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The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

doi: 10.3390/ani11102833
pmid: 34679854
pmc: PMC8532622
handle: 10807/198898 , 2108/397499 , 11570/3253418 , 2067/45971
doi: 10.3390/ani11102833
pmid: 34679854
pmc: PMC8532622
handle: 10807/198898 , 2108/397499 , 11570/3253418 , 2067/45971
Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.
- University of Messina Italy
- École Polytechnique Fédérale de Lausanne EPFL Switzerland
- Central Maine Community College United States
- University of Messina Italy
- Euro-Mediterranean Center for Climate Change Italy
Livestock, Veterinary medicine, adaptation, Review, extremes indexes, 630, landscape genomics, positive selection, heat-stress, SF600-1100, r package, Climate change, candidate loci, adaptation, genomics, climate, livestock, deep learning, Adaptation, AGR/17, livestock, climate change, QL1-991, population-genetics, cattle, genome-wide association, Settore AGRI-09/A - Zootecnia generale e miglioramento genetico, Zoology, local adaptation, Adaptation, Climate change, Livestock, LS2_12 Bioinformatics
Livestock, Veterinary medicine, adaptation, Review, extremes indexes, 630, landscape genomics, positive selection, heat-stress, SF600-1100, r package, Climate change, candidate loci, adaptation, genomics, climate, livestock, deep learning, Adaptation, AGR/17, livestock, climate change, QL1-991, population-genetics, cattle, genome-wide association, Settore AGRI-09/A - Zootecnia generale e miglioramento genetico, Zoology, local adaptation, Adaptation, Climate change, Livestock, LS2_12 Bioinformatics
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).31 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% visibility views 3 download downloads 7 - 3views7downloads
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