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UK - Department for Business, Energy and Industrial Strategy

Improvement of Barley, Rice and Chickpea by Population Sequencing

Disclaimer: The data for this page has been produced from IATI data published by UK - Department for Business, Energy and Industrial Strategy. Please contact them (Show Email Address) if you have any questions about their data.

Project Data Last Updated: 27/08/2020

IATI Identifier: GB-GOV-13-FUND--GCRF-BB_P024726_1


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.


'BRiCSeq' combines UK expertise in bioinformatics, genotyping via low-coverage genome sequencing, MAGIC and quantitative genetics with MAGIC/landrace resources and breeding activities at ICRISAT (India) and IRRI (Philippines), to provide three resources primarily focused on crop R&D: Resource 1: Bioinformatic pipelines and software for sequence-based imputation in crops. We will extend our imputation software 'STICH', developed in animals, for use in crops. This will be undertaken using existing low coverage genome sequence (LCGS) data available for Multiparental Advanced Generation Inter Cross (MAGIC) populations and landrace collections in chickpea (available via ICRISAT) and rice (via IRRI). The software will be tailored for genetic mapping and genomic prediction in the imputed genomes, and applicable to many other crops. Resource 2: Construction of a UK barley MAGIC population. We will generate a state-of-the-art biological resource to serve the UK crop R&D community: an eight-parent barley MAGIC population. Completed in the four year project duration, the population will consist of 8 elite UK winter barley founder varieties and 1,000 progeny, genotyped via LCGS. Resource 3: Fully imputed genotypic and haplotype reference panel datasets for all three target crops. These will be generated using the software, populations and existing LCGS chickpea and rice datasets described above, in combination with de novo LCGS data (900 rice MAGIC lines and 2400 chickpea landraces sequenced at ~1x coverage). These resources will be validated and explored within this project via quantitative genetic analyses of agronomic traits. All resources will be made publicly available, for the benefit of UK and global crop research and breeding.

Status - Implementation More information about project status
Project Spend More information about project funding
Participating Organisation(s) More information about implementing organisation(s)

Sectors groups as a percentage of country budgets according to the Development Assistance Committee's classifications.


A comparison across six financial years of forecast spend and the total amount of money spent on the project to date.

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