Abstract/Summary

The major aim of this project is to develop novel computational approaches for rapid identification of key omic variations, particularly SNPs that are likely to be associated with the variability of the ADP (Adenosine diphosphate) activated platelet responses. The ADP platelet response was chosen as a model system due to its distinct role during the platelet amplification and aggregation, and it is the main therapeutic target for cardiovascular disease (CVD) antiplatelet drug treatments. Based on recent studies, CVD is currently the second lethal noncommunicable disease after cancer in both developed and developing countries. Interindividual variability of the ADP platelet responses was previously reported in genetic association studies, and susceptible SNPs were identified. However, most of the standard biostatistical methods that were previously employed were found to be suboptimal, and it is assumed that other crucial SNPs might have been potentially missed. In genetics, this phenomenon is known as ‘missing heritability’ problem. Therefore, to address this issue, this study aims to employ alternative computational approaches in an integrated manner in order to identify previously unidentified key SNPs, which may underlie the ADP platelet responses variability. Additionally, the project aims to develop predictive approaches to unveil the molecular mechanisms of the identified key SNPs, which are likely to underpin the interindividual variability in the ADP platelet responses and aggregation. The molecular mechanisms underpinning these SNPs, or ‘omic variations are rarely addressed in standard genetic mapping or association studies. This may be due to the experimental hurdles related to the costs and labour that are required in pursuing such undertakings, hence our predictive approach seeks to address such inefficiencies in closing these knowledge gaps. Moreover, the project culminates in the development of a method for predicting an individuals’ ADP platelet response levels with a focus on determining the extreme cases, i.e., individuals showing high and low responses to ADP platelet activation. Predicting ADP responses levels might be suitable for determining which allelic features will contribute most to the extreme ADP platelet responses. This understanding may be useful for suggesting new drug targets or individualised treatments in the targeted CVD therapeutics or personalised medical settings for the next generation of medical practice.