Statistical Analysis of Linkage/Association on Family-Based Studies in Human Genetics

人类遗传学中基于家族的研究的连锁/关联统计分析

• 批准号: 0071930
• 负责人: Shaw-Hwa Lo
• 金额: $ 26.05万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0071930&HistoricalAwards=false
• 关键词: Statistical; Analysis; Linkage; Association; Family

The allelic association between disease genes and marker alleles allows predicting which marker allele is in coupling with the disease. There are novel statistical testing procedures proposed recently. For example, the Transmission/ Disequilibrium Testing ( TDT ) has been both a popular and powerful method to evaluate the linkage/ association between the candidate genes with disease and the markers. This approach combines the information of association and linkage to yield greater power than conventional tests for detecting linkage when the association is present. It also has great potential to play a major role in future gene mapping of human disorder, by carrying out genomewide TDT screens. Recent literature has seen an increasing trend in the number of studies that have applied TDT or its generalizations to a variety of complex family-based data, but these methods are ad hoc. There is a great need for a systematic and sensible optimal analysis of these methods. This work sets up a general statistical framework that can incorporate all procedures, different data sets and other related knowledge such as disease information, sampling designs and population information. We develop a general statistical theory, useful as a guideline for future investigators, who can design and develop their own statistical procedures (following the framework) to meet their needs in incorporating different environments. The research provides a likelihood-based approach by introducing a useful 3x2 table which applies to each nuclear family. It is natural to adopt the conditionality principle to derive and to evaluate a variety of TDT type procedures and linkage tests, using "Locally Most Powerful Testing" (LMPT ) as a central optimality criteria. This research outlines the likelihood approach and demonstrates how the method works in some special cases. The research provides the derivations and findings from the examples,which should shed light on a better understanding of the more challenging and broader problems arising from these data sets. In training doctoral students working on this increasingly important area, the research provides the basis to develop a new research level course. The proposed research intends to lead ultimately to routine guidelines for future investigators and to offer a theoretical basis for family-based studies.

疾病基因和标记等位基因之间的等位基因关联允许预测哪个标记等位基因与疾病耦合。最近提出了一些新的统计检验方法。例如，传递/不平衡测试（TDT）已经成为评估候选基因与疾病和标记之间的联系/关联的一种流行和强大的方法。这种方法结合了关联和链接的信息，在关联存在时产生比传统测试更强大的检测链接的能力。通过开展全基因组TDT筛选，它也有很大的潜力在未来人类疾病的基因定位中发挥重要作用。最近的文献显示，越来越多的研究将TDT或其推广应用于各种复杂的基于家庭的数据，但这些方法是临时的。对这些方法进行系统、合理的优化分析是十分必要的。这项工作建立了一个一般的统计框架，可以纳入所有程序、不同的数据集和其他相关知识，如疾病信息、抽样设计和人口信息。我们开发了一个通用的统计理论，作为未来研究人员的指导方针，他们可以设计和开发自己的统计程序（遵循框架），以满足他们在整合不同环境中的需求。该研究通过引入一个适用于每个核心家庭的有用的3x2表，提供了一种基于可能性的方法。使用“局部最强大测试”（LMPT）作为中心最优性标准，自然会采用条件原则来推导和评估各种TDT类型的程序和链接测试。本研究概述了似然方法，并演示了该方法如何在一些特殊情况下工作。该研究提供了来自这些例子的推导和发现，这应该有助于更好地理解这些数据集产生的更具挑战性和更广泛的问题。为了培养在这一日益重要的领域工作的博士生，本研究为开发新的研究水平课程提供了基础。拟议的研究旨在最终为未来的研究人员提供常规指导方针，并为基于家庭的研究提供理论基础。