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

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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).