Genetic analysis of lung cancer susceptibility

摘要 尽管接触烟草是肺癌的主要决定因素，但只有约15%的吸烟者 患上肺癌。在这项研究中，我们试图确定影响肺癌的基因效应。 风险，无论是独立的还是与吸烟行为联合。我们假设新的基因 并且可以确定影响肺癌易感性的宿主因素 通过基因分型。我们将追求三个目标。在目标1中，我们将(A)珠粒阵列和 23,103例肺癌病例和23,103例无关对照的全基因组阵列，以便 确定遗传变异和(B)在其他人群中验证选定的SNPs。 将对选定的变种进行验证研究，以确保结果一致 在基因分型和桑格测序分析之间。在目标2中，我们将评估力量 使用在AIM(1)和Combine中产生的额外基因类型的遗传效应 与我们现有的29,683例肺癌病例和55,586例对照相比。在这个目标中，我们采取了 利用更大样本群体的优势来确定变种并执行 对一个更大的集合的推断变体进行关联分析。最后在目标4中， 我们将构建一种风险预测算法，以提高当前肺部的准确性 癌症风险模型。这些模型将对提炼个体选择有价值 参加肺部筛查研究。

Abstract Although tobacco exposure is the major determinant of lung cancer, only about 15% of smokers develop lung cancer. In this study, we seek to identify genetic effects that influence lung cancer risk, either independently or jointly with smoking behavior. We hypothesize that novel genetic and host factors influencing the susceptibility for lung cancer can be identified by genotyping. We will pursue 3 aims. In Aim 1 we will (a) Genotype on bead arrays an 23,103 lung cancer cases and 23,103 unrelated controls on genome wide arrays in order to identify genetic variants and (b) Validate selected SNPs in additional populations. Validation studies will be conducted for selected variants to assure results are consistent between genotyping or Sanger sequencing analysis. In Aim 2, we will Evaluate strength of genetic effects using the additional genotypes generated in aim (1) and combine with our existing 29,683 lung cancer cases and 55,586 controls. In this aim, we take advantage of the larger population of samples to impute variants and perform association analyses for inferred variants for a much larger collection. Finally in Aim 4, we will Construct a risk prediction algorithm to improve the accuracy of the current lung cancer risk models. These models will be valuable for refining the selection of individuals to be enrolled in lung screening studies.