Genomic predictors of smoking and lung cancer risk

摘要-项目1 肺癌(LC)是癌症的主要原因。肺癌是导致肺癌的最好的例子之一。 来自环境暴露和遗传因素之间的复杂相互作用。生殖系遗传学研究发现 对定义高危个体做出了重要贡献，并提供了对LC病因学的关键见解。LC 全基因组关联研究已经确定了影响我们研究方法的信息位点。 并为肿瘤的发生提供了新的见解。我们已经确定了24个与癌症有关的基因座 敏感度。然而，遗传易感性和人口统计学影响之间的相互作用 环境因素尚未得到阐明。我们假设基因变异会影响吸烟和吸烟 共同导致肺癌发生的行为和细胞过程。我们的具体目标是：目标1 该项目将表征常见基因变异对肺癌病因学的贡献。我们 将分析47,506例肺癌和63,687例肺癌的GWA，以确定影响肺部的因素 根据组织学和宿主特征，癌症风险。这些变异影响癌症的机制 将使用基于eQTL的程序并通过对现有数据库的注释来探索风险。目标2将 研究LC易感性的罕见遗传变异。我们将分析外显子胚系测序 来自2,500多例肺癌病例和2,000名对照的信息用于归罪和罕见的变异 分析。目标3将确定吸烟行为的遗传效应。为此，我们将整合大规模的 我们通过肺癌队列进行了广泛的表型分析的遗传学研究 (LC3)和项目2，以确定SNPs对吸烟行为的具体影响。目标4将 描述环境和遗传交互作用对肺癌风险的联合影响。这些广泛的数据 我们收集的队列研究的信息将使我们能够模拟吸烟和遗传的联合影响 随着时间的推移，肺癌风险的影响因素。在生物统计学核心的支持下，我们将进行中介分析 多个吸烟表型和遗传因素之间的风险分配。我们还将使用基因数据来执行 孟德尔随机化评估生物标记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.