Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Int J Cancer. 2018 Apr 15;142(8):1602-1610. doi: 10.1002/ijc.31182. Epub 2017 Dec 12.
Traditional pathway analysis map single nucleotide polymorphisms (SNPs) to genes according to physical position, which lacks sufficient biological bases. Here, we incorporated genetics of gene expression into gene- and pathway-based analysis to identify genes and pathways associated with lung cancer risk. We identified expression-related SNPs (eSNPs) in lung tissues and integrated these eSNPs into three lung cancer genome-wide association studies (GWASs), including 12,843 lung cancer cases and 12,639 controls. We used SKAT-C for gene-based analysis, and conditional analysis to identify independent eSNPs of each gene. ARTP algorithm was used for pathway analysis. A total of 374,382 eSNPs in the GWAS datasets survived quality control, which were mapped to 5,084 genes and 2,752 pathways. In the gene-based analysis, nine genes showed significant associations with lung cancer risk. Among them, TP63 (3q28), RP11-650L12.2 (15q25.1) and CHRNA5 (15q25.1) were located in known lung cancer susceptibility loci. We also validated two newly identified susceptibility loci (RNASET2 and AL133458.1 in 6q27, and MPZL3 in 11q23.3). Besides, DVL3 (3q27.1), RP11-522I20.3 (9q21.32) and CCDC116 (22q11.21) were identified as novel lung cancer susceptibility genes. Pathway analysis showed that pathways involved in protein structure, the Notch signaling pathway and the nicotinic acetylcholine receptor-related pathways were associated with lung cancer risk. Combing eSNPs, gene- and pathway-based analysis identifies novel lung cancer susceptibility genes, which serves as a powerful approach to decipher biological mechanisms underlying GWAS findings.
传统的通路分析方法根据物理位置将单核苷酸多态性（SNP）映射到基因，这种方法缺乏充分的生物学基础。在这里，我们将基因表达的遗传学纳入基于基因和通路的分析中，以鉴定与肺癌风险相关的基因和通路。我们在肺组织中鉴定了与表达相关的 SNP（eSNP），并将这些 eSNP 整合到三项肺癌全基因组关联研究（GWAS）中，包括 12843 例肺癌病例和 12639 例对照。我们使用 SKAT-C 进行基于基因的分析，并使用条件分析鉴定每个基因的独立 eSNP。ARTP 算法用于通路分析。GWAS 数据集中共有 374382 个 eSNP 通过质量控制，这些 eSNP 映射到 5084 个基因和 2752 条通路。在基于基因的分析中，有九个基因与肺癌风险显著相关。其中，TP63（3q28）、RP11-650L12.2（15q25.1）和 CHRNA5（15q25.1）位于已知的肺癌易感位点。我们还验证了两个新鉴定的易感位点（6q27 上的 RNASET2 和 AL133458.1，以及 11q23.3 上的 MPZL3）。此外，DVL3（3q27.1）、RP11-522I20.3（9q21.32）和 CCDC116（22q11.21）被鉴定为新的肺癌易感基因。通路分析显示，涉及蛋白质结构、Notch 信号通路和烟碱型乙酰胆碱受体相关通路的通路与肺癌风险相关。结合 eSNP、基因和通路分析，鉴定出了新的肺癌易感基因，这为解析 GWAS 结果背后的生物学机制提供了一种有力的方法。
