Improvement of Barley, Rice and Chickpea by Population Sequencing

背景：气候变化、人口增长和其他新出现的挑战意味着需要开发适应能力更强的新作物品种。为了帮助实现这些目标，我们首先需要识别和分类现有作物品系之间的遗传变异，并评估或预测这些变异对作物产量、干旱和抗病能力的可能影响。这在英国这样的高产环境中很重要，对于在发展中国家广泛种植但尚未成为密集基因研究重点的作物来说，这一点尤为紧迫。要做到这一点，我们需要结合对遗传变异的分析，这可以通过对尽可能多的品种的基因组测序来获得，并通过以受控的方式混合这种变异形成新的种群。所谓的“魔术”群体，将多个品种的遗传变异组合成一个统一的群体，是通过实验确定基因变异对农业的影响的理想选择。有了这种遗传和表型数据的组合，我们可以更好地预测哪些现有品种应该杂交和培育，以产生适应更好的新品系。目标和产出：为了实现这一目标，该项目重点关注三种具有全球重要性的作物：水稻、大麦和鹰嘴豆。它结合了英国在遗传分析和软件开发、魔术、大麦研究和预育种方面的知识(通过UCL、NIAB和JHI)，以及在发展中国家印度(鹰嘴豆，通过ICRISAT)和菲律宾(水稻，通过IRRI)种植的主要作物的类似专业知识，以开发三个关键的生物和软件/分析资源，旨在促进作物研究和开发。我们将把我们最初在动物物种中开发的名为“Stich”的软件扩展到农作物上。基于大量品系集合的低复盖率基因组测序，Stitch允许改进“基因类型归因”(丢失遗传信息的预测)。这将分别通过IRRI和ICRISAT项目合作伙伴利用水稻和鹰嘴豆魔术种群的现有序列数据进行。2.为了提供一个专注于英国大麦研发的最先进的资源，我们将产生一个由8个亲本和1,000个衍生品系组成的大麦魔术种群，并通过低覆盖率基因组测序来表征这些品系的基因组。此外，我们将对与大麦生产相关的信息特征的魔术线进行评估。我们将使用上述1和2中创建的资源，以及在水稻魔术和鹰嘴豆‘长白’收集(早于现代育种方法的遗传多样性品系)项目中产生的低覆盖率序列数据，为所有三种目标作物生成详细的遗传变异图。我们将通过探索改进的方法来验证这些数据集，这些方法可以识别和/或预测不同的基因组合对作物表现的影响。所有产生的资源都将在可行的情况下尽快公开，以帮助最大限度地发挥它们对研究和育种的影响。归根结底，产生的资源和知识将有助于改良作物品种的发展。大麦是英国改进的重点，我们得到了英国研究人员和育种人员的大力支持。稻米和鹰嘴豆侧重于发展中国家的作物改良，并得到相关生产区域的卓越区域研究和育种中心的支持。这种改善的潜力在发展鹰嘴豆等县级作物方面尤其强劲，这些作物在历史上一直受到研发投资不足的影响。

Background: Climate change, population growth and other emerging challenges mean new, better adapted, varieties of crops need to be developed. To help achieve these goals, we first need to identify and catalogue genetic variations between existing crop strains, and assess or predict the likely impact of these variations on crop yield, drought and disease resistance. This is important in high-yielding environments such as the UK, and particularly urgent for crops that are widely grown in developing counties, but which not yet the focus of intense genetic research. To do this we need to combine an analysis of genetic variation, which we can obtain by sequencing the genomes of as many varieties as possible, with the creation of new populations formed by mixing this variation in a controlled manner. So-called "MAGIC" populations, which combine genetic variation from multiple varieties into a unified population, are ideal for establishing the agricultural impact of genetic variants experimentally. Armed with this combination of genetic and phenotypic data we can better predict which existing varieties should be crossed and bred to generate new better-adapted strains. Aims and outputs: Towards this goal, this project focuses on three crops of global importance: rice, barley and chickpea. It combines UK based knowledge in genetic analysis and software development, MAGIC, barley research and pre-breeding (via UCL, NIAB and JHI), with similar expertise in major crops grown in the developing countries India (chickpea, via ICRISAT) and the Philippines (rice, via IRRI) to develop three key biological and software/ analysis resources aimed at boosting crop research and development.1. We will extend the use of our software called 'STITCH', originally developed in animal species, for use in crops. STITCH allows improved 'genotypic imputation' (prediction of missing genetic information) based on low-coverage genome sequencing of large collections of lines. This will be undertaken using existing sequence data available for MAGIC populations in rice and chickpea via project partners IRRI and ICRISAT, respectively. 2. In order to provide a state-of-the-art resource focused on UK barley R&D, we will generate a barley MAGIC population, consisting of 8 parents and 1,000 derived lines, and characterise the genomes of these lines by low-coverage genomic sequencing. Additionally, we will undertake assessment of the MAGIC lines for informative characteristics relevant to barley production.3. We will use the resources created in 1 and 2 above, as well as low-coverage sequence data generated within the project for rice MAGIC and chickpea 'landrace' collections (genetically diverse lines that pre-date modern breeding approaches), to generate a detailed map of genetic variation for all three target crops. We will validate these datasets by exploring improved methods that identify and/or predict different combinations of genes on crop performance.All the resources generated will be made publicly available as soon as is practical, to help maximise their impact for research and breeding. Ultimately, the resources and knowledge generated will help the development of improved crop varieties. Barley is the focus of UK improvement, and we have strong support from UK researchers and breeders. Rice and chickpea focus on developing country crop improvement, and has the support of the pre-eminent regional research and breeding centres in the relevant production regions. The potential for such improvement is particularly strong in developing county crops such as chickpea, which have historically suffered from a lack of R&D investment.