Incidences of and genetic parameters for mastitis, claw disorders, and common health traits recorded in dairy cattle contract herds

Abstract

Test-day records for protein yield, protein percent, fat percent and somatic cell score combined with diagnoses for health traits from 19,870 Holstein cows kept in 9 large-scale contract herds in the region of Thuringia, Germany, were used to infer genetic parameters. From an electronic database system for recording diagnoses, 15 health disorders with highest incidences were extracted and grouped into the following 5 disease categories: claw disorders, mastitis, female fertility, metabolism, and ectoparasites. In a bayesian approach, threshold methodology was applied for binary distributed health disorders and linear models were used for gaussian test-day observations. Variances and variance ratios for health disorders were from univariate and covariance components among health disorders and between health disorders, and test-day production traits were from bivariate repeatability models. Incidences of health disorders increased with increasing parity and were substantially higher at the beginning of lactation. Only incidences for ectoparasites slightly increased with increasing stage of lactation. Heritabilities ranged from 0.00 for ectoparasites to 0.22 for interdigital hyperplasia. Heritabilities of remaining health disorders were in a narrow range between 0.04 (corpus luteum persistent) and 0.09 (dermatitis digitalis). Clustering diseases into categories did not result in higher heritabilities. The variance ratio of the permanent environmental component was higher than the heritability for the same trait, pointing to the conclusion that non-genetic factors influence repeated occurrence of health problems during lactation. Repeatabilities were relatively high with values up to 0.49 for interdigital hyperplasia. Genetic correlations among selected health disorders were low and close to zero, disproving the assumption that a cow being susceptible for a specific disease is also susceptible for other types of health disorders. Antagonistic genetic relationships between test-day protein yield and health disorders were found for ovarian cysts (0.57) and clinical mastitis (0.29). Remaining genetic correlations between diseases and production traits were close to zero. The genetic correlation between clinical mastitis and somatic cell score was 0.69. This study revealed reliable genetic parameters for health disorders and underlined the possibility of precise health data recording by farmers from contract herds that can be used for genetic evaluation of health traits.

Case–control approach application for finding a relationship between candidate genes and clinical mastitis in Holstein dairy cattle

“…an obvious infection of the udder, including dolor, rubor, change of colour and, additionally, flakes in the milk. When counting cases of CM, an interval of 5 days was required to consider an occurrence of CM as a new case of CM (Hinrichs et al 2005;Gernand et al 2012). More description about the data structure is presented by Bagheri et al (2013).…”

“…CM was diagnosed as described by Gernand et al (2012), i.e. an obvious infection of the udder, including dolor, rubor, change of colour and, additionally, flakes in the milk.…”

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…They can provide a valuable continuous documentation on diagnoses, treatments and prescriptions. This continuous documentation is urgent for the implementation of direct health data into breeding programs, but it assumes standardization and centralization (Gernand et al, 2012;ICAR, 2012). The earliest documentation for health traits in dairy cattle has been established in the Nordic countries since the 1970s (ICAR, 2012), and these data have been included in national evaluation systems (Phillipson and Lindhé, 2003).…”

Simultaneous genetic evaluation of simulated mastitis susceptibility and recovery ability using a bivariate threshold sire model

“…Such threshold models have been favored over the restricted maximum likelihood (REML) applied to generalized linear mixed models (GLMM) in several studies (e.g. Sørensen et al, 2009;Franzén et al, 2012;Gernand et al, 2012). This is because in threshold models where the trait of interest is defined on an underlying susceptibility scale, there are theoretical advantages as they are more appropriate to depict the physiological background of a disease data compared to linear models and the associated model assumptions such as normality (Yin et al, 2014).…”