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Dimensionality of the human phenome

Project description

Mapping the genetic and environmental basis of human traits

Individuals exhibit genotypic variation, differences in DNA sequences which are responsible for the phenotypic diversity of physical traits and disease risk. Despite identifying thousands of trait-associated variants, we lack a clear understanding of which are causative, how they function and how they are influenced by the environment. The ERC-funded Dimension project aims to quantify heritable phenotypes and estimate how genetic variations contribute to them. In this context, researchers will utilise genetic and phenotypic data from large biobanks and analyse them using advanced statistical approaches. The goal is to identify genetic loci and environmental factors that contribute to specific phenotypes in different individuals.

Objective

What am I going to do?
I will use data from millions of human genomes and thousands of traits (the “phenome”) to quantify the dimension of the heritable human phenome and dissect and map it to single DNA variant resolution; use whole genome sequence data in populations and families to estimate mutational trait heritability; predict future phenotypes using millions of DNA variants and thousands of environmental factors.

Why am I going to do this?
Despite the discovery of hundreds of thousands of DNA variants that are statistically associated with one or more human complex traits, we do not know which variants are causative, how many traits they affect (pleiotropy), how they function and why there is so much genetic trait variation in the first place. We understand even less of how environmental factors (the “exposome”) interact with genomic variation to cause the variation in phenotypes that we observe. What is missing is a full genome-wide characterisation of multivariate genetic architecture and estimates of genetic variation due to new mutations. A better understanding of pleiotropy, mutational variance and the exposome will inform theoretical and biological models of trait variation and improve prediction of disease in personalised medicine.

How am I going to do this?
This Project will repurpose data from multiple large biobanks containing millions of genotyped and sequenced individuals who are longitudinally measured on thousands of traits. New advanced statistical approaches will be developed, tested and applied in innovative experimental paradigms that utilise both population and pedigree data. Variance component methods will be developed to estimate mutational variance. Bayesian hierarchical mixture models will be developed and applied to fine-map multivariate trait loci and to predict future disease cases. My Team will develop general user-friendly software tools to achieve the Project aims.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-ADG

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Host institution

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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.

€ 2 499 733,00
Address
WELLINGTON SQUARE UNIVERSITY OFFICES
OX1 2JD Oxford
United Kingdom

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Region
South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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