Functional and positional genomics in beef cattle : current programs and potential progress | Des avances en génomique fonctionnelle et positionnelle chez les bovins : programmes en cours et potentialités
2008
Hocquette, Jean-François | Boichard, Didier | Cassar-Malek, Isabelle | Laville, Elisabeth | Renand, Gilles, G. | Levéziel, Hubert, H. | Picard, Brigitte, B. | Unité de Recherches sur les Herbivores (URH) ; Institut National de la Recherche Agronomique (INRA) | Génétique Animale et Biologie Intégrative (GABI) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Qualité des Produits Animaux (QuaPA) ; Institut National de la Recherche Agronomique (INRA) | Unité de Génétique Moléculaire Animale (UMR GMA) ; Université de Limoges (UNILIM)-Institut National de la Recherche Agronomique (INRA)
International audience
显示更多 [+] 显示较少 [-]英语. Genomics has brought with it a true biological revolution and has a great number of potential applications in all areas of life sciences. Thanks to the simultaneous analysis of vast numbers of genes, proteins and metabolites, genomics opens up new perspectives for a host of research programs in the genetic and physiological control of beef quality especially tenderness. Indeed, tenderness is a complex trait which depends on interplay between a great number of interacting genes associated with connective tissue, the contractile and metabolic characteristics of muscle fibres as well as proteolysis during aging. The completed bovine genome sequence and the availability of high-throughput genotyping chips help the discovery of genetic markers for beef quality and the development of marker-assisted selection and of "genomic selection". Meanwhile, transcriptomics and proteomics have favoured the study of muscle development and of muscle hypertrophy as well as the discovery of new biological markers of beef quality. The development of metabolomics is in progress. Thanks to the development of genomics, the new challenge is now to develop high-throughput pheno-typing as well as modelling approaches to better predict beef quality from the different genomic markers so far identified.
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