Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale.
BACKGROUND
Identifying functional relation of copy number variation regions (CNVRs) and gene is an essential process in understanding the impact of genotypic variations on phenotype. There have been many related works, but only a few attempts were made to normal populations.


RESULTS
To analyze the functions of genome-wide CNVRs, we applied a novel correlation measure called Correlation based on Sample Set (CSS) to paired Whole Genome TilePath array and messenger RNA (mRNA) microarray data from 210 HapMap individuals with normal phenotypes and calculated the confident CNVR-gene relationships. Two CNVR nodes form an edge if they regulate a common set of genes, allowing the construction of a global CNVR network. We performed functional enrichment on the common genes that were trans-regulated from CNVRs clustered together in our CNVR network. As a result, we observed that most of CNVR clusters in our CNVR network were reported to be involved in some biological processes or cellular functions, while most CNVR clusters from randomly constructed CNVR networks showed no evidence of functional enrichment. Those results imply that CSS is capable of finding related CNVR-gene pairs and CNVR networks that have functional significance.


BACKGROUND
http://embio.yonsei.ac.kr/~ Park/cnv_net.php.


BACKGROUND
sanghyun@cs.yonsei.ac.kr


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Computer Science, Yonsei University, South Korea, Seoul 120-749, South Korea.
Chihyun
C
Park
Jaegyoon
J
Ahn
Youngmi
Y
Yoon
Sanghyun
S
Park
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D053263:Gene Regulatory Networks; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype