HIGEN

ADVANCED STATISTICAL METHODS FOR HIGH-DIMENSIONAL GENETIC STUDIES

Coordinatore	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	1˙627˙906 €
EC contributo	1˙627˙906 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2013-CoG
Funding Scheme	ERC-CG
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-06-01   -   2019-05-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD  Organization address address: University Offices, Wellington Square city: OXFORD postcode: OX1 2JD contact info Titolo: Dr. Nome: Jonathan Lawrence Cognome: Marchini Email: send email Telefono: +44 1865 271125 Fax: +44 1865 271125	UK (OXFORD)	hostInstitution	1˙627˙906.00
2	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD  Organization address address: University Offices, Wellington Square city: OXFORD postcode: OX1 2JD contact info Titolo: Ms. Nome: Gill Cognome: Wells Email: send email Telefono: +44 1865 289800 Fax: +44 1865 289801	UK (OXFORD)	hostInstitution	1˙627˙906.00

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 Obiettivo del progetto (Objective)

'Statistical methods play a central role in the field of modern genetics. New technologies are driving an explosion of high-dimensional datasets that will sustain a continuing need for new methods, theory and computationally efficient software. My proposal has two parts that will address the statistical challenges and creation of resources at the frontier of the science in this area. The methods development will be driven by, and applied to, several cutting-edge datasets in the fields of human disease genetics, population genetics and plant and animal breeding, guaranteeing impact on exciting scientific questions.

The first part concerns a wide circle of ideas around haplotype estimation, genotype imputation and analysis of sequencing data. The overarching aim is to provide a suite of methods that can estimate haplotypes and impute genotypes in a unified and computationally efficient manner. In addition, we will work to create a reference set of haplotypes from tens of thousands of European and worldwide samples that will form a central resource for human disease and population genetic studies.

The second part concerns the development of models for high-dimensional phenotypic data in genome-wide association studies. This is poorly developed area of human disease genetics with great potential for methods development and wide ranging applications.'