Two-Phase Cancer Studies of Gene-Environment Interaction

分子生物学和癌症流行病学的最新进展共同为癌症的病因、诊断、预后和治疗研究做出了基础性的贡献。在过去的二十年中，病例对照研究越来越多地用于研究不同类型的癌症与候选基因之间的关系。最近，许多主要的癌症和健康研究机构都在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适度贡献再次强调了环境因素在癌症病因学中的作用不容忽视。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（gxe研究）。拟议的研究考虑了病例对照抽样设计的一种变体，即gx E研究的两阶段抽样设计。该设计描述了一种研究设置，其中一组廉价的协变量可用于更大的研究基础（第一阶段样本），结果暴露分层抽样已被用于选择子样本（第二阶段子样本）。在II期子样本上，测量昂贵的遗传或生物标志物数据。目标是在这样的抽样设计下研究gx E的相互作用。所提出的方法通过适当的两期联合回顾性可能性，有效地利用了I期和II期的所有可用数据。还考虑了II期子样本中非单调缺失数据的存在、放宽基因-环境独立假设、多基因模型中的变量选择等更为微妙的问题。在具体目标1和2中，分别提出了一种基于半参数轮廓似然的方法和一种可选的半参数贝叶斯方法，用于两阶段gx E研究。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剖面似然的估计策略。所提出的估计策略可以处理非单调缺失协变量数据模式，并解决了放宽基因-环境独立性假设的关键问题。具体目标2：开发一种替代的半参数贝叶斯过程来实现与目标1中相同的建模目标。贝叶斯方法可以更灵活地处理疾病风险模型中大量的主效应和相互作用项，并放宽基因-环境的独立性。将探讨将Aim 2扩展到基于单倍型的相互作用的可能性。项目团队在生物统计方法学、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业知识。来自结直肠癌分子流行病学研究的一个具体数据示例，该研究通过胆固醇合成/脂质代谢途径中的基因检查结直肠癌与长期使用他汀类药物之间关系的效应改变的证据，已被确定为所提出方法的激励和说明示例。然而，应用程序中开发的方法是通用的，可以广泛应用于采用结果暴露分层抽样方案的其他癌症流行病学研究。到目前为止，还没有贝叶斯方法用于两阶段的gxe研究。计划中的研究还将有助于填补经典频率学文献中关于在两阶段研究中处理非单调缺失数据模式的空白。该研究将为他汀类药物与结直肠癌的化学保护关联提供有价值的临床见解，因为基因型信息的变化有所改变。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 PUBLIC HEALTH RELEVANCE: Project Narrative Synergism of gene and environment play an important role in the etiology of cancer. The present application tries to develop new statistical methods for analyzing gene-environment interactions in epidemiological studies of cancer, where a two-phase, outcome-exposure stratified sampling design is used to generate the data.