BACKGROUND:
The rapid decline in the costs of genotypic profiling per individual has led to a great explosion of many forms of data in the phenotypic-genomic spectrum in recent years in the forms of marker and sequence information, gene expression phenotypes, chromatin structure, RNA expression, protein, biochemical traits, physiological traits, metabolomics and microbiome data enriched with annotations; which, in turn, is leading to the development of new genetic tools that are transforming the way we understand biology as a whole.
Genomic Selection (GS) is a new breeding approach that uses genome-wide molecular markers in prediction of breeding values for multi-genic quantitative traits. Many public and private breeding efforts have adopted GS as a new breeding tool.
Genomic selection differs from the traditional marker assisted selection (MAS) which explains a limited fraction of the genetic variation by the identification of quantitative trait loci. In MAS, genotyping is limited to a selected set of markers that tag putative genes to predict breeding values, and therefore have poor predictive ability due to the fact that only a limited proportion of genetic variance can be captured by the markers.
In GS, all markers are included in the model regardless of effect size and therefore allow for tracking and selecting complex traits with both small and large effect genes. There have been both empirical and simulation studies that have stressed that a greater gain per unit of time can be achieved by GS. Currently, GS is being adapted in breeding programs because it can increase the genetic gain per year. Although there have been great advances on the application of GS in plant breeding programs, there are many factors that affects its success: selection of a good training population, type of genotyping platform, the individuals genotyped, the generation for selection, etc.
OBJECTIVE(S):
Swift Current Research and Development Centre (SCRDC) requires the services of a Contractor to perform GS analysis for breeder selection and assist in instructing SCRDC scientists on the application of GS results.
