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

尽管它很重要，而且在英国国内和全球范围内需求不断增长，但英国农场小麦产量的增长速度一直停滞不前。为了满足全球未来的需求，小麦年产量增幅必须至少达到1.4%，而利用现代方法提高遗传改良率是实现这一目标的一种方法。以低成本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和表型组学)为提高遗传改良率提供了新的机会。遗传改良率受(1)选择的准确性、(2)育种周期时间、(3)选择强度和(4)要选择的遗传多样性的数量的影响。从中长期来看，正在通过国家和国际项目从地方品种和祖先物种中导入性状和等位基因，以解决对遗传多样性的关切。然而，小麦遗传改良率立即提高的主要障碍是育种周期的长短。即使在最快的时候，小麦育种计划也需要至少四到六个季节才能完成一个周期，主要是因为将选择的个体数量减少到一个可以密集表型的子集所需的时间。基因组选择(GS)是一种新的育种工具，除了其他优点外，还可以显著缩短育种周期，因为无需记录表型就可以进行选择。在小麦中，这意味着育种周期可以缩短到一个季节，大大提高遗传改良率。在极端情况下，使用温室每年完成两个选择周期，在目前的一个选择周期的5年时间框架内可以进行10个周期。GS使用表型和基因分型的训练群体来构建预测方程。这个方程被用来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假设用分子标记饱和所有个体的基因组，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基因座引起的很大比例的遗传变异。如果捕获的遗传变异的比例很大，并且估计得很好，预测方程将能够对育种价值做出准确的预测。同样，在表型组学中，表型可能被描述符饱和，这可能导致更好地分离其环境和遗传成分，并产生更精确的表型。创建训练种群是GS所需的投资，需要战略性地使用资源以达到所需的规模，以优化GS的成本和收益。使用基因分型和归罪策略对于降低成本至关重要。实地试验的成本也很高。使用新的高维方法来捕捉额外的特征和变量(表现学)可以提高田间试验的总体价值，以及实现更强大的GS。这项建议将使用田间试验和模拟来设计和评估基因组学和表观组学策略，以提高小麦的遗传改良率。这将包括GS培训种群设计和低成本收集基因数据、评估高维环境描述符的特性和量化其威力、评估由高维数据记录设备收集的性状表型的特性以及量化它们与标准性状的关系。结果将推广到与小麦育种计划类似的其他物种，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).