الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Variant analyses that depend only on one type of genomic data suffer from many limitations as they can’t offer a complete understanding of complex diseases. To overcome these limitations, integrative approaches that combine multiple forms of data have been widely used. Association analyses based on SNP, CNV, and gene expression profiles are the three most common paradigms used for gene set/pathway enrichment analyses. However, it has not been examined yet if integrating different pairs of data from those models can lead to different enrichment results. Knowing which types of genomic data to favor can save other researchers time and experimental effort. In this thesis, we present an intensive analysis to compare the enrichment results of the three possible combinations of data: (SNPgene expression), (CNV-gene expression) and (SNP-CNV). Also, to the best of our knowledge, there is no work done before to integrate the three paradigms all together. Thus, we present an integrated analysis that combines (SNP-CNV-gene expression) data to generate a single gene list. We provide different methods to compare this gene list with the three lists that result from the pairwise combinations. Our results show that integrating (SNP-CNV-gene expression) data give better association results than integrating any pair of them.