyze the pedigrees of Canadian Holstein and Jersey cattle to estimate the past and current rates of inbreeding and genetic diversity, and to identify the main causes of diversity loss. Completeness and depth of the pedigrees were good for both breeds. For Holsteins, the average rates of inbreeding per generation showed a decreasing trend in recent years when compared with the 1990s. The estimated current effective population size was about 115 for Holsteins and is not expected to significantly change in the near future if generation intervals stay at current value, as rates of increase in inbreeding and coancestry showed decreasing trends. For Jerseys, the estimated effective population size was about 55 and it is expected to decrease in the near future due to the observed increasing rates of coancestry and inbreeding. Ancestors with the highest marginal genetic contributions to the gene pool in current years and with the highest contributions to inbreeding were identified. The 2 most heavily used and represented ancestors in the Holstein pedigree (i.e., Round Oak Rag Apple Elevation and his son Hanoverhill Starbuck), accounted for 30% of inbreeding. Analyses revealed that the most important cause of genetic diversity loss in both breeds was genetic drift accumulated over nonfounder generations, which occurred due to small effective population size. Therefore, a need exists in both breeds, particularly in Jerseys, for managing selection and mating decisions to control future coancestry and inbreeding, which would lead to better handling of the effective population size.     

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.     

Genetic diversity and joint-pedigree analysis of two importing Holstein populations

Genetic diversity and relatedness between 2 geographically distant Holstein populations (in Luxembourg and Tunisia) were studied by pedigree analysis. These 2 populations have similar sizes and structures and are essentially importing populations. Edited pedigrees included 140,392 and 151,381 animals for Tunisia and Luxembourg, respectively. To partially account for pedigree completeness levels, a modified algorithm was used to compute inbreeding. The effective numbers of ancestors were derived from probabilities of gene origin for the 2 populations of cows born between 1990 and 2000. The 10 ancestors with the highest contributions to genetic diversity in the cow populations accounted for more than 32% of the genes. Eight of these 10 ancestors were the same in both populations. The rates of inbreeding were different in the 2 populations but were generally comparable to those found in the literature for the Holstein breed. Average inbreeding coefficients per year, estimated from the data, ranged from 0.91 and 0.50 in 1990 to 3.10 and 2.12 in 2000 for the Tunisian and Luxembourg populations, respectively. Genetic links have also strengthened with time. Average additive relationships between the 2 populations were as high as 2.2% in 2000. Results suggest that it would be possible to investigate genotype by environment interactions for milk traits using the Tunisian and Luxembourg dairy populations.     

Impact of maturity rate of daughters on genetic ranking of Holstein bulls

If genetic evaluations are calculated with a single-trait repeatability model, evaluation changes may be attributed in part to bulls that have daughters that deviate considerably from the typical response to aging. Differences in maturity rate of bull daughters were examined to determine whether they influence change in bull evaluations. Standardized milk records for Holsteins that first calved between 1960 and 1998 were used to calculate 12 tailored predicted transmitting abilities (PTA) for each bull. Predicted transmitting abilities were tailored from combinations of 4 annual cut-off dates and 3 parities. Date screening selected cows first calving before January of 1996, 1997, 1998, or 1999. Parity screening selected milk records from the first 1, 2, or 3 parities. Therefore, 4 evaluations (PTA₁) included only first-parity records available for daughters and contemporaries prior to the respective years designated. Four more evaluations (PTA_1,2) included the records from the first 2 parities for cows first calving prior to those same year cutoffs; likewise, the last 4 evaluations (PTA_1,2,3) included records from the first 3 parities. Stability of bull evaluations (standard deviations of differences as well as correlations between bull evaluations) across time was compared. Bulls born after 1984 with ≥500 daughters were of interest because of the high precision of evaluations and recent activity. Tailored PTA of those bulls had more uniformity across years in mean records per daughter than did official USDA PTA. Standard deviation of differences in PTA₁, PTA_1,2, and PTA_1,2,3 for milk between evaluation years 1996 and 1997 were 28, 28, and 27 kg compared with 63 kg for official evaluations; similarly, between 1996 and 1999, SD were 36, 32, and 32 kg compared with 80 kg. Results suggested that a modification to the current evaluation model to account for maturity rate should reduce fluctuations in individual bull PTA across time and may improve accuracy of evaluations.     

Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information

Currently, genomic evaluations use multiple-step procedures, which are prone to biases and errors. A single-step procedure may be applicable when genomic predictions can be obtained by modifying the numerator relationship matrix A to H = A + A_Δ, where A_Δ includes deviations from expected relationships. However, the traditional mixed model equations require H⁻¹, which is usually difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form that also applies for singular H and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or A_Δq, where q is a vector. The alternative equations have a nonsymmetric left-hand side. Computing A_Δq is inexpensive when the number of nonzeros in A_Δ is small, and the product Aq can be calculated efficiently in linear time using an indirect algorithm. Generalizations to more complicated models are proposed. The data included 10.2 million final scores on 6.2 million Holsteins and were analyzed by a repeatability model. Comparisons involved the regular and the alternative equations. The model for the second case included simulated A_Δ. Solutions were obtained by the preconditioned conjugate gradient algorithm, which works only with symmetric matrices, and by the bi-conjugate gradient stabilized algorithm, which also works with nonsymmetric matrices. The convergence rate associated with the nonsymmetric solvers was slightly better than that with the symmetric solver for the original equations, although the time per round was twice as much for the nonsymmetric solvers. The convergence rate associated with the alternative equations ranged from 2 times lower without A_Δ to 3 times lower for the largest simulated A_Δ. When the information attributable to genomics can be expressed as modifications to the numerator relationship matrix, the proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information.     

Effect of synchronized breeding on genetic evaluations of fertility traits in dairy cattle

Estrus detection has become more difficult over the years due to decreases in the estrus expression of high-producing dairy cows, and increased herd sizes and animal density. Through the use of hormonal synchronization protocols, also known as timed artificial insemination (TAI) protocols, it is possible to alleviate some of the challenges associated with estrus detection. However, TAI masks cows' fertility performance, resulting in an unfair comparison of treated animals and innately fertile animals. Consequently, genetically inferior and superior cows show similar phenotypes, making it difficult to distinguish between them. As genetic programs rely on the collection of accurate phenotypic data, phenotypes collected on treated animals likely add bias to genetic evaluations. In this study, to assess the effect of TAI, the rank correlation of bulls for a given trait using only TAI records were compared with the same trait using only heat detection records. A total of 270,434 records from 192,539 animals split across heifers, first and second parity cows were analyzed for the traits: calving to first service, first service to conception, and days open. Results showed large reranking across all traits and parities between bulls compared based on either having only TAI records or only heat detection records, suggesting that a bias does indeed exist. Large reranking was also observed for both the heat detection and TAI groups among the top 100 bulls in the control group, which included all records. Furthermore, breeding method was added to the model to assess its effect on bull ranking. However, there were only minor changes in the rank correlations between scenario groups. Therefore, more complex methods to account for the apparent bias created by TAI should be investigated; for this, the method by which these data are collected needs to be improved through creating a standardized way of recording breeding codes. Though the results of this study suggest the presence of bias within current fertility evaluations, additional research is required to confirm the findings of this study, including looking at high-reliability bulls specifically, to determine if the levels of reranking remain. Future studies should also aim to understand the potential genetic differences between the fertility traits split via management technology, possibly in a multiple-trait analysis.     

How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd,genomic information nucleus,and bulls

Genomic selection has been commonly used for selection for over a decade. In this time, the rate of genetic gain has more than doubled in some countries, while inbreeding per year has also increased. Inbreeding can result in a loss of genetic diversity, decreased long-term response to selection, reduced animal performance and ultimately, decreased farm profitability. We quantified and compared changes in genetic gain and diversity resulting from genomic selection in Australian Holstein and Jersey cattle populations. To increase the accuracy of genomic selection, Australia has had a female genomic reference population since 2013, specifically designed to be representative of commercial populations and thus including both Holstein and Jersey cows. Herds that kept excellent health and fertility data were invited to join this population and most their animals were genotyped. In both breeds, the rate of genetic gain and inbreeding was greatest in bulls, and then the female genomic reference population, and finally the wider national herd. When comparing pre- and postgenomic selection, the rates of genetic gain for the national economic index has increased by ~160% in Holstein females and ~100% in Jersey females. This has been accompanied by doubling of the rates of inbreeding in female populations, and the rate of inbreeding has increased several fold in Holstein bulls since the widespread use of genomic selection. Where cow genotype data were available to perform a more accurate genomic analysis, greater rates of pedigree and genomic inbreeding were observed, indicating actual inbreeding levels could be underestimated in the national population due to gaps in pedigrees. Based on current rates of genetic gain, the female reference population is progressing ahead of the national herd and could be used to infer and track the future inbreeding and genetic trends of the national herds.     

Investigation of country bias in international genetic evaluations using full-brother information

International Bull Evaluation Service evaluations from May 2005 were examined for country bias by comparing Holstein full-brother families. Countries with ≥25 bulls in multicountry full-brother families were included. The model fit evaluations of US estimated breeding values (EBV) by absorbing full-brother family and producing solutions for country of brothers. For yield and somatic cell score, 24,611 and 22,802 bulls, respectively, were included in the analysis. The study was repeated fitting evaluations on the scales of 9 countries other than the United States. On all countries’ scales, bulls from Australia, Germany, Great Britain, and Japan had greater EBV for milk yield than did their full brothers from the United States; Italian bulls had lower EBV. Bulls from Australia, Great Britain, and South Africa had an advantage in EBV for fat yield. For EBV for protein yield, bulls from Germany, Great Britain, Japan, and South Africa had an advantage, whereas bulls from the Netherlands were disadvantaged. For somatic cell score, US bulls were advantaged compared with bulls from South Africa. Significance and rankings of apparent biases were similar across country scales of the international evaluations. Causes of those differences are unknown; differences in incorporation of parental data in national and International Bull Evaluation Service evaluations are a possibility.     

Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection

The proportion of cows in the UK dairy herd whose sires were misidentified was estimated using DNA markers. Genetic marker genotypes were determined on 568 cows (from 168 milk samples and 400 hair samples) and 96 putative sires (from semen samples). The estimated pedigree error rate from the hair samples was 8.8%, and from the milk samples, 13.1%, giving an overall estimate of the error rate of 10%. This level of pedigree errors will have a relatively large impact on the efficiency of progeny testing and the accuracy of cow predicted breeding values. We predict a loss of response to selection of approximately 2 to 3% given this error rate.     

Short communication: correlations of marker-assisted breeding values with progeny-test breeding values for eight hundred ninety-nine French Holstein bulls

French artificial insemination companies have been running a marker-assisted selection program since 2001 to determine which young bulls should be progeny tested. A first batch of 899 Holstein sires receiving their first proofs based on progeny daughters has been studied. Estimated breeding values with or without marker information were computed based on information available in April 2004, and correlated to daughter yield deviations available in 2007 for production traits. Marker-assisted estimated breeding values presented greater correlations with daughter yield deviations than those calculated using only pedigree index. The average improvement in correlation was 0.043 and ranged from +0.001 for protein yield to +0.103 for fat percentage. This gain was based on the initial and suboptimal conditions of the program and is expected to increase in the coming years because of several improvements implemented since the start of the marker-assisted selection program.     

Lifetime profit as an individual trait and prediction of its breeding values in Spanish Holstein cows

Genetic parameters for lifetime profit and some productive traits were estimated from records of 42,401 Holstein cows with first calving before May 1996 from Navarra and Basque Autonomous Regions of Spain. Profit from the first, first two, and first three lactations were tested as early measures of profitability. Profit prediction was tested for another population of 2127 cows using selection indexes (Type-Production and economic indexes) and multitrait analysis for directly predicting profit from first-lactation records. High genetic correlations of actual profit with estimated profit from the first two or first three lactation records, (0.97 and 0.99, respectively) suggest that lifetime profit can be accurately estimated from data in second lactation. Profit was positively correlated to production traits (0.79 to 0.83), functional herd life (0.38), mature body weight (0.25), and days in milk (0.35), but genetic correlation was found to be close to zero with calving interval. Complicated relationships among profit and economic traits (i.e., calving interval, days in milk, and functional herd life) were found. Although the correlation between calving interval and profit was near zero, calving interval was the most important trait after production in prediction of sire profit by a stepwise regression analysis. Profit breeding values from multitrait analysis obtained higher correlation (0.48) with actual profit than Spanish official Type-Production index ICO (0.44) and economic index MEG (0.46). A correlation of 0.49 between profit breeding values and the economic index MEG2002, where stature and calving interval were included as new traits, was obtained.     

Genetic parameters of semen quality traits and genetic correlations with service sire nonreturn rate in Nordic Holstein bulls

Despite the importance of the quality of semen used in artificial insemination to the reproductive success of dairy herds, few studies have estimated the extent of genetic variability in semen quality traits. Even fewer studies have quantified the correlation between semen quality traits and male fertility. In this study, records of 100,058 ejaculates collected from 2,885 Nordic Holstein bulls were used to estimate genetic parameters for semen quality traits, including pre- and postcryopreservation semen concentration, sperm motility and viability, ejaculate volume, and number of doses per ejaculate. Additionally, summary data on nonreturn rate (NRR) obtained from insemination of some of the bulls (n = 2,142) to cows in different parities (heifers and parities 1–3 or more) were used to estimate correlations between the semen quality traits and service sire NRR. In the study, low to moderate heritability (0.06–0.45) was estimated for semen quality traits, indicating the possibility of improving these traits through selective breeding. The study also showed moderate to high genetic and phenotypic correlations between service sire NRR and some of the semen quality traits, including sperm viability pre- and postcryopreservation, motility postcryopreservation, and sperm concentration precryopreservation, indicating the predictive values of these traits for service sire NRR. The positive moderate to high genetic correlations between semen quality and service sire NRR traits also indicated that selection for semen quality traits might be favorable for improving service sire NRR.