A strategy to exploit genomic selection for achieving higher genetic gains in groundnut

利用基因组选择在花生中实现更高遗传增益的策略

• 批准号: BB/R019940/1
• 负责人: Gregor Gorjanc
• 金额: $ 69.36万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR019940%2F1
• 关键词: strategy; exploit; genomic; selection; achieving

This project will develop the technology for genomic selection in legume and oilseed crops by applying the research tools developed at Roslin for computational genetics and breeding programme design in livestock to groundnut breeding programmes. Genomic selection has revolutionised livestock breeding programmes worldwide because animals are selected for breeding on the basis of genotype information - which can be collected from very young animals - instead of accurate phenotype information (inherited characteristics) which may not be available until several years after birth. Genomic selection is now widely and successfully used in dairy cattle, pigs, sheep, and poultry and it offers new opportunities to increase the efficiency, effectiveness and sustainability of plant breeding programmes. Genomic selection promises the same benefits in pulse breeding as it has already delivered in livestock. Rates of genetic improvement in a breeding programme are determined by four factors: selection intensity, selection accuracy, breeding cycle time, and the amount of genetic diversity to be selected upon. The first three of these factors would be improved by genomic selection. In this discussion, we take the groundnut as an example but very similar limitations apply in other oilseed and pulse crops.Selection intensity is low and breeding cycle time is long because traits such as X, Y, yield and disease resistance, as well as genotype by environment interactions, can only be selected upon late in the breeding cycle, by which point most of the candidates have been eliminated on crude visual criteria. Genomic selection would allow all traits to be estimated in very large numbers of plants once low-cost genotyping technologies have been developed. We need low cost genotyping technologies to increase selection intensity.Selection accuracy is limited because selection in the early years of the breeding cycle is limited to the few traits that can be measured at the seedling stage. Important factors such as yield, X, genotype by environment interactions and Y cannot be evaluated in seedlings. Genomic selection would allow breeding values for all traits of importance to be estimated in young seedlings with a high degree of accuracy once an appropriate training population has been created and the necessary phenotype and genotype data collected. We need optimal designs for training populations to increase selection accuracy.Breeding cycle time in groundnut is a minimum of four years because of the time it takes from crossing to advance generation from F1 to F6 to achieve homozygosity from where phenotyping can be done in plots. Genomic selection could reduce the breeding cycle time to a year or even six months because genomic selection can be carried out on immature seedlings and so the new cycle can be initiated as soon as the selection candidates reach maturity through rapid generation advancement. Additionally, genomic selection also help to reduce the size from F3 generation onwards thus optimizing the resources. This reduction in generation interval represents the most obvious advantage of genomic selection in comparison to traditional groundnut breeding as it gives a potential tenfold increase in the rate of genetic improvement. We need optimised breeding programme designs and transition strategies to reduce breeding cycle time in an affordable way that minimizes risks, hence the need for the proposed research.To deliver this project we need to develop genotyping and sequencing technologies, algorithms and strategies to enable sufficient genomic data to be generated within the economic constraints of groundnut breeding programs. We need to develop a genomic selection training set. We need to develop and optimise the population improvement and product development components of the proposed redesigned breeding program. Finally, we need to implement the new design in the ICRISAT, DGR and UAS breeding programmes and test its performance.

该项目将开发豆类和油籽作物基因组选择技术，将罗斯林为牲畜计算遗传学和育种方案设计开发的研究工具应用于花生育种方案。基因组选择已经彻底改变了世界范围内的牲畜育种计划，因为动物是根据基因型信息（可以从非常年幼的动物中收集）而不是准确的表型信息（遗传特征）进行育种的，这些信息可能要到出生后几年才能获得。基因组选择目前已广泛成功地应用于奶牛、猪、羊和家禽，它为提高植物育种计划的效率、有效性和可持续性提供了新的机会。基因组选择在豆类育种中承诺了与牲畜相同的好处。育种计划中的遗传改良率由四个因素决定：选择强度，选择准确性，育种周期时间和选择的遗传多样性数量。这些因素中的前三个将通过基因组选择得到改善。在本讨论中，我们以花生为例，但其他油料作物和豆类作物也有类似的限制，选择强度低，育种周期长，因为X、Y、产量和抗病性等性状，以及基因型与环境的相互作用，只能在育种周期的后期进行选择，此时大多数候选作物都已被粗略的视觉标准淘汰。一旦开发出低成本的基因分型技术，基因组选择将允许在非常大量的植物中估计所有性状。我们需要低成本的基因分型技术来增加选择强度。选择的准确性是有限的，因为在育种周期的早期选择仅限于少数几个可以在幼苗阶段测量的性状。重要的因素，如产量，X，基因型与环境的相互作用和Y不能在幼苗中进行评估。基因组选择将允许育种值的所有重要性状，以高度的准确性，估计在年轻的幼苗，一旦一个适当的培训人口已创建和必要的表型和基因型数据收集。我们需要优化设计的训练群体，以提高选择的准确性。花生育种周期时间是最少的四年，因为它需要从杂交到先进的一代从F1到F6，以实现纯合性，从那里表型可以在地块上完成。基因组选择可以将育种周期时间缩短到一年甚至六个月，因为基因组选择可以在未成熟的幼苗上进行，因此新的周期可以在选择候选者通过快速世代推进达到成熟时立即开始。此外，基因组选择还有助于从F3代开始减少大小，从而优化资源。与传统的花生育种相比，这种世代间隔的缩短代表了基因组选择的最明显优势，因为它使遗传改良的速度提高了10倍。我们需要优化的育种计划设计和过渡策略，以降低风险的经济方式缩短育种周期，因此需要进行拟议的研究。为了实现这一项目，我们需要开发基因分型和测序技术，算法和策略，以便在花生育种计划的经济限制下生成足够的基因组数据。我们需要开发一个基因组选择训练集。我们需要开发和优化拟议的重新设计育种计划的种群改良和产品开发部分。最后，我们需要在ICRISAT，DGR和UAS育种计划中实施新设计并测试其性能。

项目成果

Modelling illustrates that genomic selection provides new opportunities for intercrop breeding

建模表明基因组选择为间作育种提供了新的机会

• DOI: 10.1101/2020.09.11.292912
• 发表时间: 2020
• 作者: Bancic J

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

Genomic and phenotypic characterization of finger millet indicates a complex diversification history.

手指小米的基因组和表型特征表明了复杂的多样化历史。

• DOI: 10.1002/tpg2.20392
• 发表时间: 2023
• 期刊: The plant genome
• 作者: Bancic J

Genomic selection for genotype performance and stability using information on multiple traits and multiple environments.

• DOI: 10.1007/s00122-023-04305-1
• 发表时间: 2023-04-07
• 期刊: THEORETICAL AND APPLIED GENETICS
• 影响因子: 5.4
• 作者: Bancic, J.;Ovenden, B.;Gorjanc, G.;Tolhurst, D. J.
• 通讯作者: Tolhurst, D. J.

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

• DOI: 10.1371/journal.pone.0235554
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Batista LG;Gaynor RC;Margarido GRA;Byrne T;Amer P;Gorjanc G;Hickey JM
• 通讯作者: Hickey JM