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FP6

FGENTCARD — Result In Brief

Project ID: 37683
Funded under: FP6-LIFESCIHEALTH

Genomics delineate the causes of arterial disease

Coronary artery diseases (CADs) represent dominant causes of premature death and disability. In order to define novel causative biomarkers associated with disease onset and progression, the Fgentcard consortium employed a genomics approach.
Genomics delineate the causes of arterial disease
The advent of functional genomics has changed the way we perform disease studies by providing the tools for generating a vast array of molecular data regarding gene expression, molecular networks, protein abundance and activity, and metabolic processes. Such information could be utilised to delineate the pathophysiological processes involved in complex diseases such as CAD.

Alongside this notion, the EU-funded ′Functional genomic diagnostic tools for coronary artery disease′ (Fgentcard) project used functional genomics technologies to generate a comprehensive and multi-dimensional description of well defined states of CAD in selected clinical cohorts and animal models. The ultimate goal was to use this information as a basis for studying the genetic causes of CAD and identifying disease biomarkers.

Effectively, these biomarkers would be used as disease prediction tools in clinical studies and for developing novel and more efficient drugs. By employing state-of-the-art proteomics, metabonomics, gene expression profiling and SNP analysis platforms, project scientists aimed to associate biomarkers with CAD risk in a large cohort of patients. For this purpose, they used patient plasma samples and organ biopsies.

Alongside tissue transcriptomic, proteomic and metabonomic profiling in animal models, the consortium provided an extensively analytical genetic profile of CAD patients which is expected to lead to predictive disease biomarkers. This approach could also be used to test the inheritance of quantitative changes in these biomarkers and identify CAD susceptibility genetic loci.

The Fgentcard approach could help identify genetic variants underlying CAD in humans and form the basis for more effective strategies for disease prevention and treatment.

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