Dear Colleagues,

Livestock breeding is a branch of applied genetics that has achieved significant improvements in the production traits of species of farm animals. Over the past seven decades, a number of statistical approaches have been developed to reliably partition phenotypic variance into genetic and environmental components. The prediction of breeding values, based on the phenotypic information of an individual and the phenotypic information of its relatives, has been optimized using different statistical approaches. In the last two decades, the contribution of molecular genetics has grown. The QTL concept, as an alternative to the infinitesimal model, has identified several candidate loci with a significant impact on quantitative traits. The development of highly informative SNP markers has enabled the identification of candidate genes to better understand the genetic architecture of production traits. The application of molecular markers in animal breeding has led to the so-called marker-assisted selection, which combines molecular and statistical approaches to improve the genetic basis of our breeding populations. The availability of a large number of informative molecular markers has allowed for the possibility to estimate genetic variability in livestock populations, and has been successfully used in modern genetic conservation programs. The last conceptual change in animal breeding was the introduction of genomic selection. This concept, based on complex genomic information, allows for selection decisions based almost exclusively on genotype information. This strategy will considerably speed up genetic progress and increase the proportion of individuals participating in selection schemes. The development of efficient methods for targeted genome editing opens a new horizon for the precise genetic optimization of farm animal genomes, resulting in a new generation of more productive, healthier, and more robust livestock.

Prof. Peter Dovč
Guest Editor

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• phenotypic selection
• marker-assisted selection
• quantitative trait loci
• candidate genes
• genomic selection
• genome editing