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Genomic Selection for Small Grain Improvement

Chapter

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.

Keywords

Genomic selection Wheat Oats Barley Rye Triticale Inbreeding crops Breeding strategy Gain from selection Marker-assisted selection Training population Whole-genome genotyping 

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

h2

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Programs, College of Agriculture and Life SciencesCornell UniversityIthacaUSA
  2. 2.Department of Plant PathologyKansas State UniversityManhattanUSA
  3. 3.Department of Plant Breeding and GeneticsCornell UniversityNew YorkUSA

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