W.F. Fikse

Variance component and breeding value estimation for genetic heterogeneity of residual variance in Swedish Holstein dairy cattle

Trait uniformity, or micro-environmental sensitivity, may be studied through individual differences in residual variance. These differences appear to be heritable, and the need exists, therefore, to fit models to predict breeding values explaining differences in residual variance. The aim of this paper is to estimate breeding values for micro-environmental sensitivity (vEBV) in milk yield and somatic cell score, and their associated variance components, on a large dairy cattle data set having more than 1.6 million records. Estimation of variance components, ordinary breeding values, and vEBV was performed using standard variance component estimation software (ASReml), applying the methodology for double hierarchical generalized linear models. Estimation using ASReml took less than 7 d on a Linux server. The genetic standard deviations for residual variance were 0.21 and 0.22 for somatic cell score and milk yield, respectively, which indicate moderate genetic variance for residual variance and imply that a standard deviation change in vEBV for one of these traits would alter the residual variance by 20%. This study shows that estimation of variance components, estimated breeding values and vEBV, is feasible for large dairy cattle data sets using standard variance component estimation software. The possibility to select for uniformity in Holstein dairy cattle based on these estimates is discussed.

Genetic parameters for dry matter intake in primiparous Holstein, Nordic Red, and Jersey cows in the first half of lactation

Dry matter intake (DMI) is a key component of feed efficiency in dairy cattle. In this study, we estimated genetic parameters of DMI over the first 24 lactation weeks in 3 dairy cattle breeds: Holstein, Nordic Red, and Jersey. In total, 1,656 primiparous cows (717 Holstein, 663 Nordic Red, and 276 Jersey) from Denmark, Finland, and Sweden were studied. For each breed, variance components, heritability, and repeatability for weekly DMI were estimated in 6 consecutive periods of the first 24 lactation weeks based on a repeatability animal model. Genetic correlations for DMI between different lactation periods were estimated using bivariate models. Based on our results, Holstein and Nordic Red cows had similar DMI at the beginning of lactation, but later in lactation Holstein cows had a slightly higher DMI than Nordic Red cows. In comparison, Jersey cows had a significantly lower DMI than the other 2 breeds within the first 24 lactation weeks. Heritability estimates for DMI ranged from 0.20 to 0.40 in Holsteins, 0.25 to 0.41 in Nordic Red, and 0.17 to 0.42 in Jerseys within the first 24 lactation weeks. Genetic and phenotypic variances for DMI varied along lactation within each breed and tended to be higher in the middle of lactation than at the beginning of the lactation. High genetic correlations were noted for DMI in lactation wk 5 to 24 in all 3 breeds, whereas DMI at early lactation (lactation wk 1 to 4) tended to be genetically different from DMI in the middle of lactation. The 3 breeds in this study might differ in their genetic variances for DMI, but the differences were not statistically significant in most of the studied periods. Breed differences for the genetic variance tended to be more obvious than for heritability. The potential breed differences in genetic variation for DMI should be considered in a future study using feed intake information from multiple breeds.

Genetic parameters for rennet- and acid-induced coagulation properties in milk from Swedish Red dairy cows.

Milk coagulation is an important processing trait, being the basis for production of both cheese and fermented products. There is interest in including technological properties of these products in the breeding goal for dairy cattle. The aim of the present study was therefore to estimate genetic parameters for milk coagulation properties, including both rennet- and acid-induced coagulation, in Swedish Red dairy cattle using genomic relationships. Morning milk samples and blood samples were collected from 395 Swedish Red cows that were selected to be as genetically unrelated as possible. Using a rheometer, milk samples were analyzed for rennet- and acid-induced coagulation properties, including gel strength (G), coagulation time, and yield stress (YS). In addition to the technological traits, milk composition was analyzed. A binary trait was created to reflect that milk samples that had not coagulated 40min after rennet addition were considered noncoagulating milk. The cows were genotyped by using the Illumina BovineHD BeadChip (Illumina Inc., San Diego, CA). Almost 600,000 markers remained after quality control and were used to construct a matrix of genomic relationships among the cows. Multivariate models including fixed effects of herd, lactation stage, and parity were fitted using the ASReml software to obtain estimates of heritabilities and genetic and phenotypic correlations. Heritability estimates (h(2)) for G and YS in rennet and acid gels were found to be high (h(2)=0.38-0.62) and the genetic correlations between rennet-induced and acid-induced coagulation properties were weak but favorable, with the exception of YSrennet with Gacid and YSacid, both of which were strong. The high heritability (h(2)=0.45) for milk coagulating ability expressed as a binary trait suggests that noncoagulation could be eliminated through breeding. Additionally, the results indicated that the current breeding objective could increase the frequency of noncoagulating milk and lead to deterioration of acid-induced coagulation through unfavorable genetic associations with protein content (0.38) and milk yield (-0.61 to -0.71), respectively. The outcome of this study suggests that by including more detailed compositional traits genetically associated with milk coagulation or by including milk coagulation properties directly within the breeding goal, it appears possible to breed cows that produce milk better suited for production of cheese and fermented products.

Statistical tools to select for robustness and milk quality

This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.