Genomic Wide Association Studies (GWAS) constitute the backbone in the discovery of common genetic
polymorphisms associated with complex diseases and phenotypes. In the current proposal we describe an innovative platform for the meta-analysis of GWAS data and the detection of potential biomarkers.
The last decade, international research projects (HapMap Project, 1000 Genomes Project) disclosed the
wide range of polymorphisms in the genome and demonstrated that most of them overlap non-coding
regions. To study the complexity of the combinatorial activity of several polymorphisms, it is essential to apply multi-factor statistical analysis models. The challenges in this method arise from the complexity of big data analysis and the need of a robust methodology in the selection of a multi-factorial model.
GNOSIS has developed the Just Add Data Bio tool (JADBio), which incorporates the
detection of statistically significant subgroups of predictive variables. DIANA-Lab has developed some of the most recognizable implementations concerning the detection of miRNA targets and the identification of regulatory regions.
In this project, we propose the development of a user-friendly and innovative implementation. a) It will extract a set of polymorphisms using multi-factorial models that will associate phenotypic mutations and b) evaluate the effect of polymorphisms in both coding and non-coding regulatory regions. The user will be able to identify major regulatory molecules as potential biomarkers and therapeutic targets for neoplastic diseases.
The above will be achieved by the following aims:
Aim I: The appropriate extension of JADBio in order to give the opportunity to non-specialists in statistical analysis (such as biologists, doctors, pharmacologists) to perform GWAS meta-analysis.
Aim II: The characterization of mutations in genome functional regulatory regions. Polymorphisms will be characterized in coding and non-coding regulatory regions.