SOFTWARE FOR INTEGRATED LINKAGE AND ASSOCIATION ANALYSIS

用于集成链接和关联分析的软件

• 批准号: 6538906
• 负责人: Elizabeth R Hauser
• 金额: $ 27.28万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6538906
• 关键词: chronic disease /disorder; computer program /software; computer simulation; computer system design /evaluation; family genetics; gene expression; genotype; human data; linkage mapping; mathematical model; method development

The genetic analysis of complex diseases holds enormous promise for increasing knowledge about the biologic mechanisms leading to common diseases such as heart disease, cancer, diabetes, and Alzheimer disease. However, the genetic analysis of complex disease is challenging because these diseases are likely due to a complex interplay of multiple genetic and environmental factors. As a result of this complexity, studies of common diseases require very large samples. New quantitative methods are needed which maximize the information obtained from each study by extending currently available methods and combining them with novel methods. Studies of complex disease require the use of multiple analytic approaches and an understanding of the advantage and disadvantage of each method as well as the ways in which the different analytic methods can complement each other to enhance understanding of genetic factors for a complex disease. The goal of this proposal is to develop new methods of linkage and association analysis, and to combine the new methods with existing linkage and association methods for nuclear families into single software package. Specifically we propose to: 1) extend existing affected-sib-pair linkage mapping software (Siblink) to allow consideration of phenotypic and environmental covariates, additional unlinked genes, and genotyping error; 2) provide enhancements to Siblink to allow for seamless estimation of empirical p-values and power, interval estimates of disease gene location, examination of robustness to model misspecification and incorporation of additional sibs; 3) develop methods for family-based association tests when parental genotyping information is missing; 4) develop methods to combine families with and without parents in a single analysis and for the use of multiple families from extended pedigrees; 5) perform simulation studies to examine optimal study design in the context of simultaneous linkage and association studies; 6) provide guidance as to the analysis method of choice under various conditions. The study of the genetics of a large number of common diseases currently underway at Duke University Center for Human Genetics and the University of Michigan provides a rich resource for application and assessment of these new methods to real data.

复杂疾病的基因分析为增加对导致常见疾病(如心脏病、癌症、糖尿病和阿尔茨海默病)的生物机制的知识带来了巨大的希望。然而，复杂疾病的遗传分析是具有挑战性的，因为这些疾病可能是由于多种遗传和环境因素的复杂相互作用造成的。由于这种复杂性，对常见疾病的研究需要非常大的样本。需要新的定量方法，通过扩展现有方法并将其与新方法相结合，最大限度地扩大从每项研究中获得的信息。对复杂疾病的研究需要使用多种分析方法，并了解每种方法的优缺点以及不同分析方法可以相互补充的方式，以加强对复杂疾病遗传因素的了解。这项建议的目标是开发新的关联和关联分析方法，并将新方法与核心家庭现有的关联和关联方法结合到单一软件包中。具体地说，我们建议：1)扩展现有的受影响同胞对连锁作图软件(Siblink)，以允许考虑表型和环境协变量、额外的未连锁基因和基因分型错误；2)对Siblink进行增强，以允许无缝估计经验p值和威力、疾病基因位置的区间估计、对模型错误指定的稳健性检查以及加入额外的同胞；3)开发当父母基因分型信息缺失时基于家庭的关联测试方法；4)开发方法将有父母和无父母的家庭结合在单一分析中，并使用来自扩展家系的多个家庭；5)进行模拟研究，在同时进行连锁和关联研究的情况下检验最优研究设计；6)就各种条件下选择的分析方法提供指导。杜克大学人类遗传学中心和密歇根大学目前正在对大量常见疾病的遗传学进行研究，这为这些新方法在真实数据中的应用和评估提供了丰富的资源。