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Genomic Selection for Small Grain Improvement

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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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Correspondence to Mark E. Sorrells .

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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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