GWAS mainly aim to identify variations associated with certain phenotypes or diseases. Recently the combined p-value approach is described as the next step after GWAS to map the significant SNPs to genes and pathways to evaluate SNP-gene-disease associations. Major bottleneck of standard GWAS approaches is the prioritization of statistically significant results. The connection between statistical analysis and biological relevance should be established to understand the underlying molecular mechanisms of diseases. There are few tools offered for SNP prioritization but these are mainly based on user-defined subjective parameters, which are hard to standardize. Our group has recently developed a novel AHP based SNP prioritization algorithm. Beside statistical association AHP based SNP prioritization algorithm scores SNPs according to their biological relevance in terms of genomic location, functional consequence, evolutionary conservation, and gene-disease association. This allows researchers to evaluate the significantly associated SNPs quickly and objectively. Here, we have investigated the performance of the AHP based prioritization as the next step in the utilization of the algorithm in comparison to the other available tools for SNP prioritization. The user-defined parameters for AHP based prioritization have been investigated and our suggestion on how to use these parameters are presented. Additionally, the GWAS results from the analysis of two different sets of Alzheimer Disease Genotyping data with the newly proposed AHP based prioritization and the integrated software, METU-SNP, it was implemented, is reported and our new findings on the association of SNPs and genes with AD based on this analysis is discussed.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613775/index.pdf |
| Date | 01 September 2011 |
| Creators | Kadioglu, Onat |
| Contributors | Aydin Son, Yesim |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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