Award details

Improvement of Barley, Rice and Chickpea by Population Sequencing

Reference	BB/P024726/1
Principal Investigator / Supervisor	Professor Richard Mott
Co-Investigators / Co-Supervisors
Institution	University College London
Department	UCL Genetics Institute
Funding type	Research
Value (£)	699,238
Status	Completed
Type	Research Grant
Start date	01/07/2017
End date	30/12/2022
Duration	66 months

Abstract

Focusing on the globally important crops rice, chickpea and barley, 'BRiCSeq' is a four year project funded by BBSRC and GCRF that establishes three biological and bioinformatic resources via research partnership between institutes in the UK (UCL, NIAB, JHI) India (ICRISAT) and the Philippines (ICRISAT). 1. We will adapt a new imputation method, STITCH (Davies et al 2016, Nature Genetics) originally developed for humans and mice, to create haplotype reference panels and imputed genomes for these three crops. This methodology requires only a reference genome and low coverage genome sequence (LCGS) data (~1x) collected across populations of hundreds or thousands of individuals. We will use this approach in rice, chickpea and barley MAGIC populations, leveraging existing population sequence data in some cases, and generating our own where necessary. 2. We will develop a new biological resource for UK crop R&D: a UK winter barley MAGIC population (8 founders, 1,000 progeny), along with genotype data via low-coverage sequencing, as well as baseline phenotypic data to pump-prime community engagement. 3. Using the bioinformatic resources generated in 1 above, in combination with genetically diverse MAGIC and landrace collections for which LCGS data is either available, or generated here (barley MAGIC, rice MAGIC, chickpea landraces), we will generate fully imputed genotypic and haplotype reference panel datasets for all three crop species. Datasets will be validated and explored via genetic analyses including QTL mapping and genomic prediction to determine/predict which combinations of genotypes have desirable agronomic characteristics. The bioinformatic and biological resources generated will enhancing R&D in rice, chickpea and barley, ultimately helping to accelerate the production of improved crop varieties . All biological and bioinformatic resources generated will be made publicly available for the UK and international research communities.

Summary

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.

Impact Summary

Our proposal develops crop resources and statistical methods that are applicable to agriculture in both developing and developed countries. The project focuses on three crops as exemplars, namely chickpea (ICRISAT, India), rice (ILRI, Phillipines) and barley (UK). Chickpeas are important crops in many developing counties although the focus of this project is on Indian varieties. Rice and Barley iare important cereals throughout the world. The project addresses the problem of how to establish the genetic architecture of crop landraces and experimental multiparent inter-crosses without the need to create genotyping arrays and haplotype reference panels. Instead it only requires a reference genome and then combines data from cheap low-coverage whole genome sequencing of hundreds or thousands of strains to simultaneously impute the complete genome sequences and reference panels of the population under study. Once this has been achieved it is straightforward to perform genetic association to identify loci associated with agronomically important traits and to perform genomic prediction. The proposal will accelerate the development of genetic resources for crop improvement and in particular enable the exploitation of standing genetic variation in the creation of varieties more adapted to changing climate. Developing software to impute crop genomes from low-coverage sequence will accelerate the use of genetics in crop breeding. We propose to first pilot this approach using chickpea, in collaboration with ICRISAT, who have a large collection of MAGIC and landrace accessions. This will provide us with a suitable test crop to optimise our statistical-genetic approach and to perfect the software necessary for it to be rolled out for use in the field, in particular in LIMC. We will then extend this methodology to other crops.

Committee	Research Committee B (Plants, microbes, food & sustainability)
Research Topics	Crop Science, Plant Science
Research Priority	X – Research Priority information not available
Research Initiative	GCRF BBR Highlight [2017]
Funding Scheme	X – not Funded via a specific Funding Scheme

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