Genomics, Transcriptomics, and Computational Biology for Biodiversity Studies and Quality-Related Traits Selection in Livestock
A topical collection in Animals (ISSN 2076-2615). This collection belongs to the section "Animal Genetics and Genomics".
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Topical Collection Information
Dear Colleagues,
Research efforts in livestock include the identification of the chromosomal regions and candidate genes related to novel complex and economically important traits, together with biodiversity and local breeds preservation.
Taking advantage of modern multi-omic approaches for complex traits like high-throughput genotyping, whole-genome sequencing, expression analysis (e.g., GWA studies and RNA sequencing analysis) and so on, it is useful to understand the degree of activation of the metabolic and physiological mechanisms involved with the traits of interest.
Additionally, molecular data are useful for biodiversity characterization, with the final purpose to gain knowledge about phylogenetic and molecular evolution, and the origin of breeds, so as to assess and exploit the genetic diversity of animal populations, in order to help the development of new selection programs using local breeds.
Moreover, performing studies at a DNA and RNA level, among breeds or populations, involves the use and the development of computational–bioinformatics tools to highlight, interpret, and visualize the results.
We invite original research papers on animal breeding, population genetics, genetic association studies, transcriptomics, and quantitative genomics in livestock, which address the biological mechanisms underlying the expression of complex (quantitative) traits, to give insight into the origin and impact of genetic variation and population stratification at the genome scale, so as to explore genomics opportunities for long-term selection strategies.
Dr. Mariasilvia D'Andrea
Collection Editor
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Keywords
- genetics
- genomics
- transcriptomics
- animal breeding and selection
- population genetics
- novel candidate genes
- GWAS
- RNAseq
- computational biology