Identifying disease associations via genome-wide association studies
Background ::: Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels – the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.
With rapid increase in influenza A virus database, an important issue is whether the predictions are similar based on different datasets. Here we stratify 482 H3N2 hemagglutinins from influenza A virus in North America to different datasets. The predictions are made using logistic regression. The results show that the different datasets have significant impact on the predictions.