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Generative Artificial Intelligence in Population Genetics

Project description

AI tool uncovering demographic histories of populations and species

Conservation and breeding programmes rely on knowledge of demographic history to protect and study wild and domesticated species effectively. However, inferring demographic histories from genetic data is challenging due to model non-identifiability, where different histories can produce the same genetic patterns. This issue is compounded by the reliance on summary statistics rather than complete genetic data. With the support of the Marie Skłodowska-Curie Actions programme, the PopGenAI project will tackle non-identifiability in demographic inference by developing an innovative generative AI tool. This tool will reveal which historical scenarios are distinguishable from specific summaries of population genomic data and provide new insights into gene flow among recently diverged and domesticated species.

Objective

Understanding the demographic history of populations and species is essential, as it plays a critical role in conservation and breeding programmes for both wild and domesticated species. However, inferring demographic history from genetic data faces the problem of model non-identifiability, where different demographic histories fit the genetic data equally well. This issue is amplified by the use of summary statistics instead of complete genetic data. Addressing the model non-identifiability problem is fundamental for improving the precision of demographic inference, particularly in complex cases involving multiple independent admixture events or barriers to gene flow.

This project aims to resolve the problem of model non-identifiability in demographic inference by developing a generative artificial intelligence (AI) tool that can uncover histories that are indistinguishable when a particular summary statistic is used. This generative AI will serve three core purposes: (1) identify non-identifiable demographic histories, helping researchers better understand the range of possible historical events that could explain the data; (2) apply this tool to clarify complex demographic histories in domesticated species, such as cattle, providing more accurate insights into their genetic background and informing breeding programmes to maintain genetic diversity; and (3) detect barriers to gene flow in recently diverged species, offering deeper insights into local adaptation and speciation processes valuable for conservation.

By addressing the model non-identifiability problem and applying this approach to key biological questions, this project has the potential to transform the study of demographic history in population genetics and open new perspectives for research on species evolution and adaptation.

Fields of science (EuroSciVoc)

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Keywords

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

THE UNIVERSITY OF EDINBURGH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 276 187,92
Address
OLD COLLEGE, SOUTH BRIDGE
EH8 9YL Edinburgh
United Kingdom

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Region
Scotland Eastern Scotland Edinburgh
Activity type
Higher or Secondary Education Establishments
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Total cost

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