Project description
Increasing statistical power of genome-wide studies for understanding human diseases
In human genetics, a genome-wide association study usually investigates links between single-nucleotide polymorphism and heritable traits, including predisposition to major diseases. However, the impact of rare genetic variants is poorly understood and current available data and analytical tools are not sufficient to study this class of variation. The goal of the EU-funded ARGPHENO project is to develop new computational methods for evaluation of the phenotypic traits of rare genetic variations. The project will focus on methods for accurate reconstruction of genealogy using sequencing data for subsequent identification of rare genetic variants and their hereditary traits and cross-association with common genetic variants.
Objective
Large-scale genome-wide association studies (GWAS) have yielded thousands of genetic as-sociations to heritable traits, but for most common diseases, these signals collectively explain only a small fraction of phenotypic variation. The phenotypic impact of recent, rare genetic variants, in particular, is poorly understood, but currently available data sets and analytical tools cannot be used to effectively study this class of variation. To address this problem, we propose to develop new computational methodology that will enable studying the phenotypic role of recent, rare genetic variation. This will improve our understanding of the architecture of heritable complex traits, inform the design of future studies, and increase our ability to detect novel associations.
This project will address three specific aims. The first aim is to devise new methods to accurately reconstruct the complex network of genealogical relationships of individuals using high/low-coverage sequencing or microarray data. The second is to leverage these genealogical structures to infer the presence of unobserved genetic variation, with the goal of analyzing variance components of narrow sense heritability attributable to rare variants and studying the evolutionary history of heritable traits. Finally, in the third aim, we will develop new approaches to detect association to both rare and common variants, increasing the statistical power of GWAS methodology.
Fields of science
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Funding Scheme
ERC-STG - Starting GrantHost institution
OX1 2JD Oxford
United Kingdom