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新的、适应性更好的作物品种。为了帮助实现这些目标，我们首先需要确定和编目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、抗旱性和抗病性的可能影响。这在英国这样的高产环境中很重要，对于那些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尤其迫切。为了做到这一点，我们需要结合对基因变异的分析，我们可以通过对尽可能多的品种进行基因组测序来获得，并通过以受控的方式混合这些变异来创造新的种群。所谓的“MAGIC”群体，将多个品种的遗传变异组合成一个统一的群体，是通过实验确定遗传变异对农业影响的理想群体。有了这种遗传和表型数据的结合，我们可以更好地预测哪些现有品种应该杂交和培育，以产生新的适应能力更好的品种。目标和产出：为实现这一目标，该项目侧重于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、MAGIC、大麦研究和预育种（通过UCL、NIAB和JHI）方面的知识，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）种植的主要作物方面的类似专业知识，开发了三种关键的生物和软件/分析资源，旨在促进作物研究和开发1。我们将把最初在动物物种中开发的名为“STITCH”的软件扩展到农作物中。STITCH基于对大量品系的低覆盖率基因组测序，可以改进“基因型插入”（预测缺失的遗传信息）。这项工作将分别利用项目合作伙伴国际水稻研究所和ICRISAT提供的水稻和鹰嘴豆MAGIC种群的现有序列数据进行。2. 为了提供最先进的资源，专注于英国大麦研发，我们将产生一个大麦MAGIC群体，由8个亲本和1000个衍生系组成，并通过低覆盖率基因组测序来表征这些系的基因组。此外，我们将对MAGIC品系进行评估，以获得与大麦生产相关的信息特性。我们将使用上述1和2中创建的资源，以及在水稻MAGIC和鹰嘴豆“地方品种”（在现代育种方法之前的遗传多样性品系）项目中生成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。我们将通过探索识别和/或预测不同基因组合对作物性能影响的改进方法来验证这些数据集。所有产生的资源将在可行的情况下尽快向公众提供，以帮助最大限度地提高它们对研究和育种的影响。最终，所产生的资源和知识将有助于改良作物品种的发展。大麦是英国改良的重点，我们得到了英国研究人员和育种者的大力支持。水稻和鹰嘴豆的重点是发展中国家的作物改良，并得到有关生产区卓越的区域研究和育种中心的支持。在鹰嘴豆等发展中国家作物中，这种改良的潜力特别大，这些作物历来受到缺乏研发投资的困扰。

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.