Project 1: Smoking, environmental and genomic factors in lung cancer and managing risk

项目概要/摘要 肺癌（LC）是一种遗传和环境风险相互作用引起的疾病的“海报儿童” 因素我们正在进行的LC全基因组关联研究（GWAS）已经确定了40多个相关基因座 在LC易感性方面的研究，极大地提高了对LC风险遗传结构的理解。然而，在这方面， 在进行的GWAS中存在显著的偏差：大多数研究涉及欧洲血统的人群。 遗传学通过祖先特异性生殖系多态性在LC风险中起重要作用 包括环境暴露在内的社会文化差异。我们假设“一种尺寸（祖先）适合 所有”模式不适用于信用证，探讨信用证风险及其跨源转换方式 将提高不同人群对LC风险和预防的理解。我们的具体目标 包括以下内容。目标1。精确描述遗传变异对肺癌的贡献 病因学遗传方法将包括基因分型阵列和低通全基因组测序（WGS）， 输入到大型多种族参考面板中，以产生世界上最大的肺发现资源 癌症易感性变异在一系列种族中影响肺癌风险。目标二。执行后- GWAS分析，以确定影响肺癌发展的环境和宿主特异性因素。 孟德尔随机化将使得能够鉴定因果分子因素，如生物标志物， 影响肺癌，这有助于制定肺癌筛查计划（项目2和3。在 此外，我们还将使用正向和反向孟德尔随机化来划分因果效应， 根据顺式作用遗传成分，反式作用成分相关的肺癌风险生物标志物 更广泛的宿主暴露和外部因素。我们将开发和使用新的注释方法来识别大多数 可能的因果变异，以改善多基因风险评分的发展。目标3：开发特定人群， 以及多血统多基因风险评分（PRS），以改善少数人群LC的风险估计 人口。在这个目标中，我们将首先从基因组分析中构建和表征PRS，以识别 患肺癌的高危人群1.其次，迄今为止，研究集中在分析 欧洲血统的人口。目标4。评估返回遗传风险因素信息的影响 激励不同人群的行为改变，并仔细研究 我们的祖先特异性和跨祖先研究的基因组发现。在这一转化过程中，我们 将通过采用快速循环研究方法，让患者和医疗保健提供者参与进来， 从发现到实践的时滞，同时适应不断发展的基因组证据基础。这个项目提供 数据的其他项目，并发挥了关键作用，在开发翻译应用程序，以评估利益， 社区进行生物标志物知情的筛选和降低风险的招聘。

Project Summary/Abstract Lung cancer (LC) is a “poster child” for a disease resulting from interactions between genetic and environmental risk factors. Ongoing LC genome wide association studies (GWAS) conducted by us have identified over 40 loci involved in LC susceptibility that have dramatically improved the understanding of the genetic architecture of LC risk. However, there is a significant bias in the conducted GWASs: most studies have involved populations of European descent. Ancestry plays an important role in LC risk through contribution of ancestry- specific germline polymorphisms as well as socio-cultural differences including environmental exposures. We hypothesize that a “one size (ancestry) fits all” model is not applicable to LC and that exploring risk of LC and its translation across ancestries manner will improve the understanding of LC risk and prevention in diverse human populations. Our specific aims include the following. Aim 1. To precisely characterize the contribution of genetic variation to lung cancer etiology. The genetic approach will include genotyping arrays and low pass whole genome sequencing (WGS) and imputation into the large multi-ethnic reference panels to generate the world largest resource for the discovery of lung cancer susceptibility variants influencing lung cancer risk across a range of ethnicities. Aim 2. To perform post- GWAS analysis to identify environmental and host-specific factors influencing lung cancer development. Mendelian randomization will enable the identification of causal molecular factors, such as biomarkers, influencing lung cancer, which facilitates developing screening programs for lung cancer (Projects 2 and 3. In addition, we will also use forward and reverse Mendelian randomization to partition the causal effects of biomarkers on lung cancer risk according to cis-acting genetic components, trans-acting components related to broader host-exposures and external factors. We will develop and use novel annotation methods to identify most likely causal variants to improve polygenic risk score development. Aim 3. To develop population-specific as well as multi-ancestry polygenic risk scores (PRSs) to refine risk estimation of LC for minority populations. In this aim, we will first build and characterize PRSs from genomic analyses to identify subsets of individuals at high risk for lung cancer development1. Second, to date, studies have focused on analysis in European-descent populations. Aim 4. To evaluate the impact of returning genetic risk factors information to motivate behavior change in diverse populations and to scrutinize direct translational implications of the genomic findings from our ancestry-specific and cross-ancestry studies. In this translational aim, we will engage both patients and healthcare providers by adopting a rapid cycle research approach to reduce the time lag from discovery to practice while accommodating evolving genomic evidence base. This project provides data to the other projects and plays a key role in developing translational applications to evaluate interest in communities for biomarker-informed recruitment for screening and risk reduction.