Search

In recent years, the genetic analysis of cattle breeds has shown that most of the genetic variability is located within breeds instead that between breeds (Mac Hugh et al., 1998). Moreover, the amount of genetic diversity assessed within breeds is quite high (Edwards et al., 2000). A selection pressure in favour of the heterozygotes can be a possible explanation for this. In this case the management of cattle breeds would represent a tool to preserve genetic variability, instead of the cause of its decline. The Italian Friesian is the main Italian dairy cattle breed. The herd book has been created in 1959 and is managed by the ANAFI (National Association of Italian Friesian Breeders). Each year ANAFI updates the herd book and uses productive data for computing genetic evaluations by an animal model analysis. These genetic evaluations are available to the farmers who use them to choose semen for artificial insemination.........

Chantier qualité GA Liste des auteurs de l'European Cattle Genetic Consortium, classés par pays : Austria: R. Baumung, S. Manatrinon, BOKU University, Vienna; Belgium: G. Mommens, University of Ghent, Merelbeke; Denmark: L.-E. Holm, Aarhus University, Tjele; K.B. Withen, B.V. Pedersen, P. Gravlund, University of Copenhagen, Copenhagen; Estonia: H. Viinalass, Estonian University of Life Sciences, Tartu; Finland: J. Kantanen, I. Tapio, M.H. Li, MTT, Jokioinen; France: K. Moazami-Goudarzi, M. Gautier, Denis Laloë, INRA, Jouy-en-Josas; A. Oulmouden, H. Levéziel, INRA, Limoges; P. Taberlet, Université Joseph Fourier et CNRS, Grenoble; Germany: B. Harlizius, School of Veterinary Medicine, Hannover; H. Simianer, H. Täubert, Georg-August-Universität, Göttingen; G. Erhardt, O. Jann, C. Weimann, E.-M. Prinzenberg, Justus-Liebig Universität, Giessen; I. Medugorac, A. Medugorac, M. Förster, Ludwig-Maximilians Universität, Munich; H.M. Mix, Naturschutz International, Grünheide; C. Looft, E. Kalm, Christian-Albrechts-Universität, Kiel; Ireland: D.G. Bradley, C.J. Edwards, D.E. MacHugh, A.R. Freeman, Trinity College, Dublin; Italy: P. Ajmone Marsan, R. Negrini, Università Cattolica del S. Cuore, Piacenza; M. Longeri, G. Ceriotti, M. Zanotti, Università degli Studi di Milano; D. Marletta, A. Criscione, Universita degli Studi di Catania, Catania; A. Valentini, C. Marchitelli, L. Pariset, M.C. Savarese, Università della Tuscia, Viterbo; F. Pilla, Università del Molise, Campobasso; Latvia: Z. Grislis, Latvia University of Agriculture, Jelgava; Lithuania: I. Miceikienė, Lithuanian Veterinary Academy, Kaunas; Netherlands: I.J. Nijman, Utrecht University; W. van Haeringen, L. van de Goor, Van Haeringen Laboratory, Wageningen; Norway: I. Olsaker, Norwegian School of Veterinary Science, Oslo; Portugal: C. Ginja, L.T.Gama, Instituto Nacional de Recursos Biológicos—INIA, Lisbon University, Lisboa; L.T. Gama, Instituto Nacional de Recursos Biológicos-INIA, I.P., Vale de Santarem; J.C. Mateus, Universidade de Trás-os-Montes e Alto Douro, Vila Real; A. Beja-Pereira, N. Ferrand, Oporto University; Russia: Z. Ivanova, R. Popov, I. Ammosov, Yakutian Research Institute of Agricultural Sciences, Yakutsk, Sakha; T. Kiselyova, All-Russian Research Institute for Farm Animals and Breeding, St. Petersburgh-Pushkin; N. Marzanov, All-Russian Research Institute of Animal Husbandry, Dubrovitsy; Serbia: S. Stojanovic, Ministry of Agriculture and Water Management, Belgrade; Slovenia: M. Simčič, P. Dovč, D.Kompan, University of Ljubljana, Domzale; Spain: S. Dunner, Universidad Complutense de Madrid; C. Rodellar, I. Martín-Burriel, Veterinary Faculty, Zaragoza; A. Sánchez, J. Piedrafita, Universitat Autònoma de Barcelona; P.J. Azor, J.V. Delgado, A. Martínez-Martínez, A. Molina, E. Rodero, University of Córdoba; Switzerland: G. Dolf, University of Berne; UK: J.L. Williams, P. Wiener, Roslin Institute; M.W. Bruford, T.C. Bray, Cardiff University, Cardiff; L. Alderson, Countrywide Livestock Ltd, Shrewsbury; USA: M.C.T. Penedo. University of California, Davis, CA; International audience; Classification of cattle breeds contributes to our understanding of the history of cattle and is essential for an effective conservation of genetic diversity. Here we review the various classifications over the last two centuries and compare the most recent classifications with genetic data. The classifications devised during the 19th to the late 20th century were in line with the Linnaean taxonomy and emphasized cranial or horn morphology. Subsequent classifications were based on coat color, geographic origin or molecular markers. Several theories were developed that linked breed characteristics either to a supposed ancestral aurochs subspecies or to a presumed ethnic origin. Most of the older classifications have now been discarded, but have introduced several Latin terms that are still in use. The most consistent classification was proposed in 1995 by Felius and emphasizes the geographic origin of breeds. This is largely in agreement with the breed clusters indicated by a biochemical and molecular genetic analysis, which reflect either groups of breeds with a common geographic origin or single breeds that have expanded by export and/or crossbreeding. We propose that this information is also relevant for managing the genetic diversity of cattle.

Background SNP data of goats of three Mediterranean countries were used for population studies and reconstruction of geographical patterning. 496 individuals belonging to Italian, Albanian and Greek breeds were genotyped to assess the basic population parameters. Results A total of 26 SNPs were used, for a total of 12,896 genotypes assayed. Statistical analysis revealed that breeds are not so similar in terms of genetic variability, as reported in studies performed using different markers. The Mantel test showed a strongly significant correlation between genetic and geographic distance. Also, PCA analysis revealed that breeds are grouped according to geographical origin, with the exception of the Greek Skopelos breed. Conclusion Our data point out that the use of SNP markers to analyze a wider breed sample could help in understanding the recent evolutionary history of domestic goats. We found correlation between genetic diversity and geographic distance. Also PCA analysis shows that the breeds are well differentiated, with good correspondence to geographical locations, thus confirming the correlation between geographical and genetic distances. This suggests that migration history of the species played a pivotal role in the present-day structure of the breeds and a scenario in which coastal routes were easier for migrating in comparison with inland routes. A westward coastal route to Italy through Greece could have led to gene flow along the Northern Mediterranean.

The use of SNPs in combination with Bayesian statistics for the geographic traceability of cattle were evaluated using a dataset comprising 24 breeds from Italy,France,Spain, Denmark, the Netherlands,Switzerland and UK genotyped with 90 polymorphic markers. The percentage of correct assignment of the individuals to their Country of origin was 90%, with an average assignment probability of 93% and an average specificity of 92%. The higher value was observed for UK breeds (97% of correct assignment) while Swiss animals were the most difficult to allocate (77% of correct assignment). Tracing of Protected Geographic Indication (PGI) products, the approach correctly assigned 100% of Guaranteed Pure Highland Beef; 97% of “Vitellone dell’Appennino Centrale” breeds; 84% of Ternera de Navarra, and 80% of Boeuf de Chalosse. Methods to verify Products of Designated Origin (PDO) and Protected Geographic Indication (PGI) products will help to protect regional foods and promote the economic growth of marginal rural areas by encouraging the product on of high quality niche market foods.

Background In this study we compare outlier loci detected using a FST based method with those identified by a recently described method based on spatial analysis (SAM). We tested a panel of single nucleotide polymorphisms (SNPs) previously genotyped in individuals of goat breeds of southern areas of the Mediterranean basin (Italy, Greece and Albania). We evaluate how the SAM method performs with SNPs, which are increasingly employed due to their high number, low cost and easy of scoring. Results The combined use of the two outlier detection approaches, never tested before using SNP polymorphisms, resulted in the identification of the same three loci involved in milk and meat quality data by using the two methods, while the FST based method identified 3 more loci as under selection sweep in the breeds examined. Conclusion Data appear congruent by using the two methods for FST values exceeding the 99% confidence limits. The methods of FST and SAM can independently detect signatures of selection and therefore can reduce the probability of finding false positives if employed together. The outlier loci identified in this study could indicate adaptive variation in the analysed species, characterized by a large range of climatic conditions in the rearing areas and by a history of intense trade, that implies plasticity in adapting to new environments.

Italian Brown is a cattle breed largely exploited in the production of many dairy products in Italy, including typical and traditional cheeses. For this reason, the improvement of selection methods is of economic relevance while a deeper understanding of the genetic mechanisms regulating milk production is of general scientific interest. We selected a total of 561 samples, representing virtually all Italian Brown bull population, to test for association between milk production traits and 29 known genes harbouring 106 single nucleotide polymorphisms (SNPs). After filtering, a total of 31 SNPs in 22 candidate genes and 473 bulls were retained. Associations between each SNP and milk traits were tested by a mixed model approach, obtaining seven significantly associated SNPs, two of which (in β-Lactoglobulin) associated with all traits, and four (in Chemokin receptor I, αs1 casein, k casein, fatty acid synthase, thyroid hormone responsive and Oxytocin prepropetide genes) associated with at least one trait.