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Genome wide association studies (GWAS) aim to identify genomic variance associated with certain disease conditions. Major bottleneck of standard GWAS approaches are the prioritization and subset selection after the statistically significant SNPs are determined. Our group has recently developed an analysis pipeline, where p-value and combined p value statistics are integrated with the novel AHP based SNP prioritization algorithm for the detailed analysis of associated SNPs considering statistical significance and biological relevance. Following this pipeline, we have done the GWAS of Alzheimer's Disease (AD) on ADNI SNP genotyping data set where 148 AD cases and 182 controls are investigated. Among the top 100 SNPs 18 of them mapped to AD linked genes. Additionally further analysis of the gene and pathway results suggested new associations, providing basis for new hypothesis for the AD Biomarker research.