About this Research Topic
To maintain the balance between conservation status and selection of local and transboundary breeds it is necessary to view both aspects in the context of global current problems like climate change and product demand. Several advances have been made in developing and implementing genomic tools and strategies for a more efficient use of AnGR and to support sustainable productivity. Genomics has an important role in addressing several research challenges for example assessing the level of inbreeding, the introgression of foreign alleles and the genomic identification of candidate genes that regulate the complex mechanism of adaptation. But predicting the population/breed viability or capacity to adapt to climate change based on genomic information will require not only the identification of relevant loci and specific variants, but also a quantitative estimate of their connection to fitness, resilience, efficiency, and demographic parameters. To incorporate several analytical approaches with the most efficient techniques for detecting DNA sequences differences and a growing list of bioinformatic tools currently available is the right way to address these challenges.
We welcome articles that contribute to the goals of this Research Topic which are:
- to better understand the mechanism involved in the process of breed formation, including the adaptation to environmental conditions, the genetic differentiation of population and breeds, to detect footprints of selection, genomic inbreeding and inbreeding depression.
- to dissect the populations history, past migrations and other demographic events that shaped the present-day breeds.
- to genetically characterise indigenous breeds for their revaluation and preservation, as source of biodiversity and genetic heritage elucidating genetic signals of origin, spread, and introgression in small landraces populations.
- to integrate novel and the state-of-the art- genomic techniques as well as bioinformatic tools and ecological analysis to get information useful to cope with these important challenges.
- to explore the field of machine learning in animal genetics, which aims at applied algorithms that improve with experience, allows to assist in the analysis of large and complex datasets through the analysis of genome sequencing datasets, including annotation of elements of sequence and epigenetic, proteomic, or metabolomic data.
Keywords: Conservation genomics, Environmental adaptation, Selection signature, Goats, Sheep, Genomic approaches, Animal genetic resources, Inbreeding, Crossbreeding, Introgression, Domestication, Demographic history, Whole genome sequence, SNP Arrays
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.