Variance components for test-day milk, fat, and protein yield, and somatic cell score for analyzing management information.

Test-day (TD) models are used in most countries to perform national genetic evaluations for dairy cattle. The TD models estimate lactation curves and their changes as well as variation in populations. Although potentially useful, little attention has been given to the application of TD models for management purposes. The potential of the TD model for management use depends on its ability to describe within- or between-herd variation that can be linked to specific management practices. The aim of this study was to estimate variance components for milk yield, milk component yields, and somatic cell score (SCS) of dairy cows in the Ragusa and Vicenza areas of Italy, such that the most relevant sources of variation can be identified for the development of management parameters. The available data set contained 1,080,637 TD records of 42,817 cows in 471 herds. Variance components were estimated with a multilactation, random-regression, TD animal model by using the software adopted by NRS for the Dutch national genetic evaluation. The model comprised 5 fixed effects [region x parity x days in milk (DIM), parity x year of calving x season of calving x DIM, parity x age at calving x year of calving, parity x calving interval x stage of pregnancy, and year of test x calendar week of test] and random herd x test date, regressions for herd lactation curve (HCUR), the animal additive genetic effect, and the permanent environmental effect by using fourth-order Legendre polynomials. The HCUR variances for milk and protein yields were highest around the time of peak yield (DIM 50 to 150), whereas for fat yield the HCUR variance was relatively constant throughout first lactation and decreased following the peak around 40 to 90 DIM for lactations 2 and 3. For SCS, the HCUR variances were relatively small compared with the genetic, permanent environmental, and residual variances. For all the traits except SCS, the variance explained by random herd x test date was much smaller than the HCUR variance, which indicates that the development of management parameters should focus on between-herd parameters during peak lactation for milk and milk components. For SCS, the within-herd variance was greater than the between-herd variance, suggesting that the focus should be on management parameters explaining variances at the cow level. The present study showed clear evidence for the benefits of using a random regression TD model for management decisions.

A pathway analysis tool for analyzing microarray data of species with low physiological information.

Pathway information provides insight into the biological processes underlying microarray data. Pathway information is widely available for humans and laboratory animals in databases through the internet, but less for other species, for example, livestock. Many software packages use species-specific gene IDs that cannot handle genomics data from other species. We developed a species-independent method to search pathways databases to analyse microarray data. Three PERL scripts were developed that use the names of the genes on the microarray. (1) Add synonyms of gene names by searching the Gene Ontology (GO) database. (2) Search the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database for pathway information using this GO-enriched gene list. (3) Combine the pathway data with the microarray data and visualize the results using color codes indicating regulation. To demonstrate the power of the method, we used a previously reported chicken microarray experiment investigating line-specific reactions to Salmonella infection as an example.

Comparison of prenatal muscle tissue expression profiles of two pig breeds differing in muscle characteristics.

The objective of this study was to compare purebred Duroc and Pietrain prenatal muscle tissue transcriptome expression levels at different stages of prenatal development to gain insight into the differences in muscle tissue development in these pig breeds. Commercial western pig breeds have been selected for muscle growth for the past 2 decades. Pig breeds differ for their muscle phenotypes (i.e., myofiber numbers and myofiber types). Duroc and Pietrain pig breeds are extremes; Duroc pigs have redder muscle fiber types with more intramuscular fat, and Pietrain pigs have faster-growing and whiter muscle fiber types. Pietrain pigs are more muscular than Duroc pigs, whereas Duroc pigs are fatter than Pietrain pigs. The genomic background underlying these breed-specific differences is poorly known. Myogenesis is a complex exclusive prenatal process involving proliferation and differentiation (i.e., fusion) of precursor cells called myoblasts. We investigated the difference in the prenatal muscle-specific transcriptome profiles of Duroc and Pietrain pigs using microarray technology. The microarray contained more than 500 genes affecting myogenesis, energy metabolism, muscle structural genes, and other genes from a porcine muscle cDNA library. The results indicated that the expression of the myogenesis-related genes was greater in early Duroc embryos than in early Pietrain embryos (14 to 49 d of gestation), whereas the opposite was found in late embryos (63 to 91 d of gestation). These findings suggest that the myogenesis process is more intense in early Duroc embryos than in Pietrain embryos but that myogenesis is more intense in late Pietrain fetuses than in Duroc fetuses. Transcriptomes of muscle structural genes followed that pattern. The energy metabolism genes were expressed at a higher level in prenatal Pietrain pigs than in prenatal Duroc pigs, except for d 35, when the opposite situation was found. Fatty acid metabolism genes were expressed at a higher level in early (14 to 49 d of gestation) Duroc embryos than in Pietrain embryos. Better understanding of the genomic regulation of tissue formation leads to improved knowledge of the genome under selection and may lead to directed breed-specific changes in the future.

Prediction of daily milk yields from a limited number of test days using test day models.

A data set of weekly milk yield records was used to compare different test day models for their ability to interpolate and extrapolate missing milk yields. The criteria to compare the models were 1) the (co)variance structure modeled compared with the observed (co)variance structure in the data and 2) mean square error of predictions of missing observations (MSEP), which compared the predicted value of a missing record to the known value of the record. The test day models used were LEG(m), which are Legendre polynomials with an order of fit of m, and EXP, which is an exponential lactation function. When fitting the LEG(m) models, criteria 1) and 2) generally improved with an increasing order of fit as expected. The model EXP, which contains three random regression coefficients, was between LEG(1) and LEG(2), which contain two and three coefficients, respectively. The improvement of the criteria with m in LEG(m) became negligible after LEG(5). Thus, a 5th order Legendre polynomial yields a good fit with a minimum number of parameters. Also, the correlation structure of milk yields among days in milk modeled by LEG(5) resembled the correlation structure that was observed in the data. However, the modeled variances at the end of lactation were larger than those observed in the data except when LEG(0) was used. Legendre polynomials with a fit less than five yielded correlation structures that clearly deviated from the observed correlations, especially in the case of LEG(0). Overall, LEG(5) is preferred to develop a genetic TDM for breeding value estimation.

Genetic parameters of legendre polynomials for first parity lactation curves.

Variance components of the covariance function coefficients in a random regression test-day model were estimated by Legendre polynomials up to a fifth order for first-parity records of Dutch dairy cows using Gibbs sampling. Two Legendre polynomials of equal order were used to model the random part of the lactation curve, one for the genetic component and one for permanent environment. Test-day records from cows registered between 1990 to 1996 and collected by regular milk recording were available. For the data set, 23,700 complete lactations were selected from 475 herds sired by 262 sires. Because the application of a random regression model is limited by computing capacity, we investigated the minimum order needed to fit the variance structure in the data sufficiently. Predictions of genetic and permanent environmental variance structures were compared with bivariate estimates on 30-d intervals. A third-order or higher polynomial modeled the shape of variance curves over DIM with sufficient accuracy for the genetic and permanent environment part. Also, the genetic correlation structure was fitted with sufficient accuracy by a third-order polynomial, but, for the permanent environmental component, a fourth order was needed. Because equal orders are suggested in the literature, a fourth-order Legendre polynomial is recommended in this study. However, a rank of three for the genetic covariance matrix and of four for permanent environment allows a simpler covariance function with a reduced number of parameters based on the eigenvalues and eigenvectors.