Two-Phase Cancer Studies of Gene-Environment Interaction

项目描述（申请人提供）：项目概述分子生物学和癌症流行病学的最新发展共同为癌症的病因学、诊断、预后和治疗的研究做出了重要贡献。在过去的二十年里，病例对照研究越来越多地用于研究不同类型癌症与候选基因之间的关联。最近，许多主要的癌症和健康研究机构已经开始努力形成针对各种类型癌症的大型病例对照全基因组关联研究（GWAS）的全球联盟。GWAS研究结果在解释癌症风险方面的贡献不大，这再次强调了环境因素在癌症病因学中的作用不可忽视。在后GWAS时代，许多流行病学研究正在探索基因-环境相互作用（G x E研究）。拟议的研究考虑了病例对照抽样设计的变化，即G x E研究的两阶段抽样设计。该设计描述了一种研究环境，其中在较大的研究基础上（I期样本）提供了一组廉价的协变量，并采用结局-暴露分层抽样来选择子样本（II期子样本）。在II期子样本上，测量昂贵的遗传或生物标志物数据。我们的目标是调查G × E的相互作用下，这样的抽样设计。所提出的方法导致有效地利用所有可用的数据在第一阶段和第二阶段通过适当的两阶段联合回顾的可能性。更微妙的问题，如存在的非单调缺失数据在第二阶段的子样本，放宽基因环境独立性假设，变量选择在多基因模型进行了考虑。半参数轮廓似然为基础的方法和替代半参数贝叶斯方法提出了两个阶段的G × E研究中的具体目标1和2。具体目标1：为基因-环境相互作用的两阶段研究开发基于半参数轮廓似然的估计策略。所提出的估计策略可以处理非单调缺失协变量数据模式，并解决了放松基因-环境独立性假设的关键问题。具体目标2：开发替代半参数贝叶斯程序，以实现与目标1相同的建模目标。贝叶斯方法将提供更大的灵活性，以处理大量的疾病风险模型中的主效应和交互作用项，放松基因-环境的独立性。将探讨将目标2扩展到基于单倍型的相互作用的可能性。该项目小组在生物统计方法学、癌症流行病学、人类遗传学、癌症治疗学和临床研究方面具有专长。来自结直肠癌分子流行病学研究的具体数据示例，其检查了胆固醇合成/脂质代谢途径中的基因对结直肠癌和长期使用他汀类药物之间的关联的效应修饰的证据，已被确定为所提出的方法的激励和说明性示例。然而，在申请中开发的方法是通用的，可以广泛应用于其他癌症流行病学研究，采用结果暴露分层抽样方案。到目前为止，还没有现有的贝叶斯方法用于两阶段G x E研究。计划中的研究还将有助于填补经典频率论文献中关于处理两阶段研究中非单调缺失数据模式的空白。这项研究将提供有价值的临床洞察力的化学保护协会他汀类药物与结直肠癌的基因型信息的变化进行修改。1

DESCRIPTION (provided by applicant): Project Summary Recent developments in molecular biology and cancer epidemiology jointly are making fundamental contributions to the study of etiology, diagnosis, prognosis and treatment of cancers. Case-control studies have been increasingly used for studying the association between different types of cancers and a candidate gene in the last two decades. More recently, many premier cancer and health re- search institutes have undertaken efforts to form global consortium of large case-control genome-wide association studies (GWAS) for various types of cancer. The modest contribution of GWAS findings in terms of explaining cancer risk have again emphasized that the role of environmental factors can- not be ignored in cancer etiology. In the post-GWAS era, many epidemiologic studies are exploring gene-environment interactions (G x E studies). The proposed research considers a variation of the case-control sampling design, namely the two-phase sampling design for G x E studies. The design describes a study setting where a set of inexpensive covariates are available on a larger study base (Phase I sample) and outcome-exposure stratified sampling has been employed to select a sub-sample (Phase II sub-sample). On the Phase II sub-sample, expensive genetic or biomarker data are measured. The goal is to investigate G x E interactions under such sampling designs. The proposed methods lead to efficient use of all available data in Phase I and Phase II through an appropriate two-phase joint retrospective likelihood. More subtle issues like existence of non-monotone missing data in Phase II sub-sample, relaxing the gene-environment independence assumption, variable selection in a multi-gene model are considered. A semiparametric profile likelihood based approach and an alternative semiparametric Bayes approach is proposed for two-phase G x E studies in Specific Aims 1 and 2 respectively. Specific Aim 1: Development of semiparametric profile likelihood based estimation strategy for two- phase studies of gene-environment interaction. The proposed estimation strategy can handle non- monotone missing covariate data patterns and addresses the critical issue of relaxing gene-environment independence assumption. Specific Aim 2: Development of an alternative semiparametric Bayesian procedure to accomplish the same modeling objectives as in Aim 1. The Bayesian methods would offer more flexibility to handle large number of main effects and interaction terms in the disease risk model and to relax gene-environment independence. The possibility of extending Aim 2 to haplotype-based interactions will be explored. The project team has expertise in biostatistical methodology, cancer epidemiology, human genetics, cancer therapeutics and clinical research. A concrete data example from the Molecular Epidemiology of Colorectal Cancer Study, that examines the evidence of effect modification of the association between colorectal cancer and long-term use of statins by genes in the cholesterol synthesis/lipid metabolism pathway has been identified as a motivating and illustrating example for the proposed methods. However, the methods developed in the application are generic and may be broadly applied to other cancer epidemiology studies that employ outcome-exposure stratified sampling schemes. There are no existing Bayesian approaches for two-phase G x E studies so far. The planned research will also contribute towards filling a gap in the classical frequentist literature on handling non-monotone missing data patterns in two-phase studies. The research will provide valuable clinical insight on the chemoprotective association of statins with colorectal cancer as modified by variation in genotypic information. 1