GplusE: Genomic selection and Environment modelling for next generation wheat breeding

Despite its importance and growing demand within the UK, and globally, the rate of increase in wheat yields on UK farms have stagnated. To meet global future demand, annual wheat yield increases must grow to at least 1.4% and increasing the rate of genetic improvement using modern approaches is one way to do this. The ability to record vast quantities of genetic and phenotypic information cheaply (e.g. genetic markers and spectral images of field trials - termed in this proposal as Genomics and Phenomics) presents a new opportunity for increasing the rate of genetic improvement.
The rate of genetic improvement is affected by (1) the accuracy of selection, (2) breeding cycle time, (3) selection intensity, and (4) the amount of genetic diversity to be selected upon. In the medium to long term, concerns about genetic diversity are being addressed through national and international projects to introgress traits and alleles from landraces and progenitor species. However, the major barrier to the immediate increase in the rate of genetic improvement in wheat is the length of the breeding cycle time. Even at their fastest wheat breeding programs require at least four to six seasons to complete a cycle, principally due to the time required to reduce the number of individuals for selection to a subset that can be intensively phenotyped. Genomic selection (GS) is a new breeding tool that, amongst other advantages, can dramatically reduce breeding cycle time as selection can occur without the need to record phenotypes. In wheat this means breeding cycle time could be reduced to one season, dramatically increasing the rate of genetic improvement. In the extreme, using glasshouses to complete 2 cycles of selection per year, 10 cycles could be undertaken in the 5-year time frame currently taken for a single selection cycle.
GS uses a training population that is phenotyped and genotyped to construct a prediction equation. This equation is used to predict the breeding values of unphenotyped individuals, which, in wheat, would allow reduction of the breeding cycle to one season. GS assumes that saturating the genome of all individuals with molecular markers and estimating the effect of these markers (i.e. training the prediction equation) will allow capture of a large proportion of the genetic variation caused by the underlying quantitative trait loci. If the proportion of the captured genetic variation is large and well estimated the prediction equation will be able to make accurate predictions about breeding values. Similarly, in Phenomics the phenotype could be saturated with descriptors, which could lead to a better separation of its environmental and genetic components as well as generating more precise phenotypes.
Creation of training populations is a required investment for GS and strategic use of resources to achieve the required size is needed to optimize the cost and benefit of GS. Use of a genotyping and imputation strategy is paramount for reducing costs. Field trials are also costly. Use of novel high-dimensional approaches for capturing extra traits and variables (Phenomics) could enhance the value of field trials generally, as well as enabling more powerful GS.
This proposal will use field trials and simulation to design and evaluate Genomics and Phenomics strategies for increasing rates of genetic improvement in wheat. This will include GS training population designs and low cost collection of genotype data, assessment of the properties of high-dimensional environmental descriptors and quantification of their power, assessment of the properties of trait phenotypes collected by high-dimensional data recording devices and quantification of their relationships to standard traits. Results will be generalised to other species with breeding programs similar to those of wheat as well as to other type of experiments and field trials (e.g. National List evaluations).

This proposal will develop and evaluate strategies for next generation wheat breeding based on the use of Genomic and Phenomic data, which can now be recorded in large quantities cheaply. It will bring "Big Data" to wheat breeding. Genomic selection (GS) could revolutionize wheat breeding: cycle times could be shortened; accuracy and intensity of selection maintained or increased; and more widespread selection for hard to measure traits could be undertaken. The key advantage of GS in comparison to traditional breeding is the ability to use high-dimension genetic marker information to make accurate predictions of genetic value for selection candidates without having to wait for phenotypes to be collected.
Analogous to GS, Phenomics uses instruments to record high dimensional phenotypic (e.g. spectral images) and environmental (e.g. soil electro-magnetic imaging) data. These may be determinants or indicators of complex trait phenotypes such as yield. More accurate modelling of environment and phenotype could enable faster genetic improvement in wheat, or increased efficiency of assessment in any field trial. Phenomics is highly valuable for wheat breeding. This project will scale that value to field level, enabling it to drive genetic improvement.
This project will use a large field trial combined with simulation to study the key factors in the application of GS to wheat breeding: training population design and genotyping strategy. It will integrate GS with Phenomics so that they empower each other.

Genomic selection and Environment modelling for next generation wheat breeding (GplusE) links Phenomics and Genomics to deliver to the wheat breeding community a platform to greatly increase the rate of progress possible through breeding. Wheat is the UK's major crop and has the 3rd largest production of any cereal globally. This project has the potential to benefit individuals and organisations worldwide for whom improvement in wheat yields is important. This ranges from farmers in the developing world, through millers and bakers, to anyone buying bread in their local supermarket. The impact of the methods we develop will be seen first by the UK wheat breeders involved in this project, with delivery of improved varieties to market in the following few years.
More specifically:
The commercial breeding partners will benefit immediately by:
1. Initiation of propriety training populations of direct relevance to their breeding programs;
2. Protocols for cost reduction of genotyping by use of imputation and of phenotyping by remote capture of covariates;
3. Access to data generated in the project and to source code for programs required to implement GplusE.
Other breeders will benefit by publication of protocols for the application of GplusE within their own breeding programmes, including access to compiled versions of software developed within the project. This will be available following their publication.
Agronomists and field crop researchers will benefit from exemplars of the use of Phenomics and precision agriculture to improve the accuracy of treatment comparisons in field trials, for example in trials comparing agronomic inputs.
Suppliers of precision agriculture services and genotypes will benefit from new market opportunities in supplying services to field trials operators, including breeders.
The academic crop research community will benefit from improved techniques for field trials and demonstration of how novel physiological and other traits could be incorporated into breeding programmes within a quantitative genetics framework.
Longer term (>8 years) the linking of Phenomics and Genomics may result in development of varieties more adapted to specific environment conditions allowing, for example, automated switching of varieties during drilling to match micro-environmental conditions. This benefits the farmer in improving yield and the environment by reducing inputs currently required to compensate for variety weaknesses.
NIAB / RI will benefit from exposure to complementary expertise in each other's institutes. For NIAB this will result in improved application of big-data methods to their research interests in agricultural science and genetics. For RI, it will open opportunities for broader application of its quantitative genetics research and future funding and collaboration with both private and public sector crop and plant science communities.
The PDRAs funded by this project will benefit from contact with each other and with staff outside their host institutes. They will gain knowledge and expertise beyond the confines of their project areas.
This project will genotype, phenotype and analyse 3,000 lines. The size of the project and the disciplines involved, encompassing quantitative genetics, molecular genetics, plant physiology, Phenomics and agronomy will provide opportunities for training of scientific and technical staff in these fields. The research will also generate opportunities for field visits and workshops to stakeholders from outside RI and NIAB. NIAB Innovation Farm showcases innovation in agriculture, provides free support and assistance for small to medium businesses in the East of England and hosts national and international workshops. This infrastructure will be used to inform a wider public and scientific community.