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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.
|Number of pages||12|
|Journal||The Plant Genome|
|Early online date||17 Mar 2020|
|Publication status||Published - 12 May 2020|
- oats; genomic prediction; genotyping; breeding
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- Faculty of Earth and Life Sciences, Institute of Biological, Environmental & Rural Sciences (IBERS) - Reader - IBERS
Person: Teaching And Research
- 1 Finished
BBSRC Core Strategic Programme in Resilient Crops: Oats
01 Apr 2017 → 31 Mar 2022
Project: Externally funded research