More Oats in the Bowl: Genetic Improvements in Oats


An oat field experiment ready for harvest

The central objective of the InnovOat project currently under-way at Aberystwyth University’s Institute of Biological, Environmental and Rural Sciences (IBERS) is to apply state of the art high throughput breeding and phenotyping approaches to the genetic improvement of oats. We are focusing on yield, and grain and milling quality - key targets for the economic sustainability of the crop and for the milling industry. This 5 year project, funded through the BBSRC- Stand Alone LINK with support from the BOBMA Research Group and Senova, also includes NIAB and the James Hutton Institute as academic partners and addresses some of the major challenges facing UK agriculture in terms of the sustainable production of safe and nutritious food.

The overall aim of this project is to incorporate high throughput approaches to the IBERS oat breeding programme, to develop strategies to improve yield and other targets ranked as priorities by our industrial partners which are currently difficult or impossible to select for at early stages of breeding cycles.

Marker assisted selection (MAS) represents one route to achieve this. It has been successful for introgression of major traits controlled by one or a few genes of large effect, but is difficult with more complex traits governed by many genes, each with a small effect. MAS is used in the IBERS oat breeding programme, largely based on predictions derived from a few markers linked to large effect quantitative trait loci (QTL). Association mapping (AM) will be used to identify further marker-trait associations enabling rapid selection or introgression within the breeding programme.

In this project, genomic selection (GS) will be applied to a range of traits, and selections will be validated by comparison with breeder and conventional marker assisted (MAS) selections. Increasingly complex models will be developed in the course of the programme, and an accelerated breeding cycle driven by GS and MAS will be initiated. Traits which may predict yield will be identified by detailed phenomic and field trial analysis of a model winter oat population and an association genetics panel of advanced breeding lines. Metabolic profiling and micro-scale analytical methods will be used to develop further predictive screens. Chip-based high throughput genotyping will be used to predict breeding values; genotype and phenotype data will be incorporated into a pedigree database to further facilitate 'intelligent' breeding design. Genotyping by Sequencing will become the main platform by the end of the project to take advantage of expected sequence throughput improvements. Taken together, InnovOat applies the best cutting edge technologies and approaches to deliver step-change advancements in oat breeding.

Professor Athole Marshall from Aberystwyth University

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