Genomic prediction of serum biomarkers of health in early lactation

Abstract

respectively), and moderate for albumin, globulins, and albumin-to-globulin ratio (0.27 ± 0.06, 0.46 ± 0.06, and 0.41 ± 0.06, respectively). The heritability of haptoglobin concentration was close to 0. The magnitude of genetic correlations between traits (estimated using bivariate models) varied considerably (0.01 to 0.96), and standard errors of these correlations were high (0.02 to 0.44). Interestingly, the direction of most genetic correlations was favorable, suggesting that selecting 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 favorable, whereas correlations with breeding values for milk production traits were small (≤0.15). Accuracies of GEBV were evaluated by using 5-fold cross validation, and by calculating accuracies from prediction error variances associated with the GEBV. 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. Although increasing the size of the reference population should theoretically improve accuracies, our results suggest that genomic prediction of health biomarkers may allow identification of cows that are less susceptible to diseases in early lactation.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

“…For all traits, heritabilities were low to moderate, and significantly greater than zero. Estimates obtained with the G matrix were lower than those published by Luke et al (2019a), although the differences were not significant. The cows in the analyses of Luke et al (2019a) were included in our data; hence, the differences observed illustrate the uncertainty of estimates of genetic parameters using small datasets.…”

“…Estimates obtained with the G matrix were lower than those published by Luke et al (2019a), although the differences were not significant. The cows in the analyses of Luke et al (2019a) were included in our data; hence, the differences observed illustrate the uncertainty of estimates of genetic parameters using small datasets. Other studies have reported various estimates for the heritability of BHB, ranging from 0.07 (Tsiamadis et al, 2016) to 0.40 (Oikonomou et al, 2008).…”

“…For ST_M_G, prediction accuracies were lower than the accuracies published by Luke et al (2019a). The accuracy of genomic prediction increases when the size of the reference population increases (Goddard, 2009); therefore, we expected higher prediction accuracies in our study than in the study by Luke et al (2019a), due to the larger reference population. However, prediction accuracies in our study were lower.…”

“…However, serum concentrations of these biomarkers are difficult and costly to measure and stressful to the animals, especially in the case of genomic selection, where a large reference population with recoded phenotypes is needed for accurate prediction. Recently, Luke et al (2019a) published accuracies of 0.29, 0.36, and 0.30 for genomic prediction of fatty acids, BHB, and urea, respectively, using a data set of 1,393 Australian Holstein cows. As collecting additional phenotypes is logistically challenging, opportunities to dramatically increase the number of phenotypes in the near future is not realistic, and alternatives should be explored.…”

A Tale of Two Biomarkers: Untargeted 1H NMR Metabolomic Fingerprinting of BHBA and NEFA in Early Lactation Dairy Cows

“…Elevated concentrations of both BHBA and NEFA can be observed in clinically healthy animals (i.e., showing no visible signs of illness), and are associated with (1) reduced reproductive performance [11,17], (2) an increased incidence of clinical diseases such as displaced abomasa and metritis [15,17,18], (3) decreased milk production [6,11,19] and (4) an increased risk of culling [6,15,20]. However, despite these similarities, both the phenotypic [21,22] and genetic [23] correlations between these two biomarkers are low. This is not necessarily important if biomarkers are being used for management purposes (such as the identification of sick animals or the assessment of nutritional status) but may be significant if the biomarkers are used as phenotypes for genetic selection for improved animal health and resilience.…”