16 ERA-CAPS Barley yield associated networks
Lead Research Organisation:
University of Dundee
Department Name: School of Life Sciences
Abstract
Our goal is to identify and characterize novel barley genes that regulate yield, specifically those affecting seed, spike (the inflorescence) and tiller (seed bearing stems) traits. We have previously
generated exome capture sequence data from a geo-referenced collection of 192 two-row landraces, revealing over 1.6 million SNP alleles. Here we plan to phenotype this germplasm collection for yield-related parameters with particular focus on seed, spike, and productive tiller traits. We will assess the same traits, in the same way at each partners location. The uniqueness of our approach lies in our proposal to add layers of transcriptome sequence data (six trait related tissues per genotype) and to analyse how gene expression relates to trait development. In plants, the power of the approach has recently been demonstrated in brassicas under the banner of 'Associative Transcriptomics' to identify genes controlling seed compositional traits. However in yeast and mammalian model organisms, 'chains of causality' are now being identified that link SNPs to transcript abundance variation, to physiological transformation and ultimately risk of disease. We propose that by using multiple tissue types we will take this form of analysis beyond the state-of-the-art, allowing deleterious SNPs in expressed sequences, patterns of transcript abundance, and expression networks to unravel complex yield-related genetic trait interactions.
To enable accurate quantification of transcript read depth, we propose to develop a reference transcript dataset by using deep paired-end Illumina RNA-seq and PacBio ISO-seq data from the six tissues
from cv. Morex. We will use this to quantify transcript abundance from RNA-seq data collected from the same six tissues sampled across the population. RNA-seq derived SNP alleles will supplement the exome capture SNPs and both these data and transcript read depth variation will be used for analysis of the yield-related traits. Gene co-expression networks will be constructed using the reference transcript dataset from Morex and the RNA-seq data from the six tissues sampled from the landrace collection. Both datasets will be integrated to identify candidate genes for key regulators of yield-related traits. Functional characterization of candidate genes will be initiated by identifying deleterious alleles from TILLING populations available in each of the three partner labs. The project will provide global community resources including: a reference transcript dataset, additional SNPs derived from the landrace collection, and gene co-expression networks for exploring key regulatory genes and their relationship to yield-related traits. We will gain an understanding of GxE interactions for key traits. Our approach is only feasible now due to the imminent barley genome release and the unique assembled and characterised germplasm available to the consortium.
generated exome capture sequence data from a geo-referenced collection of 192 two-row landraces, revealing over 1.6 million SNP alleles. Here we plan to phenotype this germplasm collection for yield-related parameters with particular focus on seed, spike, and productive tiller traits. We will assess the same traits, in the same way at each partners location. The uniqueness of our approach lies in our proposal to add layers of transcriptome sequence data (six trait related tissues per genotype) and to analyse how gene expression relates to trait development. In plants, the power of the approach has recently been demonstrated in brassicas under the banner of 'Associative Transcriptomics' to identify genes controlling seed compositional traits. However in yeast and mammalian model organisms, 'chains of causality' are now being identified that link SNPs to transcript abundance variation, to physiological transformation and ultimately risk of disease. We propose that by using multiple tissue types we will take this form of analysis beyond the state-of-the-art, allowing deleterious SNPs in expressed sequences, patterns of transcript abundance, and expression networks to unravel complex yield-related genetic trait interactions.
To enable accurate quantification of transcript read depth, we propose to develop a reference transcript dataset by using deep paired-end Illumina RNA-seq and PacBio ISO-seq data from the six tissues
from cv. Morex. We will use this to quantify transcript abundance from RNA-seq data collected from the same six tissues sampled across the population. RNA-seq derived SNP alleles will supplement the exome capture SNPs and both these data and transcript read depth variation will be used for analysis of the yield-related traits. Gene co-expression networks will be constructed using the reference transcript dataset from Morex and the RNA-seq data from the six tissues sampled from the landrace collection. Both datasets will be integrated to identify candidate genes for key regulators of yield-related traits. Functional characterization of candidate genes will be initiated by identifying deleterious alleles from TILLING populations available in each of the three partner labs. The project will provide global community resources including: a reference transcript dataset, additional SNPs derived from the landrace collection, and gene co-expression networks for exploring key regulatory genes and their relationship to yield-related traits. We will gain an understanding of GxE interactions for key traits. Our approach is only feasible now due to the imminent barley genome release and the unique assembled and characterised germplasm available to the consortium.
Technical Summary
We propose to both phenotype and perform RNA-seq on six tissues from each of a collection of 192 diverse and geo-referenced barley accessions that have already been exome capture sequenced and perform 'associative transcriptomics' and network analysis on the resulting data. By using multiple tissue types we will take this form of analysis beyond the state-of-the-art, allowing deleterious SNPs in expressed sequences, patterns of transcript abundance, and expression networks to unravel complex yield-related genetic trait interactions. We propose to develop a reference transcript dataset by using deep paired-end Illumina RNA-seq and PacBio ISO-seq data from the six tissues from cv. Morex to enable transcript level quantification of gene expression. RNA-seq derived SNP alleles will supplement the exome capture SNPs and both these data and transcript read depth variation will be used for analysis of the yield-related traits. Gene co-expression networks will be constructed using the reference transcript dataset from Morex and the RNA-seq data from the six sampled tissues. Phenotypic and molecular datasets will be integrated to identify candidate genes for key regulators of yield-related traits. Functional characterization of candidate genes will be initiated by identifying deleterious alleles from TILLING populations. The project will provide a reference transcript dataset, additional SNPs derived from the landrace collection, and gene co-expression networks for exploring key regulatory genes and their relationship to yield-related traits. We will gain an understanding of GxE interactions for key traits. Our approach is only feasible now due to release of the barley genome sequence and the unique characterised germplasm available to the consortium.
Planned Impact
Who will benefit from this research?
Barley is a dominant component of European agriculture and 70-80% of the crop provides a major source of calories for inclusion in animal feed. Based on the use of approximately 30% of the UK crop, barley underpins the multi-billion pound beer and whisky industries that are key pillars of the UK food and drink sector. Developing novel types of high yielding, high quality barley will ultimately provide support for and benefit commercial breeders, farmers, maltsters, feed compounders, and processors.
How will they benefit from this research?
The entire value chain, from breeder to retailer, will benefit from higher revenues from increased production and consumption of traditional products and the development of novel high value products. Farmers will benefit from being able to produce and sell improved varieties with no additional investment, for both conventional and high value products. Processors will benefit from a resilient supply chain and retailers from the increasing demand of barley derived products - including health conscious populations seeking healthy products without additives. Demand for high quality malted barley is already increasing in Asia and this commercial pull could encourage farmers to grow more malting barley allowing themselves, maltsters and distributors to realise increased profits from high value commodity exports.
What will be done to ensure that they have the opportunity to benefit from this research?
The conduit through which almost all genetic advances in crop production must pass to release their benefits to the broader community is the plant breeding / biotech sector. Translational activities from basic science to application are therefore crucial and we will maintain and develop these throughout the proposed program. The UK boasts one of the most efficient and successful commercial cereal breeding sectors in Europe and the applicants have long standing collaborations with the majority of the UK breeding companies. All applicants have strong links within the academic sector and each has a strong reputation and identity within the global community. A key distinguishing feature of this project is the international collaboration and the added-value through participation of IPK Gatersleben and the University of Minneapolis. Collectively, the PI's have has many years' experience in researching barley, and have the relevant expertise, track-record and motivation to ensure the project reaches a successful conclusion: while also carrying out excellent fundamental research on a globally important crop.
Barley is a dominant component of European agriculture and 70-80% of the crop provides a major source of calories for inclusion in animal feed. Based on the use of approximately 30% of the UK crop, barley underpins the multi-billion pound beer and whisky industries that are key pillars of the UK food and drink sector. Developing novel types of high yielding, high quality barley will ultimately provide support for and benefit commercial breeders, farmers, maltsters, feed compounders, and processors.
How will they benefit from this research?
The entire value chain, from breeder to retailer, will benefit from higher revenues from increased production and consumption of traditional products and the development of novel high value products. Farmers will benefit from being able to produce and sell improved varieties with no additional investment, for both conventional and high value products. Processors will benefit from a resilient supply chain and retailers from the increasing demand of barley derived products - including health conscious populations seeking healthy products without additives. Demand for high quality malted barley is already increasing in Asia and this commercial pull could encourage farmers to grow more malting barley allowing themselves, maltsters and distributors to realise increased profits from high value commodity exports.
What will be done to ensure that they have the opportunity to benefit from this research?
The conduit through which almost all genetic advances in crop production must pass to release their benefits to the broader community is the plant breeding / biotech sector. Translational activities from basic science to application are therefore crucial and we will maintain and develop these throughout the proposed program. The UK boasts one of the most efficient and successful commercial cereal breeding sectors in Europe and the applicants have long standing collaborations with the majority of the UK breeding companies. All applicants have strong links within the academic sector and each has a strong reputation and identity within the global community. A key distinguishing feature of this project is the international collaboration and the added-value through participation of IPK Gatersleben and the University of Minneapolis. Collectively, the PI's have has many years' experience in researching barley, and have the relevant expertise, track-record and motivation to ensure the project reaches a successful conclusion: while also carrying out excellent fundamental research on a globally important crop.
Organisations
- University of Dundee (Lead Research Organisation)
- Council for Agricultural Research and Agricultural Economy Analysis (Collaboration)
- Murdoch University (Collaboration)
- Carlsberg Group (Collaboration)
- University of Zurich (Collaboration)
- Zhejiang University (Collaboration)
- Okayama University (Collaboration)
- University of Minnesota (Collaboration)
- Martin Luther University of Halle-Wittenberg (Collaboration)
- IPK Gatersleben (Collaboration)
- Helmholtz Association of German Research Centres (Collaboration)
- Academy of Sciences of the Czech Republic (Collaboration)
- University of Saskatchewan (Collaboration)
- Leibniz Association (Collaboration)
- Indiana University (Collaboration)
- University of Adelaide (Collaboration)
People |
ORCID iD |
Robbie Waugh (Principal Investigator) |
Publications
Coulter M
(2022)
BaRTv2: a highly resolved barley reference transcriptome for accurate transcript-specific RNA-seq quantification.
in The Plant journal : for cell and molecular biology
Guo W
(2022)
The value of genotype-specific reference for transcriptome analyses in barley
in Life Science Alliance
Liu L
(2022)
Conserved signalling components coordinate epidermal patterning and cuticle deposition in barley.
in Nature communications
Milne L
(2020)
EoRNA, a barley gene and transcript abundance database
Rapazote-Flores P
(2019)
BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq.
in BMC genomics
Raubach S
(2022)
GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization.
in BMC bioinformatics
Description | We examined levels of RNA transcript abundance in six different tissues from 211 different barley cultivars selected on the basis of their year of introduction into european agriculture. Year of introduction has a strong correlation with yield potential so we also collected a range of yield related trait information from field trials conducted in Europe, UK and USA. The idea was to combine transcript abundance data with phenotypic and genotypic data to try to identify and understand the networks of genes underlying the measured yield related traits. While we are still analysing all of the data (generation was delayed by COVID with some institutional genomics facilities tied up with virus testing) it is already clear that we have been able to identify strong associations between traits and transcripts. All participants were awarded extensions due to COVID and this in some ways helped with the time dedicated to analysis of the data but during covid contributed to a delay in obtaining the orthologous datasets across all three sites. We have now submitted two manuscripts for publication describing some of our iitial findings. It is however fair to say that the analyses are complex and due to the quantitative nature of each of the phenotypic traits, analysis of the data is not as straightforward as we had hoped and that new ways of looking at the enormous amount of data generated in the project may need to be explored. At the moment, further manuscripts are being planned/conceived where we anticipate that further analysis of the data will represent the major scientific outputs of the project. We have also made all of the data generated available to the broader community for detailed exploration or reuse |
Exploitation Route | We had hoped that we would have beenable to identify genetic components of different yield parameters that could have been used to 1. Develop molecular diagnostics that can be used in crop improvement for yield enhancement 2. Promote fundamental studies to understand how combinations of different transcript abundances combine to affect components of yield e.g. through common genetic control. 3. Provide evidence of major shifts in genetic diversity around the time of introduction of the semi-dwarf 1 locus in the context of the emergence of (high Nitrogen) intensive agriculture. 4. Interrogate the impact of structural genome rearrangements (e.g. inversions causing changes in occupancy of given nuclear domains) and their impact on the transcriptional landscape of certain genotyes and potentially influence specific traits. While we will still explore the data further, It has also been made available for any other group to interrogate further if desired.. |
Sectors | Agriculture Food and Drink Education Environment |
URL | https://pubmed.ncbi.nlm.nih.gov/35704392/ |
Title | Additional file 1 of A high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis |
Description | Additional file 1: Table S1. Plant material for RNA samples for Iso-Seq. Table S2. Read statistics for Iso-seq libraries. Table S3A and B. Number and percentage of splice junctions with sequencing mismatches in positions L1 to L30 for A) upstream (left) and B) downstream (right) of splice junctions. Table S4. Position Weight Matrix scores for consensus splice site sequences of introns. Table S5A and B. Filtering of SJs on basis of mismatches in each position. Table S6. Sequence motifs for validation of TSS and TES sites. Table S7. Number of genes and transcripts contributed to AtIso from each Iso-seq library. Table S8. Saturation curve of the number of unique genes and transcripts added to AtIso with the addition of each library. Table S9. AtRTD3 - Transcript characteristics and translations from TransFeat. Table S10A. TranSuite output of AtRTD3 for mono-exonic/multi-exonic genes with single or multiple transcript isoforms; B Comparison of TranSuite output of AtRTD3 gene and transcript characterisation. Table S11. AtRTD3 - novel genes. Table S12. Functional analysis of transcripts from novel genes in AtRTD3 with TRAPID 2.0. Table S13. AtRTD3 - Chimeric Genes and transcripts. Table S14. Frequency of AS event type among AtRTD3, AtIso and Araport11. Table S15A. Frequency of AS event type among AtRTD3, AtIso and Araport11. Table S15B. Gene descriptions of genes containing non-stop RNAs. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://springernature.figshare.com/articles/dataset/Additional_file_1_of_A_high-resolution_single-m... |
Title | Additional file 1 of A high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis |
Description | Additional file 1: Table S1. Plant material for RNA samples for Iso-Seq. Table S2. Read statistics for Iso-seq libraries. Table S3A and B. Number and percentage of splice junctions with sequencing mismatches in positions L1 to L30 for A) upstream (left) and B) downstream (right) of splice junctions. Table S4. Position Weight Matrix scores for consensus splice site sequences of introns. Table S5A and B. Filtering of SJs on basis of mismatches in each position. Table S6. Sequence motifs for validation of TSS and TES sites. Table S7. Number of genes and transcripts contributed to AtIso from each Iso-seq library. Table S8. Saturation curve of the number of unique genes and transcripts added to AtIso with the addition of each library. Table S9. AtRTD3 - Transcript characteristics and translations from TransFeat. Table S10A. TranSuite output of AtRTD3 for mono-exonic/multi-exonic genes with single or multiple transcript isoforms; B Comparison of TranSuite output of AtRTD3 gene and transcript characterisation. Table S11. AtRTD3 - novel genes. Table S12. Functional analysis of transcripts from novel genes in AtRTD3 with TRAPID 2.0. Table S13. AtRTD3 - Chimeric Genes and transcripts. Table S14. Frequency of AS event type among AtRTD3, AtIso and Araport11. Table S15A. Frequency of AS event type among AtRTD3, AtIso and Araport11. Table S15B. Gene descriptions of genes containing non-stop RNAs. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://springernature.figshare.com/articles/dataset/Additional_file_1_of_A_high-resolution_single-m... |
Title | BaRT |
Description | A database containing a reference set of transcripts expressed from the barley cultivar Morex. Used for rapid and accurate analysis of RNA-seq data |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | Rapid and accurate analysis of RNA-seq data using alignment free methods |
URL | https://ics.hutton.ac.uk/barleyrtd/index.html |
Description | Barley Pan Genome |
Organisation | IPK Gatersleben |
Country | Germany |
Sector | Private |
PI Contribution | Provide a reference quality sequence of the cultivar Golden Promise |
Collaborator Contribution | Reference Quality sequences of other barley genotypes (consortium effort) |
Impact | No outcomes yet |
Start Year | 2017 |
Description | Barley Pan-Transcriptome |
Organisation | Academy of Sciences of the Czech Republic |
Department | Institute of Experimental Botany |
Country | Czech Republic |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Carlsberg Group |
Department | Carlsberg Research Centre |
Country | Denmark |
Sector | Private |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Council for Agricultural Research and Agricultural Economy Analysis |
Country | Italy |
Sector | Public |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Helmholtz Association of German Research Centres |
Department | Helmholtz Zentrum Munchen |
Country | Germany |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Indiana University |
Department | School of Medicine |
Country | United States |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Leibniz Association |
Department | Leibniz Institute of Plant Genetics and Crop Plant Research |
Country | Germany |
Sector | Charity/Non Profit |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Martin Luther University of Halle-Wittenberg |
Country | Germany |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Murdoch University |
Country | Australia |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Okayama University |
Country | Japan |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | University of Adelaide |
Country | Australia |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | University of Saskatchewan |
Country | Canada |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | University of Zurich |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Pan-Transcriptome |
Organisation | Zhejiang University |
Country | China |
Sector | Academic/University |
PI Contribution | Principle Investigators and coordinators |
Collaborator Contribution | Funding, data, data analysis, data interpretation, paper writing, project management |
Impact | https://doi.org/10.21203/rs.3.rs-3787876/v1 |
Start Year | 2019 |
Description | Barley Yield associated Networks (BARN) |
Organisation | IPK Gatersleben |
Country | Germany |
Sector | Private |
PI Contribution | BARN is an ERA CAPS collaborative award with three partners. We will provide a Reference Transcript dataset and RNA seq information from 2 tissues from 200 barley cultivars. We will jointly analyse the resulting data |
Collaborator Contribution | Each has common and specific tasks. The Reference Transcript dataset and RNA seq information will be used to interrogate expression in a further 2 tissues from 200 barley cultivars. We will also survey sequence all 200 lines and build cultivar specific RTD's to assist analysis. The partners will jointly analyse the resulting data |
Impact | Too early |
Start Year | 2018 |
Description | Barley Yield associated Networks (BARN) |
Organisation | University of Minnesota |
Country | United States |
Sector | Academic/University |
PI Contribution | BARN is an ERA CAPS collaborative award with three partners. We will provide a Reference Transcript dataset and RNA seq information from 2 tissues from 200 barley cultivars. We will jointly analyse the resulting data |
Collaborator Contribution | Each has common and specific tasks. The Reference Transcript dataset and RNA seq information will be used to interrogate expression in a further 2 tissues from 200 barley cultivars. We will also survey sequence all 200 lines and build cultivar specific RTD's to assist analysis. The partners will jointly analyse the resulting data |
Impact | Too early |
Start Year | 2018 |
Description | SAB |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | BBSRC requested we establish a Science Advisory Board for our Barley Reference Transcript Database (RTD) project and we extended the remit of this SAB to cover a range of other related projects and to gather their expert feedback more widely. We received written feedback from Mario Caccammo and Philippa Borill at our first meeting last April. The SAB will meet again in April though we have extended its composition to include Ian Bancroft (who couldnt make the first meeting) and representation from the EBI (Bruno Contreras) and the barley Pan Genome Consortium (Nils Stein). |
Year(s) Of Engagement Activity | 2019 |