We have added proteomics data and conducted analysis on it, gene-environmental interactions, additional GWAS analysis, work on case stratification and exploration of the methods used for that, improved batch effect correction of RNASeq data. We digitised information from angiograms and also family trees with medical conditions. We collated data from multiple sources (medical history, questionnaire data, tests done in hospital, tests done by us, 10 year follow up data and use of medications) to get more accurate data. We built an MI timeline for all the research participants in the MAMI Study so we better map events such as onset of 1st MI, recurrent MI, death from cardiovascular events (CVE), occurrences of cardiovascular events. We conducted analysis of the 10 year follow up data identifying people who developed 1st MI or CVE, diabetes, hypertension and hypercholesterolaemia after we collected their blood, together with time from blood collection, enabling us to study results in a prospective manner avoiding issues of reverse causation. We started work on risk algorithm development including validating the SCORE2 in the Maltese population, and developing and assessing polygenic risk scores from the whole genome sequencing data (Objective 4). We identified high risk groups. We have updated annotations of our RNA-Seq and WGS data. We further developed computational pipelines for transcriptomic and proteomic analysis and conducted QC and data analysis for untargeted proteomics. We are conducting differential expression analysis. Together with the portfolio partners and EU programme manager we have developed a strategic plan for the portfolio work and have started this work. We have automated the TargetID strategy to analyse the multilayered data and identify better candidate drug targets in a high throughput manner and added improvements to it. We are now well poised to finalise these analyses and move to the next stages of the project.
