SNP and gene networks construction and analysis from classification of copy number variations data.

ABSTRACT:

Background

Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genome-wide data due to high data volume and the complexity of interactions.

Results

In this paper, we incorporate the 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. More than 80% accuracy can be obtained using our classification model and 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 a statistical software PLINK is employed to

SUBMITTER: Liu Y 

PROVIDER: S-EPMC3226254 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature