Molecular Improvement of Disease Resistance in Barley (MIDRIB)

Lead Research Organisation: University College London
Department Name: UCL Genetics Institute

Abstract

This project develops an approach, genomic selection, to increase the rate at which varieties of Spring barley are developed. This is a very important crop in national agriculture, particularly for the malting, brewing and distilling industries. It is important that the rate with which improved varieties are created is increased so that more effort can be placed by breeders on improving disease resistance while maintaining or increasing grain yield and grain quality, which remain of greatest importance to growers and end users.Genomic selection represents a way of predicting traits purely from genetic markers rather than by direct measurement. These predictions require that a set of plants is first measured for the target traits so that the effect of each marker can be estimated. However, after that, selection can occur for several generations purely on markers.Direct measurement of many traits can take much longer than a single growing season: seed must first be bulked up over several generations to provide a sufficient quantity for yield trials. In contrast, marker data can be collected within the generation time of any crop and is therefore much faster than conventional selection.Other approaches to plant breeding using genetic molecular markers have been in use for many years. In these, a very small numbers of markers with strong evidence of an affect on a trait are first identified. These are then tracked through the breeding programme. Genomic selection differs in that all available markers are used to predict traits: the more markers the better. The inclusion of all markers gives more accurate prediction of overall trait values even though the precise involvement of each marker is known with less certainty.Our study has four themes. Firstly, throughout the life of the project, we shall develop new statistical methods to establish relationships between very high numbers of genetic markers and traits. The methods we develop will be more focussed on the problems of plant breeding: most methods to date have been targeted at animal breeding. Secondly, we shall test methods which are available now using historical data available from to an existing Spring barley scheme. Results will be used immediately to make selections within this scheme. We expect to register new varieties from these selections within the five year life of the project.Next, we shall use results from the analysis of the historical data together with any early methodological developments we make to create crosses specifically to exploit genomic selection. These crosses may not necessarily be the typical crosses between two parents which are commonly used by breeders but may involve more complicated crossing schemes involving, for example four parents. Within the life of the project, we shall test whether this approach gives a greater response to selection that achieved by more conventional breeding, but there will be insufficient time to resister a new variety.Finally, we shall integrate results and methods from the first three phases to completely redesign the breeding programme to get the greatest advantage out of genomic selection.In short, we plan to develop a new approach to Spring barley breeding .Genomic selection could result in a fundamental change to the way crops are bred and enable targets for increased food production and environmental sustainability to be met. Compared to other temperate crops, Spring barley has a short generation time which make it well suited to develop and test these ideas, which may also be applicable to other crops.

Publications

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Bentley AR (2014) Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

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Scutari M (2013) Identifying significant edges in graphical models of molecular networks. in Artificial intelligence in medicine

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Scutari M (2013) Improving the efficiency of genomic selection. in Statistical applications in genetics and molecular biology

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Scutari M (2014) Multiple Quantitative Trait Analysis in Statistical Genetics with Bayesian Networks in Graphical Causality Models: Tree, Bayesian Networks and Big Data; ENBIS-SFdS Spring Meeting

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Scutari M (2014) Multiple Quantitative Trait Analysis in Statistical Genetics with Bayesian Networks in Integrating the Genome with the Phenome; BCGES-SEGEG Annual Meeting

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Scutari M (2014) Multiple Quantitative Trait Analysis in Statistical Genetics with Bayesian Networks in International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

 
Description We have developed a protocol for selecting new barley varieties displaying favourable traits, such as yield and disease resistance, using genetic information available in the context of a commercial selection program. This protocol has the potential to reduce the number of field trials required by the program, and has the advantage to both speed it up and reduce it operational costs. The protocol is based on statistical models that predict such traits in future generations using historical records from the selection program itself.

We have explored the limits of current statistical methods for this task, which relate to how different new barley plants are compared to those in the selection program's records. Two common situations in which such limits are tested are exporting barley varieties to new countries and trying to implement predictions far into the future for optimising the speed of selection. We have developed a protocol to estimate when the statistical models produce reliable predictions in such circumstances.

The project has curated a comprehensive set of treated/untreated trial data for hundreds of barley varieties.

We have thoroughly investigated which conditions make field trials problematic for use in the statistical models above, in terms of the countries the trials are performed in and differing weather patterns in different years.
Exploitation Route See above. Results are being used by our industrial partner Limagrain, and through publication the wider crop breeding industry.
Sectors Agriculture, Food and Drink

 
Description The findings of the project have been used to produce the first barley variety submitted to the National Recommended List that was selected using only genetic information, as opposed to classic evaluation of a set of phenotypes. Investigations into which trials are most useful in this approach to selection have prompted changes in Limagrain's planning procedures for new trials. Using only genetic information for selection has changed Limagrain's standard procedures in designing new breeding programs, reducing the number of experimental stages (and thus the number of field trials). In particular, screening new varieties using statistical models based on genetic information makes the first stage trials redundant; only select varieties then require field testing thus reducing operational costs.
First Year Of Impact 2012
Sector Agriculture, Food and Drink
Impact Types Economic