Implementing within-cross genomic prediction to reduce oat breeding costs

Greg Mellers, Ian Mackay, Sandy Cowan, Irene Griffiths, Pilar Martinez-Martin, Jesse A. Poland, Wubishet Bekele, Nicholas A. Tinker, Alison R. Bentley*, Catherine J. Howarth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
49 Downloads (Pure)


A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow-base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow-base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.

Original languageEnglish
Article numbere20004
JournalPlant Genome
Issue number1
Early online date17 Mar 2020
Publication statusPrint publication - Mar 2020

Bibliographical note

© 2019 The Authors. The Plant Genome published by Wiley Periodicals, Inc. on behalf of Crop Science Society of America.


  • Avena/genetics
  • Breeding
  • Genome
  • Genomics
  • Genotype


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