Classification of genome-wide copy number variations and their associated SNP and gene networks analysis

Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. In this paper, we incorporate DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. By shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and find out the unique genes that those SNPs are located in. A gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their p-values. By using two real genome-wide data sets, we further demonstrate SNP and gene networks play a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to disease study.