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Identification of a Potential Regulatory Variant for Colorectal Cancer Risk Mapping to 3p21.31 in Chinese Population

Genome-wide association studies (GWAS) have established chromosome 3p21.31 as a susceptibility locus for colorectal cancer (CRC) that lacks replication and exploration in the Chinese population. We searched potentially functional single nucleotide polymorphisms (SNPs) in the linkage disequilibrium (...

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Detalles Bibliográficos
Autores principales: Ke, Juntao, Lou, Jiao, Zhong, Rong, Chen, Xueqin, Li, Jiaoyuan, Liu, Cheng, Gong, Yajie, Yang, Yang, Zhu, Ying, Zhang, Yi, Chang, Jiang, Gong, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848543/
https://www.ncbi.nlm.nih.gov/pubmed/27120998
http://dx.doi.org/10.1038/srep25194
Descripción
Sumario:Genome-wide association studies (GWAS) have established chromosome 3p21.31 as a susceptibility locus for colorectal cancer (CRC) that lacks replication and exploration in the Chinese population. We searched potentially functional single nucleotide polymorphisms (SNPs) in the linkage disequilibrium (LD) block of 3p21.31 with chromatin immunoprecipitation-sequencing (ChIP-seq) data of histone modification, and tested their association with CRC via a case-control study involving 767 cases and 1397 controls in stage 1 and 528 cases and 678 controls in stage 2. In addition to the tag SNP rs8180040 (odds ratio (OR) = 0.875, 95% confidence interval (95% CI) = 0.793−0.966, P = 0.008, P-FDR (false discovery rate) = 0.040), rs1076394 presented consistently significant associations with CRC risk at both stages with OR = 0.850 (95% CI = 0.771−0.938, P = 0.001, P-FDR = 0.005) under the additive model in combined analyses. Supported by the analyses of data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it was suggested that rs1076394 served as an expression Quantitative Trait Loci (eQTL) for gene CCDC12 and NME6, while NME6’s expression was obviously higher in CRC tissues. Using biofeature information such as ChIP-seq and RNA sequencing (RNA-seq) data might help researchers to interpret GWAS results and locate functional variants for diseases in the post-GWAS era.