Quantitative methods for genetic linkage heterogeneity

遗传连锁异质性的定量方法

• 批准号: 7318339
• 负责人: Daniel J. Schaid
• 金额: $ 20.98万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7318339
• 关键词: Accounting; Algorithms; Behavioral; Cardiovascular system; Communities; Complex; Complex Genetic Trait; Computing Methodologies; Development; Diabetes Mellitus; Disease; Disease Clusterings; Disease regression; Environmental Risk Factor; Etiology; Evaluation; Family; Foundations; Future; Genes; Genetic; Genomics; Goals; Hereditary Disease; Heterogeneity; Human; Knowledge; Malignant Neoplasms; Methods; Modeling; Modems; Phenotype; Play; Probability; Procedures; Range; Regression Analysis; Research; Research Design; Risk Factors; Role; Signal Transduction; Source; Statistical Computing; Statistical Methods; Susceptibility Gene; Technology; Trees; base; cost; gene environment interaction; gene interaction; genetic linkage; genome sequencing; human disease; improved; positional cloning; success; trait; user friendly software

DESCRIPTION (provided by applicant): The evolving understanding of the human genome sequence and recent advances in genomic technologies provide a strong foundation to resolve the genetic basis of common human diseases. However, common diseases that cluster in families, such as many cancers, cardiovascular, and diabetes, have much heterogeneity in their etiology and their phenotypic expression. Sources of this heterogeneity range from genetic, to phenotype, to environmental and behavioral risk factors, to study design, to a combination of all of these sources. The success of linkage studies for common diseases has been limited, and future success may hinge on the ability to account for (and control) a wide range of heterogeneity. The goals of this proposed research are to improve the ability to identify and characterize susceptibility genes for complex human traits by developing statistical and computational methods that account for linkage heterogeneity. Aim 1. Recursive Partitioning Trees: To improve identification of susceptibility genes that play critical roles in subsets of families, we will develop new quantitative methods for evaluation of genetic linkage heterogeneity across subsets based on recursive partitioning trees. These new developments, based on modem statistical and computing algorithms, should improve the power to detect linkage in the presence of a large amount of heterogeneity caused by non-genetic factors that influence disease in non-linear ways. Aim 2. Regression Models: To increase the power to detect linkage for complex genetic traits, and refine the regions of promising linkage, we will develop new statistical methods that provide flexible ways to directly model the influence of critical factors on identity-by-descent sharing probabilities for genetic linkage studies. Aim 3. User-friendly software: User-friendly software that implements the proposed methods, including well-documented procedures and examples of their usage, will be provided free to the scientific community.

描述（由申请人提供）：对人类基因组序列的不断发展的理解和基因组技术的最新进展为解决常见人类疾病的遗传基础提供了坚实的基础。但是，聚集在家庭中的常见疾病，例如许多癌症，心血管和糖尿病，其病因学和表型表达具有很大的异质性。这种异质性的来源范围从遗传到表型，到环境和行为风险因素，再到设计设计，再到所有这些来源的组合。对常见疾病的连锁研究的成功是有限的，未来的成功可能取决于（并控制）广泛的异质性。这项拟议的研究的目标是通过开发统计和计算方法来提高识别和表征复杂人类特征的易感基因的能力，以解释链接异质性。 目的1。递归分区树：为了改善在家庭子集中起作用至关重要的易感基因的识别，我们将开发新的定量方法，以评估基于递归分配树的跨亚集的遗传连锁异质性。这些基于现代统计和计算算法的新发展应提高在存在大量异质性的情况下，这是由非遗传因素引起的，这些因素以非线性方式影响疾病。 目标2。回归模型：为了增加检测复杂遗传特征的联系的能力，并完善了有希望的联系的区域，我们将开发新的统计方法，这些方法可以提供灵活的方法，以直接模拟关键因素对遗传联系研究的关键因素对逐渐逐渐差异的概率的影响。 AIM 3。用户友好的软件：实现拟议方法的用户友好软件，包括有据可查的程序和其使用的示例，将免费提供给科学界。