The project aims to: 1) perform in-depth analysis of nsSNPs at protein interfaces; 2) develop an algorithm to prioritise and characterise nsSNPs, by combining data from the interactome, with those from currently available structure and sequence-based tools; 3) implement this algorithm to identify new common nsSNPs associated with complex diseases. The following hypotheses will be addressed: 1) a structural system biology method can enhance the number of identified deleterious nsSNPs, compared to standard sequence and structure methods; 2) the location of nsSNPs on different interfaces of the same can explain protein pleiotropism; 3) a predictive algorithm based on the functional effects of nsSNP can be developed, maximising the ability of GWAS to identify disease SNPs; 4) the algorithm can help identify new common nsSNPs associated with cardiovascular and metabolic disorders in Indian-Asians and in cancer. The study comprises of four work packages: 1) Analysis of nsSNPs located at interfaces: this will involve characterisation of wild type residues and nsSNPs by means of structure, sequence, biophysical and structural system biology methods. A dedicated database will be created to disseminate results; 2) Manual analysis of nsSNPs strongly associated with disease, from the LOLIPOP study; 3) Development of a prediction algorithm and dedicated web server to predict deleterious nsSNPs. The performance of this method will be evaluated using nsSNPs annotated in Uniprot, for both training and testing, with a support vector machine and a 5-fold cross-validation; 4) Application of the prediction algorithm for the analysis of common nsSNPs identified in the LOLIPOP study. The project will generate invaluable information on the genetics of cardiovascular and metabolic disorders. The prediction algorithm will be freely available for implementation in any disease, thus, aiding the medical community to develop clinical algorithms for risk stratification.