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Next generation methods to preserve farm animal bi.. (NextGen)
Next generation methods to preserve farm animal biodiversity by optimizing present and future breeding options
(NextGen)
Date du début: 1 avr. 2010,
Date de fin: 31 mars 2014
PROJET
TERMINÉ
NEXTGEN proposes the bold step of using whole genome data to develop and optimise conservation genetic management of livestock diversity for the foreseeable future. The rationale for choosing whole genome data is to ‘future-proof’ DNA-based analysis in livestock conservation against upcoming changes in technology and analysis. Thus, in the context of whole genome data availability, our global objective is to develop cost-effective optimized methodologies for preserving farm-animal biodiversity, using cattle, sheep, and goats as model species. More specifically, NEXTGEN will: - produce whole genome data in selected populations; - develop the necessary bioinformatics approaches, taking advantage of the 1000 human genomes project, and focusing on the identification of genomic regions under recent selection (adaptive / neutral variation); - develop the methods for optimizing breeding and biobanking, taking into account both neutral and adaptive variations; - develop innovative biobanking methods based on freeze-dried nuclei; - provide guidelines for studying disease resistance and genome/environment relationships in a spatial context; - assess the value of wild ancestors and breeds from domestication centres as genetic resources; - assess the performance of a surrogate marker system compared with whole genome sequence data for preserving biodiversity; - provide efficient training and a wide dissemination of the improved methodologies. The consortium has been designed to specifically reach these objectives, and encompasses skills in conservation genetics, bioinformatics, biobanking and breeding technologies, GIScience. The work plan has been established with great care. The sequencing task has been postponed to year 3 to take advantage of cost dynamics, while the two first years are dedicated to bioinformatics and to an innovative sampling strategy that fully integrates the spatial aspect and that offers more value at the data analysis stage.
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