Quantitative Methods for Genetic Epidemiology

遗传流行病学的定量方法

• 批准号: 7232321
• 负责人: Daniel J. Schaid
• 金额: $ 34.04万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7232321
• 关键词: Accounting; Address; Area; Arts; Candidate Disease Gene; Case-Control Studies; Charge; Class; Clinical; Cohort Studies; Communities; Complex; Data; Data Analyses; Development; Diagnosis; Disease; Documentation; Dose; Environmental Risk Factor; Etiology; Face; Genes; Genetic; Genetic Markers; Genetic Models; Genome; Genomics; Goals; Haplotypes; Headache; Health; Human; Human Genetics; Human Genome; Hypertension; Knowledge; Lead; Major Depressive Disorder; Malignant neoplasm of prostate; Methodology; Methods; Metric; Modeling; Nonparametric Statistics; Numbers; Pharmacogenomics; Phenotype; Pliability; Population Genetics; Procedures; Range; Research; Research Personnel; Role; Scanning; Specific qualifier value; Statistical Methods; Structure; Techniques; Testing; Trees; Variant; base; case control; gene discovery; gene interaction; genetic association; genetic epidemiology; genetic pedigree; genetic variant; genome sequencing; genome wide association study; human disease; improved; insight; malignant breast neoplasm; member; novel; outcome forecast; programs; research study; response; statistics; theories; trait; user friendly software

DESCRIPTION (provided by applicant): The completion of the human genome sequence has led to an immense amount of information on the structure of the human genome. We now face the challenge of using this rich information to improve human health through a better understanding of common human diseases. This challenge is intensified by complex genetic and environmental mechanisms that contribute to common complex diseases. Our long term goals are to develop state-of-the-art statistical and quantitative methods to analyze genetic association studies in order to extract the maximum amount of genetic information. Our short term goals are to develop new "model-free" statistical methods for genetic association studies, in order to provide robust, yet powerful, methods as we gain knowledge on the genetic and environmental factors that lead to disease and response to treatment. By taking this strategy, we anticipate that our proposed research plans will succeed at providing the research community with the much-needed statistical methods to evaluate the association of large scale genomic variation with complex human traits. Our planned specific aims are to: 1) Develop Nonparametric Statistical Methods For Genetic Association Studies: Based on our recent developments of a new class of nonparametric statistics, we plan to extend our methods to be more powerful for detecting gene-gene interactions, to create "scan" statistics for genome-wide analyses, and to account for a variety of traits; 2) Develop New Genomic Scan Statistics: To consider a large number of candidate genes, or a genome-wide association study, we plan to develop new scan statistics for evaluating the association of haplotypes with a variety of traits; 3) Develop Nonparametric Statistical Methods and Scan Statistics for Pedigree Data: Aims 1-2 are focused on unrelated subjects, and we plan to extend these methods to pedigree data; 4) Develop User-Friendly Software and Documentation: We plan to provide, at no charge to the scientific community, user-friendly software that implements our methods, including well-documented procedures and examples on their usage; 5) Apply New Methods to Collaborative Research Studies: All methods developed in Aims 1-4 will be applied to ongoing collaborative studies in order to gain insights to their strengths and weaknesses, and to provide potential clinical benefits for our collaborative studies. Translational Potential: Our research plans address a critically important scientific and clinical problem--to make optimal use of large-scale genomic information to evaluate its role in human health. Through development of new quantitative methods, our research has the potential to improve the diagnosis, prognosis, and treatment of complex genetic human diseases, as well as other human traits.

描述（由申请人提供）：人类基因组序列的完成导致有关人基因组结构的大量信息。现在，我们面临着通过对常见人类疾病的更好理解来改善人类健康的挑战。这一挑战是由有助于常见复杂疾病的复杂遗传和环境机制加强的。我们的长期目标是开发最先进的统计和定量方法，以分析遗传关联研究，以提取最大的遗传信息。我们的短期目标是为遗传关联研究开发新的“无模型”统计方法，以便提供强大而强大的方法，因为我们了解导致疾病和对治疗反应的遗传和环境因素的知识。通过采用这一策略，我们预计我们提出的研究计划将成功地为研究社区提供急需的统计方法，以评估大规模基因组变异与复杂人类特征的关联。我们计划的具体目的是：1）为遗传关联研究开发非参数统计方法：基于我们最近对新的非参数统计的发展，我们计划将方法扩展到更强大的基因相互作用，以检测基因组全基因组分析的“扫描”统计数据，并考虑到各种特质的统计； 2）开发新的基因组扫描统计：要考虑大量候选基因或全基因组关联研究，我们计划开发新的扫描统计数据，以评估单倍型与多种特征的关联； 3）为谱系数据开发非参数统计方法和扫描统计：目标1-2集中于无关的受试者，我们计划将这些方法扩展到谱系数据； 4）开发用户友好的软件和文档：我们计划免费向科学社区提供用户友好的软件，以实现我们的方法，包括记录的程序和示例的使用； 5）将新方法应用于协作研究：AIM 1-4中开发的所有方法都将应用于正在进行的协作研究中，以了解其优势和劣势，并为我们的协作研究提供潜在的临床益处。翻译潜力：我们的研究计划涉及一个至关重要的科学和临床问题 - 最佳利用大规模基因组信息来评估其在人类健康中的作用。通过开发新的定量方法，我们的研究有可能改善复杂遗传疾病以及其他人类特征的诊断，预后和治疗。