Limits...
Prioritization of cancer-related genomic variants by SNP association network.

Liu C, Xuan Z - Cancer Inform (2015)

Bottom Line: SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C).We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs).We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66).

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA. ; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.

No MeSH data available.


Related in: MedlinePlus

The general idea of SAN construction: an example network. Gi (or Gj) represents a gene set in the chromosomal region of SNPi (or SNPj). The computing method for SAS is as shown in Formula 1.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4384763&req=5

f1-cin-suppl.2-2015-057: The general idea of SAN construction: an example network. Gi (or Gj) represents a gene set in the chromosomal region of SNPi (or SNPj). The computing method for SAS is as shown in Formula 1.

Mentions: In GWASs, when an SNP is connected with a specific disease, it actually means that the chromosomal region around this SNP has one or more function elements, such as protein-coding genes, that are related to this disease.26 Considering that those genes that are involved in the same disease tend to have closer functional interactions in the gene interaction network (GIN) than other genes,27 we can exploit the gene interaction information to evaluate functional associations between genomic loci. Figure 1 shows a simple example of how SAN is constructed for three SNP-tagged genomic loci based on gene interactions. We can calculate the SNP association score (SAS, Formula 1 in the Method section) between each pair of SNPs and obtain a symmetric SAS matrix for all SNP pairs. SAS is calculated based on the connectivity between genes inside of the loci. The higher the score, the more the possibility that is there a functional association between these two loci. For this SAS matrix, we can further test the significance of each SAS by random permutation. After filtering out SNP pairs with nonsignificant SAS, we can finally construct the SAN.


Prioritization of cancer-related genomic variants by SNP association network.

Liu C, Xuan Z - Cancer Inform (2015)

The general idea of SAN construction: an example network. Gi (or Gj) represents a gene set in the chromosomal region of SNPi (or SNPj). The computing method for SAS is as shown in Formula 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4384763&req=5

f1-cin-suppl.2-2015-057: The general idea of SAN construction: an example network. Gi (or Gj) represents a gene set in the chromosomal region of SNPi (or SNPj). The computing method for SAS is as shown in Formula 1.
Mentions: In GWASs, when an SNP is connected with a specific disease, it actually means that the chromosomal region around this SNP has one or more function elements, such as protein-coding genes, that are related to this disease.26 Considering that those genes that are involved in the same disease tend to have closer functional interactions in the gene interaction network (GIN) than other genes,27 we can exploit the gene interaction information to evaluate functional associations between genomic loci. Figure 1 shows a simple example of how SAN is constructed for three SNP-tagged genomic loci based on gene interactions. We can calculate the SNP association score (SAS, Formula 1 in the Method section) between each pair of SNPs and obtain a symmetric SAS matrix for all SNP pairs. SAS is calculated based on the connectivity between genes inside of the loci. The higher the score, the more the possibility that is there a functional association between these two loci. For this SAS matrix, we can further test the significance of each SAS by random permutation. After filtering out SNP pairs with nonsignificant SAS, we can finally construct the SAN.

Bottom Line: SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C).We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs).We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66).

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA. ; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.

No MeSH data available.


Related in: MedlinePlus