Prediction of fatty acid composition using milk spectral data and its associations with various mid-infrared spectral regions in Michigan Holsteins.

Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.

Association between days open and milk spectral data in dairy cows.

Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm-1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5-90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm-1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm-1 [related to fat-A (C = O stretch)], wn 1,546 cm-1 (related to protein), wn 1,465 cm-1 (related to urea and fat), wn 1,399 to 1,245 cm-1 (related to acetone), and wn 1,110 cm-1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.

Genetic parameters for fertility traits assessed in herds divergent in milk energy output in Holstein-Friesian, Brown Swiss, and Simmental cattle.

In this study, we aimed to investigate differences in the genetics of fertility traits (heritability of traits and correlations between traits in divergent environments) in dairy cows of different production levels defined on the basis of the herd-average daily milk energy output (herd-dMEO). Data were obtained from Holstein-Friesian (n = 37,359 for fertility traits, 381,334 for dMEO), Brown Swiss (n = 79,638 for fertility traits, 665,697 for dMEO), and Simmental cows (n = 63,048 for fertility traits, 448,445 for dMEO) reared in northeastern Italy. Fertility traits under study were interval from calving to first service, interval from first service to conception, days open, calving interval, calving rate, and nonreturn rate at d 56. We classified herds into low and high productivity based on the herd-average dMEO (inferred using mixed effects models). We estimated genetic parameters using Bayesian bivariate animal models, where expressions of a phenotype in the low and high dMEO herds were taken as being different-albeit correlated-traits. Fertility traits were more favorable in Simmental than in Holstein-Friesian cows, whereas for all traits, Holstein-Friesian had the highest estimates of intraherd heritability [ranging from 0.021 (0.006-0.038) to 0.126 (0.10-0.15)] and Simmental the lowest [ranging from 0.008 (0.001-0.017) to 0.101 (0.08-0.12)]. The genetic correlations between fertility traits and dMEO were moderate and unfavorable, ranging, in absolute values, from 0.527 (0.37-0.68) to 0.619 (0.50-0.73) in Holstein-Friesian; from 0.339 (0.20-0.47) to 0.556 (0.45-0.66) in Brown Swiss; and from 0.340 (0.10-0.60) to 0.475 (0.33-0.61) in Simmental cattle. The only exception was the nonreturn rate at d 56, which had weak genetic correlations with dMEO in all 3 breeds. The herd correlations between fertility and dMEO tended to be modest and favorable and the residual correlations modest and variable. The heritability of fertility traits tended to be greater in the low dMEO than in the high dMEO herds in the case of the Holstein-Friesians, but not in the case of the Brown Swiss or Simmentals. The additive genetic correlations between fertility traits in the low and high dMEO herds were always lower than 1 [0.329 (-0.17 to 0.85) to 0.934 (0.86 to 0.99)] for all traits considered in all breeds. The correlation was particularly low for the threshold characters and the interval from first service to conception in Holstein-Friesian, suggesting that the relative performances of genotypes vary significantly between herds of different dMEO levels. Although there was large variability in the estimates, results might support making separate genetic evaluations of fertility in the different herd production groups. Our results also indicate that Simmental, a dual-purpose breed, has higher fertility and lower environmental sensitivity than Holstein-Friesian, with Brown Swiss being intermediate.

A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows.

Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.

High-density genotyping of immune loci in Kawasaki disease and IVIG treatment response in European-American case-parent trio study.

Kawasaki disease (KD) is a diffuse and acute small-vessel vasculitis observed in children, and has genetic and autoimmune components. We genotyped 112 case-parent trios of European decent (confirmed by ancestry informative markers) using the immunoChip array, and performed association analyses with susceptibility to KD and intravenous immunoglobulin (IVIG) non-response. KD susceptibility was assessed using the transmission disequilibrium test, whereas IVIG non-response was evaluated using multivariable logistic regression analysis. We replicated single-nucleotide polymorphisms (SNPs) in three gene regions (FCGR, CD40/CDH22 and HLA-DQB2/HLA-DOB) that have been previously associated with KD and provide support to other findings of several novel SNPs in genes with a potential pathway in KD pathogenesis. SNP rs838143 in the 3'-untranslated region of the FUT1 gene (2.7 × 10(-5)) and rs9847915 in the intergenic region of LOC730109 | BRD7P2 (6.81 × 10(-7)) were the top hits for KD susceptibility in additive and dominant models, respectively. The top hits for IVIG responsiveness were rs1200332 in the intergenic region of BAZ1A | C14orf19 (1.4 × 10(-4)) and rs4889606 in the intron of the STX1B gene (6.95 × 10(-5)) in additive and dominant models, respectively. Our study suggests that genes and biological pathways involved in autoimmune diseases have an important role in the pathogenesis of KD and IVIG response mechanism.

Predictive ability of alternative models for genetic analysis of clinical mastitis.

Mastitis in cows can be defined as a binary trait, reflecting presence or absence of clinical mastitis (CM), or as a count variable, number of mastitis cases (NCM), within a defined time interval. Many different models have been proposed for genetic analyses of mastitis, and the objective of this study was to evaluate the predictive ability and sire predictions of a set of models for genetic evaluation of CM or NCM. Linear- and threshold liability models for CM, and linear, censored ordinal threshold, and zero-inflated Poisson (ZIP) models for NCM were compared in a cross-validation study. To assess the ability of these models to predict future data, records from 620492 first-lactation Norwegian Red cows, which were daughters of 3064 sires, were evaluated in a fourfold cross-validation scheme. The mean squared error of prediction was used for model comparison. All models but ordinal threshold model equally performed when comparing the overall predictive ability. This result was on average, across sick and healthy cows; however, the models behaved differently for each category of animals. For example, healthy cows were predicted better by the threshold and linear models for binary data and ZIP model, whereas for mastitic cows, the ordinal threshold model was by far the best model. Predicted sire effects and rankings of sires were highly correlated across all models. For practical purposes, the linear models are very competitive with the nonlinear models.

Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins.

Genome-enabled prediction of breeding values using high-density panels (HDP) can be highly accurate, even for young sires. However, the cost of the assay may limit its use to elite animals only. Low-density panels (LDP) containing a subset of single nucleotide polymorphisms (SNP) may give reasonably accurate predictions and could be used cost-effectively with young males and females. This study evaluates strategies for selecting subsets of SNP for several traits, compares predictive ability of LDP with that of HDP, and assesses the benefits of including parent average (PA) as a predictor in models using LDP. Data consisting of progeny-test predicted transmitting ability (PTA) for net merit and 6 other traits of economic interest from 4,783 Holstein sires were evaluated using testing and training sets with regressions on their high-density genotypes and parent averages for net merit index. Additionally, SNP subsets of different sizes were selected using different strategies, including the "best" SNP based on the absolute values of their estimated effects from HDP models for either the trait itself or lifetime net merit, and evenly spaced (ES) SNP across the genome. Overall, HDP models had the best predictive ability, setting an upper bound for the predictive ability of LDP sets. Low-density panels targeting the SNP with strongest effects (for either a single trait or lifetime net merit) provided reasonably accurate predictions and generally outperformed predictions based on evenly spaced SNP. For example, evenly spaced sets would require at least 5,000 to 7,500 SNP to reach 95% of the predictive ability provided by HDP. On the other hand, this level of predictive ability can be achieved with sets of 2,000 SNP when SNP are selected based on magnitude of estimated effects for the trait. Accuracy of predictions based on LDP can be improved markedly by including parent average as a fixed effect in the model; for example, a set with the 1,000 best SNP using the parent average achieved the 95% of the accuracy of a HDP model.

Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle.

The objective of the present study was to evaluate the predictive ability of direct genomic values for economically important dairy traits when genotypes at some single nucleotide polymorphism (SNP) loci were imputed rather than measured directly. Genotypic data consisted of 42,552 SNP genotypes for each of 1,762 Jersey sires. Phenotypic data consisted of predicted transmitting abilities (PTA) for milk yield, protein percentage, and daughter pregnancy rate from May 2006 for 1,446 sires in the training set and from April 2009 for 316 sires in the testing set. The SNP effects were estimated using the Bayesian least absolute selection and shrinkage operator (LASSO) method with data of sires in the training set, and direct genomic values (DGV) for sires in the testing set were computed by multiplying these estimates by corresponding genotype dosages for sires in the testing set. The mean correlation across traits between DGV (before progeny testing) and PTA (after progeny testing) for sires in the testing set was 70.6% when all 42,552 SNP genotypes were used. When genotypes for 93.1, 96.6, 98.3, or 99.1% of loci were masked and subsequently imputed in the testing set, mean correlations across traits between DGV and PTA were 68.5, 64.8, 54.8, or 43.5%, respectively. When genotypes were also masked and imputed for a random 50% of sires in the training set, mean correlations across traits between DGV and PTA were 65.7, 63.2, 53.9, or 49.5%, respectively. Results of this study indicate that if a suitable reference population with high-density genotypes is available, a low-density chip comprising 3,000 equally spaced SNP may provide approximately 95% of the predictive ability observed with the BovineSNP50 Beadchip (Illumina Inc., San Diego, CA) in Jersey cattle. However, if fewer than 1,500 SNP are genotyped, the accuracy of DGV may be limited by errors in the imputed genotypes of selection candidates.

Integrating biological information into the statistical analysis and design of microarray experiments.

Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.

Technical note: an R package for fitting generalized linear mixed models in animal breeding.

Mixed models have been used extensively in quantitative genetics to study continuous and discrete traits. A standard quantitative genetic model proposes that the effects of levels of some random factor (e.g., sire) are correlated accordingly with their relationships. For this reason, routines for mixed models available in standard packages cannot be used for genetic analysis. The pedigreemm package of R was developed as an extension of the lme4 package, and allows mixed models with correlated random effects to be fitted for Gaussian, binary, and count responses. Following the method of Harville and Callanan (1989), a correlation between levels of the grouping factor (e.g., sire) is induced by post-multiplying the incidence matrix of the levels of this random factor by the Cholesky factor of the corresponding (co)variance matrix (e.g., the numerator relationship matrix between sires). Estimation methods available in pedigreemm include approximations to maximum likelihood and REML. This note describes the classes of models that can be fitted using pedigreemm and presents examples that illustrate its use.

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins.

Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest for the Poisson model, followed by the ordinal threshold model. In general, the models for count variables more accurately identified diseased animals and more accurately predicted mastitis costs. Healthy animals were more accurately identified by the probit model.

Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows.

This study had 3 objectives: to estimate genetic parameters and predict sires' transmitting abilities for clinical mastitis in a Spanish Holstein population, to propose a methodology for comparing models with different response variables by using a cost-based loss function, and to evaluate alternative genetic evaluation models by using this methodology. On-farm records for clinical mastitis from herds in 3 Spanish regions were analyzed as a binary trait (CM) and as number of episodes (NCM) per lactation. Linear and probit models were fitted for CM, whereas linear and Poisson models were used for NCM. Predictive ability of the models was evaluated by using the average predicted residual sum of squares from cross-validation and an alternative cost-based loss function. The loss function for model comparison was calculated by using average mastitis costs depending on the NCM and average cost per infected lactation. The average cost per infected lactation was $345.58, whereas the cost per lactation ranged from $204.86 to $985.44 for lactations with 1 to 5 cases, respectively. Management and hygiene practices on individual farms had a large impact on clinical mastitis because the herd-year variance was larger than that of other random effects considered. The sire variance was significantly different from zero, confirming that genetic variation exists for clinical mastitis. Estimates of heritability for CM using the linear and probit models were 0.07 and 0.10 on the underlying scale, respectively. For NCM, the estimate of heritability for the linear model was 0.10 and estimates for the Poisson model evaluated at the mean and the median of lambda on the underlying scale were 0.09 and 0.07, respectively. Regarding ranking of sires, the definition of response variable (CM or NCM) was of greater importance than the choice of statistical model. Cross-validation results indicated that models with the best fit for CM and NCM were the probit model and the linear model, respectively. However, a comparison across all models using the alternative cost-based loss function showed that using NCM as a response variable with a Poisson model provided the most accurate predictions of future costs associated with clinical mastitis.

Effect of suckling restriction with nose plates and premature weaning on postpartum anestrous interval in primiparous cows under range conditions.

Suckling and nutrition are generally recognized as two major factors controlling the duration of the postpartum anovulatory period. In the present study, the effect of premature weaning and suckling restriction with nose plates (NPs) on cow and calf performance was evaluated. The study was conducted over 2 years; primiparous Hereford cows, weighing (mean+/-S.E.M.) 344+/-3.5kg and with 4.1+/-0.05 units of body condition score (BCS) (scale 1-8 [Vizcarra, J.A., Ibañez, W., Orcasberro, R., 1986. Repetibilidad y reproductibilidad de dos escalas para estimar la condición corporal de vacas Hereford. Investigaciones Agronómicas 7 (1), 45-47]) at calving, remained with their calves until 72.5+/-1.2 days postpartum (day 0). They were then assigned to one of three treatments: (i) calves with free access to their dams and ad libitum suckling (S, n=29); (ii) calves fitted with NPs for 14 days, but remained with their dams (NP, n=29), and (iii) calves that were weaned from their dams (W, n=28). All cows were anestrus at the time treatments commenced (day 0). All cows were blood sampled twice weekly from 1 week before the beginning of the experiment until the end of the mating period (day 74) for progesterone analysis. The mating period began on day 14. Cows in W treatment had ovulations earlier (P<0.05) than those in NP and S groups. Cows in the NP group had longer (P<0.05) intervals between the first progesterone increase and normal luteal phase than cows in the other two treatments groups (23.3+/-3.2 vs. 6.5+/-3.2 and 5.2+/-3.3 days for NP, S and W cows, respectively). Fifty per cent of the cows with NP had a short cycle (7 days) but there was a group of cows that had longer (P<0.05) intervals (66 days) between first progesterone increase and normal estrous activity. In the NP group, 8 of 29 cows had a short luteal phase and then a normal one; for 9 of these 29 cows progesterone concentrations remained low for 6 weeks from the beginning of the treatment; and for 12 of these 29 cows progesterone concentrations initially increased after treatment initiation, but these animals became anestrus thereafter. Short-term suckling restriction with NPs led to a variable response in primiparous cows of moderate body condition under range conditions.

Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows.

Clinical mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of mastitis, it has rarely been used for studying the genetics of mastitis. Many models have been proposed for genetic analysis of mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were clinical mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, ..). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.