Functional Data Analysis of Longitudinally Measured Genetic Traits.

描述(由申请人提供)：人们对研究血压、胆固醇水平或生长速度等时变功能特征的遗传结构越来越感兴趣。然而，文献中提出的方法中很少有足够普遍的方法以计算可行的方式应用于复杂的情况。这项研究的目的是开发通用的、更强大的统计方法来定位功能数量遗传性状。更具体地说，在第一步中，我们提出了一种非参数置换检验，通过检验家族聚集性来检验功能性状的总体遗传效应。当有证据表明基因有贡献时，自然的第二步就是估计这种总体的多基因效应。然后，我们发展了基于混合效应模型的估计方法，并使用泛函主成分分析来总结多基因效应的主要时间变化。当总体遗传效应相当强时，研究兴趣在于在基因组上定位有影响力的基因。在第三步中，我们提出了通用的功能方差分量模型，以在全基因组连锁研究中使用标记基因型数据来检验和估计数量性状基因座(QTL)的遗传效应。当前的特别方法要么在单变量分析中使用重复测量的平均值，要么在纵向分析中指定依赖时间的遗传效应的参数形式。我们提出了一系列的基础系统来捕捉遗传效应和估计特定年龄的QTL遗传力。这种基本系统的灵活性允许确定任何形式的时间趋势。在这个功能定位框架内，我们可以回答QTL效应何时表达来影响一个性状，基因是如何影响性状变化率等研究问题。最后，我们建议使用遗传分析研讨会(GAW)13的模拟数据来研究我们的方法，将它们应用于Framingham心脏研究数据，并在一个软件包中实现它们。弗雷明翰心脏研究是一项关于心血管疾病的大型前瞻性研究，旨在调查这种疾病的危险因素和遗传结构。GAW13的仿真数据是紧密基于Framingham研究产生的，这为方法评估和比较提供了现实而有价值的资源。将开发的方法应用于Framingham数据可能会增强我们对心血管疾病相关特征的遗传结构的理解。开发的软件将免费向所有调查人员公开提供。与公共卫生相关：由于其病因的复杂性，剖析血压、胆固醇水平或生长速度等复杂时变功能特征的遗传决定因素一直是遗传学研究中最艰巨的任务之一。该项目开发了新的统计方法来绘制易患复杂功能特征的遗传变异图，并将方法应用于弗雷明翰心脏研究数据。这项研究将为功能数量遗传性状的定位提供更通用、更强大的分析方法，并回答基因何时表达影响一个性状、遗传效应持续多长时间、基因如何影响性状变化率等研究问题。

DESCRIPTION (provided by applicant): There has been a growing interest in investigating genetic architecture of time-varying functional traits such as blood pressure, cholesterol levels or growth rate. Few of the methods proposed in the literature, however, are sufficiently general to apply to complicated situations in a computationally feasible fashion. The goal of this research is to develop general and more powerful statistical methods to map functional quantitative genetic traits. More specifically, in the first step we propose a non-parametric permutation test for overall genetic effect of functional traits by examining familial aggregation. When there is evidence for genetic contribution, the natural second step is to estimate this overall polygenic effect. We then develop methods based on mixed effects models for estimation and use functional principal components analysis to summarize the major temporal variation of the polygenic effect. When the overall genetic effect is reasonably strong, research interest lies in locating influential genes on the genome. In the third step, we propose general functional variance components models to test and estimate quantitative trait locus (QTL) genetic effects using marker genotype data in a genome-wide linkage study. Current ad-hoc methods either uses averages of repeated measurements in a univariate analysis or specifies a parametric form of time- dependent genetic effects in a longitudinal analysis. We propose a family of basis systems to capture genetic effects and estimate age-specific QTL heritability. The flexibility of such basis systems allow for identification of temporal trends of any shape. Within this functional mapping framework, we can answer research questions such as when is a QTL effect expressed to affect a trait, how does gene affect rate of change of traits and so on. Lastly, we propose to investigate our methods using Genetic Analysis Workshop (GAW) 13 simulated data, apply them to the Framingham Heart Study data, and implement them in a software package. Framingham Heart Study is a large prospective study of cardiovascular disease which aims to investigate risk factors and genetic architecture of this disease. The GAW13 simulation data was generated closely based on the Framingham Study, which provides a realistic and valuable resource for methods evaluation and comparison. An application of the developed methods to Framingham data may enhance our understanding of the genetic architecture of cardiovascular disease related traits. The developed software will be made publicly available to all investigators free of charge. PUBLIC HEALTH RELEVANCE: Dissecting genetic determinants of complex time-varying functional traits such as blood pressure, cholesterol levels or growth rate has been one of the most daunting tasks in genetic studies due to complicated nature of their etiology. This project develops new statistical methods to map genetic variants predisposing complex functional traits and applies methods to the Framingham Heart Study data. The study will offer general and more powerful analysis methods for mapping functional quantitative genetic trait and to answer research questions such as when is a gene expressed to affect a trait, how long does genetic effect last, and how does gene affect rate of change of traits.