Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds.

Recently, the amount of available single nucleotide polymorphism (SNP) marker data has considerably increased in dairy cattle breeds, both for research purposes and for application in commercial breeding and selection programs. Bayesian methods are currently used in the genomic evaluation of dairy cattle to handle very large sets of explanatory variables with a limited number of observations. In this study, we applied 2 bayesian methods, BayesCπ and bayesian least absolute shrinkage and selection operator (LASSO), to 2 genotyped and phenotyped reference populations consisting of 3,940 Holstein bulls and 1,172 Montbéliarde bulls with approximately 40,000 polymorphic SNP. We compared the accuracy of the bayesian methods for the prediction of 3 traits (milk yield, fat content, and conception rate) with pedigree-based BLUP, genomic BLUP, partial least squares (PLS) regression, and sparse PLS regression, a variable selection PLS variant. The results showed that the correlations between observed and predicted phenotypes were similar in BayesCπ (including or not pedigree information) and bayesian LASSO for most of the traits and whatever the breed. In the Holstein breed, bayesian methods led to higher correlations than other approaches for fat content and were similar to genomic BLUP for milk yield and to genomic BLUP and PLS regression for the conception rate. In the Montbéliarde breed, no method dominated the others, except BayesCπ for fat content. The better performances of the bayesian methods for fat content in Holstein and Montbéliarde breeds are probably due to the effect of the DGAT1 gene. The SNP identified by the BayesCπ, bayesian LASSO, and sparse PLS regression methods, based on their effect on the different traits of interest, were located at almost the same position on the genome. As the bayesian methods resulted in regressions of direct genomic values on daughter trait deviations closer to 1 than for the other methods tested in this study, bayesian methods are suggested for genomic evaluations of French dairy cattle.

A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle.

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS were used with a reference population of 3,940 genotyped and phenotyped French Holstein bulls and 39,738 polymorphic single nucleotide polymorphism markers. Partial least squares regression reduces the number of variables by projecting independent variables onto latent structures. Sparse PLS combines variable selection and modeling in a one-step procedure. Correlations between observed phenotypes and phenotypes predicted by PLS and sparse PLS were similar, but sparse PLS highlighted some genome regions more clearly. Both PLS and sparse PLS were more accurate than pedigree-based BLUP and generally provided lower correlations between observed and predicted phenotypes than did genomic BLUP. Furthermore, PLS and sparse PLS required similar computing time to genomic BLUP for the study of 6 traits.

Genomic selection in the French Lacaune dairy sheep breed.

Genomic selection aims to increase accuracy and to decrease generation intervals, thus increasing genetic gains in animal breeding. Using real data of the French Lacaune dairy sheep breed, the purpose of this study was to compare the observed accuracies of genomic estimated breeding values using different models (infinitesimal only, markers only, and joint estimation of infinitesimal and marker effects) and methods [BLUP, Bayes Cπ, partial least squares (PLS), and sparse PLS]. The training data set included results of progeny tests of 1,886 rams born from 1998 to 2006, whereas the validation set had results of 681 rams born in 2007 and 2008. The 3 lactation traits studied (milk yield, fat content, and somatic cell scores) had heritabilities varying from 0.14 to 0.41. The inclusion of molecular information, as compared with traditional schemes, increased accuracies of estimated breeding values of young males at birth from 18 up to 25%, according to the trait. Accuracies of genomic methods varied from 0.4 to 0.6, according to the traits, with minor differences among genomic approaches. In Bayes Cπ, the joint estimation of marker and infinitesimal effects had a slightly favorable effect on the accuracies of genomic estimated breeding values, and were especially beneficial for somatic cell counts, the less heritable trait. Inclusion of infinitesimal effects also improved slopes of predictive regression equations. Methods that select markers implicitly (Bayes Cπ and sparse PLS) were advantageous for some models and traits, and are of interest for further quantitative trait loci studies.

Dimensional analysis of milk fat globules in sow milk: effects of the lactation stage and fat content and comparison with vaccine milk.

To assess the differences in the granulometry of milk fat globules between swine and bovine species, milk samples from 30 lactating sows were analyzed for fat globule dimensions and compared with cow milk samples. Results showed differences between the fat globules: sow milk presents reduced globule diameters compared with cow milk (volume-weighted diameter 2.62 vs. 3.27 microm, p < 0.001) and reduced interglobular distance. A positive relationship was observed between milk fat content and globule diameter, while a slight, insignificant inverse trend was detected between the day of lactation and fat globule diameter. These complex interactions between milk lipids, globule membrane proteins, and globule dimensions provide a better understanding of digestion/absorption phenomena in the design of milk replacers.

Haematology and plasma biochemistry of Stamboek pre-pubertal gilts in Italy: reference values.

Blood samples were taken between February and April from 105 healthy Stamboek pre-pubertal gilts, aged 1-3 months, which were housed at a modern pig farm in northern Italy. The blood was analysed for nine haematological and nine selected haematochemical variables by means of automated and semiautomated blood analysis apparatus. After detection and rejection of outliers, the data were submitted to reference limits evaluation, also taking into account the limits for the red blood cell volume histogram as the anisocytosis index. Some haematological reference values deal with previously published data; in the haematochemical parameters, several discrepancies between evaluated limits and existing reference limits were noted, mainly for aspartate aminotransferase, alanine aminotransferase and lactate dehydrogenase levels and total protein concentration. The results confirm the relevance of age in determining blood reference intervals and that 'normal' values should be determined by each laboratory, taking into account the age of subjects, the sample size and methods of analysis.