Novel approaches in linkage analysis for complex traits

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危险因素。有强有力的证据表明高血压有遗传基础。在这一应用中，我们建议开发新的方法来更好地了解有助于测量血压水平、诊断类别(高血压与正常血压)和相关特征的遗传机制。为了实现这一目标，在目标1中，我们建议定位影响血压水平、诊断类别及其相关性的基因。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，并利用与血压测量和/或诊断类别相关的特征簇。在目标2中，我们建议开发用于复杂性状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测量、诊断类别及其相关性的基因的能力。为此，我们将把回归分析中使用的诊断工具扩展到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庭，因为之前的研究表明，具有离群值的家庭会产生假阳性结果。我们还将把树状结构模型扩展到谱系数据。基于树的建模是一种探索性技术，用于揭示数据中的结构。树结构模型的使用是有利的，因为探索数据结构或推导简约模型不需要任何假设。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个功能整合到S+软件中。S+之所以被选中，是因为它具有分析大数据集的能力和灵活性。我们将分析罗切斯特家庭心脏研究的家庭数据，这些数据收集在之前NIH资助的拨款(R01 HL30428“钠运输：遗传学与高血压”和R01 HL51021“原发性高血压的分子流行病学”)中。不会为这项拨款提案收集新的数据。该应用程序显然符合NHLBI拨款计划的目标，因为它将使用通过NHLBI支持收集的现有数据集来探索新的方法。

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.