Genetic variants and risk of gastric cancer: a pathway analysis of a genome-wide association study

SpringerPlus, May 2015

This study aimed to discover candidate single nucleotide polymorphisms (SNPs) for hypothesizing significant biological pathways of gastric cancer (GC). We performed an Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis using a GC genome-wide association study (GWAS) dataset, including 472,342 SNPs in 2,240 GC cases and 3,302 controls of Asian ethnicity. By integrating linkage disequilibrium analysis, functional SNP annotation, and pathway-based analysis, seven candidate SNPs, four genes and 12 pathways were selected. The ICSNPathway analysis produced 4 hypothetical mechanisms of GC: (1) rs4745 and rs12904 → EFNA1 → ephrin receptor binding; (2) rs1801019 → UMPS → drug and pyrimidine metabolism; (3) rs364897 → GBA → cyanoamino acid metabolism; and (4) rs11187870, rs2274223, and rs3765524 → PLCE1 → lipid biosynthetic process, regulation of cell growth, and cation homeostasis. This pathway analysis using GWAS dataset suggests that the 4 hypothetical biological mechanisms might contribute to GC susceptibility.

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Genetic variants and risk of gastric cancer: a pathway analysis of a genome-wide association study

Lee et al. SpringerPlus Genetic variants and risk of gastric cancer: a pathway analysis of a genome-wide association study Ju-Han Lee 0 Younghye Kim 0 Jung-Woo Choi Young-Sik Kim 0 Equal contributors Department of Pathology, Korea University Ansan Hospital , 123, Jeokgeum-Ro, Danwon-Gu, Ansan-Si, Gyeonggi-Do 425-707 , Republic of Korea This study aimed to discover candidate single nucleotide polymorphisms (SNPs) for hypothesizing significant biological pathways of gastric cancer (GC). We performed an Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis using a GC genome-wide association study (GWAS) dataset, including 472,342 SNPs in 2,240 GC cases and 3,302 controls of Asian ethnicity. By integrating linkage disequilibrium analysis, functional SNP annotation, and pathway-based analysis, seven candidate SNPs, four genes and 12 pathways were selected. The ICSNPathway analysis produced 4 hypothetical mechanisms of GC: (1) rs4745 and rs12904 EFNA1 ephrin receptor binding; (2) rs1801019 UMPS drug and pyrimidine metabolism; (3) rs364897 GBA cyanoamino acid metabolism; and (4) rs11187870, rs2274223, and rs3765524 PLCE1 lipid biosynthetic process, regulation of cell growth, and cation homeostasis. This pathway analysis using GWAS dataset suggests that the 4 hypothetical biological mechanisms might contribute to GC susceptibility. Genome-wide association study; Pathway-based analysis; Gastric cancer - Introduction Despite a decline in its incidence, gastric cancer (GC) is still the second most common cause of cancer-related death worldwide (Hohenberger and Gretschel 2003). Furthermore, GC remains one of the most prevalent high-mortality cancers in Northeast Asia (Hohenberger and Gretschel 2003). Helicobacter pylori infection is the strongest risk factor for GC (Polk and Peek 2010), but only a small proportion of infected individuals develop malignancy. Thus, genetic factors such as polymorphisms in GC-related genes, in addition to dietary factors and environmental factors, substantially contribute to GC susceptibility (Milne et al. 2009). Genome-wide association studies (GWAS) have proved successful in identifying associations between specific genes and complex diseases (Manolio 2010), and opened a new phase in researching the genetic causes of disease. Furthermore, GWAS datasets are increasingly being used to recognize the biological pathways underlying complex diseases (Ramanan et al. 2012), because the functional pathway analysis using genomic datasets has high statistical power to detect the biological mechanisms of disease causation (Ramanan et al. 2012). Recently, (Zhang et al. 2011a) developed the pathway analysis tool called Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis. This method highlights the candidate SNPs and their corresponding candidate pathways from GWAS data by integrating linkage disequilibrium (LD) analysis, functional SNP annotation, and pathway-based analysis (PBA) (Zhang et al. 2011a). The ICSNPathway analysis provides candidate SNPs and their corresponding candidate pathways using GWAS data, thereby making it easier to link variants to biological mechanisms. We conducted ICSNPathway analysis using a GC GWAS dataset available online to identify candidate SNPs and promising biological mechanisms that contribute to GC susceptibility. Methods GWAS dataset The GC GWAS dataset is publicly available from the NCBI dbGap (http://www.ncbi.nlm.nih.gov/gap). The dataset includes genotypes of 472,342 SNPs on Illumina 660 W Quad chip from 2,240 GC cases and 3,302 controls of Chinese ethnicity (Abnet et al. 2010; Li et al. 2013). Study participants were drawn from the Shanxi Upper Gastrointestinal Cancer Genetics Project and the Linxian Nutrition Intervention Trial, which included a total of 1,625 GC cases and 2,100 controls. Six hundred and fifteen GC cases and 1,202 controls from the Shanghai Mens Health Study, the Shanghai Womens Health Study, and the Singapore Chinese Health Study were also included in the database. Controls were matched for age (5 years), sex, and geographical location and they were all cancer-free at the time of enrollment (Abnet et al. 2010; Li et al. 2013). The dataset was filtered to prevent genotyping errors. The SNPs were excluded if they showed a call rate lower than 90% in cases or controls or significant deviation from Hardy-Weinberg equilibrium in the controls (P < 104). Finally, 470,698 SNPs were left for downstream pathway analysis. ICSNPathway analysis We conducted ICSNPathway analysis using the GC GWAS dataset in two-stages (Zhang et al. 2011a). First, candidate causal SNPs were pre-selected by LD analysis and the most significant functional SNPs were annotated. Next, biological mechanisms for the pre-selected candidate causal SNPs were found using PBA. A full list of GWAS SNP P-values was used for the ICSNPathway analysis. The ICSNPathway analysis is based on LD analysis and the discovery of functional SNPs using i (...truncated)


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Ju-Han Lee, Younghye Kim, Jung-Woo Choi, Young-Sik Kim. Genetic variants and risk of gastric cancer: a pathway analysis of a genome-wide association study, SpringerPlus, 2015, pp. 215, Volume 4, Issue 1, DOI: 10.1186/s40064-015-1005-8