Genomic selection in plant breeding: From theory to practice

Jean Luc Jannink, Aaron J. Lorenz, Hiroyoshi Iwata

Research output: Contribution to journalArticlepeer-review

737 Scopus citations

Abstract

We intuitively believe that the dramatic drop in the cost of DNA marker information we have experienced should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker-assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait,GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this article are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation and empirical studies.We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection.

Original languageEnglish (US)
Article numberelq001
Pages (from-to)166-177
Number of pages12
JournalBriefings in Functional Genomics and Proteomics
Volume9
Issue number2
DOIs
StatePublished - Feb 15 2010

Keywords

  • Breeding value prediction
  • Linkage disequilibrium
  • Machine learning
  • Marker-assisted selection
  • Ridge regression

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