Genetic Analysis of Somatic Cell Score in Danish Holsteins Using a Liability-Normal Mixture Model

Abstract

Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cases of mastitis. Here, putative mastitis statuses and breeding values for liability to putative mastitis were inferred solely from SCS observations. In total, there were 395,906 test-day records for SCS from 50,607 Danish Holstein cows. Four different statistical models were fitted: A) a classical (nonmixture) random regression model for test-day SCS; B1) an LNM test-day model assuming homogeneous (co)variance components for SCS from healthy (IMI-) and infected (IMI+) udders; B2) an LNM model identical to B1, but assuming heterogeneous residual variances for SCS from IMI- and IMI+ udders; and C) an LNM model assuming fully heterogeneous (co)variance components of SCS from IMI- and IMI+ udders. For the LNM models, parameters were estimated with Gibbs sampling. For model C, variance components for SCS were lower, and the corresponding heritabilities and repeatabilities were substantially greater for SCS from IMI- udders relative to SCS from IMI+ udders. Further, the genetic correlation between SCS of IMI- and SCS of IMI+ was 0.61, and heritability for liability to putative mastitis was 0.07. Models B2 and C allocated approximately 30% of SCS records to IMI+, but for model B1 this fraction was only 10%. The correlation between estimated breeding values for liability to putative mastitis based on the model (SCS for model A) and estimated breeding values for liability to clinical mastitis from the national evaluation was greatest for model B1, followed by models A, C, and B2. This may be explained by model B1 categorizing only the most extreme SCS observations as mastitic, and such cases of subclinical infections may be the most closely related to clinical (treated) mastitis.

Estimation of genetic and phenotypic parameters for bacteriological status of the udder, somatic cell score, and milk yield in dairy sheep using a threshold animal model

“…Using the same Bayesian approach, Penasa et al (2010) obtained a value of 0.05 for Table 1 Descriptive statistics for milk yield and somatic cell score both considering the whole sample and according to the health status (infected or not infected). the SCS heritability in a small autochthonous dairy cattle breed; whereas, other authors found higher values in German Holstein (Hinrichs et al, 2005) and Danish Holstein breed (Madsen and Odegard, 2006). The posterior mean estimate of heritability for liability to mastitis (i.e., infection status) was equal to 0.09, which is the same as the estimate reported by Riggio et al (2010) in a previous study on the same breed, using a threshold animal model, assuming a probit link function.…”

Genetic selection for reduced somatic cell counts in sheep milk: A review

“…non-infected) and infected animals, is treated equally. However, several researchers suggested that, in cattle, SCS in healthy and infected animals are different traits (Detilleux and Leroy, 2000;Boettcher et al, 2007;Madsen et al, 2008). This was also confirmed in sheep by Riggio et al (2010), who showed that SCS in healthy and infected animals can indeed be considered as different traits-with different heritabilities, and with a genetic correlation between bacteria negative and bacteria positive SCS of 0.62.…”

Genetic correlation between heart ratio and body weight as a function of ascites frequency in broilers split up into sex and health status

“…Kause et al (2005) found that the genetic correlation between BW and skeletal deformations changed from positive to negative when trout got older and more fish showed skeletal deformations. Further, several studies indicated that somatic cell scores in healthy and infected cattle are genetically different traits (Boettcher et al, 2007;Madsen et al, 2008). When more energy is needed to overcome problems due to infections or metabolic disorders, less energy is available for production.…”