Genomic prediction of serum biomarkers of health in early lactation.

Improved animal health and resilience are important breeding objectives for the dairy cow. Many diseases affecting the cow occur in the first 30 days after calving. Some of these diseases are associated with metabolic disorders such as ketosis and milk fever, which can have deleterious effects on animal health and welfare and farm profitability. Although heritability estimates of metabolic disorders are generally low, sufficient genetic variation exists suggesting that improvement in metabolic health through selection should be possible. Also, it has been reported that favourable genetic correlations between different metabolic disorders and between metabolic disorders and diseases such as mastitis and reproductive disorders exist. This suggests that selecting for improvements in metabolic health may lead to improvements in overall animal health. Advances in genomic selection methodology may provide a potential application for achieving genetic improvement in economically important but difficult-to-measure and lowly heritable health traits. This may be achieved by using data obtained from a relatively small genotyped reference population in combination with high quality phenotypic data. To test this, Dr T. D. W. Luke and colleagues in a study (1) estimated the genetic parameters of serum biomarkers of health in early-lactation dairy cows using data collected from a genotyped female reference population, and (2) studied the accuracy of genomic predictions of serum biomarker concentrations. Their results were published in the Journal of Dairy Science, Volume 102 of 2019, page 11142 to 11152. The title of the study was: Genomic prediction of serum biomarkers of health in early lactation.

In the study, they estimated genetic parameters and genomic prediction accuracies of serum biomarkers of health in early-lactation dairy cows. A single serum sample was taken from 1393 cows, located on 14 farms in south-eastern Australia, within 30 days after calving. The samples were analyzed for biomarkers of energy balance (β-hydroxybutyrate and fatty acids), macro-mineral status (calcium and magnesium), protein nutritional status (urea and albumin), and immune status (globulins, the albumin to globulin ratio, and haptoglobin). After editing, 47162 SNP marker genotypes were used to estimate genomic heritabilities and estimated breeding values (GEBV) for these traits in ASReml. Genetic correlations between traits were estimated by using bivariate models. 

The calculated heritabilities were low for β-hydroxybutyrate, fatty acids, calcium, magnesium and urea (approximately 0.09, 0.18, 0.07, 0.19 and 0.18, respectively), and moderate for albumin, globulins and the albumin-to-globulin ratio (0.27, 0.46, and 0.41, respectively). The heritability of the haptoglobin concentration was close to 0. The magnitude of the genetic correlations between traits varied considerably (0.01 to 0.96), and standard errors of these correlations were high (0.02 to 0.44). On the positive side, the direction of most genetic correlations was favourable, suggesting that selection for more optimal concentrations of one biomarker may result in more optimal concentrations of other biomarkers. Correlations between biomarker GEBV and existing breeding values for survival, somatic cell count and daughter fertility were small to moderate (0.07 to 0.45) and favourable, whereas correlations with breeding values for milk production traits were small (≤0.15). Accuracies of GEBV predicted using 5-fold cross validation were low (0.05 to 0.27), whereas the means of individual accuracies were greater, ranging from 0.31 to 0.51.

The authors concluded that by increasing the size of the reference population accuracies should be improved. Nevertheless, even with the smaller reference population used here, the results suggest that genomic prediction of health biomarkers should enable identification of cows that are less susceptible to diseases in early lactation.