Genetic parameters for female fertility in Nordic Holstein and Red Cattle dairy breeds.

Genetic evaluation of female fertility in Danish, Finnish, and Swedish dairy cows was updated in 2015 to multiple-trait animal model evaluation, where heifer and cow fertility up to third parity are considered as separate traits. A model for conception rate was also developed, which required variance component estimation for Nordic Holstein and Nordic Red Dairy Cattle. We used a multiple-trait multiple-lactation sire model to determine variance components for interval from calving to first insemination, length of service period, and conception rate. Monte Carlo Expectation Maximization REML allowed estimation of all 11 traits simultaneously. Study data were sampled from Swedish Holstein (n = 140,040) and Red Dairy Cattle (n = 101,315) heifers and cows. Conception rate observations are binomial observations with various numbers of failures preceding an observation of success. Using a simulation study, we confirmed that including a service number effect into the conception rate model allowed us to model the change in expectation of successful AI with increasing number of services. Heifers outperformed cows in all fertility traits according to the phenotypic means in the records. Heritabilities for the traits varied from 3 to 7% for interval from calving to first insemination, from 1 to 5% for length of service period, and from 1 to 3% for conception rate. Genetic correlations within traits (i.e., between parities) were favorable, ranging from moderate to high; genetic correlations between heifer and cow traits were lower than between cow traits in different parities. Lowest genetic correlations between traits were for interval from calving to first insemination and conception rate, intermediate for interval from calving to first insemination and length of service period, and highest for length of service period and conception rate. The variance components estimated in this study have been used in Nordic fertility breeding value evaluations since 2016.

Principal component and factor analytic models in international sire evaluation.

BACKGROUND: Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured. METHODS: Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries. RESULTS: In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared. CONCLUSIONS: Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.

Principal component approach in variance component estimation for international sire evaluation.

BACKGROUND: The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model. METHODS: This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix. RESULTS: Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time. CONCLUSIONS: In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.

Composition of poorly and non-coagulating bovine milk and effect of calcium addition.

Ninety-nine individual milk samples from 37 cows in lactation week 10-35, selected for producing well or poorly/non-coagulating milk, were compared regarding protein composition, total calcium content, casein micelle size, pH, and coagulating properties after addition of 0·05% CaCl2. The results showed that a low κ-casein concentration in milk was a risk factor for non-coagulation. CaCl2 addition improved coagulating properties (coagulation time, curd firmness) of nearly all samples and eliminated differences between poorly/non-coagulating and well-coagulating milk, particularly regarding curd firmness. A second, independent data set with 18 non-coagulating or well-coagulating milk samples were analysed for protein composition, where indications of a similar association with κ-casein was observed.

Chromosomal regions underlying noncoagulation of milk in Finnish Ayrshire cows.

About 10% of Finnish Ayrshire cows produce noncoagulating milk, i.e., milk that does not form a curd in a standard 30-min testing time and is thus a poor raw material for cheese dairies. This phenomenon is associated with peak and midlactation, but some cows produce noncoagulating milk persistently. A genomewide scan under a selective DNA pooling method was carried out to locate genomic regions associated with the noncoagulation of milk. On the basis of the hypothesis of the same historical mutation, we pooled the data across sires. Before testing pools for homogeneity, allele intensities were corrected for PCR artifacts, i.e., shadow bands and differential amplification. Results indicating association were verified using daughter design and selective genotyping within families. Data consisted of 18 sire families with 477 genotyped daughters in total, i.e., 12% of each tail of the milk coagulation ability. Data were analyzed using interval mapping under maximum-likelihood and nonparametric methods. BMS1126 on chromosome 2 and BMS1355 on chromosome 18 were associated with noncoagulation of milk across families on an experimentwise 0.1% significance level. By scanning gene databases, we found two potential candidate genes: LOC538897, a nonspecific serine/threonine kinase on chromosome 2, and SIAT4B, a sialyltransferase catalyzing the last step of glycosylation of kappa-casein on chromosome 18. Further studies to determine the role of the candidates in the noncoagulation of milk are clearly needed.

Repeatability estimates for milk coagulation traits and non-coagulation of milk in Finnish Ayrshire cows.

Effects of systematic environmental factors and milk production and quality traits on milk coagulation properties (MCP), and on repeatability of those traits were estimated from 979 milk samples collected once a month over a period of 2 years from 83 Finnish Ayrshire cows. Estimation was based on a multitrait animal model and REML methodology. In addition, persistence of non-coagulation of milk in individual cows, and factors associated with it were established from a sub sample of 24 cows producing non-coagulating (NC) milk at least once. MCP were at their best during the first lactation, at the beginning and at the end of lactation, and during grazing seasons. Variation in MCP with systematic environmental factors was partly due to variation in composition and quality of milk, especially in pH and ln (somatic cell count, SCC). Coefficients of repeatability for milk coagulation time and curd firmness were 0.65 and 0.68. These estimates were of the same magnitude as those for protein content, but were higher than those for daily milk yield, fat content, pH, and SCC. Based on the repeatability estimates for the milk coagulation traits and effects of the environmental factors, cows should be sampled at least three times during a lactation to estimate reliably breeding values for the milk coagulation traits. A total of 10% of the milk samples did not coagulate in 30 min after addition of rennet. Cows that produced NC milk at least once (30% of the cows) could be classified into those that produced NC milk only a few times during a lactation and those that produced NC milk at almost every sampling. Based on logistic regression analyses, peak and mid-lactation, high milk yield, low protein and fat content and high pH increased the risk of non-coagulation of milk.