Integrated Analysis of SNP, CNV and Gene Expression Data in Genetic Association Studies

Abstract

Integrative approaches that combine multiple forms of data can more accurately capture CGEway associations and so provide a comprehensive understanding of the molecular mechanisms that cause complex diseases. Association analyses based on SNP genotypes, CNV genotypes, and gene expression profiles are the three most common paradigms used for gene set/ CGEway enrichment analyses. Many work has been done to leverage information from two types of data from these three paradigms.

However, to the best of our knowledge, there is no work done before to integrate the three paradigms all together. In this paper, we present an integrated analysis that combine SNP, CNV, and gene expression data to generate a single gene list. We present different methods to compare this gene list with the other three possible lists that result from the combinations of the following pairs of data: SNP genotype with gene expression, CNV genotype with gene expression, and SNP genotype with CNV genotype. The comparison is done using three different cancer datasets and two different methods of comparison. Our results show that integrating SNP, CNV, and gene expression data give better association results than integrating any pair of three data.

Graphical Abstract

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