Limits...
Pathway-based analysis using genome-wide association data from a Korean non-small cell lung cancer study.

Lee D, Lee GK, Yoon KA, Lee JS - PLoS ONE (2013)

Bottom Line: After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status.Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (PGSEA≤0.025, false discovery rate≤0.25).The top-ranked pathways were ABC Transporters (PGSEA<0.001, PARTP = 0.001), VEGF Signaling Pathway (PGSEA<0.001, PARTP = 0.008), G1/S Check Point (PGSEA = 0.004, PARTP = 0.013), and NRAGE Signals Death through JNK (PGSEA = 0.006, PARTP = 0.001).

View Article: PubMed Central - PubMed

Affiliation: Lung Cancer Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi, Republic of Korea.

ABSTRACT
Pathway-based analysis, used in conjunction with genome-wide association study (GWAS) techniques, is a powerful tool to detect subtle but systematic patterns in genome that can help elucidate complex diseases, like cancers. Here, we stepped back from genetic polymorphisms at a single locus and examined how multiple association signals can be orchestrated to find pathways related to lung cancer susceptibility. We used single-nucleotide polymorphism (SNP) array data from 869 non-small cell lung cancer (NSCLC) cases from a previous GWAS at the National Cancer Center and 1,533 controls from the Korean Association Resource project for the pathway-based analysis. After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status. Pathway statistics were evaluated using Gene Set Enrichment Analysis (GSEA) and Adaptive Rank Truncated Product (ARTP) methods. Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (PGSEA≤0.025, false discovery rate≤0.25). Candidate pathways were validated using the ARTP method and similarities between pathways were computed against each other. The top-ranked pathways were ABC Transporters (PGSEA<0.001, PARTP = 0.001), VEGF Signaling Pathway (PGSEA<0.001, PARTP = 0.008), G1/S Check Point (PGSEA = 0.004, PARTP = 0.013), and NRAGE Signals Death through JNK (PGSEA = 0.006, PARTP = 0.001). Our results demonstrate that pathway analysis can shed light on post-GWAS research and help identify potential targets for cancer susceptibility.

Show MeSH

Related in: MedlinePlus

Overview of the Study.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3675130&req=5

pone-0065396-g001: Overview of the Study.

Mentions: In the present study, we used the GSEA-based pathway analysis suggested by Wang et al. [24] with our previous Korean lung cancer GWAS data, from 869 cases and 1,533 controls, with the hope of finding additional susceptibility loci and of obtaining insights into the underlying pathogenesis (Figure 1). Pathways showing high statistical significance were validated using another pathway-based method called adaptive rank truncated product (ARTP) [31]. In contrast to GSEA, ARTP is a self-contained test [32] that directly associates genes in a pathway to diseases and works independently of genes outside the pathway. We report seven pathways categorized into four cellular processes that showed consistent associations with Korean NSCLC susceptibility.


Pathway-based analysis using genome-wide association data from a Korean non-small cell lung cancer study.

Lee D, Lee GK, Yoon KA, Lee JS - PLoS ONE (2013)

Overview of the Study.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675130&req=5

pone-0065396-g001: Overview of the Study.
Mentions: In the present study, we used the GSEA-based pathway analysis suggested by Wang et al. [24] with our previous Korean lung cancer GWAS data, from 869 cases and 1,533 controls, with the hope of finding additional susceptibility loci and of obtaining insights into the underlying pathogenesis (Figure 1). Pathways showing high statistical significance were validated using another pathway-based method called adaptive rank truncated product (ARTP) [31]. In contrast to GSEA, ARTP is a self-contained test [32] that directly associates genes in a pathway to diseases and works independently of genes outside the pathway. We report seven pathways categorized into four cellular processes that showed consistent associations with Korean NSCLC susceptibility.

Bottom Line: After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status.Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (PGSEA≤0.025, false discovery rate≤0.25).The top-ranked pathways were ABC Transporters (PGSEA<0.001, PARTP = 0.001), VEGF Signaling Pathway (PGSEA<0.001, PARTP = 0.008), G1/S Check Point (PGSEA = 0.004, PARTP = 0.013), and NRAGE Signals Death through JNK (PGSEA = 0.006, PARTP = 0.001).

View Article: PubMed Central - PubMed

Affiliation: Lung Cancer Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi, Republic of Korea.

ABSTRACT
Pathway-based analysis, used in conjunction with genome-wide association study (GWAS) techniques, is a powerful tool to detect subtle but systematic patterns in genome that can help elucidate complex diseases, like cancers. Here, we stepped back from genetic polymorphisms at a single locus and examined how multiple association signals can be orchestrated to find pathways related to lung cancer susceptibility. We used single-nucleotide polymorphism (SNP) array data from 869 non-small cell lung cancer (NSCLC) cases from a previous GWAS at the National Cancer Center and 1,533 controls from the Korean Association Resource project for the pathway-based analysis. After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status. Pathway statistics were evaluated using Gene Set Enrichment Analysis (GSEA) and Adaptive Rank Truncated Product (ARTP) methods. Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (PGSEA≤0.025, false discovery rate≤0.25). Candidate pathways were validated using the ARTP method and similarities between pathways were computed against each other. The top-ranked pathways were ABC Transporters (PGSEA<0.001, PARTP = 0.001), VEGF Signaling Pathway (PGSEA<0.001, PARTP = 0.008), G1/S Check Point (PGSEA = 0.004, PARTP = 0.013), and NRAGE Signals Death through JNK (PGSEA = 0.006, PARTP = 0.001). Our results demonstrate that pathway analysis can shed light on post-GWAS research and help identify potential targets for cancer susceptibility.

Show MeSH
Related in: MedlinePlus