Genomic Selection: Towards Meeting Challenge of Food Security | ||
| Annals of Agricultural Science, Moshtohor | ||
| Article 1, Volume 59, Issue 5, 2021, Pages 1-8 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/assjm.2021.183328 | ||
| Author | ||
| Hamdy Abdel-Shafy* | ||
| Department of Animal Production, Faculty of Agriculture, Cairo University | ||
| Abstract | ||
| Breeding is a powerful science and remains a core component of most agriculture production. Although traditional selection has proven to be a successful approach for improving animal and plant production in the last decades, several studies have reported a worrying shortage between the current rates of production and expected global future demands for food. These challenges, coupled with a constant loss of suitable land for agricultural production, needs to implement new breeding strategies to significantly increase the genetic gain and meet the future requirements. Recent advances in genotyping capacity with affordable costs have allowed selection based on genomic information. These technologies would reshape the breeding programs, accelerate the genetic gain and maximize the productivity per unit. Although this concept was suggested many decades ago, the applications of marker-assisted selection were limited due to the low number of genotyped markers and low number of identified QTL. In contrast, genomic selection (GS) utilizes the dense genetic markers across the whole genome to predict the effect of all QTL that are assumed to be in linkage disequilibrium with at least one genetic marker. Nowadays, GS is widely implemented in several species for breeding purposes and attracted attention for human disease risk predictions. It has been proven for many traits to double the rate of the genetic gain and reduce the generation interval, which would efficiently contribute to more food security. Here, we introduce the general concept of GS, and briefly describe the GS models along with the recent progress in some farm animal species. | ||
| Keywords | ||
| complex traits; genomic prediction; livestock species; whole genome sequencing | ||
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