Novel approaches in linkage analysis for complex traits

复杂性状连锁分析的新方法

• 批准号: 6562233
• 负责人: Mariza de ANDRADE
• 金额: $ 14.45万
• 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: 2002-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6562233
• 关键词: clinical research; family genetics; genetic models; genetic susceptibility; human data; human genetic material tag; hypertension; linkage mapping; mathematical model

DESCRIPTION (provided by applicant): Hypertension affects 50 million Americans and is the single greatest risk factor contributing to diseases of the brain, heart, and kidneys. There is a strong evidence that hypertension has a genetic basis. In this application, we propose to develop novel approaches to better understand the genetic mechanisms contributing to measures of blood pressure (BP) level, diagnostic category (hypertension versus normotension) and correlated traits. To achieve this goal, in the aim 1 we propose to localize genes influencing measures of blood pressure levels, diagnostic category and their correlates. To do so we will apply genome-wide multivariate linkage analyses based on the variance components approach and utilizing clusters of traits correlated with measures of blood pressure and/or diagnostics category. In the aim 2, we propose to develop exploratory diagnostic tools for linkage analysis of complex traits to further enhance our ability to localize genes influencing measures of blood pressure, diagnostic category and their correlates. To do so, we will extend the diagnostic tools used in regression analysis to the variance components approach used for linkage analysis of quantitative traits. In our case, for example, it can be used to identify outlier families since previous studies have shown that families with outlier values yield false-positive results. We will also extend tree-structure models to pedigree data. Tree-based modeling is an exploratory technique for uncovering structure in the data. The use of tree-structure models is advantageous because no assumptions are necessary to explore the data structure or to derive parsimonious model. These models are accurate classifiers (binary outcome) and predictors (quantitative outcomes). Thus, they will help us to better understand the underlying structure of the familial data. All these tools will be incorporated in the S-Plus software as a function. S-Plus was selected due to its capability and flexibility for analyzing large data sets. We will analyze family data from the Rochester Family Heart Study, collected in previously NIH funded grants (R01 HL30428 "Sodium Transport: Genetics and Hypertension", and R01 HL51021 "Molecular Epidemiology of Essential Hypertension"). No new data will be collected for this grant proposal. The application clearly fits the goal of this NHLBI grant program since it will explore new approaches using an existing data set collected through NHLBI support.

描述（由申请人提供）： 高血压影响着5000万美国人，是导致大脑、心脏和肾脏疾病的最大风险因素。 有强有力的证据表明，高血压有遗传基础。在本申请中，我们建议开发新的方法，以更好地了解有助于测量血压（BP）水平，诊断类别（高血压与血压正常）和相关性状的遗传机制。 为了实现这一目标，在目标1中，我们提出了本地化基因影响血压水平，诊断类别及其相关的措施。为此，我们将应用基于方差分量方法的全基因组多变量连锁分析，并利用与血压和/或诊断类别相关的性状簇。 在目标2中，我们建议开发用于复杂性状的连锁分析的探索性诊断工具，以进一步提高我们定位影响血压、诊断类别及其相关指标的基因的能力。为此，我们将扩展用于回归分析的诊断工具，用于数量性状连锁分析的方差分量方法。例如，在我们的案例中，它可以用来识别离群值家族，因为以前的研究表明，具有离群值的家族会产生假阳性结果。 我们还将树结构模型扩展到系谱数据。基于树的建模是一种用于揭示数据结构的探索性技术。 使用树结构模型是有利的，因为不需要假设来探索数据结构或导出简约模型。 这些模型是准确的分类器（二元结果）和预测器（定量结果）。 因此，它们将帮助我们更好地理解家族数据的潜在结构。 所有这些工具都将作为一项功能纳入S-Plus软件。选择S-Plus是因为其分析大型数据集的能力和灵活性。 我们将分析来自罗切斯特家族心脏研究的家族数据，这些数据是在以前NIH资助的赠款中收集的（R 01 HL 30428“钠转运：遗传学和高血压”和R 01 HL 51021“原发性高血压的分子流行病学”）。 不会为该赠款提案收集新的数据。 该应用程序显然符合NHLBI资助计划的目标，因为它将使用通过NHLBI支持收集的现有数据集探索新方法。