Mining the allohexaploid wheat genome for useful sequence polymorphisms

面包小麦对英国、欧洲和世界农业至关重要，估计2007年世界产量约为5.5亿吨。在英国，约有180万公顷的土地种植了小麦，每公顷产量约为7.2吨，农场价值为26亿英镑。英国拥有理想的小麦生长条件和世界级的作物改良计划。尽管它很重要，但世界范围内的小麦生产并没有跟上需求的增长，生产力受到病害、肥料成本增加、对优质农业用地的竞争、资源限制和急剧降低最佳产量的不利环境条件的威胁。据估计，欧洲的生产率必须翻一番才能满足需求并保持稳定的价格。因此，通过缩小最大产量和实际产量之间的差距，并增加最大潜在产量，可以确保世界上最重要的作物之一的可持续和充足生产。小麦产量的大幅度增加主要是由于选育优良品系带来的遗传改良。通过整合更广泛的遗传多样性和加速鉴定表现最佳的基因型，可以增强育种的力量。这可以通过DNA序列标记来确定关键性状的遗传多样性来实现。我们的目标是使用下一代测序和一种新的计算和比较基因组学策略来确定5个关键品种基因组中的序列差异，这些差异可用于定义不同品种中单个基因的不同版本。在小麦中找到这种类型的标记过去一直很困难，因为小麦是六倍体，每个基因可能有3个拷贝，而小麦品系的大部分序列差异是在一个品种的基因的这三个拷贝之间，而不是在不同品种的基因之间。有了这些信息和一组标记，育种公司和学术科学家将能够识别和选择不同品种基因组的特定区域，并利用这些信息分离基因，并从杂交中选择含有该区域DNA的品系。这种能力将从根本上改变小麦研究，使育种中能够使用更多样化的品系，包括具有大量未被开发的性状（包括抗逆性）的野生品种。最后，这项基因分型研究将促进英国重要作物的更高水平的学术研究。我们旨在开发的测序和信息学策略也将建立小麦全基因组测序的方法。目前，基因组的大尺寸，其六倍体组成和主要的重复组成，是一个很大的障碍。然而，下一代测序的高通量和低成本为小麦基因组的规模化提供了解决方案。我们提出的工作将使测序集中在基因丰富的区域，并增加组装基因丰富的基因组序列的潜力。此外，使用一种新的生物信息学策略，使用一个密切相关物种的完整基因组序列作为“模板”来识别基因结构（如内含子）和基因的近似顺序，我们的工作将定义组装基因序列和小麦染色体中基因顺序的新方法。这将降低未来大规模基因组测序和分析工作的障碍。最后，该项目通过WGIN与英国育种界密切联系，通过Monogram网络与英国研究小麦的学术实验室密切联系，并通过国际小麦基因组测序联盟与国际小麦基因组学界密切联系。这将确保向关键利益攸关方迅速传递信息。

Bread wheat is of fundamental importance to UK, European and world agriculture, with an estimated 2007 world harvest of ~ 550 m tonnes. In the UK, ~1.8 m hectares are planted with wheat, yielding ~7.2 tonnes per hectare, with a farm-gate value of £2.6 billion. The UK has ideal growth conditions for wheat and has a world-class crop improvement programme. Despite its importance, wheat production world-wide has not kept pace with increased demand, and productivity is threatened by disease, increased fertiliser costs, competition for high quality agricultural land, resource limitations, and adverse environmental conditions that dramatically reduce optimal yields. It has been estimated that in Europe productivity has to be doubled to keep pace with demand and to maintain stable prices. Therefore by narrowing the gap between maximal yields and actual yields, and increasing maximal potential yields, sustainable and adequate production of one of the world's most importance crops could be secured. The large increases in wheat yield have been primarily due to genetic improvements brought about by selective breeding of elite lines. The power of breeding can be increased by enabling the incorporation of wider genetic diversity and accelerating the identification of best-performing genotypes. This can be achieved using DNA sequence markers to identify genetic diversity underlying key traits. We aim to use next generation sequencing and a novel computational and comparative genomics strategy to identify sequence differences in the genomes of 5 key varieties that can be used to define different versions of a single gene in different varieties. Finding this type of marker in wheat has been problematic in the past because wheat is a hexaploid, with potentially 3 copies of each gene, and most of the sequence differences in wheat lines are between these three copies of a gene in a variety, rather than between genes in different varieties. With this information and a set of markers, breeding companies and academic scientists will be able to identify and select specific regions of the genomes of different varieties, and use this information to isolate genes and select lines with that region of DNA in it from crosses. This capability will fundamentally alter wheat research by enabling the use of more diverse lines in breeding, including wild species that have a wealth of under-exploited traits, including stress tolerance. Finally this genotyping study will facilitate a far greater level of academic research in a key UK crop. The sequencing and informatics strategies we aim to develop will also establish ways to sequence the complete genome of wheat. Currently the large size of the genome, its hexaploid composition and predominant repeat composition, is a large barrier to progress. However, the high throughput and low cost of next generation sequencing provides a solution to the scale of the wheat genome. Our proposed work will enable sequencing to focus on gene-rich regions and increase the potential for assembling gene-rich genome sequences. Furthermore, using a novel bioinformatics strategy that uses the complete genome sequence of a closely-related species as a 'template' for identifying both gene structures such as introns and an approximate order of genes, our work will define new ways of assembling gene sequences and the order of genes in wheat chromosomes. This will lower the barriers for future work aimed at larger-scale genome sequencing and analysis. Finally this project is closely linked to the UK breeding community through WGIN, to academic laboratories studying wheat in the UK through the Monogram Network, and to the international wheat genomics community through the International Wheat Genome Sequencing Consortium. This will ensure the rapid transfer of information to key stakeholders.