Objective
The project is focused on the definition of a comprehensive genetic epidemiological model of complex traits like Essential Hypertension (EH) and intermediate phenotypes of hypertension dependent/associated Target Organ Damages (TOD). To identify the common genetic variants relevant for the pathogenesis of EH and TODs, we will perform a Whole Genome Association (WGA) study of 4.000 subjects recruited from historical well-characterized European cohorts. Genotyping will be done with the Illumina Human 1M BeadChip. Well-established multi-variate techniques and innovative genomic analyses through machine learning techniques will be used for the WGA investigations. Using machine learning approach we aim at developing a disease model of EH integrating the available information on EH and TOD with relevant validated pathways and genetic/environmental information to mimic the clinician's recognition pattern of EH/TOD and their causes in an individual patient. Our statistical design is with two samples run in parallel, each with 1,000 cases and 1,000 controls, followed by a replication/joint analysis. This design is more powerful than replication alone and allows also a formal testing of the potential heterogeneity of findings compared to a single step (one large sample) design. The results represent the source to build a customized and inexpensive genetic diagnostic chip that can be validated in our existing cohorts (n=12,000 subjects). HYPERGENES is in the unique position to propose a ground-breaking project, improving the methodology of genetic epidemiology of chronic complex diseases that have a high prevalence among EU populations. Designing a comprehensive genetic epidemiological model of complex traits will also help us to translate genetic findings into improved diagnostic accuracy and new strategies for early detection, prevention and eventually personalised treatment of a complex trait. The ultimate goal will be to promote the quality of life of EU populations.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health scienceshealth sciencespublic healthepidemiology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesbiological sciencesgeneticsgenomes
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Keywords
Call for proposal
FP7-HEALTH-2007-A
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Funding Scheme
CP-IP - Large-scale integrating projectCoordinator
20122 Milano
Italy
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Participants (18)
3000 Leuven
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49527 Petach Tikva
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Participation ended
5000 Nova Gorica
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20091 Bresso
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620089 Novosibirsk
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SW7 2AZ LONDON
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75654 Paris
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CV4 8UW COVENTRY
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07100 Sassari
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20864 Agrate Brianza
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1015 LAUSANNE
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75008 PARIS
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16149 GENOVA
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200025 Shanghai
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116 36 Praha 1
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35122 Padova
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80210 Gdansk
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31007 KRAKOW
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