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

GplusE：下一代小麦育种的基因组选择和环境建模

• 批准号: BB/L022141/1
• 负责人: Ian Mackay
• 金额: $ 72.87万
• 依托单位: National Institute of Agricultural Botany
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FL022141%2F1
• 关键词: GplusE; Genomic; selection; Environment; modelling

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).

尽管其在英国和全球范围内的重要性和不断增长的需求，英国农场小麦产量的增长速度停滞不前。为了满足全球未来的需求，小麦年产量必须增长到至少1.4%，使用现代方法提高遗传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）为提高遗传改良率提供了新的机会。遗传改良率受以下因素影响：（1）选择的准确性，（2）育种周期时间，（3）选择强度，以及（4）待选择的遗传多样性的量。从中长期来看，正在通过国家和国际项目解决对遗传多样性的关切，以使地方品种和祖先物种的性状和等位基因渐渗。然而，直接提高小麦遗传改良速度的主要障碍是育种周期的长度。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，主要是由于减少用于选择的个体数量到可以进行密集表型分析的子集所需的时间。基因组选择（GS）是一种新的育种工具，除其他优点外，它可以显着减少育种周期时间，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。在极端的情况下，使用温室每年完成2个选择周期，可以在目前单个选择周期所用的5年时间框架内进行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。该方程用于预测未分型个体的育种值，在小麦中，这将允许将育种周期减少到一个季节。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，则预测方程将能够对育种值做出准确的预测。类似地，在表型组学中，表型可以被描述符饱和，这可以导致其环境和遗传组分的更好分离以及产生更精确的表型。创建训练群体是GS所需的投资，并且需要战略性地使用资源以实现所需的规模，以优化GS的成本和效益。使用基因分型和插补策略对于降低成本至关重要。实地试验也很昂贵。使用新的高维方法来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，并实现更强大的GS。该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，以提高小麦遗传改良的速度。这将包括GS训练群体设计和基因型数据的低成本收集、评估高维环境描述符的属性并量化其功效、评估高维数据记录设备收集的性状表型的属性并量化其关系与标准性状。结果将推广到其他物种，育种计划类似于小麦以及其他类型的实验和田间试验（例如国家清单评估）。

项目成果

How remote sensing is offering complementing and diverging opportunities for precision agriculture users and researchers

遥感如何为精准农业用户和研究人员提供互补和分散的机会

• DOI: 10.1017/s2040470017000930
• 发表时间: 2017
• 期刊: Advances in Animal Biosciences
• 作者: Jackson R

Annual Plant Reviews online

年度植物评论在线

• DOI: 10.1002/9781119312994.apr0538
• 发表时间: 2018
• 作者: Urbanova T

Plant breeders should be determining economic weights for a selection index instead of using independent culling for choosing parents in breeding programs with genomic selection

植物育种者应该确定选择指数的经济权重，而不是在基因组选择育种计划中使用独立剔除来选择亲本

• DOI: 10.1101/500652
• 发表时间: 2018
• 作者: Batista L

The effects of training population design on genomic prediction accuracy in wheat

训练群体设计对小麦基因组预测准确性的影响

• DOI: 10.1101/443267
• 发表时间: 2018
• 作者: Edwards S

Phenomic and genomic prediction of yield on multiple locations in winter wheat.

• DOI: 10.3389/fgene.2023.1164935
• 发表时间: 2023
• 期刊: FRONTIERS IN GENETICS
• 影响因子: 3.7
• 作者: Jackson, Robert;Buntjer, Jaap B.;Bentley, Alison R.;Lage, Jacob;Byrne, Ed;Burt, Chris;Jack, Peter;Berry, Simon;Flatman, Edward;Poupard, Bruno;Smith, Stephen;Hayes, Charlotte;Barber, Tobias;Love, Bethany;Gaynor, R. Chris;Gorjanc, Gregor;Howell, Phil;Mackay, Ian J.;Hickey, John M.;Ober, Eric S.
• 通讯作者: Ober, Eric S.