Statistical Methods for Gene Environment Interactions In Lung Cancer

描述(由申请人提供)： 肺癌是反映遗传和环境因素相互作用的最著名的疾病之一。虽然烟草烟雾中的致癌化合物是肺癌的主要危险因素，但只有大约14%的长期吸烟者会患上癌症。家庭研究表明，遗传易感性在决定肺癌风险方面起着重要作用。这项研究计划的目标是开发和应用方法来确定肺癌的遗传易感因素，这些因素受到烟草烟雾影响的调节。为了实现这一目标，我们正在利用两个广泛的数据来源，这两个来源反映了致力于了解肺癌原因的科学家团队的努力。第一个数据来源来自肺癌联盟的遗传流行病学，该联盟自2000年以来一直在收集患有肺癌的三个或更多亲属的大家庭。分析结果显示，有证据表明，染色体6q上的一种遗传易感因素会强烈增加肺癌风险，但这对吸烟者的风险有更深远的影响。另一个主要的数据来源是从1993年开始，由德州大学安德森癌症中心的玛格丽特·斯皮茨博士开展的一项超大型病例对照研究。目前，用于识别肺癌易感性遗传危险因素的候选基因研究已针对51个基因多态性完成。此外，一项新启动的计划将在未来3个月内提供数据，该计划将对1200名吸烟的高加索肺癌病例和1200名匹配的对照组进行全基因组关联分析。这项建议的一个主要目标是开发基于模拟的方法来评估许多相互竞争的分析方法的有效性，以表征基因-环境相互作用在疾病病因中的作用。我们的研究涉及一支强大的分析团队，他们在基因环境建模、机器学习工具的应用和家庭数据研究方面拥有丰富的经验。此外，我们拥有模拟数据的独特技能和方法，将通过拟议的研究资金进一步完善这一点。我们研究的模拟方法和分析发现将广泛分布。(摘要结束)

DESCRIPTION (provided by applicant): Lung cancer is among the best known examples of a disease that reflects the interaction of genetic and environmental factors. While carcinogenic compounds in tobacco smoke constitute the major risk factor for lung cancer, only about 14% of long-time smokers will develop cancer. Family studies indicate the role that genetic susceptibility plays in determining lung cancer risk. The goal of this research proposal is to develop and apply methods to identify genetic susceptibility factors for lung cancer that are modulated by the effects of tobacco smoke. Toward this goal we are taking advantage of two extensive sources of data that reflect the efforts of teams of scientists who have been devoted to understanding the causes of lung cancer. The first source of data is from the Genetic Epidemiology of Lung Cancer Consortium, which has been collecting extended families with three or more relatives affected with lung cancer since 2000. Analytical results show evidence for a genetic susceptibility factor on chromosome 6q that strongly increases lung cancer risk, but that has a much more profound effect on risk among tobacco smokers. The other major source of data is an extremely large, case-control study developed by Dr. Margaret Spitz at the U.T. M.D. Anderson Cancer Center, starting in 1993. Currently, candidate gene studies to identify genetic risk factors of lung cancer susceptibility have been completed for 51 polymorphisms. In addition, data from a newly launched initiative to perform a genome-wide association analysis of 1200 ever-smoking Caucasian lung cancer cases and 1200 matched controls will become available within the next 3 months. A major goal of this proposal is to develop simulation-based approaches to evaluate the efficacy of many competing analytical approaches for characterizing the role of gene-environment interactions in disease causation. Our research involves a strong analytical team with extensive experience in gene-environment modeling, application of machine learning tools, and the study of family data. In addition, we have unique skills and methods for simulating data, which will be further refined through the proposed research funding. The simulation approaches and analytical discoveries from our research will be widely distributed. (End of Abstract)