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Multi-omics for genotype-phenotype associations (RIA)


Proposals submitted to this topic will work on the integration of different 'omics datasets and different data types towards the definitive goal of fully understanding the causal relations between the genome of an organism and its phenotype, i.e. how biological systems respond to variations in their genetic make-up or in their external environment.

Proposals should also;

  • Focus on systems biology solutions and will develop methods that integrate ‘omics datasets and use data collected in several experiments. A description of the datasets available for the given application should be included together with clarification of the potential use of big data analytics, machine learning or artificial intelligence in order to analyse available data sets.
  • Take into consideration the study of interactions between different data types; the combination of data from multiple time points and different individual entities;
  • Tackle the challenges posed by data quality. The outcomes should allow for replication and validation, expanding the capacity to generate biological knowledge.
  • Involve at least two case studies for the application in one or two industrial sectors where biotechnology can provide added value, excluding healthcare.

Proposals submitted under this topic should include a business case and exploitation strategy, as outlined in the Introduction to the LEIT part of this Work Programme.

Activities should start at TRL 4 and achieve TRL 6 at the end of the project.

The Commission considers that proposals requesting a contribution from the EU between EUR 6 and 8 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.

Large-scale biological datasets (e.g. genomics, proteomics, metabolomics, epigenetics) have been populated thanks to modern high-throughput technologies, the decreasing costs of data generation, unprecedented improvement in data processing and analysis and the increasing capacity to save and store these datasets. However, less progress has been made in associating genome information (genotype) with the complex variation of observable traits (phenotype) of a living being. This knowledge is key to addressing important societal needs in a variety of sectors.

The exploitation of existing biological data types for an increased number of useful applications requires new computational and statistical approaches that integrate data and perform complementary analyses with the different –omics datasets. This is needed to draw meaningful information about genotype-phenotype associations that complete the picture of how biological models function and how phenotypes are established. An improved understanding of phenotypes will make possible the development of new predictive models for living beings applicable in different industrial sectors. Health-related applications have benefited from developments in this area and this could also be valuable for other areas of application.

  • A comprehensive analysis and interpretation of the complexity of genotype-environment interactions in biological systems ensuring its applicability to different industrial sectors;
  • The development of models that leverage ‘omics information to realistically predict phenotypic effects, including performance, and to answer specific biological questions about phenotypic variation;
  • A significant improvement to the exploitation of existing databases for new biotechnological applications in industry, excluding health.

Relevant indicators and metrics, with baseline values, including demonstration activities should be clearly stated in the proposal.