Molecular marker-assisted plant breeding on a genome wide scale

维持或增加农业粮食生产和安全是满足不断增长的人口需求的首要任务。气候变化和减少农业环境足迹的必要性进一步突出了这一挑战。因此，迫切需要加快改进作物品种，提高产量，提高肥料和水等资源的使用效率。作物植物中这些性状的遗传改良已经通过基于选择和杂交表型上级植物的植物育种来实现。在过去20年左右的时间里，分子标记已被用于一些育种计划，但主要是在一个特设的基础上，以改善一些目标性状。在过去的五年中，更经济实惠的高通量（下一代）测序和基因分型的出现，使得有可能以比迄今为止更全面的方式利用分子标记。我们指的是基因组选择（GS），它代表了一种新的方式来改善复杂的农艺和生物性状的表型，这些性状由许多基因控制，每个基因的影响很小。GS已经开始改变牛和猪等牲畜的养殖，但尚未对作物产生实际影响。GS是基于密集覆盖整个基因组的分子标记的集体组成的选择。拟议的生物，环境和农村科学研究所（IBERS）和计算机科学系在阿伯里斯特威斯大学之间的合作，使我们有机会测试GS的经验和理论。IBERS是英国唯一一个拥有植物育种计划的大学部门，我们将利用这一独特的地位，开发我们的多年生黑麦草育种计划。它是基于反复循环的轮回选择和杂交，非常适合GS，因为我们有全面的表型数据，为当前一代和前几代的这个成功的计划。我们将使用当前一代的母株作为“训练群体”，通过覆盖整个基因组的3000多个分子标记对其进行基因分型。目的是至少一个分子标记接近影响感兴趣表型的基因组区域（数量性状基因座或QTL）。从育种计划中已经获得的表型数据将与基因型数据相结合，使用已建立的统计方法生成复杂的预测模型，同时还使用计算机科学系开发的最先进的机器学习技术，用于计算基因组估计育种值（GEBV），并测试模型在育种计划中的性能。然后使用计算模型来计算验证群体中的GEBV，验证群体不同于训练群体，仅使用基因型数据。所得GEBV将用于仅基于基因型选择用于子代生产的个体。在基因组覆盖度较高的情况下，同一性状的多个QTL的组合效应可以通过同时合并所有等位基因的效应而得到可测量的改善。这种方法在多年生作物中特别有利，如黑麦草和其他牧草，因为可以减少对冗长的小区试验的需要。然而，这并不是GS的唯一优势。这里开发的基因组和统计资源和模型将为我们提供一个发现基因的平台，并促进解开复杂的农艺和生物学重要性状的结构。

Maintaining or increasing agricultural food production and security is a priority in order to meet the needs of a growing population. This challenge is put into further focus by climate change and the need to reduce the environmental footprint of agriculture. There is thus an urgent need to increase the speed of improvement of crop varieties in terms of yield and increased efficiency of use of resources, such as fertiliser and water. Genetic improvement of these traits in crop plants has been achieved by plant breeding on the basis of selection and crossing of phenotypically superior plants. In the last 20 years or so molecular markers have been used in some breeding programmes, but largely on an ad hoc basis for improvement of a few target traits. The advent of more affordable high throughput (next generation) sequencing and genotyping in the last five years has made it possible to make use of molecular markers in a more comprehensive way than hitherto. We refer to genomic selection (GS) which represents a novel way to improve the phenotype of complex agronomic and biological traits governed by many genes each with a small effect. GS is already beginning to transform the breeding of livestock such as cattle and pigs, but has yet to make an impact at a practical level for crop plants. GS is selection based on the collective composition of molecular markers densely covering the entire genome. The proposed collaboration between the Institute of Biological, Environmental and Rural Sciences (IBERS) and the Computer Science Department at Aberystwyth University gives us an opportunity to test GS empirically and theoretically. IBERS is the only university department in the UK with plant breeding programmes, and we will use this unique position by exploiting our perennial ryegrass breeding programme. It is based on repeated cycles of recurrent selection and crossing and is well suited for GS, as we have comprehensive phenotypic data for the current generation and earlier generations of this successful scheme. We will use the current generation of motherplants as a "training population" by genotyping it with over 3000 molecular markers covering the entire genome. The aim is that at least one molecular marker is close to a genomic region influencing the phenotype of interest (quantitative trait locus or QTL). The phenotypic data already available from the breeding programme will be combined with the genotype data to generate complex prediction models using established statistical methods, but also state-of-the-art machine learning techniques developed at the Computer Science Department, for the calculation of a genomic estimated breeding value (GEBV), and to test the performance of the models in the breeding programme. The computational models are then used to calculate the GEBV in a validation population, which is different from the training population, using only genotypic data. The resulting GEBV will be used to select individuals for progeny production based on genotype only. Given a dense coverage of the genome, the combined effect of many QTL for the same trait can be improved measurably by incorporating the effect of all alleles simultaneously. This approach will be particularly advantageous in perennial crops, such as ryegrass and other forages, as the need for lengthy plot trials can be reduced. However, this is not the only benefit of GS. The genomic and statistical resources and models developed here will provide us with a platform for discovery of genes and facilitate the unravelling of the architecture of complex traits of agronomic and biological importance.