Genome-wide Association Mapping of Metabolic, Lung and Cardiovascular Phenotypes

代谢、肺和心血管表型的全基因组关联图谱

• 批准号: 7102898
• 负责人: Nancy J Cox
• 金额: $ 28.48万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-15 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7102898
• 关键词: Internet; cardiovascular disorder; clinical research; computer program /software; computer system design /evaluation; cooperative study; data management; gene expression; genetic mapping; genetic screening; high throughput technology; human data; human genetic material tag; human population genetics; human tissue; linkage disequilibriums; longitudinal human study; lung disorder; metabolism disorder; method development; single nucleotide polymorphism

DESCRIPTION: (provided by applicant) We, as a society, have made tremendous investments in developing the scientific infrastructure to enable breakthrough discoveries on the primary biological risk factors for common disorders, such as diabetes, asthma, and cardiovascular disease (and related quantitative traits), that account for a disproportionate and growing share of public health care expenditures. While it is clear that the relationships between genetic variation and common disease phenotypes are complex, there is good preliminary evidence that identifying genetic risk factors for common disorders will improve understanding of their etiology and pathogenesis, leading to more specific and effective therapies. Moreover, since virtually all common disease arises as a consequence of the actions and interactions of both genetic and non-genetic risk factors, the identification of genetic risk factors should allow design of epidemiological studies to identify more specific non-genetic risk factors that might be cost-effective targets for prevention of disease. The recent development of high throughput platforms for very large-scale SNP genotyping makes genome-wide association mapping a viable additional tool in the effort to identify genetic risk factors for common disease. Effective use of this tool, however, will require considerable methodological development. Thus, we propose to develop (including software tools and web interfaces) and apply a variety of approaches for facilitating genome-wide association studies, including 1) improved allele/genotype calling algorithms for high-throughput genotyping platforms (to reduce non-random patterns of missing data), and 2) measures of information content for linkage disequilibrium mapping, analogous to those previously developed for linkage mapping. We will utilize these tools, as well as novel analytic approaches that we are developing, to conduct genome-wide association mapping studies on 3) more than 100,000 SNPs typed genome-wide in 350 Mexican Americans with type 2 diabetes and a random sample of 350 Mexican Americans from Starr County, TX; and 4) more than 500,000 SNPs typed in 700 Hutterites with data on a variety of phenotypes related to metabolic, pulmonary and cardiovascular disease. Our proposal builds on long-standing collaborations in methods development and data analysis among members of our research team, which includes human, population, and statistical geneticists and statisticians.

描述：（由申请人提供）作为一个社会，我们在开发科学基础设施方面进行了巨大的投资，以使对普通疾病的主要生物危险因素（例如糖尿病，哮喘和心血管疾病（以及相关定量性状））的主要生物学危险因素（以及相关定量性状），这是公共份额和生长的份额。虽然很明显，遗传变异与常见疾病表型之间的关系很复杂，但有良好的初步证据表明，鉴定常见疾病的遗传危险因素将改善对其病因和发病机理的理解，从而导致更具体而有效的治疗方法。此外，由于几乎所有常见的疾病都是由于遗传和非遗传危险因素的作用和相互作用而产生的，因此遗传危险因素的鉴定应允许设计流行病学研究，以确定可能是预防疾病的更具体的非遗传危险因素。对于非常大规模的SNP基因分型的高吞吐量平台的最新发展使整个基因组的关联映射成为可行的额外工具，以识别常见疾病的遗传危险因素。但是，有效使用该工具将需要大量的方法论发展。因此，我们建议开发（包括软件工具和网络接口），并采用各种方法来促进全基因组的关联研究，包括1）改进了高等位基因/基因型，用于高通量基因分型平台的呼叫算法（以减少缺失数据模式），以及2）以前开发的链接内容，以互动的链接模仿，以衡量这些链接内容，以衡量这些链接模仿的模仿，这些链接模仿了这些链接。我们将利用这些工具以及正在开发的新型分析方法进行全基因组的关联映射研究3）3）在350名墨西哥裔美国人中，有100,000多个SNP打字的全基因组，患有2型糖尿病，并随机样本的350名来自德克萨斯州斯塔尔县的墨西哥裔美国人； 4）超过500,000个SNP在700个Hutterites中键入了有关各种表型的数据，这些表型与代谢，肺和心血管疾病有关。我们的建议基于在方法开发和数据分析的长期合作基础上，包括人类，人群，统计遗传学家和统计学家。