Improvement of Barley, Rice and Chickpea by Population Sequencing

通过群体测序改良大麦、水稻和鹰嘴豆

• 批准号: BB/P024726/1
• 负责人: Richard Mott
• 金额: $ 89.1万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP024726%2F1
• 关键词: Improvement; Barley; Rice; Chickpea; Population

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.

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、适应性更强的作物品种。为了帮助实现这些目标，我们首先需要识别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，对于在发展中国家广泛种植但尚未成为密集遗传研究重点的作物尤为紧迫。要做到这一点，我们需要将对遗传变异的联合收割机分析（我们可以通过对尽可能多的品种的基因组进行测序来获得）与通过以受控的方式混合这种变异而形成的新种群的创造结合起来。所谓的“魔术”群体，即联合收割机将来自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选择。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育种，以产生新的适应性更好的菌株。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，大麦研究和育种前（通过UCL，NIAB和JHI）的知识，以及发展中国家印度（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。我们将把最初在动物物种中开发的名为“STITCH”的软件用于农作物。STITCH允许基于大量品系的低覆盖率基因组测序的改进的“基因型插补”（缺失遗传信息的预测）。这将分别通过项目合作伙伴IRRI和ICRISAT使用水稻和鹰嘴豆中MAGIC种群的现有序列数据进行。2.为了提供专注于英国大麦研发的最先进的资源，我们将产生一个大麦MAGIC群体，由8个亲本和1,000个衍生系组成，并通过低覆盖率基因组测序对这些系的基因组进行测序。此外，我们将对MAGIC系进行评估，以获得与大麦生产相关的信息特征。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”收集项目中生成的低覆盖率序列数据（早于现代育种方法的遗传多样性品系），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/或预测作物性能基因不同组合的改进方法来验证这些数据集。所有产生的资源将尽快公开，以帮助最大限度地发挥其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开发。大麦是英国改良的重点，我们得到了英国研究人员和育种家的大力支持。水稻和鹰嘴豆的重点是发展中国家作物改良，并得到相关生产地区杰出的区域研究和育种中心的支持。这种改进的潜力在鹰嘴豆等发展中国家作物中尤其强大，这些作物历来缺乏研发投资。

项目成果

Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness.

• DOI: 10.1111/pbi.13516
• 发表时间: 2021-05
• 期刊: Plant biotechnology journal
• 影响因子: 13.8
• 作者: Misra G;Badoni S;Parween S;Singh RK;Leung H;Ladejobi O;Mott R;Sreenivasulu N
• 通讯作者: Sreenivasulu N

Additional file 2 of Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding

附加文件 2 有限的单倍型多样性是 70 年小麦育种多基因性状结构的基础

• DOI: 10.6084/m9.figshare.14552126
• 发表时间: 2021
• 作者: Scott M

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.

• DOI: 10.1038/s41437-020-0336-6
• 发表时间: 2020-12
• 期刊: Heredity
• 影响因子: 3.8
• 作者: Scott MF;Ladejobi O;Amer S;Bentley AR;Biernaskie J;Boden SA;Clark M;Dell'Acqua M;Dixon LE;Filippi CV;Fradgley N;Gardner KA;Mackay IJ;O'Sullivan D;Percival-Alwyn L;Roorkiwal M;Singh RK;Thudi M;Varshney RK;Venturini L;Whan A;Cockram J;Mott R
• 通讯作者: Mott R

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

• DOI: 10.1101/2022.11.08.515457
• 发表时间: 2022-11
• 期刊: bioRxiv
• 作者: N. Fradgley;Keith A. Gardner;A. Bentley;P. Howell;I. Mackay;M. F. Scott;R. Mott;J. Cockram

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat

多性状整体基因组预测和循环选择模拟强调了复杂性状遗传结构对于小麦长期遗传增益的重要性

• DOI: 10.1093/insilicoplants/diad002
• 发表时间: 2023
• 期刊: in silico Plants
• 影响因子: 3.1
• 作者: Fradgley N