Genetic Evaluation Of Dairy Cattle Research Articles

Genetic evaluation of productive life in Iranian Holstein using survival analysis

Improvement of herd life increases profitability due to lower replacement costs of heifers, higher proportion of mature cows that produce at their maximum potential and increased opportunity for voluntary culling. Functional productive life (PL) after adjustment for production is the ability of a cow to remain healthy and delay involuntary culling (Ducrocq et al 1988). Survival or failure time analysis has replaced linear model approaches for routine genetic evaluation of dairy cattle in several countries (Sewalem et. al. 2003). It allows proper treatment of censored data, inclusion of time-dependent covariates and skewed or non normal distribution of data. Approximate estimates of the heritability of longevity traits typically range from 0.05 to 0.10 and 0.15 to 0.20 using linear models and survival analysis, respectively (Vollema et al. 2000 and Caraviello et. al. 2004). The objective of this study was to apply survival analysis methodology for assessing the most important factor influencing PL, estimation of genetic parameters of productive life and genetic evaluation in Iranian Holstein dairy cattle.

Duration of Herd Participation in Dairy Herd Improvement Milk Recording in the United States

Participation in milk-recording programs that provide data for national genetic evaluations of dairy cattle in the United States is voluntary, but the effectiveness of the evaluation system increases with the number of herds that contribute data. To investigate patterns of herd participation in Dairy Herd Improvement (DHI) testing, periods of continuous testing were computed based on the year that a herd initiated or terminated testing and by geographical region. Continuous testing was defined as at least one test per 6-mo period. Some herds discontinued testing and then re-enrolled. Across all years (1960 through 2002), 65% of herds had one period of continuous testing (no testing lapse). The percentage of herds with testing lapses decreased as the number of lapses increased and as the initial test year became more recent; overall, only 1.5% of herds had more than 6 continuous testing periods. For herds that terminated DHI testing from 1960 through 2002, 64% were on continuous test for <3 yr. In general, herd frequencies decreased as continuous test period increased except for continuous testing of ≥20 yr, which increased to 13% for years 2000 to 2002. Herds with more recent termination dates had remained on continuous test longer, and one-third of herds that were still on test after June 2002 had been on test for at least 20 yr. The duration of herd participation was longest for the northeastern and mideastern United States and shortest for the southeastern United States. Multiple periods of testing with lapses of >6 mo between test periods represent a loss of data that could have enhanced the study and evaluation of genetic characteristics of US dairy cattle.

Overview of genetic evaluation in dairy cattle

It is costly and time-consuming to carry out dairy cattle selection on a large experimental scale. For this reason, sire and cow evaluations are almost exclusively based on field data, which are highly affected by a large array of environmental factors. Therefore, it is crucial to adjust for those environmental effects in order to accurately estimate the genetic merits of sires and cows. Index selection is a simple extension of the ordinary least squares under the assumption that the fixed effects are assumed known without error. The mixed-model equations (MME) of Henderson provide a simpler alternative to the generalized least squares procedure, which is computationally difficult to apply to large data sets. Solution to the MME yields the best linear unbiased estimator of the fixed effects and the best linear unbiased predictor (BLUP) of the random effects. In an animal breeding situation, the random effects such as sire or animal represent the animal's estimated breeding value, which provides a basis for selection decision. The BLUP procedure under sire model assumes random mating between sires and dams. The genetic evaluation procedure has progressed a long way from the dam-daughter comparison method to animal model, from single trait to multiple trait analysis, and from lactational to test-day model, to improve accuracy of evaluations. Multiple-trait evaluation appears desirable because it takes into account the genetic and environmental variance-covariance of all traits evaluated. For these reasons, multiple-trait evaluation would reduce bias from selection and achieve a better accuracy of prediction as compared to single-trait evaluation. The number of traits included in multiple-trait evaluation should depend upon the breeding goal. Recent advances in molecular and reproductive technologies have created great potential for quantitative geneticists concerning genetic dissection of quantitative traits, and marker-assisted genetic evaluation and selection.

Derivation and Calculation of Approximate Reliabilities and Daughter Yield-Deviations of a Random Regression Test-Day Model for Genetic Evaluation of Dairy Cattle

Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.

Application of random regression models in animal breeding

Random regression models (RRM) have become common for the analysis of longitudinal data or repeated records on individuals over time. Applications in animal breeding research are emphasized while recognizing that RRM are used in many biological situations including human health. The best known application of RRM has been to genetic evaluation of dairy cattle using test day production records. The basic structure of a RRM is given. Other applications include growth traits in all species, genotype by environment interactions, and ad hoc proposals for the analysis of survival data and fertility data. RRM allow the researcher to study changes in genetic variability with time and allow selection of individuals to alter the general patterns of response over time.

Modelling and analysis of incomplete and short lactations

AbstractAdvantages of the use of test-day records for genetic evaluation of dairy cattle are now widely accepted. In particular, longitudinal models such as random regression avoid using ad hoc extrapolation procedures to reconstruct complete lactations as they provide individual predictions even for incomplete data. However, these predictions and parameter estimates obtained in the model do not take into account the lactation length. This can be an important drawback for phenotypic and genetic analysis of milk production of cows with shorter lactations. The aim of this paper is to propose a methodology that would correct these predictions, weighting them by the probability at each point in time of each cow being dried off. The proposed procedure is easy to implement and calculations are fast to compute. A simulation study and an application on real data for daily milk records show that the proposed methodology provides a more accurate estimation for individual cumulative production as well as genetic values, and avoids predicting negative productions at the end of the lactation as is often the case with random regression models.

Multivariate REML estimates of genetic parameters of monthly test day milk production traits in first parity Iranian Holstein cows with the use of a repeatability test day model

In practical dairy cattle breeding programmes, usually a small number of animals (selected from a large population) have a major influence on the genetic gain of the concerned population over a period of time (Hofer, 1998). Candidate animals are usually selected based on their breeding values that are predicted by using animal models. In order to predict breeding values, genetic parameters (calculated from variance and covariance components) of the traits under consideration should be estimated to be used in genetic evaluation systems either based on lactation or test day models. The use of test day models has increasingly become of interest in genetic evaluation of dairy cattle due to the fact that they can take more accurate account of the effects of environmental factors influencing test day milk yield over the course of lactation. The main objective of this study was to use a repeatability test day animal model to estimate genetic parameters of monthly test day milk production traits in first parity Iranian Holsteins.

Genetic evaluation of Iranian Holstein dairy cows with the use of a Covariance Function Model

The use of test day models has increasingly become of interest in genetic evaluation of dairy cattle. Traditionally, in most dairy cattle breeding programmes genetic evaluation of dairy sires and cows has been primarily based on using 305-day lactation yield. Since in this system of evaluation many incomplete lactation records have to be extended to complete lactations this could result in biased prediction of breeding values due to any over or underestimation of total lactation yields. In genetic evaluation of dairy cattle, test day models have many advantages over the traditional method (Lactation Animal Model) because of the fact that they can account for the lactation curve of individual cows and take more accurate account of the effects of environmental factors influencing test day milk yield over the course of lactation. The main aim of this study was to use a Covariance Function Test day Animal Model (CFTAM) in genetic evaluation of first parity Iranian Holsteins.

Estimation des paramètres génétiques et phénotypiques de la production laitière des vaches de race Frisonnes Holstein au Maroc

L’héritabilité et la répétabilité du rendement laitier, de la quantité de matières grasses et du taux butyreux, ainsi que leurs corrélations génétiques et phénotypiques, ont été estimées en utilisant un modèle animal à partir de 20 140 données relevées sur 11 618 vaches Frisonnes Holstein soumises au contrôle laitier officiel au Maroc, entre 1990 et 1999. Les héritabilités du rendement laitier, de la quantité de matières grasses et du taux butyreux ont été respectivement de 0,29, 0,27 et 0,39, leurs répétabilités ont été respectivement de 0,33, 0,32 et 0,39. Les corrélations génétiques du rendement laitier avec la quantité de matières grasses et le taux butyreux ont été respectivement de 0,96 et –0,32, et celle de la quantité de matières grasses avec le taux butyreux a été de –0,06. Les corrélations phénotypiques ont été de 0,96 entre les rendements en lait et en matières grasses, de –0,28 entre le rendement en lait et le taux butyreux, et de –0,03 entre le rendement en matières grasses et le taux butyreux. Il a été conclu que ces paramètres génétiques et phénotypiques pourraient être utilisés pour l’estimation des valeurs génétiques des bovins laitiers au Maroc.

Genetic Evaluation of Dairy Cattle Using Test-Day Models

Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called “biological functions”, various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.

Parallel Computing Applied to Breeding Value Estimation in Dairy Cattle

Continuous genetic evaluation of dairy cattle with test-day models is desired in Finland. However, the computing time for the genetic evaluation is 4 d and exceeds the minimum of a weekend. Three parallel implementations of the preconditioned conjugate gradient iterative solver were programmed and compared to identify the best strategy for solving mixed model equations using parallel computing. The programs were used to solve two random regression test-day models with approximately 7.28 and 49.9million unknowns. The latter model will be used in the Finnish dairy cattle evaluation. Computing times for the smaller model with the four processors available were 52, 32, and 27% of the single processor program when the complexity of the parallel program was increased. In practice, the best program required the most programming because the other parallel programs could not solve the larger model because of excess memory requirements. Parallel computing with four processors reduced the time to obtain solutions of Finnish dairy cattle evaluations to under 2 d. Benefit from parallel computing will be increased if amount of computing memory is increased.

Approaches to Estimating Daily Yield from Single Milk Testing Schemes and Use of a.m.-p.m. Records in Test-Day Model Genetic Evaluation in Dairy Cattle

Statistical models were presented to estimate daily yields from either morning or evening test results. The 64,451 test-day records from 10,392 lactations of 8800 cows were available for analysis from experiments that were designed to investigate the accuracy of an alternate morning and evening four-weekly milk-testing scheme. The experiments were conducted in 152 herds from six German states and covered a span from 1994 to 1998. Milk yield, fat, and protein percentage were recorded for all of the morning and evening milkings. Seven statistical models were fitted to the data to derive formulas for estimating daily yields from morning or evening yields. In general, use of evening milkings less accurately estimated yields than did use of morning milkings. Among the three yield traits the lowest accuracy of estimation of daily yield was found for fat yield. Although the models do not differ much in the correlation between estimated and true daily yields, systematic under- and overestimation of daily yield at the beginning and end of lactation were observed in all models with the exception of model 6, which accounted for heterogeneous variances by parity class, milking interval class, and lactation stage by fitting separate regression formulas within each combination of the three factors. A study to validate the models showed that model 6 is also robust for the analyzed populations. Smoothing model 6 regression formulas across lactation stages caused a systematic pattern of estimation error, although loss in accuracy was minimal by fitting far fewer parameters in the regression formulas. Differences in the accuracy of alternate milking schemes to predict daily yields were found between traits, between morning and evening milkings, and between parity classes. Compared with true daily yields from different lactation stages, variances and correlations of the estimated yields were reduced, which must be accounted for in genetic evaluation. The use of estimated daily yields from morning or evening milkings has a smaller impact on estimated breeding values of bulls than cows. As a result of lower heritability and repeatability of estimated daily yields than true daily yields, the weight on own test-day records for estimating cows’ breeding values is lower when cows are in a.m.–p.m. than conventional monthly testing schemes. However, the difference in the weights between estimated and true daily yields decreases as lactation progresses. Use of estimated daily yields is less reliable for estimating breeding value than use of true daily yields.

Genetic evaluation of dairy cattle for conformation traits using random regression models

Summary Random regression models were applied to eight conformation traits (i.e. stature, rump angle, thurl width, rear leg set, rear udder width, rear udder height, udder depth, and fore udder attachment) of Holstein cows from the northeastern United States. Covariates for fixed and random regressions included age and age-squared for six of the traits, and two additional covariates were included for rear udder width and rear udder height. Other effects in the model were herd—year-classifier and months in milk. Fixed covariates were nested within year of birth of the cow. Variance components were estimated using Bayesian theory and Gibbs sampling procedure. Estimated breeding values from the random regression models were compared to two single trait models. The first model utilized only the first classification record of the cow in first lactation, and the second model utilized all classifications of the cow in a simple repeatability model. Additive genetic merit for conformation traits changed with the age of the animal. Some traits were affected by age more than others. The single trait, single record model and the simple repeatability model were not appropriate in predicting breeding values at mature ages for rear udder width and rear udder height.

Genetic evaluation of dairy cattle for conformation traits using random regression models

SummaryRandom regression models were applied to eight conformation traits (i.e. stature, rump angle, thurl width, rear leg set, rear udder width, rear udder height, udder depth, and fore udder attachment) of Holstein cows from the northeastern United States. Covariates for fixed and random regressions included age and age‐squared for six of the traits, and two additional covariates were included for rear udder width and rear udder height. Other effects in the model were herd—year‐classifier and months in milk. Fixed covariates were nested within year of birth of the cow. Variance components were estimated using Bayesian theory and Gibbs sampling procedure. Estimated breeding values from the random regression models were compared to two single trait models. The first model utilized only the first classification record of the cow in first lactation, and the second model utilized all classifications of the cow in a simple repeatability model. Additive genetic merit for conformation traits changed with the age of the animal. Some traits were affected by age more than others. The single trait, single record model and the simple repeatability model were not appropriate in predicting breeding values at mature ages for rear udder width and rear udder height.

The use of test day models with small size of contemporary groups

IntroductionFor many years genetic evaluation of dairy cattle has been based on the analysis of 305‐day lactation milk production. Thus, to account for environmental variance, records are usually grouped in herd–year–season (HYS) classes. The 305‐day milk yield is computed on the basis of single test day records. An alternative approach to genetic evaluation is an analysis of individual test day records (P tak and S chaeffer 1993). A complete set of records contains more information than 305‐day milk yield, and, in consequence, by including the single herd–test date (HTD) effect, this approach leads to an improvement in the accuracy of genetic evaluation. Modeling the time–herd effect in this way reduces residual variance. In populations with a small herd size a number of records with no contemporaries in HTD classes could appear. It should be noted that accurate estimation and prediction in mixed linear models requires both model adequacy and respective data structure. Unfortunately, inclusion of a HTD effect when herds are small (typical situation for European cattle breeding) causes a decomposition of data structure (more unbalanced experimental design). Hence, a modification to the grouping of the records was recommended (S walve 1995; S trabel 1997).When a lactation model is used various modifications to HYS definition could be implemented to improve its adjustment to environmental effects (S chmitz et al. 1991; S trandberg and G randinson 1997; C rump et al. 1997). The definition of herd–test day class is so precise that it does not allow for a different assignment of records to classes. What could be done is to combine HTD classes within the herd by joining the consecutive records (S walve 1995) or across herds looking for records from ‘similar’ herds taken at ‘similar’ periods of time (S trabel 1997). HTD effect can also be set as random to avoid the loss of information especially when the number of single HTD classes is high (R eents et al. 1995; P&amp; ouml; s&amp; ouml; et al. 1996). Finally, the HTD effect can be replaced by HYS, typical for the traditional model (P tak and Żarnecki 1998).The objective of the present study was to compare animal model evaluations using test day (TD) models with different definitions of contemporary groups (CG). Evaluations based on the 305‐day lactation model were also carried out.

Comparison of Possible Covariates for Use in a Random Regression Model for Analyses of Test Day Yields

Random regression models have been proposed for the genetic evaluation of dairy cattle using test day records. Random regression models contain linear functions of fixed and random coefficients and a set of covariates to describe the shapes of lactation curves for groups of cows and for individual cows. Previous work has used a linear function of five covariates to describe lactation shape. This study compared the function of five covariates with a function of only three covariates in three random regression models. Comparisons of estimates of components of variances and covariances, as well as comparisons of EBV and their prediction errors for milk yield, were made among models. Small practical differences existed between models in all respects. The model using regressions with five covariates had a slight advantage for comparison of prediction error variances of daily yields.

Genetic Evaluation of Dairy Cattle Using Test Day Yields and Random Regression Model

A model for analyzing test day records that contains both fixed and random regression coefficients was applied to the genetic evaluation of first lactation data for Canadian Holstein dairy cows. Data were 5.1million test day records with milk, fat, and protein yields from calvings between 1988 and 1995 from herds in four regions of Canada. Each evaluated animal received five predictions for each trait representing the random regression coefficients. From these solutions, a range of estimated breeding values for various parts of the lactation could be calculated. Three genetic measures of persistency were compared. Bulls could be selected for both yields and persistency of their daughters in whatever combination was desirable. Test day analyses could result in monthly genetic evaluations for yield traits.

Effect of Variance of Interaction Effects of Sire and Herd on Selection for Milk and Fat Yield

The animal model for genetic evaluations of dairy cattle by the USDA currently includes a term for interaction effects of sire and herd. The relative magnitude of the variance of that effect was established in the 1960s as 14% of the total variances, but recent research has shown that the proportion is 2% or less. This report compared EBV using either the 14% or the actual estimate from 20 samples of records from herds in California, New York, and Pennsylvania. From 6 to 22% of bulls or cows selected for milk and fat yields based on evaluation with 14% of the total variance would not be selected using the sample estimates, depending on selection intensity, region, and whether only first or up to three lactations were used in the evaluations. Nevertheless, the average EBV of the bulls and cows selected based on 14% of the total variance were only slightly less than for those selected on 2%. This pilot research suggests that further study of the national data be done to establish the appropriate proportion of variance from interaction effects of sire and herd to use with national evaluations. Kinds of evaluations of bulls and ages of cows and bulls should be considered.

A comparison of the efficiency of the repeatability model on observed or transformed yields to a multi-variate analysis

A multi-variate animal model Best Linear Unbiased Prediction (BLUP) on milk, fat and protein yields in different lactations, as different traits, is the optimum method for genetic evaluation in dairy cattle for production. However, this is computationally demanding and usually a repeatability model is performed separately for milk, fat and protein yields. A simulation study by Visscher (1991) showed that the repeatability model on canonical transformed yield rather than on observed yield was a better approximation to a multi-variate analysis using selection index. The aim of this study is to verify, using an animal model, the efficiency of the repeatability model relative to a multi-variate analysis on observed or transformed yields.

A comparison of the efficiency of the repeatability model on observed or transformed yields to a multi-variate analysis

A multi-variate animal model Best Linear Unbiased Prediction (BLUP) on milk, fat and protein yields in different lactations, as different traits, is the optimum method for genetic evaluation in dairy cattle for production. However, this is computationally demanding and usually a repeatability model is performed separately for milk, fat and protein yields. A simulation study by Visscher (1991) showed that the repeatability model on canonical transformed yield rather than on observed yield was a better approximation to a multi-variate analysis using selection index. The aim of this study is to verify, using an animal model, the efficiency of the repeatability model relative to a multi-variate analysis on observed or transformed yields.