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Joint analysis of two related studies of different data types and different study designs using hierarchical modeling in detecting gene-environment interactions.

机译：联合分析两个不同数据类型和不同研究设计的相关研究，并使用层次模型来检测基因与环境之间的相互作用。

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摘要

Identifying causal susceptibility alleles for asthma poses many challenges. These include the multigenic nature of the disease, the lack of reliable assessment of individual exposures, and complex interactions with environmental factors. The completion of the Human Genome Project has greatly accelerated the technology development for linkage analysis, candidate gene association studies, and, more recently, genome-wide association studies (GWAS). At the same time, high-throughput technologies for profiling gene expression, DNA methylation, and protein abundances have advanced our understanding of the molecular basis of disease etiology, disease heterogeneity and classification. Accordingly, the best statistical method to use for elucidating the genetic basis of a complex disease depends on the questions of interest, the design of the study, assumptions made about genetic models (i.e. recessive, additive, dominant), how a disease is defined, and the type of genetic markers under investigation. Conventional analyses of gene-environment (G x E) interaction require much larger sample size than for studying main effects. This can lead to false discoveries or false negatives due to lack of power. Hence, there is a need to develop statistical methods aimed at detecting interactions between factors. The observational epidemiology Southern California Children's Health Study (CHS) and the experimental UCLA Challenge Study together offer a unique opportunity to investigate the interactions between genetic variation and exposure to particulates on the risk of asthma through joint modeling of effects attributable to differential responses of asthma-related immune phenotypes.;In this dissertation, I first review the asthma-associated risk factors, the background of the CHS and UCLA Challenge Study, and my research goals. The second section summarizes currently used statistical approaches to the discovery of genetic determinants for complex diseases including conventional regression models, exploratory data mining techniques, biology-driven methods, and prior hypothesis-driven approaches. Next, I propose two distinct Bayesian hierarchical modeling approaches to inform the analysis of one study with information derived from another through joint analysis. This is illustrated using data from the CHS and the UCLA Challenge Study for estimating G x E effects. I describe the simulation design to evaluate the performance of my proposed method, and then illustrate the application for genetic association studies using real candidate gene data from the two studies. Finally, I conclude with a discussion of implications and future challenges for this statistical methodology framework.
机译：识别哮喘的因果易感性等位基因面临许多挑战。这些包括疾病的多基因性，缺乏对个体暴露的可靠评估以及与环境因素的复杂相互作用。人类基因组计划的完成极大地加快了连锁分析，候选基因关联研究以及最近的全基因组关联研究（GWAS）的技术开发。同时，用于分析基因表达，DNA甲基化和蛋白质丰度的高通量技术使我们对疾病病因，疾病异质性和分类的分子基础有了更深入的了解。因此，用来阐明复杂疾病的遗传基础的最佳统计方法取决于所关注的问题，研究的设计，对遗传模型的假设（即隐性，累加性，显性），疾病的定义，以及正在研究的遗传标记的类型。基因-环境（G x E）相互作用的常规分析需要比研究主要效应大得多的样本量。由于缺乏权力，这可能导致错误的发现或错误的否定。因此，需要开发旨在检测因子之间相互作用的统计方法。观察性流行病学南加州儿童健康研究（CHS）和实验性UCLA挑战研究共同提供了独特的机会，通过联合建模可归因于哮喘不同反应的效应，研究遗传变异与暴露于颗粒物对哮喘风险的相互作用。相关的免疫表型。本文首先回顾了哮喘相关的危险因素，CHS和UCLA挑战研究的背景以及我的研究目标。第二部分概述了当前使用的统计方法来发现复杂疾病的遗传决定因素，包括常规回归模型，探索性数据挖掘技术，生物学驱动的方法以及先前的假设驱动的方法。接下来，我提出了两种截然不同的贝叶斯分层建模方法，以通过联合分析从另一项研究获得的信息来指导一项研究的分析。使用来自CHS和UCLA挑战研究的数据来估算G x E效应可以说明这一点。我描述了仿真设计以评估我提出的方法的性能，然后说明了使用这两项研究中的真实候选基因数据进行遗传关联研究的应用。最后，我最后讨论了此统计方法框架的影响和未来挑战。

著录项

  • 作者

    Li, Rui Rachel.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Biology Biostatistics.;Biology Genetics.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 185 p.
  • 总页数 185
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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