Improvement of Barley, Rice and Chickpea by Population Sequencing
Lead Research Organisation:
University College London
Department Name: UCL Genetics Institute
Abstract
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.
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.
Technical Summary
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.
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.
Planned Impact
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.
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.
People |
ORCID iD |
| Richard Mott (Principal Investigator) |
Publications
Mackintosh C
(2021)
X-linked meiotic drive can boost population size and persistence
in Genetics
Misra G
(2021)
Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness.
in Plant biotechnology journal
Scott MF
(2019)
A 3,000-year-old Egyptian emmer wheat genome reveals dispersal and domestication history.
in Nature plants
Scott MF
(2020)
Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.
in Heredity
Scott MF
(2021)
Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding.
in Genome biology
Yang CJ
(2022)
Analysis of historical selection in winter wheat.
in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
| Description | We have sequenced 800 rice genomes and 1920 chickpea genomes using DNA supplied by our collaborators in the LMIC countries India (chickpea) and Philippines (rice). We have started to analyse the data to identify sequencing variants that are associated with traits of agricultural importance. The sequencing technique we use is "Low Coverage Whole Genome Sequencing" (LCWGS) which means that we sample the genome variation of each individual randomly. LCWGS is very economical which meant we could sequence many genomes at low cost. We then use a statistical technique called imputation to fill in the missing genotypes in each individual. Imputation relies on the fact that the number of different chromosomal segments (haplotypes) present in any small region of the genome is small - usually a dozen or so - and that each chromosome can be built as a mosaic of these haplotypes. We use a algorithm called STITCH developed previously in our group, that simultaneously finds these haplotypes, builds the mosaics and then infers the genomic variation in each individual. We have done this in both the rice and chickpea sequence data. In the case of the chickpeas, the 1920 genomes are a subset of landraces (varieties grown by farmers, often saving their own seed, with no attempt at crop improvement, but which can hold a great deal of genetic variation lost in commercially-developed varieties. These "lost" variations might contain useful alleles for disease resistance or for combating the effects of climate change. One of our collaborators on the grant, Rajeev Varshney at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT, Hyderabad, India) has access to over 10,000 chickpea landraces and our 1920 landraces are part of a larger project with ICRISAT and the Beijing Genomics Institute (BGI) to sequence over 10,000 landraces using LCWGS. An important prerequisite is to know how accurately we can impute the genomes using this cost-effective strategy. Therefore we have performed a simulation study using an additional 400 landraces that were sequenced by ICRISAT at high coverage, and where the majority of genetic variants are known. We combined our 1920 LCWGS genomes with down-sampled versions of the 400 to test if we could recover the fully imputed genotypes in the 400. We conclude that, for 1.6 million common variants where STITCH can impute accurately, we are likely to be able to impute chickpea landraces reliably with accuracy exceeding 98%. This is important for future sequencing efforts. In the case of rice, our collaborators Hei Leung and Ken McNally in the Philippines at the International Rice Research Institute (IRRI) have provided phenotype data for the 800 rice genomes we have sequenced by LCWGS and imputed. The rice genomes form the rice HEAT MAGIC population, a specially bred population descended from eight varieties chosen for their variation in response to heat stress. Using these variants we have identified genetic regions associated with variations in phenotypes. A subset of the population has now been grown at six different locations that have different levels of heat stress and we are in the process of analysing these data. We published a paper with our collaborators in the International Rice research Institute, Philippines, to study of chalkiness in rice (Plant Biotechnol J. 2020 Nov 21. doi: 10.1111/pbi.13516). The COVID19 pandemic delayed and curtailed planned activities in 2020, (for example visits) due to the ban on international travel. We have arranged for a one year no-cost extension to the award in order to carry out as many of these delayed activities as possible in the future |
| Exploitation Route | We will publish our findings and make the data publicly available for others to use, once our analyses are complete. The chickpea landrace data will be combined with more landrace sequence to characterise the genetic variation in chickpea and be used to re-introduce potentially useful alleles into cultivated varieties. The HEAT MAGIC population (including seeds and genotypes) could be used in future studies. The specific ODA relevance of this work includes the need to improve rice and chickpea varieties better adapted to high temperatures and other climate change parameters. These two crops are staple sources of nutrition in many LMIC countries (rice for carbohydrates, chickpea for protein) . By partnering with these two CGIAR institutes we are able to leverage our work across many other LMIC countries. For example, some of the yield data we are analysing in the rice project has been collected in other countries such as Myanmar. The workshop on multiparental populations we held enable researchers from IRRI And ICRISAT to present their work and to establish collaborations with other attendees at the workshop. Our collaorators in the International Rice research Institute, Philippines, used the sequence data we generated on the rice HEAT MAGIC population to produce genotypes that were used in a study of chalkiness in rice (Plant Biotechnol J. 2020 Nov 21. doi: 10.1111/pbi.13516). These results may be useful for breeding improved rice. This paper is the first publication that directly uses the outputs from this award and demonstrates its utility. |
| Sectors | Agriculture, Food and Drink |
| URL | https://pubmed.ncbi.nlm.nih.gov/33220119/ |
| Description | We have endeavoured to use this GCRF grant to advance both gender and racial equality. The post-doc employed on the grant is a Nigerian woman. The grant has a travel budget partially designed to provide training and research visits for scientists at the collaborating institutions in the Philippines (IRRI) and India (ICRISAT). Unfortunately the corona virus epidemic has delayed two women from IRRI from visiting us in 2020 for this purpose, and visa problems prevented our Nigerian post-doc from visiting ICRISAT in October 2019. Visa issues are an ongoing concern. The workshop we held in July 2019 was partially funded by this grant, with all travel for visiting scientists from ICRISAT and IRRI being covered in this way. Unfortunately the gender balance of these visitors was entirely male, even though about half of all those invited were female. We have a difficulty enforcing gender balance as the choice of visitors is not up to us, but we will attempt at the next workshop to ensure a balance of genders. As the award is still in progress it is too soon to report economic and societal impacts on the collaborating LMIC countries of India and Philippines. The development of crop varieties suited to changing climate is likely to benefit all genders by increasing food security and helping economic development in the agricultural sector. Moreover it is generally the case that some economic development will improve women's education (eg by increasing educational attainment as measured by the number of years in formal education). We are planning a further workshop on multiparental populations towards the end of this award (ie 2022). We will use the lessons leant in the first workshop to increase the gender balance of attendees from LMIC and elsewhere, eg by funding travel in a gender-balanced manner. |
| First Year Of Impact | 2019 |
| Sector | Agriculture, Food and Drink |
| Impact Types | Societal,Economic |
| Description | What determines protein abundance in plants? |
| Amount | £3,354,456 (GBP) |
| Funding ID | BB/T002182/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 11/2019 |
| End | 10/2024 |
| Title | Low Coverage Sequencing of 1500 chickpea genomes |
| Description | We sequenced approx1500 chickpea genomes at about 1x coverage. This work was in collaboration with the International Crop Research Institute for the Semi-Arid Tropics, India (ICRISAT). Although 2000 genomes were attempted approximately 500 did not produce high quality data and are in the process of being resequenced by the Earlham Institute. These genomes are from the collection of landraces held at ICRISAT, and in collaboration with other sequencing efforts, will eventually result in the sequencig of most of the ~20,000 chickpea landraces in the ICRISAT gene bank. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | No |
| Impact | NA |
| Title | Low Coverage Sequencing of 800 rice genomes |
| Description | We sequenced 800 rice genomes at approximately 1x coverage. These genomes are from the HEAT MAGIC population bred at International Rice Research Institute, Philippines (IRRI). We have analysed the data and imputed variants at high resolution across the population, and have mapped quantitative trait loci for flowering time and other phenotypes. We plan to write up and publish the research in collaboration with IRRI and make the data publicly available as soon as possible. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | No |
| Impact | NA |
| Title | NIAB DIVERSE MAGIC GENOTYPES AND PHENOTYPES |
| Description | SNP Genotype and Phenotype datasets for the NIAB DIVERSE MAGIC wheat population and its founders. The diverse MAGIC wheat population was developed at the National Institute for Applied Botany (NIAB), from whom germplasm is available (contact James Cockram).Summary of the Data Sets available here:(i) Founder_Consensus_Genotypes.calls.adjusted.txt, All_MAGIC_Consensus_Genotypes.calls.adjusted.txt: Tab-delimited genotypes of the 16 founders of the NIAB DIVERSE MAGIC wheat population and for 550 MAGIC lines, obtained using the 35k Wheat Breeders' Array. Calls were made using the Axiom Best Practices Genotyping Analysis workflow with an inbreeding penalty of 4. The released genotypes have consensus calls where multiple samples were genotyped from the same line. In addition, the genotypes at sites with no minor homozygous calls have been adjusted.(ii) FOUNDERS.tar, MAGIC_PLINK.tar: Genotypes in PLINK format of 1.1M imputed SNPs from exome capture in the 16 founders and and low -coverage sequencing in 505 MAGIC lines.(iii) MAGIC_PLINK_PRUNED.tar 55k tagging SNP genotypes of 505 MAGIC lines, suitable for GWAS(iv) MAGIC_PHENOTYPES.txt Phenotypes for the MAGIC lines and founders.(v) BASIC_GWAS.tar contains the genotypes and phenotypes and analysis scripts packaged into one file. We provide a simple pipeline for genetic mapping with these data.Once unpacked, the 'DATA' subdirectory contains the phenotypic data and the tagging set of ~55k SNP sites called in 504 inbred lines. In this directory, we include R functions for association mapping (file mixed.model.functions.r), including a mixed model transformation to remove the inflationary effects of unequal relatedness on genetic associations. Association mapping can be run on the basis of SNPs or the inferred founder haplotype dosages. To run, follow the steps in the R script example.analysis.r (this will run without modification if the downloaded directory is used as the R working directory). We also include a function for plotting the results as a manhattan plot (plot.functions.r). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rdr.ucl.ac.uk/articles/dataset/NIAB_DIVERSE_MAGIC_GENOTYPES_AND_PHENOTYPES/14388461 |
| Title | NIAB DIVERSE MAGIC GENOTYPES AND PHENOTYPES |
| Description | SNP Genotype and Phenotype datasets for the NIAB DIVERSE MAGIC wheat population and its founders. The diverse MAGIC wheat population was developed at the National Institute for Applied Botany (NIAB), from whom germplasm is available (contact James Cockram).Summary of the Data Sets available here:(i) Founder_Consensus_Genotypes.calls.adjusted.txt, All_MAGIC_Consensus_Genotypes.calls.adjusted.txt: Tab-delimited genotypes of the 16 founders of the NIAB DIVERSE MAGIC wheat population and for 550 MAGIC lines, obtained using the 35k Wheat Breeders' Array. Calls were made using the Axiom Best Practices Genotyping Analysis workflow with an inbreeding penalty of 4. The released genotypes have consensus calls where multiple samples were genotyped from the same line. In addition, the genotypes at sites with no minor homozygous calls have been adjusted.(ii) FOUNDERS.tar, MAGIC_PLINK.tar: Genotypes in PLINK format of 1.1M imputed SNPs from exome capture in the 16 founders and and low -coverage sequencing in 505 MAGIC lines.(iii) MAGIC_PLINK_PRUNED.tar 55k tagging SNP genotypes of 505 MAGIC lines, suitable for GWAS(iv) MAGIC_PHENOTYPES.txt Phenotypes for the MAGIC lines and founders.(v) BASIC_GWAS.tar contains the genotypes and phenotypes and analysis scripts packaged into one file. We provide a simple pipeline for genetic mapping with these data.Once unpacked, the 'DATA' subdirectory contains the phenotypic data and the tagging set of ~55k SNP sites called in 504 inbred lines. In this directory, we include R functions for association mapping (file mixed.model.functions.r), including a mixed model transformation to remove the inflationary effects of unequal relatedness on genetic associations. Association mapping can be run on the basis of SNPs or the inferred founder haplotype dosages. To run, follow the steps in the R script example.analysis.r (this will run without modification if the downloaded directory is used as the R working directory). We also include a function for plotting the results as a manhattan plot (plot.functions.r). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rdr.ucl.ac.uk/articles/dataset/NIAB_DIVERSE_MAGIC_GENOTYPES_AND_PHENOTYPES/14388461/1 |
| Description | Collaboration with IRRI and ICRISAT |
| Organisation | International Crops Research Institute for the Semi-Arid Tropics |
| Country | India |
| Sector | Charity/Non Profit |
| PI Contribution | We have set up formal collaborations with the International Rice Research Institute (IRRI), Philippines, and with the International Center for Crop Research in the Semi-Arid Tropics (Hyderabad, India) as part of the original. Formal collaboration agreements between UCL and IRRI and ICRISAT were signed in summer 2019. Both India and Philippines are described as LMIC in the 2020 DAC list. Richard Mott (PI of project) visited IRRI and ICRISAT in September 2017 during which time he gave seminars and held discussions and planning meetings with IRRI staff including Hei Leung, the IRRI PI, and ICRISAT staff including Rajeev Varshney, the ICRISAT PI. We have sequenced and analysed 1920 chickpea landrace genomes and 900 rice HEAT MAGIC genomes as described in the grant proposal. We have analysed several other datasets of chickpea and rice genomes shared with us by ICRISAT and IRRI. We organised, funded and held a workshop on Multiparental Crops at NIAB, Cambridge in July 2019, with over 50 attendees. 7 attendees were from LMIC and 25% were women. The project financially supported travel for 7 attendees from LMIC. Richard Mott visited ICRISAT for a workshop/progress meeting in October 2019. Attendees were from India, China, Egypt, UK. At the workshop we discussed sequencing further chickpea landraces and planned how the work would be divided.He also visited ICBA, Dubai https://www.biosaline.org for discussions about potential new collaborations around the rice HEAT MAGIC population that we sequenced as part of the project. We started collaborating in December 2019 with the Max Planck Institute of Plant Molecular Physiology who are also working with IRRI on the rice HEAT MAGIC. The MPIMP have shared phenotype data with us for analysis. |
| Collaborator Contribution | IRRI to sent us DNA from 800 samples the rice HEAT MAGIC population, and ICRISAT sent us DNA from 1920 chickpea landraces, to the Earlham Institute for sequencing. Funding was from this GCRF grant. These samples have now been sequenced as planned using low-coverage sequencing and the data have been shared with IRRI (rice) or ICRISAT (chickpea). IRRI have shared phenotype data from the HEAT MAGIC data and we have analysed these data with the genotypes we obtained from sequencing. In addition invited two researchers from IRRI to visit the UK in 2020 to attend a course on quantitative genetics and to discuss the collaboration. Unfortunately the coronavirus epidemic has prevented these visits and we now plan for them to take place at a later time. IRRI have also shared sequenced data from another rice MAGIC population with us for analysis. ICRISAT have shared phenotypes and sequence data from 1200 chickpea MAGIC lines with us, which we have imputed and analysed. |
| Impact | DNA sequence data have been generated and shared, and analyses performed as agreed. One paper has been published so far (Plant Biotechnol J. 2020 Nov 21. doi: 10.1111/pbi.13516. PMID: 33220119) Our contributions to these collaborations have been financial (paying for and organising the sequencing of rice and chickpea crop genomes) and analytical imputation of sequence variants, genome-wide assocation analysis of phenotypes collected by our collaborators. |
| Start Year | 2017 |
| Description | Collaboration with IRRI and ICRISAT |
| Organisation | International Rice Research Institute |
| Country | Philippines |
| Sector | Charity/Non Profit |
| PI Contribution | We have set up formal collaborations with the International Rice Research Institute (IRRI), Philippines, and with the International Center for Crop Research in the Semi-Arid Tropics (Hyderabad, India) as part of the original. Formal collaboration agreements between UCL and IRRI and ICRISAT were signed in summer 2019. Both India and Philippines are described as LMIC in the 2020 DAC list. Richard Mott (PI of project) visited IRRI and ICRISAT in September 2017 during which time he gave seminars and held discussions and planning meetings with IRRI staff including Hei Leung, the IRRI PI, and ICRISAT staff including Rajeev Varshney, the ICRISAT PI. We have sequenced and analysed 1920 chickpea landrace genomes and 900 rice HEAT MAGIC genomes as described in the grant proposal. We have analysed several other datasets of chickpea and rice genomes shared with us by ICRISAT and IRRI. We organised, funded and held a workshop on Multiparental Crops at NIAB, Cambridge in July 2019, with over 50 attendees. 7 attendees were from LMIC and 25% were women. The project financially supported travel for 7 attendees from LMIC. Richard Mott visited ICRISAT for a workshop/progress meeting in October 2019. Attendees were from India, China, Egypt, UK. At the workshop we discussed sequencing further chickpea landraces and planned how the work would be divided.He also visited ICBA, Dubai https://www.biosaline.org for discussions about potential new collaborations around the rice HEAT MAGIC population that we sequenced as part of the project. We started collaborating in December 2019 with the Max Planck Institute of Plant Molecular Physiology who are also working with IRRI on the rice HEAT MAGIC. The MPIMP have shared phenotype data with us for analysis. |
| Collaborator Contribution | IRRI to sent us DNA from 800 samples the rice HEAT MAGIC population, and ICRISAT sent us DNA from 1920 chickpea landraces, to the Earlham Institute for sequencing. Funding was from this GCRF grant. These samples have now been sequenced as planned using low-coverage sequencing and the data have been shared with IRRI (rice) or ICRISAT (chickpea). IRRI have shared phenotype data from the HEAT MAGIC data and we have analysed these data with the genotypes we obtained from sequencing. In addition invited two researchers from IRRI to visit the UK in 2020 to attend a course on quantitative genetics and to discuss the collaboration. Unfortunately the coronavirus epidemic has prevented these visits and we now plan for them to take place at a later time. IRRI have also shared sequenced data from another rice MAGIC population with us for analysis. ICRISAT have shared phenotypes and sequence data from 1200 chickpea MAGIC lines with us, which we have imputed and analysed. |
| Impact | DNA sequence data have been generated and shared, and analyses performed as agreed. One paper has been published so far (Plant Biotechnol J. 2020 Nov 21. doi: 10.1111/pbi.13516. PMID: 33220119) Our contributions to these collaborations have been financial (paying for and organising the sequencing of rice and chickpea crop genomes) and analytical imputation of sequence variants, genome-wide assocation analysis of phenotypes collected by our collaborators. |
| Start Year | 2017 |
| Description | Collaboration with IRRI and ICRISAT |
| Organisation | Max Planck Society |
| Department | Max Planck Institute of Molecular Plant Physiology |
| Country | Germany |
| Sector | Charity/Non Profit |
| PI Contribution | We have set up formal collaborations with the International Rice Research Institute (IRRI), Philippines, and with the International Center for Crop Research in the Semi-Arid Tropics (Hyderabad, India) as part of the original. Formal collaboration agreements between UCL and IRRI and ICRISAT were signed in summer 2019. Both India and Philippines are described as LMIC in the 2020 DAC list. Richard Mott (PI of project) visited IRRI and ICRISAT in September 2017 during which time he gave seminars and held discussions and planning meetings with IRRI staff including Hei Leung, the IRRI PI, and ICRISAT staff including Rajeev Varshney, the ICRISAT PI. We have sequenced and analysed 1920 chickpea landrace genomes and 900 rice HEAT MAGIC genomes as described in the grant proposal. We have analysed several other datasets of chickpea and rice genomes shared with us by ICRISAT and IRRI. We organised, funded and held a workshop on Multiparental Crops at NIAB, Cambridge in July 2019, with over 50 attendees. 7 attendees were from LMIC and 25% were women. The project financially supported travel for 7 attendees from LMIC. Richard Mott visited ICRISAT for a workshop/progress meeting in October 2019. Attendees were from India, China, Egypt, UK. At the workshop we discussed sequencing further chickpea landraces and planned how the work would be divided.He also visited ICBA, Dubai https://www.biosaline.org for discussions about potential new collaborations around the rice HEAT MAGIC population that we sequenced as part of the project. We started collaborating in December 2019 with the Max Planck Institute of Plant Molecular Physiology who are also working with IRRI on the rice HEAT MAGIC. The MPIMP have shared phenotype data with us for analysis. |
| Collaborator Contribution | IRRI to sent us DNA from 800 samples the rice HEAT MAGIC population, and ICRISAT sent us DNA from 1920 chickpea landraces, to the Earlham Institute for sequencing. Funding was from this GCRF grant. These samples have now been sequenced as planned using low-coverage sequencing and the data have been shared with IRRI (rice) or ICRISAT (chickpea). IRRI have shared phenotype data from the HEAT MAGIC data and we have analysed these data with the genotypes we obtained from sequencing. In addition invited two researchers from IRRI to visit the UK in 2020 to attend a course on quantitative genetics and to discuss the collaboration. Unfortunately the coronavirus epidemic has prevented these visits and we now plan for them to take place at a later time. IRRI have also shared sequenced data from another rice MAGIC population with us for analysis. ICRISAT have shared phenotypes and sequence data from 1200 chickpea MAGIC lines with us, which we have imputed and analysed. |
| Impact | DNA sequence data have been generated and shared, and analyses performed as agreed. One paper has been published so far (Plant Biotechnol J. 2020 Nov 21. doi: 10.1111/pbi.13516. PMID: 33220119) Our contributions to these collaborations have been financial (paying for and organising the sequencing of rice and chickpea crop genomes) and analytical imputation of sequence variants, genome-wide assocation analysis of phenotypes collected by our collaborators. |
| Start Year | 2017 |
| Description | IWGSC Webinar: NIAB Diverse MAGIC dissecting trait genetic architecture across 70 years of wheat breeding |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Michael Scott gave a webinar to the International Wheat Genome Sequencing Consortium on the NIAB Diverse MAGIC wheat population. The recording is available to the public via YouTube |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.youtube.com/watch?v=5lPyjn2NOL0&feature=youtu.be |
| Description | Online magazine article on ancient wheat genetics for "The Conversation" website |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Mike Scott, a PDRA funded by the BBSRC grants BB/P024726/1, BB/M011585/1 wrote a online magazine-style article entitled "What 3,000-year-old Egyptian wheat tells us about the genetics of our daily bread" for the website "The Conversation". This described in layperson's terms the findings of his Nature Plants publication https://doi.org/10.1038/s41477-019-0534-5 on the genome of an ancient wheat. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://theconversation.com/what-3-000-year-old-egyptian-wheat-tells-us-about-the-genetics-of-our-da... |
| Description | Organised Workshop on Multiparental Crops, Cambridge July 2020 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | In collaboration with NIAB (James Cockram, Keith Gardiner) we organised a workshop on multiparental crop populations in July 2019. the workshop attracted over 50 attendees from around the world, including our key collaborators from ICRISAT (India) and IRRI (Philippines). The website for eh workshop is |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://mtweb.cs.ucl.ac.uk/mus/www/MAGICdiverse/MAGIC_workshop.htm |
| Description | Poster Presentation at Monogram 2019 Meeting, Nottingham UK. (Won best poster prize) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Monogram meetings (http://www.monogram.ac.uk/MgNW2019.php) are the main annual meetings for the UK cereal research community, that also attract considerable international and industrial participation. Mike Scott, a PDRA funded from BBSRC grants BB/P024726/1 and BB/M011585/1 presented our poster "Genetic resources for the 16-founder NIAB MAGIC diverse wheat population" for which he won best poster prize. |
| Year(s) Of Engagement Activity | 2016,2017,2018,2019 |
| Description | Poster Presentation at the Plant and Animal Genome XXVIII conference (PAGC 2020), San Diego USA, January 2020 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Mike |Scott, a PDRA employed on the grants BB/M011585/1 and BB/P024726/1 gave a poster presentation "Imputation and QTL Mapping in Multiparental Crop Populations from Low Coverage Sequence Data" at the PAGC 2020 Meeting in San Diego. This is one of the major international meetings on plant and animal genomics. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Presentation at the The UK Rice Research Consortium 4th annual meeting |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Funmi Ladejobi gave a talk entitled "QTLs in the Rice 'Heat MAGIC' population under varying environmental conditions" at the 4th annual meeting of The UK Rice Research Consortium (UKRRC: http://ukrrc.org/) in Aberdeen |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.abdn.ac.uk/events/conferences/programme-1773.php |
| Description | Webinar for UK Plant Science (Garnet): Funmi Ladejobi and Mike Scott summarise the potential for using MAGIC populations in Plant Breeding |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Funmi Ladejobi and Mike Scott gave a webinar to summarise the potential for using MAGIC populations in Plant Breeding |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.youtube.com/watch?v=CNhoJ-tuao0 |
| Description | Workshop on MAGIC in crop plants |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This is a planned workshop "Applications of MAGIC populations in crops" held at the NIAB innovation farm, Cambridge, with international collaborators from CGIAR centres IRRI and ICRISAT scheduled to attend. Other researchers will be invited to present as well as crop breeders and industry colleagues from the project review boards. This was an opportunity to advertise and promote the resources we have developed in wheat, rice, and chickpeas. The wider goal of the workshop is to highlight and guide the translation of the growing research in crop MAGIC populations into practical applications and results. Of the ~40 attendees (nearly all of whom gave a talk), the breakdown of attendees was as follows: UK universities: 8 (3 UCL, 2 Reading, 1 Leeds/JIC, 2 Oxford, 1 Bath - could not attend) UK research institutes: 14 (7 NIAB, 3 SRUC, 1 Roslin, 1 Rothamsted, 1 NHM, 1 IBERS/Aberystwyth) CGIAR centres: 8 (3 IRRI, 3 ICRISAT, 1 CSIRO, 1 ICBA) Worldwide Universities: 7 (2 Wageningen, 1 Pisa, 1 Wisconsin, 1 Hainan, 1 UFRGS (Brazil), 1 INTA (Argentina)) UK Breeders: 2 (1 RAGT, 1 KWS/NIAB LMIC countries: 7 Approximately 25% of attendees were women. We invited close to 50% women but the eventual fraction was disappointingly lower. The number of attendees from LMIC countries was reduced by last-minute cancellations. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://mtweb.cs.ucl.ac.uk/mus/www/MAGICdiverse/MAGIC_workshop.htm |