The association of immune response and colostral immunoglobulin G in Canadian and US Holstein-Friesian dairy cows

In cattle, maternal immunoglobulins are transferred through colostrum to provide passive immunity to the neonatal calf once they are absorbed into circulation. Cows can be assessed for antibody- and cell-mediated immune responses (AMIR and CMIR, respectively), and through estimated breeding values (EBV) and genomic parent averages (GPA), cows can be classified as having high, average, or low immune response (IR). The objective of this study was to identify associations of colostral IgG concentrations with IR in dairy cows. High IR dairy cows identified by GPA or EBV were hypothesized to produce higher colostral IgG concentrations than cows with average or low IR. Colostrum was collected from Holstein dairy cows from 3 large commercial herds (n = 590) in the United States and 1 research herd at the Ontario Dairy Research Centre (n = 275) in Canada. For the US herds, IR GPA were available through genotyping. For the Canadian herd, IR EBV were available through phenotyping and pedigree information. Colostral IgG concentrations were measured by radial immunodiffusion and analyzed using general linear models in SAS. Based on a prediction equation, cows in US herds with a CMIR GPA of 1 would have colostral IgG concentrations 6.3 g/L higher on average than cows with a CMIR GPA of 0. High CMIR cows produced statistically greater colostral IgG concentrations (least squares mean ± standard error of the mean, 107.5 ± 7.7 g/L) than low CMIR cows (91.4 ± 7.1 g/L), with intermediate values for average CMIR cows (105.1 ± 5.6 g/L). No differences were found among AMIR categories in US cows. The Canadian herd showed a trend for cows with high CMIR EBV (continuous variable) to produce greater colostral IgG. No differences were observed among high, average, and low AMIR EBV classifications in Canadian cows. The findings suggest that selective breeding of Holstein cows to enhance CMIR could contribute to higher-quality colostrum in succeeding generations.

Genomic clustering helps to improve prediction in a multibreed population

Genomic prediction for crossbred beef cattle has shown limited results using low- to moderate-density SNP panels. The relationship between the training and validation populations, as well as the size of the reference population, affects the prediction accuracy for genomic selection. Rotational crossbreeding systems require the usage of crossbred animals as sires and dams of future generations, so crossbred animals require accurate evaluation. Here, a novel method for grouping of purebred and crossbred animals (based exclusively on genotypes) for genomic selection was investigated. Clustering of animals to investigate the genetic similarity among different groups was performed using several genomic relationship criteria between individuals. Hierarchical clusters based on average-link criteria (computed as the mean distance between elements of each subcluster) were formed. The accuracy of genomic prediction was assessed using 1,500 bulls genotyped for 54,609 markers. Estimated breeding values based on all available phenotypic records for birth weight, weaning gain, postweaning gain, and yearling gain were calculated using BLUP methodologies and deregressed to ensure unbiased comparisons could be made across populations. A 5-fold validation technique was used to calculate direct genomic values for all genotyped bulls; the addition of unrelated animals in the reference population was also investigated. We demonstrate a decrease in genomic selection accuracy after including animals from disconnected clusters. A method to improve genomic selection for crossbred and purebred animals by clustering animals based on their genotype is suggested. Unlike traditional approaches for genomic selection with a fixed reference population, genomic prediction using clusters (GPC) chooses the best reference population for better accuracy of genomic prediction of crossbred and purebred animals using clustering methods based on genotypes. An overall average gain in accuracy of 1.30% was noted over all scenarios across all traits investigated when the GPC approach was implemented. Further investigation is required to assess this difference in accuracy when a larger genotyped population is available, especially for the comparison of groups with higher genetic dissimilarity, such as those found in industry-wide across-breed genetic evaluations.