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An experiment was conducted in order to evaluate the effects of gestational undernutrition of rabbit does on growth, carcass composition and meat quality of the offsprings. Thirty primiparous non lactating rabbit does were artificially inseminated and randomly divided in three treatment groups: Control (C; fed to 100% of maintenance requirements throughout gestation, n = 10), early undernourished (EU; fed to 50% of maintenance requirements during days 7-19 of gestation, n = 10) and late undernourished (LU; fed to 50% of maintenance requirements during days 20-27 of gestation, n = 10). During the 4th week of the gestation period, LU does significantly lost weight compared to C and EU groups (P<0.05). At kindling, C does produced litters with higher proportions of stillborn kits (P<0.05) while the total litter size (alive and stillborn kits) was not different among groups (10.7, 12.8 and 12.7 kits in C, EU and LU groups, respectively). Kit birth weight tended to be lower in the LU group. During fattening, body weight and feed intake were not different among offsprings of the three experimental groups. Moreover, the maternal undernutrition did not have any impact on carcass composition of the offsprings in terms of carcass parts and internal organs weights as well as meat quality of L. lumborum muscle (pH24, colour, water holding capacity and shear values) at slaughter (70 days of age). Therefore, it can be concluded that the gestational undernutrition of the mother does not have detrimental effects on the productive and quality traits of the offsprings.

The recent release of the Bovine HapMap dataset represents the most detailed survey of bovine genetic diversity to date, providing an important resource for the design and development of livestock production. We studied this dataset, comprising more than 30,000 Single Nucleotide Polymorphisms (SNPs) for 19 breeds (13 taurine, three zebu, and three hybrid breeds), seeking to identify small panels of genetic markers that can be used to trace the breed of unknown cattle samples. Taking advantage of the power of Principal Components Analysis and algorithms that we have recently described for the selection of Ancestry Informative Markers from genomewide datasets, we present a decision-tree which can be used to accurately infer the origin of individual cattle. In doing so, we present a thorough examination of population genetic structure in modern bovine breeds. Performing extensive cross-validation experiments, we demonstrate that 250-500 carefully selected SNPs suffice in order to achieve close to 100% prediction accuracy of individual ancestry, when this particular set of 19 breeds is considered. Our methods, coupled with the dense genotypic data that is becoming increasingly available, have the potential to become a valuable tool and have considerable impact in worldwide livestock production. They can be used to inform the design of studies of the genetic basis of economically important traits in cattle, as well as breeding programs and efforts to conserve biodiversity. Furthermore, the SNPs that we have identified can provide a reliable solution for the traceability of breed-specific branded products.