Final Report Summary - FGENTCARD (Functional genomic diagnostic tools for coronary artery disease)
Progress in functional genomics technologies have dramatically increased the density of gene expression datasets providing information on the expression of individual genes and gene networks at the levels of transcription, translation, protein abundance and activity, and metabolic processes. They will impact prevalent human complex disorders by providing new information for disease prevention and treatment. Risk factors of Coronary artery disease (CAD) represent dominant causes of premature death and disability. Owing to the complexity of CAD, our understanding of the pathophysiological processes involved is limited. In this programme implemented by international investigators and Small and medium-sized enterprises (SMEs), we shall use functional genomics technologies (metabonomics, proteomics and transcriptomics) to generate a comprehensive and multidimensional description of well-defined states of CAD in selected clinical cohorts and animal models, which will be used as a platform for studying the causes of CAD. The project would initially test the power of high density functional genomic datasets in separating patients from probands and disease models from controls. Subsequent genetic studies would test the inheritance of both classical morphological, biochemical and hormonal phenotypes and quantitative traits derived from functional genomics datasets. This multidisciplinary strategy was expected to ultimately:
i) define novel disease causative biomarkers associated with or predicting disease onset and progression;
ii) provide genetic and genomic information for identifying the underlying genes.
This information will form a basis for more rational and effective strategies for CAD prevention and treatment. The methodologies that are developed will have wide application to other diseases.
The general theme of the FGENTCARD project was to develop methodologies that integrate functional genomic tools and genetic strategies to address major unresolved questions concerning the molecular basis of common diseases using CAD as an example.
The main goal of the FGENTCARD project was to generate biomarkers associated with CAD risk factors using network biology that can be used as disease prediction tools in clinical studies and targets for developing novel and more efficient drugs. The main objectives within FGENTCARD were:
- to develop analytical models for integrating functional genomic datasets towards improved understanding of gene, protein and metabolite function in relation to CAD risk factors;
- to apply functional genomics tools for identifying novel biomarkers associated with CAD risk factors in animal models and humans;
- to test the inheritance of quantitative changes in these biomarkers in relation to classical phenotypes and identify CAD susceptibility genetic loci;
- to identify gene variants underlying these effects in animal models and human;
- to create an integrated knowledge framework to facilitate dissection of CAD risk factor phenotypes with the ultimate aim of improving human health.
FGENTCARD use state of the art functional genomic and genetic technologies including:
i) 1H-NMR metabonomics profiling of biofluids and organ biopsies;
ii) automated Clinprot proteomics platform for plasma and organ samples;
iii) microarray based Illumina gene transcription profiling;
iv) SNP-based genome wide association for identifying disease susceptibility loci.
FGENTCARD research was objective-driven and applied the following steps:
1. Characterisation of CAD phenotypes using classical physiological and biochemical methods in a large cohort of patients and in animal models.
2. Generation of functional genomic quantitative trait datasets using:
- plasma and urine metabonomic profiling in animal models and humans;
- plasma proteomic profiling in animal models and humans;
- tissue transcriptomic, proteomic and metabonomic profiling in animal models.
3. Genetic studies:
- to test the association between plasma biomarkers and CAD risk factors in humans;
- to test the inheritance of plasma urine and organ biomarkers in animal models.
The consortium has successfully developed tools and models to genetically map complex traits in CAD patients and rodent models of CAD. Novel methods optimised in rodent crosses and congenic strains to carry out QTL mapping with functional datasets and integrate the genetic control of traits underlying different dimensions of gene expression (metabolome and transcriptome) will have direct application in human quantitative genetics to ultimately lead to the discovery of predictive disease biomarkers.
i) define novel disease causative biomarkers associated with or predicting disease onset and progression;
ii) provide genetic and genomic information for identifying the underlying genes.
This information will form a basis for more rational and effective strategies for CAD prevention and treatment. The methodologies that are developed will have wide application to other diseases.
The general theme of the FGENTCARD project was to develop methodologies that integrate functional genomic tools and genetic strategies to address major unresolved questions concerning the molecular basis of common diseases using CAD as an example.
The main goal of the FGENTCARD project was to generate biomarkers associated with CAD risk factors using network biology that can be used as disease prediction tools in clinical studies and targets for developing novel and more efficient drugs. The main objectives within FGENTCARD were:
- to develop analytical models for integrating functional genomic datasets towards improved understanding of gene, protein and metabolite function in relation to CAD risk factors;
- to apply functional genomics tools for identifying novel biomarkers associated with CAD risk factors in animal models and humans;
- to test the inheritance of quantitative changes in these biomarkers in relation to classical phenotypes and identify CAD susceptibility genetic loci;
- to identify gene variants underlying these effects in animal models and human;
- to create an integrated knowledge framework to facilitate dissection of CAD risk factor phenotypes with the ultimate aim of improving human health.
FGENTCARD use state of the art functional genomic and genetic technologies including:
i) 1H-NMR metabonomics profiling of biofluids and organ biopsies;
ii) automated Clinprot proteomics platform for plasma and organ samples;
iii) microarray based Illumina gene transcription profiling;
iv) SNP-based genome wide association for identifying disease susceptibility loci.
FGENTCARD research was objective-driven and applied the following steps:
1. Characterisation of CAD phenotypes using classical physiological and biochemical methods in a large cohort of patients and in animal models.
2. Generation of functional genomic quantitative trait datasets using:
- plasma and urine metabonomic profiling in animal models and humans;
- plasma proteomic profiling in animal models and humans;
- tissue transcriptomic, proteomic and metabonomic profiling in animal models.
3. Genetic studies:
- to test the association between plasma biomarkers and CAD risk factors in humans;
- to test the inheritance of plasma urine and organ biomarkers in animal models.
The consortium has successfully developed tools and models to genetically map complex traits in CAD patients and rodent models of CAD. Novel methods optimised in rodent crosses and congenic strains to carry out QTL mapping with functional datasets and integrate the genetic control of traits underlying different dimensions of gene expression (metabolome and transcriptome) will have direct application in human quantitative genetics to ultimately lead to the discovery of predictive disease biomarkers.