Comparison between linear models and survival analysis for genetic evaluation of clinical mastitis in dairy cattle

Clinical mastitis was analyzed with mixed linear models (LM) and survival analysis (SA) using data from the first 3 lactations of >200,000 Swedish Holstein cows having their first calving between 1995 and 2000. The model for both methods included fixed effects of year-month and age at calving, fixed regressions of proportions of heterosis and North American Holstein genes, and random effects of herd-year at calving and sire. For the LM, clinical mastitis was defined as a binary trait measured from 10 d before to 150 d after calving. For the SA, clinical mastitis was defined either as the time period from 10 d before calving to the day of first treatment or culling because of mastitis (uncensored record) or from 10 d before to the day of next calving, culling for reasons other than mastitis, movement to a new herd, or to lactation d 240 (censored record). The heritability estimates from SA (0.03 to 0.04) were higher than those obtained with the LM (0.01 to 0.03). Consequently, the accuracies of estimated transmitting abilities were also higher for the trait analyzed with SA. The difference between estimates from the 2 methods was greater for later lactations. This study reveals the potential of analyzing clinical mastitis data with SA.     

International genetic evaluation for direct longevity in dairy bulls

The aims of this study were to document, present, and discuss the procedure used to calculate the international estimated breeding value (EBV) for longevity for Brown Swiss, Guernsey, Holstein, Jersey, Red Dairy Cattle, and Simmental breeds. Data from 19 countries and 123,833 national sires’ breeding value were used for this purpose. Trait definitions and national genetic evaluation procedures were first summarized; and this showed that differences among countries existed. International breeding values for direct longevity were calculated using a multi-trait across-country evaluation model. The data editing method was identical to the one used for the February 2007 routine international genetic evaluation. Estimated genetic correlations presented in this study were similar to those presented in the literature and, in general, differed from unity because of differences in trait definitions, culling reasons, data included, evaluation procedures, genotype-environment interactions, and weak genetic ties among countries. The average genetic correlations for Holstein ranged from 0.49 to 0.76. The genetic correlations for Brown Swiss and Guernsey ranged from 0.29 to 0.95 and from 0.30 to 0.89, respectively. For Jersey and Red Dairy Cattle the genetic correlations ranged from 0.39 to 0.61 and from 0.30 to 0.96, respectively. For Simmental the genetic correlation was 0.59. Different predictors were used at national levels to define combined longevity. These predictors were combined using economic and empirical weights. Three out of 15 countries published international EBV of direct longevity only and 12 out of 15 countries combined direct longevity with predictors (combined longevity). International breeding values for longevity were combined into the total merit index by most of the member organizations and made available to breeders across the world through magazines and Web sites. Even if some breeders are not familiar with longevity EBV, they will select for this trait automatically if they use the published total merit indexes.     

Economic trade-offs between genetic improvement and longevity in dairy cattle

Genetic improvement in sires used for artificial insemination (AI) is increasing faster compared with a decade ago. The genetic merit of replacement heifers is also increasing faster and the genetic lag with older cows in the herd increases. This may trigger greater cow culling to capture this genetic improvement. On the other hand, lower culling rates are often viewed favorably because the costs and environmental effects of maintaining herd size are generally lower. Thus, there is an economic trade-off between genetic improvement and longevity in dairy cattle. The objective of this study was to investigate the principles, literature, and magnitude of these trade-offs. Data from the Council on Dairy Cattle Breeding show that the estimated breeding value of the trait productive life has increased for 50 yr but the actual time cows spend in the herd has not increased. The average annual herd cull rate remains at approximately 36% and cow longevity is approximately 59 mo. The annual increase in average estimated breeding value of the economic index lifetime net merit of Holstein sires is accelerating from $40/yr when the sire entered AI around 2002 to $171/yr for sires that entered AI around 2012. The expectation is therefore that heifers born in 2015 are approximately $50 more profitable per lactation than heifers born in 2014. Asset replacement theory shows that assets should be replaced sooner when the challenging asset is technically improved. Few studies have investigated the direct effects of genetic improvement on optimal cull rates. A 35-yr-old study found that the economically optimal cull rates were in the range of 25 to 27%, compared with the lowest possible involuntary cull rate of 20%. Only a small effect was observed of using the best surviving dams to generate the replacement heifer calves. Genetic improvement from sires had little effect on the optimal cull rate. Another study that optimized culling decisions for individual cows also showed that the effect of changes in genetic improvement of milk revenue minus feed cost on herd longevity was relatively small. Reduced involuntary cull rates improved profitability, but also increased optimal voluntary culling. Finally, an economically optimal culling model with prices from 2015 confirmed that optimal annual cull rates were insensitive to heifer prices and therefore insensitive to genetic improvement in heifers. In conclusion, genetic improvement is important but does not warrant short cow longevity. Economic cow longevity continues to depends more on cow depreciation than on accelerated genetic improvements in heifers. This is confirmed by old and new studies.     

Survival analysis applied to genetic evaluation for female fertility in dairy cattle

The objective of this research was to study whether survival analysis results in a more accurate genetic evaluation for female fertility traits compared with the usual methodology based on linear models. The fertility trait studied was interval between calving and last insemination. A stochastic simulation describing the reproductive cycle of first-parity cows was done, in which true breeding values for conception rate were created. A model containing effects of sire and herd was used both with survival analysis and with mixed linear model analysis to predict sire breeding values. Correlations between true breeding values for conception rate and breeding values for calving to last insemination predicted by the best survival analysis model or the best linear model were 0.77 and 0.68, respectively. The results showed that when pregnancy status is known, survival analysis is a better method than linear models for genetic evaluation of conception rate when using observations on the interval between calving and last insemination.     

Estimation of genetic parameters and heterosis for longevity in crossbred Danish dairy cattle

Crossbreeding has been shown to improve the longevity of dairy cattle in countries across the world. The aim of this study was to estimate heterosis, breed effects, and genetic parameters for longevity in crossbred dairy cattle among Danish Holstein (DH), Danish Red (DR), and Danish Jersey (DJ) breeds. Data were provided from 119 Danish commercial herds that use systematic crossbreeding (i.e., rotational crossbreeding). Additional data from 11 mixed-breed herds with DH and DJ were included to estimate reliable breed effects for DJ. Survival information on 73,741 cows was analyzed with a linear animal model using the artificial insemination–REML algorithm in the DMU package. Five longevity (L) traits were defined: days from first calving until the end of first lactation or culling (L1), days from first calving until the end of second lactation or culling (L2), days from first calving until the end of third lactation or culling (L3), days from first calving until the end of fourth lactation or culling (L4), and days from first calving until the end of fifth lactation or culling (L5). Heritabilities ranged between 0.022 and 0.090. Additive breed effects in units of days were estimated relative to DH for DR as ?0.5 (L1), +10.5 (L2), +18.5 (L3), +11.9 (L4), and +28.6 (L5), and corresponding figures for DJ were +2.0, +0.5, +14.2, +27.7, and +44.0. Heterosis effects in L1 were low (1.2%) but favorable in crosses between DH and DR, whereas negative heterosis effects were estimated for crosses between DH and DJ (?2.5%) and DR and DJ (?1.2%). The largest heterosis effects for L2, L3, L4, and L5 were found in DH × DR and were favorable (+3.3, +5.7, +7.7, and +8.5%, respectively). Corresponding figures for heterosis effects in DH × DJ and DR × DJ were favorable as well: +2.3, +4.1, +5.6, and +6.2% in DH × DJ and +3.1, +7.3, +6.9, and +7.2% in DR × DJ. The favorable heterosis effects show that crossbreeding is an efficient tool for improving longevity in Danish dairy cattle.     

Comparison between sire-maternal grandsire and animal models for genetic evaluation of longevity in a dairy cattle population with small herds

Survival analysis techniques for sire-maternal grandsire (MGS) and animal models were used to test the genetic evaluation of longevity in a Slovenian Brown cattle population characterized by small herds. Three genetic models were compared: a sire-MGS model for bulls and an approximate animal model based on estimated breeding values (EBV) from the sire-MGS model for cows, an animal model, and an animal model based on the estimated variance components from the sire-MGS model. In addition, modeling the contemporary group effect was defined as either a herd or a herd-year (HY) effect. With various restrictions on the minimum HY group size (from 1 to 10 cows per HY), changes in estimates of variance components, and consequently also in EBV, were observed for the sire-MGS and animal models. Variance of contemporary group effects decreased when an HY effect was fitted instead of a herd effect. In the case of a sire-MGS model, estimates of additive genetic variance were mostly robust to changes in minimum HY group size or fitting herd or HY effect, whereas they increased in the animal model when HY instead of herd effects was fitted, possibly revealing some confounding between cow EBV and contemporary group effect. Estimated heritabilities from sire-MGS models were between 0.091 and 0.119 and were mainly influenced by the restriction on the HY group size. Estimated heritabilities from animal models were higher: between 0.125 and 0.160 when herd effect was fitted and between 0.171 and 0.210 when HY effect was fitted. Rank correlations between the animal model and the approximate animal model based on EBV from the sire-MGS model were high: 0.94 for cows and 0.93 for sires when a herd effect was fitted and 0.90 for cows and 0.93 for sires when an HY effect was fitted. Validation performed on the independent validation data set revealed that the correlation between sire EBV and daughter survival were slightly higher with the approximate animal model based on EBV from the sire-MGS model compared with the animal model. The correlations between the sire EBV and daughter survival were higher when the model included an HY effect instead of a herd effect. To avoid confounding and reduce computational requirements, it is suggested that the approximate animal model based on EBV from the sire-MGS model and HY as a contemporary group effect is an interesting compromise for practical applications of genetic evaluation of longevity in cattle populations.     

Alternative subclinical mastitis traits for genetic evaluation in dairy cattle

Chronic subclinical mastitis (SCM), characterized by changes in milk composition and high somatic cell count (SCC) in milk for a prolonged period of time, is often caused by a bacterial infection. Different levels of SCC have been suggested and used as threshold to identify subclinical infection. The aim of this study was to examine different definitions of SCM based on test-day SCC and estimate genetic parameters for these traits and their genetic correlation to milk production. Test-day SCC records from 1,209,128 Norwegian Red cows in lactation 1 to 3 were analyzed. Twelve SCM traits were defined as binary with 2 test-day SCC in a row above SCC thresholds from 50,000 to 400,000 cells/mL (SCM50, SCM100, SCM150, SCM200, SCM250, SCM300, SCM350, and SCM400), with 3 test-day SCC in a row above 200,000 and 400,000 cells/mL (SCM200_3 and SCM400_3), and the number of days before the first case with SCM50 (D50) or SCM400 (D400). The heritability and genetic correlations were estimated for SCM traits and the mean lactation-average somatic cell score (LSCS) using linear animal repeatability models. The total mean frequency of SCM ranged from 1.2% to 51.8%, for different trait definitions, high for low SCC threshold (SCM50) and low for the highest SCC threshold (SCM400_3). For the 2 traits based on number of days, the mean values were 104 (D50) and 117 (D400) days. The mean LSCS was 4.4 (equivalent to around 82,000 SCC). Heritabilities for the 12 alternative SCM traits were low and varied from 0.01 (SCM400_3) to 0.1 (SCM100), whereas for LSCS the estimated heritability was 0.3 and standard error varied from 0.001 to 0.003. Genetic correlations among the SCM traits ranged from 0.7 (D50 and SCM400) to 1 (SCM350 and SCM400), whereas between SCM traits and milk production the correlation ranged from 0.07 (LSCS) to 0.3 (D400). The standard error for genetic correlations varied from 0.001 to 0.06. The heritability was low and the genetic correlations were strong among SCM traits. Genetic correlations lower than 1 suggest that the alternative SCM traits are genetically different from LSCS, the trait currently used in genetic evaluations for Norwegian Red. Hence, the alternative traits will add information and improve breeding for better udder health.     

Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis

The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis treatment was assumed to have an effect on functional longevity until the end of the lactation had the highest likelihood, and the model in which mastitis treatment had an effect for only a short period had the lowest likelihood. A cow with mastitis had 1.69 times greater risk of being culled than did a healthy herdmate with all other effects being the same. A model without mastitis treatment was used to predict transmitting abilities of bulls for risk of being culled, based on longevity records of their daughters, and was expressed in terms of risk of being culled. The correlation between the risk of being culled and the national evaluations of the bulls for mastitis resistance was approximately -0.4, indicating that resistance against mastitis was genetically correlated with a lower risk of being culled and, thus, a longer functional length of productive life.     

Genetic evaluation of dairy cattle using a simple heritable genetic ground

The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value. Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation. Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index. Automated genotyping enables the determination of many single‐nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied). However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB. Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors. A one‐step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population. An augmented pedigree–genomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals. Copyright © 2010 Society of Chemical Industry     

Short communication: Multivariate outlier detection for routine Nordic dairy cattle genetic evaluation in the Nordic Holstein and Red population

It is of practical importance to ensure the data quality from a milk-recording system before use for genetic evaluation. A procedure was developed for detection of multivariate outliers based on an approximation for Mahalanobis distance and was implemented in the Nordic Holstein and Red population. The general target of this procedure is based on the Nordic Cattle Genetic Evaluation yield model, which is a 9-trait model for milk, protein, and fat in the first 3 lactations. The procedure is based on the phenotypic correlation structure as a function of days in milk (DIM) and on computation of trait means and standard deviations within a production year, lactation, and DIM. For each record in the data, a Mahalanobis distance value was computed based on the trait mean and the covariance matrix for the actual production year, lactation, and DIM. A set of cutoff values, ranging from 10 to 100 with steps of 10, for discarding multivariate outliers was investigated. Prediction accuracy was calculated as the Pearson correlations between estimated breeding values predicted by full data set and estimated breeding values predicted by reduced data set for cows without records in the reduced data set and with 1 or more records deleted due to the editing rules on Mahalanobis distance. The results showed that, averaged over all scenarios, gains of 0.005 to 0.048 on prediction accuracy have been obtained by deleting the multivariate outliers. The improvements were more profound for progeny of young bulls compared with progeny of proven bulls. It is easy to implement this multivariate outlier-detection procedure in the routine genetic evaluation for different dairy cattle breeds; however, an optimal cutoff value for Mahalanobis distance needs to be defined to achieve an acceptable compromise between genetic evaluation accuracy and data deletion.     

Simultaneous genetic evaluation of sires and cows for a large population of dairy cattle

A strategy for simultaneous sire and cow evaluation of genetic merit was implemented. First lactation records of 1,074,971 Holstein cows in 20,065 herds were included. After inclusion of ancestors, there were 1,741,356 equations: 1,505,938 cow, 229,394 herd-year-season, 6000 sire, and 24 genetic group equations. All known genetic relationships among animals were considered. Genetic group coefficients were assigned based on animals that had one or more parents unidentified. Mixed model equations from an animal model were transformed to solve for total additive genetic merit. The coefficient matrix was sparse with .00039% nonzero elements. Equations were blocked by herds. A final block included sires, groups, and cows that had no daughters or records. Effects of herd-year-season were solved last within each herd. All herd blocks were solved before sire equations. A form of block iteration with successive overrelaxation was used to obtain solutions. A total of 30 rounds were completed. Number of iterations per round for herd blocks decreased from an average of 4.93 in round 1 to the minimum allowed, 3.00, in round 30. The correlation between Northeast Artificial Insemination Sire Comparisons and sire solutions from this study stabilized at .94. At round 30, 96.4, 95.2, 99.4, and 75.0% of solutions for cow, sire, herd-year-season, and group effects changed less than 4.54 kg from the previous round.     

Effect of misidentification on genetic gain and estimation of breeding value in dairy cattle populations

The effect of pedigree errors on estimated breeding value and genetic gain for a sex-limited trait with heritability of 0.25 was evaluated. Ten populations of 100,000 milking cows were simulated with correct paternity identification for all animals, and 10 populations were simulated with 10% incorrect paternal identification. The initial populations consisted of 100,000 unrelated individuals, and simulations were continued for 20 yr. The BLUP genetic evaluations were computed every year by an animal model analysis for each complete population. Estimated breeding values for the populations with 10% incorrect paternity were biased, especially in the later generations. Genetic gains were 4.3% higher with correct paternity identification. Reduction of pedigree errors by paternity confirmation of daughters of test sires by DNA microsatellites may result in considerable economic benefits, depending on the cost of testing in each country.     

Genetic evaluation for longevity of Dutch dairy bulls

Parameters needed for survival analysis of longevity records of cows to predict breeding values of their sires were estimated with data on Dutch Black and White and Red and White cows. The heritabilities of functional productive life were 0.041 and 0.036 on the log scale for Black and White and Red and White cows, respectively. Although the heritabilities and other parameters differed between both breeds, the resulting breeding values were hardly affected: the correlation between breeding values of Red and White bulls using either Red and White parameters or Black and White parameters was 0.992. Genetic correlations between the direct breeding value for functional longevity (based solely on longevity of sires' daughters) and breeding values for conformation, health, and fertility traits were calculated. Several alternative selection indices were investigated using these correlations. Based on the resulting reliabilities, it was concluded that the Dutch breeding value for functional longevity should be based on longevity, rump angle, teat placement, udder depth, feet and legs, and somatic cell count. The index was expressed on a scale with average of 100 and a standard deviation of 4 points (at 80% reliability). The economic value was Dfl. 65 per genetic standard deviation, which was 0.46 times the economic value of INET (Net Milk Revenue Index). For the breeding value for functional longevity that was first published in August 1999, slight modifications in the model were made.     

Use of repeated measures analysis for evaluation of genetic background of dairy cattle behavior in automatic milking systems

Milking frequencies measured at official test days were used with repeated measurement analysis to reveal the environmental and genetic impact on the milking frequency of cows in automatic milking systems. Repeated measurements were 3 test-day observations per cow within days in milk (DIM) classes, with 1,216 cows in DIM class 1 (d 0 to 99), from 1,112 cows in DIM class 2 (d 100 to 199), and from 1,004 cows in DIM class 3 (d 200 to 299) kept in 15 farms. Selection criteria for models analyzing repeated measurements were Akaike and Schwarz Bayesian values, which favored the autoregressive [AR(1)] covariance structure over the compound symmetry model. Results from the AR(1) model indicated a significant impact of fixed herd and parity effects. Milking frequencies decreased with increasing parities and were greatest for first-parity cows. High daily milk yield was associated with higher milking frequencies. Heritabilities for milking frequency were 0.16, 0.19, and 0.22 in DIM classes 1, 2, and 3, respectively, from the AR(1) model. Higher heritabilities in the later stage of lactation were due to a substantial reduction of the residual variance. Genetic correlations between test-day milk yield and daily milking frequency were in the range of 0.46 to 0.57 for all DIM classes and between milking frequency and somatic cell score were near zero. For verification of results, milking frequencies of the same cows obtained from herd management programs were averaged within DIM classes. Heritabilities were slightly above the values from the AR(1) model. In conclusion, heritabilities for milking frequency in automatic milking systems are moderate enough to incorporate this behavioral trait in a combined breeding goal. The inevitable improvement of labor efficiency in dairy cattle farming demands such cows going easily and voluntarily in automatic milking systems.     

Predictive abilities of different statistical models for analysis of survival data in dairy cattle

The objective of this study was to compare alternative trait definitions and statistical models for genetic evaluation of survival in dairy cattle. Data from the first 5 lactations of 808,750 first-crop daughters of 3,064 Norwegian Red sires were analyzed. Seven sire models were used for genetic analyses: linear and threshold cross-sectional models for binary survival scores from first lactation; a linear multi-trait model for survival scores from the first 3 lactations; linear and threshold repeatability models for survival scores from the first 5 lactations; a Weibull frailty model for herd life in first lactation; and a Weibull frailty model for herd life in the first 5 lactations. The models were compared to assess predictive ability of sire estimated breeding values with respect to average survival 365 d after first calving for second-crop daughters (not included in calculation of predicted transmitting abilities) of 375 elite sires. Generally, the linear multi-trait model analyzing survival in the first 3 lactations as correlated traits gave more-accurate predicted sire breeding values compared with both linear and Weibull frailty models using data from first lactation only, even when the latter models were extended to include data up to the sixth lactation. The Weibull frailty models did not improve predictive ability of sire estimated breeding values over what was obtained using a simple cross-sectional linear model for binary survival in first lactation.     

Economic evaluation of pregnancy diagnosis in dairy cattle: a decision analysis approach

Cost-benefit evaluations of several pregnancy diagnosis schemes were performed. The strategy using on-farm milk progesterone test on d 19 after service, followed by treatment of nonpregnant cows with prostaglandin, was the most profitable returning $10.50 per cow above the cost of the intervention. An increase in efficiency of detection of estrus of greater than 20% among cows diagnosed nonpregnant and an error rate in pregnancy diagnosis of less than or equal to 3% were needed to ensure profitability. Pregnancy diagnosis by uterine palpation per rectum on d 35 after service, combined with the use of pressure-sensitive mounting devices on nonpregnant cows was the second most profitable strategy and returned $5.10 per cow. An increase in efficiency of detection of estrus of greater than or equal to 20% was required to ensure profitability. Embryonic mortality was also critical and an increase from a baseline value of 10% to 12%, as a result of early uterine palpation, made this scheme unprofitable ($-4.80 per cow). Pregnancy diagnosis by uterine palpation per rectum at 50 or 65 d was less profitable, with a return of $2.50 and $.10 per cow, respectively.     

Genetic analysis of cow survival in the Israeli dairy cattle population.

The linear model method of VanRaden and Klaaskate for analyzing herd life was expanded. Information on conception and protein yield was included in the estimation of predicted herd life of Israeli Holsteins. Variance components were estimated by a multitrait animal model. Heritability was slightly higher for herd life than for number of parities, but genetic correlations were close to unity. Animal model heritability estimates of herd life were higher than were sire model estimates. The expected herd life of pregnant cows was 420 d greater than for open cows. Each kilogram of increase in protein yield increased expected herd life by 9.5 d. Heritability of expected herd life increased from 0.11 for cows 6 mo after first calving to 0.14 for cows 3 yr from first calving. The genetic correlation of expected and actual herd life increased from 0.87 for records cut after 6 mo to 0.99 for records cut 3 yr after first calving. Phenotypic correlations increased from 0.61 to 0.94. Sire genetic evaluations based on predicted herd life of live cows were strongly biased if all records were weighted equally, and evaluations derived by weighting incomplete records to account for the effects of current herd life on variance components were nearly unbiased.     

Using conformation traits to improve reliability of genetic evaluation for herd life based on survival analysis

Genetic evaluation for herd life based on survival analysis utilizes information available on all animals, dead (uncensored) and alive (censored), but the reliability of bulls' breeding values depends only on the number of uncensored daughters. Therefore, information on correlated conformation traits scored on daughters during their first lactation may be essential for the evaluation of young bulls with mostly censored daughters. Currently available programs for genetic evaluation based on survival analysis cannot combine indirect information on conformation traits with direct information on herd life, nor can they estimate genetic covariances between herd life and conformation traits. In this study, an alternative approach has been developed and tested using data on Swiss Simmental and Red & White cattle. Genetic covariances were approximated using breeding values for herd life from a survival analysis and BLUP breeding values for 26 linear conformation traits from a separate multivariate analysis. An index combining direct breeding values for herd life and indirect breeding values obtained from conformation traits was constructed. The relative weighting of both information sources varied depending on the amount of available information. The maximum reliability based only on conformation traits was 0.64. Except for old bulls with >100 uncensored daughters, the combined reliability was always higher than the direct reliability from survival analysis.     

Genetic evaluation of mastitis in dairy cattle using linear models, threshold models, and survival analysis: a simulation study

The objective was to study, by simulation, whether survival analysis results in a more precise genetic evaluation for mastitis in dairy cattle than cross-sectional linear models and threshold models by using observation periods for mastitis of 2 lengths (the first 150 d of lactation, and the full lactation, respectively). True breeding values for mastitis liability on the underlying scale were simulated for daughters of 400 sires (average daughter group size, 60 or 150), and the possible event of a mastitis case within lactation for each cow was created. For the linear models and the threshold models, mastitis was defined as a binary trait within either the first 150 d of lactation or the full lactation. For the survival analysis, mastitis was defined as the number of days from calving to either the first case of mastitis (uncensored record) or to the day of censoring (i.e., day of culling, lactation d 150 or day of next calving; censored record). Cows could be culled early in lactation (within 10 d after calving) for calving-related reasons or later on because of infertility. The correlation between sire true breeding values for mastitis liability and sire predicted breeding values was greater when using the full lactation data (0.76) than when using data from the first 150 d (0.70) with an average of 150 daughters per sire. The corresponding results were 0.60 and 0.53, respectively, with an average of 60 daughters per sire. Under these simulated conditions, the method used had no effect on accuracy. The higher accuracy of sire breeding values can be translated into a greater genetic gain, unless counteracted by a longer generation interval.