Quantitative methods for genetic linkage heterogeneity

遗传连锁异质性的定量方法

• 批准号: 6846048
• 负责人: Daniel J. Schaid
• 金额: $ 22.13万
• 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 2008-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6846048
• 关键词: computer human interaction; computer program /software; computer system design /evaluation; family genetics; gene environment interaction; genetic models; genetic susceptibility; human data; linkage mapping; mathematical model; model design /development; neoplasm /cancer genetics; prostate neoplasms; quantitative trait loci; statistics /biometry

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(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人类常见疾病的遗传基础提供了坚实的基础。然而，常见的疾病聚集在家庭中，如许多癌症、心血管疾病和糖尿病，其病因和表型表达具有很大的异质性。这种异质性的来源从遗传到表型，到环境和行为风险因素，到研究设计，再到所有这些来源的组合。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解释(和控制)广泛异质性的能力。这项拟议研究的目标是通过开发解释连锁异质性的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。 目的1.递归划分树：为了更好地识别在家族子集中起关键作用的易感基因，我们将基于递归划分树开发新的定量方法来评估子集之间的遗传连锁异质性。这些基于现代统计和计算算法的新发展，应该会提高在存在由以非线性方式影响疾病的非遗传因素引起的大量异质性的情况下检测连锁的能力。 目的2.回归模型：为了提高对复杂遗传性状的连锁检测能力，并提炼有希望的连锁区域，我们将开发新的统计方法，为遗传连锁研究提供灵活的方法，直接模拟关键因素对血统共享概率的影响。 目标3.用户友好型软件：将免费向科学界提供实施拟议方法的用户友好型软件，包括详细记录的程序及其使用实例。