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）我们作为一个社会，在发展科学基础设施方面投入了大量资金，以便能够在糖尿病、哮喘和心血管疾病（以及相关数量性状）等常见疾病的主要生物风险因素方面取得突破性发现），在公共医疗保健支出中所占份额不成比例且不断增加。虽然很明显遗传变异与常见疾病表型之间的关系很复杂，但有充分的初步证据表明，识别常见疾病的遗传风险因素将提高对其病因和发病机制的了解，从而产生更具体和有效的治疗方法。此外，由于几乎所有常见疾病都是遗传和非遗传风险因素作用和相互作用的结果，遗传风险因素的识别应允许流行病学研究的设计，以识别更具体的非遗传风险因素，这些因素可能是预防疾病的成本效益目标。最近开发的用于超大规模 SNP 基因分型的高通量平台使得全基因组关联图谱成为识别常见疾病遗传风险因素的可行的附加工具。然而，有效使用这一工具需要大量的方法开发。因此，我们建议开发（包括软件工具和网络界面）并应用各种方法来促进全基因组关联研究，包括1）改进的高通量基因分型平台的等位基因/基因型调用算法（以减少非随机模式）缺失数据），以及 2）用于连锁不平衡绘图的信息内容测量，类似于先前为连锁绘图开发的信息内容。我们将利用这些工具以及我们正在开发的新颖分析方法，对 350 名患有 2 型糖尿病的墨西哥美国人和 350 名墨西哥裔美国人的随机样本中的 3) 超过 100,000 个 SNP 进行全基因组分析，进行全基因组关联图谱研究。来自德克萨斯州斯塔尔县的墨西哥裔美国人； 4) 对 700 名哈特人进行了超过 500,000 个 SNP 分型，其中包含与代谢、肺部和心血管疾病相关的各种表型数据。我们的提案建立在我们研究团队成员（包括人类、人口、统计遗传学家和统计学家）在方法开发和数据分析方面的长期合作的基础上。