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Integrated analysis of SNP , CNV and gene expression data in genetic association studies
Author(s) -
Momtaz R.,
Ghanem N.M.,
ElMakky N.M.,
Ismail M.A.
Publication year - 2018
Publication title -
clinical genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.543
H-Index - 102
eISSN - 1399-0004
pISSN - 0009-9163
DOI - 10.1111/cge.13092
Subject(s) - genetics , snp , biology , gene , gene expression , genetic association , snp array , genotype , single nucleotide polymorphism
Integrative approaches that combine multiple forms of data can more accurately capture pathway associations and so provide a comprehensive understanding of the molecular mechanisms that cause complex diseases. Association analyses based on single nucleotide polymorphism ( SNP ) genotypes, copy number variant ( CNV ) genotypes, and gene expression profiles are the 3 most common paradigms used for gene set/pathway enrichment analyses. Many work has been done to leverage information from 2 types of data from these 3 paradigms. However, to the best of our knowledge, there is no work done before to integrate the 3 paradigms all together. In this article, 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 3 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 3 different cancer datasets and 2 different methods of comparison. Our results show that integrating SNP , CNV , and gene expression data give better association results than integrating any pair of 3 data.