Genomic predictors of smoking and lung cancer risk

摘要-项目1 肺癌（LC）是癌症的主要原因。肺癌是最好的例子之一， 环境暴露和遗传因素之间复杂的相互作用。生殖系基因的发现使 对高危个体的定义做出了重要贡献，并为LC病因学提供了关键见解。LC 全基因组关联研究（GWAS）已经确定了影响我们研究方法的信息位点， 烟草控制，并提供了新的见解肿瘤。我们已经确定了24个与癌症有关的基因座， 易感性然而，遗传易感性与人口统计学和 环境因素尚未阐明。我们假设遗传变异影响吸烟和 行为和细胞过程共同导致肺癌的发展。我们的具体目标是：目标1 本项目的第二部分将描述常见遗传变异对肺癌病因学的贡献。我们 将分析47，506例肺癌病例和63，687例肺癌的GWAS，以确定影响肺癌的因素 根据组织学和宿主特征的癌症风险。这些变异影响癌症的机制 将使用基于eQTL的程序并通过现有数据库的注释来探索风险。目标2将 研究LC易感性的不常见遗传变异。我们将分析外显子组种系测序， 来自2,500多例肺癌病例和2,000例对照的信息使用这些数据进行插补， 分析.目标3将确定遗传对吸烟行为的影响。为此，我们将整合大规模的 我们进行了遗传研究，并通过肺癌队列进行了广泛的表型分析 联盟（LC 3）和项目2，以确定SNPs对吸烟行为的具体影响。目标4将 表征环境和遗传相互作用对肺癌风险的联合影响。这些广泛的数据 从我们收集的队列研究中获得的信息将使我们能够模拟吸烟和遗传因素的联合影响， 随着时间的推移，肺癌的风险因素。在生物统计学核心的支持下，我们将进行中介分析， 在多种吸烟表型和遗传因素之间划分风险。我们还将使用基因数据来执行 孟德尔随机化评估生物标志物在预测肺癌风险中的相关性，以协助项目2。

Abstract – Project 1 Lung cancer (LC) is the leading cause of cancer. Lung cancer is among the best examples of a disease resulting from a complex interaction between environmental exposures and genetic factors. Germline genetic findings make an important contribution to the definition of high-risk individuals and provide key insights into LC etiology. LC genome wide association studies (GWAS) have identified informative loci that have influenced our approach to tobacco control and provided new insights into tumorigenesis. We have identified 24 loci with involved in cancer susceptibility. However, the interplay between inherited susceptibility and effects from demographic and environmental factors has not been elucidated. We hypothesize that genetic variation influences both smoking behaviors and cellular processes that jointly lead to lung cancer development. Our specific aims are: Aim 1 of this project will characterize the contribution of common genetic variation to lung cancer etiology. We will analyze GWAS of lung cancer from 47,506 lung cancer cases and 63,687 to identify factors influencing lung cancer risk according to histology and host-characteristics. Mechanisms by which these variants influence cancer risk will be explored using eQTL based procedures and through annotation of existing databases. Aim 2 will investigate uncommon genetic variants for LC susceptibility. We will analyze exome germline sequencing information from over 2,500 lung cancer cases and 2,000 controls use these for imputation and rare variant analysis. Aim 3 will identify genetic effects on smoking behavior. For this aim, we will integrate the large-scale genetic studies we have conducted with extensive phenotyping performed through the lung cancer cohort consortium (LC3) and project 2 to identify the specific impacts of SNPs on smoking behaviors. Aim 4 will characterize joint effects of environmental and genetic interactions on lung cancer risk. This extensive data and information from cohort studies we have assembled will allow us to model joint effects of smoking and genetic factors on lung cancer risk over time. Supported by the biostatistical core we will perform mediation analyses to partition risk among multiple smoking phenotypes and genetic factors. We will also use the genetic data to perform Mendelian randomization to evaluate the relevance of biomarkers in predicting lung cancer risk, to assist project 2.