The main research goals, to be carried out during 2011-2015, are as follows:

1. Recontact 1000 participants of the Estonian Biobank, carry out new phenotyping (fill in questionnaire and objective measurements) and collect new blood samples. Additionally,

a) contact 250 Grave's disease patients, phenotype and collect new biological samples
b) contact 250 NSC Lung cancer patients, phenotype and collect new biological samples
c) include 1000 CAD/Ml patients into the study, phenotype and collect new biological samples

2. Carry out resequencing of whole genome, full exomes or microarray analysis of the samples, reference alignment and analysis for genomic variation (single nucleotide polymorphisms, copynumber variation, insertion-deletions, inversions) using lllumina platform technology.

3. Include exome sequencing of family trios with familiar forms of the chosen diseases (NSC Lung Cancer, CAD/MI, Graves disease)

4. Create a catalogue of genomic information which forms the basis for the additional analyses of understanding the associations between genome variation and (endo)phenotypes, lifestyle and disease outcome.

5. Perform paired-end RNA-seq analysis combined with miRNA analysis using the SOLID platform to describe and analyze the transcriptional profile of human PBMCs in the study population.

6. Identify a pattern of epigenetic modifications in peripheral blood of healthy human mononuclear cells (CD4+, CD8+ T-cells and monocytes). Additionally, implement chromatin immunoprecipitation to generate at least 100 reference epigenomes from the Estonian population to elucidate the correlation of DNA methylation and histone modifications with the genomic, transcriptomic and metabolomic information.

7. Carry out proteomic profiling of blood plasma samples to study disease-associated disturbances in relation to protein signatures or interactions and compare results to the genomic variants or differential mRNA expression.

8. Carry out metabolomic untargeted and targeted profiling of blood samples and analyze differences in the metabolomic profiles of single donors and of trios by supervised and unsupervised multivariate data mining methods.

9. Develop a visualization tool called "Personal Genome Browser" for easy viewing of all the -omics data where the genomics, transcriptomics, epigenomics, proteomics and metabolomics data will be overlaid to the genome sequence. Variations that have a known effect on health or phenotype will be provided with description and comments.