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

尽管它在英国和全球范围内的重要性和需求不断增长，英国农场的小麦产量增长率却停滞不前。为了满足全球未来的需求，小麦的年产量增长必须至少达到1.4%，而使用现代方法提高基因改良的速度是实现这一目标的一种方法。廉价记录大量遗传和表型信息的能力（例如，遗传标记和田间试验的光谱图像-在本提案中称为基因组学和表型组学）为提高遗传改良的速度提供了新的机会。遗传改良的速度受以下因素影响：(1)选择的准确性；(2)育种周期时间；(3)选择强度；(4)可供选择的遗传多样性的数量。从中长期来看，对遗传多样性的关注正在通过国家和国际项目来解决，这些项目旨在从地方品种和祖先物种中引入性状和等位基因。然而，小麦遗传改良速度立即提高的主要障碍是育种周期时间的长短。即使在他们最快的小麦育种计划中，也需要至少四到六个季节来完成一个周期，这主要是因为需要时间来减少个体的数量，以选择一个可以集中表型的子集。基因组选择（GS）是一种新的育种工具，除其他优点外，它可以大大缩短育种周期时间，因为选择可以在不需要记录表型的情况下进行。在小麦中，这意味着育种周期可以缩短到一个季节，大大提高了遗传改良的速度。在极端情况下，使用温室每年完成2个周期的选择，在目前一个选择周期的5年时间框架内可以进行10个周期。GS使用表型和基因型的训练群体来构建预测方程。该方程用于预测非表型个体的育种值，在小麦中，这将允许将育种周期缩短到一个季节。GS假设用分子标记使所有个体的基因组饱和并估计这些标记的影响（即训练预测方程）将允许捕获由潜在数量性状位点引起的大部分遗传变异。如果捕获的遗传变异的比例很大，并且估计得很好，那么预测方程将能够对育种值做出准确的预测。同样，在表型组学中，表型可以被描述符饱和，这可以更好地分离其环境和遗传成分，并产生更精确的表型。建立培训人口是对GS的一项必要投资，需要战略性地利用资源来达到所需的规模，以优化GS的成本和效益。使用基因分型和归算策略对于降低成本至关重要。实地试验也很昂贵。利用新的高维方法捕获额外的性状和变量（表型组学）可以提高田间试验的价值，并使更强大的GS成为可能。本提案将使用田间试验和模拟来设计和评估基因组学和表型组学策略，以提高小麦的遗传改良率。这将包括GS训练种群设计和低成本的基因型数据收集，对高维环境描述符的特性进行评估并对其能力进行量化，对高维数据记录设备收集的性状表型的特性进行评估，并对其与标准性状的关系进行量化。结果将推广到具有与小麦类似的育种计划的其他物种，以及其他类型的实验和田间试验（例如国家名录评估）。

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).