MARVEL

Multi-phenotype Analysis of Rare Variants – devELopment of an analysis method and software with implementation to large-scale data to unravel pleiotropic genetic effects behind cardiometabolic traits

Coordinatore	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE    more  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Tatjana Cognome: Palalic Email: send email Telefono: +44 20 7595 6265
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	221˙606 €
EC contributo	221˙606 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-IEF
Funding Scheme	MC-IEF
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-08-01   -   2016-07-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Tatjana Cognome: Palalic Email: send email Telefono: +44 20 7595 6265	UK (LONDON)	coordinator	221˙606.40

Mappa

 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

 Obiettivo del progetto (Objective)

'The obesity rates are rapidly increasing worldwide concomitantly with rising prevalence of chronic diseases, including cardiovascular disease and type 2 diabetes. Individual trait genome-wide association studies of common variants (minor allele frequency, MAF>5%) have highlighted complex genetic relationships between related cardiometabolic phenotypes with an intriguing pattern of associated overlapping DNA sequence variant effects, which does not always follow the epidemiological correlations. Joint analysis of multiple correlated traits: (i) increases power for variant discovery; and (ii) facilitates dissection of the genetic mechanisms underlying multi-phenotype association signals, including evaluation of the evidence for pleiotropy. Multi-phenotype analysis methods for common variants have been proposed, however, with the current focus being in low-frequency and rare variants (MAF<5%/1%), novel method development for identification of such effects is required. The project has three goals: 1) To develop a multi-phenotype analysis method for rare variants and to test it on at least 20000 individuals directly available to me. The availability of high-throughput “omics” data, including sequencing data and serum metabolites, adds further challenges to the methods development, e.g. due to genotype uncertainty and hundreds of correlated traits. I will extend the methodological development to the methods for meta-analysis of rare variants, given the need to combine genetic effects across many individual studies; 2) To create an efficient publicly available software tool for the developed methods; 3) To dissect the genetic architecture behind cardiometabolic phenotypes by conducting a large-scale multi-phenotype meta-analysis of rare variant effects on metabolic traits within international consortia. This timely and highly relevant project will allow me to embark on an independent academic career in the field of statistical genetics where my research interests lie.'