Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle

Rekaya, Romdhane; Gianola, Daniel; Shook, G.E.

doi:10.1186/1297-9686-35-6-457

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…Another explanation for the lower heritabilities in our study is probably the much lower CM frequency. In our data, the overall CM frequency was about 10% to 11% compared to 18% (Rekaya et al, 2003) and 23% (Chang, 2002;Heringstad et al, 2003;Chang et al, 2004b) in other longitudinal studies of CM.…”

Section: Random Regression Models For Clinical Mastitiscontrasting

confidence: 43%

“…Heritability estimates of the liability to CM from threshold models are generally higher than those from linear models (Heringstad et al, 2000). Rekaya et al (2003) reported heritabilities for test-day records of CM of 27% at day 14, 5% at day 60, 3% at day 120 and 7% at day 305, from a Bayesian threshold RRM on 329 first-lactation Holstein cows. Heringstad et al (2003) and Chang et al (2004b) studied the same data from first-lactation Norwegian cattle using similar approaches as Rekaya et al (2003).…”

Section: Random Regression Models For Clinical Mastitismentioning

confidence: 97%

“…Rekaya et al (2003) reported heritabilities for test-day records of CM of 27% at day 14, 5% at day 60, 3% at day 120 and 7% at day 305, from a Bayesian threshold RRM on 329 first-lactation Holstein cows. Heringstad et al (2003) and Chang et al (2004b) studied the same data from first-lactation Norwegian cattle using similar approaches as Rekaya et al (2003). They presented heritabilities for CM between 7% and 13% before calving and between about 3% to 4% and 11% to 15% between calving and day 260.…”

Section: Random Regression Models For Clinical Mastitismentioning

confidence: 99%

“…milk production and SCC (Schaeffer, 2004). To a lesser extent, RRM have also been used for longitudinal binary data, in the context of a linear model for survival data (Veerkamp et al, 2001) and a threshold model for fertility (Averill et al, 2006) as well as for CM (Heringstad et al, 2003;Rekaya et al, 2003;Chang et al, 2004b). According to Veerkamp et al (2001), RRM for longitudinal binary data can be expected to combine some of the advantages of survival analysis and LMVM.…”

Section: Introductionmentioning

confidence: 99%

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Random regression models for genetic evaluation of clinical mastitis in dairy cattle

Carlén

Grandinson

Emanuelson

et al. 2009

Animal

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A genetic analysis of longitudinal binary clinical mastitis (CM) data recorded on about 90 000 first-lactation Swedish Holstein cows was carried out using linear random regression models (RRM). This method for genetic evaluation of CM has theoretical advantages compared to the method of linear cross-sectional models (CSM), which is currently being used. The aim of this study was to investigate the feasibility and suitability of estimating genetic parameters and predicting breeding values for CM with a linear sire RRM. For validation purposes, the estimates and predictions from the RRM were compared to those from linear sire longitudinal multivariate models (LMVM) and CSM. For each cow, the period from 10 days before to 241 days after calving was divided into four 1-week intervals followed by eight 4-week intervals. Within each interval, presence or absence of CM was scored as '1' or '0'. The linear RRM used to explain the trajectory of CM over time included a set of explanatory variables plus a third-order Legendre polynomial function of time for the sire effect. The time-dependent heritabilities and genetic correlations from the chosen RRM corresponded fairly well with estimates obtained from the linear LMVM for the separate intervals. Some discrepancy between the two methods was observed, with the more unstable results being obtained from the linear LMVM. Both methods indicated clearly that CM was not genetically the same trait throughout lactation. The correlations between predicted sire breeding values from the RRM, summarized over different time periods, and from linear CSM were rather high. They were, however, less than unity (0.74 to 0.96), which indicated some re-ranking of sires. Sire curves based on the time-specific breeding values from the RRM illustrated differences in intercept and slope among the best and the worst sires. To conclude, a linear sire RRM seemed to work well for genetic evaluation purposes, but was sensitive for estimation of genetic parameters.Keywords: dairy cattle, genetic evaluation, clinical mastitis, random regression model ImplicationsLinear random regression models (RRM) were used as a new longitudinal approach for genetic evaluation of clinical mastitis data in dairy cattle. This approach has theoretical advantages compared to the method of linear cross-sectional models (CSM) currently used. The estimated genetic parameters and predicted breeding values from the RRM were in good agreement with the results from linear longitudinal multivariate and linear CSM. However, the RRM approach was sensitive to parameter estimation in the current setting.

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Section: Random Regression Models For Clinical Mastitiscontrasting

confidence: 43%

Section: Random Regression Models For Clinical Mastitismentioning

confidence: 97%

Section: Random Regression Models For Clinical Mastitismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Random regression models for genetic evaluation of clinical mastitis in dairy cattle

Carlén

Grandinson

Emanuelson

et al. 2009

Animal

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“…Furthermore, two mastitis incidence scenarios were used (Table 1). Scenario one was chosen to reflect previous estimates of the incidence of mastitis in field data of Swedish first-parity cows [5,11,33], where a case of mastitis was defined as a veterinary-treated clinical mastitis. The recurrence rate of CM was based on data from the research herd of the Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences [1].…”

Section: Methodsmentioning

confidence: 99%

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

et al. 2012

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Background Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.

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Multivariate dynamic model for ordinal outcomes

Chaubert

Mortier²,

André³

2008

Journal of Multivariate Analysis

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Individual or stand-level biomass is not easy to measure. The current methods employed, based on cutting down a representative sample of plantations, make it possible to assess the biomasses for various compartments (bark, dead branches, leaves, . . . ). However, this felling makes individual longitudinal follow-up impossible. In this context, we propose a method to evaluate individual biomasses by compartments when these are ordinals. Biomass is measured visually and observations are therefore not destructive. The technique is based on a probit model redefined in terms of latent variables. A generalization of the univariate case to the multivariate case is then natural and takes into account of dependency between compartment biomasses. These models are then extended to the longitudinal case by developing a Dynamic Multivariate Ordinal Probit Model. The performance of the MCMC algorithm used for the estimation is illustrated by means of simulations built from known biomass models. The quality of the estimates and the impact of certain parameters, are then discussed.

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Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle

Cited by 13 publications

References 16 publications

Random regression models for genetic evaluation of clinical mastitis in dairy cattle

Random regression models for genetic evaluation of clinical mastitis in dairy cattle

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

Multivariate dynamic model for ordinal outcomes

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