Abstract
In this chapter we present an overview of genomic selection (GS) research in the small grain cereals and interpret some of the results across studies where there is a growing body of information. We also provide the reader with approaches to implementation of GS in applied breeding programs and how various scenarios affect gain from selection and cost relative to conventional breeding. Training population optimization is discussed as well as the factors that affect prediction accuracy. We conclude with comments on future research directions required to improve the efficiency of GS.
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Abbreviations
- BB:
-
BayesB
- BRR:
-
Bayesian ridge regression
- CIMMYT:
-
International Maize and Wheat Improvement Center
- DArT:
-
Diversity Array Technology
- DHs:
-
Doubled haploids
- DON:
-
Deoxynivalenol
- ECs:
-
Environmental covariates
- FHB:
-
Fusarium head blight
- GBS:
-
Genotyping by sequencing
- GEBV:
-
Genomic estimated breeding value
- GS:
-
Genomic selection
- GxE:
-
Genotype-by-environment interaction
- h 2 :
-
Heritability
- HTP:
-
High-throughput phenotyping
- LD:
-
Linkage disequilibrium
- MAS:
-
Marker-assisted selection
- MEs:
-
Mega-environments
- MET:
-
Multi-environment trials
- MxE:
-
Marker-by-environment interaction
- PS:
-
Phenotypic selection
- QTL:
-
Quantitative trait loci, RR-BLUP, ridge-regression best linear unbiased prediction
- SNP:
-
Single nucleotide polymorphism
- TPE:
-
Target population of environments
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Rutkoski, J.E., Crain, J., Poland, J., Sorrells, M.E. (2017). Genomic Selection for Small Grain Improvement. In: Varshney, R., Roorkiwal, M., Sorrells, M. (eds) Genomic Selection for Crop Improvement. Springer, Cham. https://doi.org/10.1007/978-3-319-63170-7_5
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