Bayesian Analysis for Studies of Gene-Environment Interaction

基因-环境相互作用研究的贝叶斯分析

• 批准号: 0706935
• 负责人: Bhramar Mukherjee
• 金额: $ 13.45万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706935&HistoricalAwards=false
• 关键词: Bayesian; Analysis; Studies; Gene; Environment

ABSTRACT In case-control studies of gene-environment association with disease, when genetic and environmental exposures can be assumed to be independent in the underlying population, one may exploit theindependence assumption in order to derive more efficient estimation techniques than the traditional logistic regression analysis. Many of the classical results for case-control analysis, which assume the covariate distribution to be non-parametric, do not hold under a constrained space of exposure distributions. However, the gain in efficiency of modern retrospective methods comes at the cost of lack of robustness, since large biases are introduced in the retrospective estimates under violation of the gene-environment independence assumption. The main goal of this research proposal is to find natural analytical tools to solve the model specification dilemma of modern retrospective analysis of studies of gene-environment interaction, under some commonly used epidemiological designs. Using the profile-likelihood framework developed by Chatterjee and Carroll (2005, Biometrika), the investigator proposes a Bayesian approach that incorporates uncertainty regarding the assumed constraint of gene-environment independence in a natural data adaptive way. The proposed shrinkage estimator, conceived from a Bayesian standpoint, is designed to maintain attractive efficiency properties, without relying on unverifiable model constraints. Theoretical properties of the proposed estimator are studied under varying scenarios of gene-environment association. The investigator considers both empirical Bayes and hierarchical Bayes methods to relax gene-environment independence assumption. The proposal explores the connection of the Bayesian approaches to an alternative random-effects model. The methods are extended beyond the commonly used unmatched case-control study design to two-phase and family-based studies of gene-environment interaction.Two scientific streams are currently playing extremely important roles in clinical medicine and public health: the molecular biology approach with an emphasis on genetics, and the quantitative approach with an emphasis on epidemiology. The developments in these areas jointly are making fundamental contributions to the study of etiology, diagnosis, prognosis and treatment of complex diseases. Phenomenal advancement of medical science and genetic technology is giving rise to many complex design and analysis issues which statisticians and epidemiologists have never confronted before. This proposal lies in that new interface of human genetics, epidemiology and statistics. Case-control studies are being increasingly used for studying theassociation between a disease and a candidate gene. However, except for some rare diseases, such as Huntington or Tay Sachs disease which may be the result of a deficiency of a single gene product,most common human diseases have a multifactorial etiology involving complex interplay of many genetic and environmental factors. By identifying and characterizing such complicated gene-environment interactions through clinical and epidemiological studies, one has more opportunities to understand the genesis and etiology of complex diseases and to develop targeted intervention strategies for high-risk individuals. The proposal presents robust and efficient statistical techniques to investigate the synergism between gene and environment in studying complex diseases. The high-performance computing tools developed in the proposal makes it feasible to use the methods in large-scale applications such as genome-wide association studies.

摘要 在基因-环境与疾病关联的病例对照研究中，当遗传和环境暴露在潜在人群中可以被假设为独立时，人们可以利用独立性假设来获得比传统的逻辑回归分析更有效的估计技术。许多经典的病例对照分析的结果，假设协变量分布是非参数的，不成立下的曝光分布的约束空间。然而，现代回顾性方法的效率提高是以缺乏鲁棒性为代价的，因为在违反基因-环境独立性假设的情况下，在回顾性估计中引入了大的偏差。本研究提案的主要目标是在一些常用的流行病学设计下，找到自然的分析工具来解决基因与环境相互作用研究的现代回顾性分析的模型规范困境。利用Chatterjee和卡罗尔（2005，Biometrika）开发的轮廓似然框架，研究者提出了一种贝叶斯方法，该方法以自然数据自适应的方式结合了关于基因-环境独立性的假设约束的不确定性。建议的收缩估计，从贝叶斯的角度来看，旨在保持有吸引力的效率属性，而不依赖于无法验证的模型约束。在不同的基因-环境关联的情况下，所提出的估计的理论性质进行了研究。研究者考虑经验贝叶斯和分层贝叶斯方法放松基因-环境独立性假设。该提案探讨了贝叶斯方法与另一种随机效应模型的联系。该方法已超出了常用的不匹配的病例对照研究设计，两个阶段和家庭为基础的研究基因-环境interaction.Two科学流目前在临床医学和公共卫生中发挥着极其重要的作用：分子生物学方法，重点是遗传学，和定量方法，重点是流行病学。这些领域的发展共同为复杂疾病的病因学、诊断、预后和治疗的研究做出了重要贡献。 医学科学和遗传技术的惊人进步正在引起许多统计学家和流行病学家以前从未遇到过的复杂设计和分析问题。 这一建议存在于人类遗传学、流行病学和统计学的新界面中。病例对照研究正越来越多地用于研究疾病与候选基因之间的关联。 然而，除了一些罕见的疾病，如亨廷顿或泰萨克斯病，这可能是一个单一的基因产物的缺陷的结果，大多数常见的人类疾病有一个多因素的病因，涉及许多遗传和环境因素的复杂相互作用。通过临床和流行病学研究来识别和表征这种复杂的基因-环境相互作用，人们有更多的机会了解复杂疾病的起源和病因，并为高危个体制定有针对性的干预策略。该提案提出了强大而有效的统计技术，以研究复杂疾病中基因与环境之间的协同作用。该提案中开发的高性能计算工具使得在全基因组关联研究等大规模应用中使用这些方法变得可行。