Functional Data Analysis of Longitudinally Measured Genetic Traits.

描述（由申请人提供）：人们对研究随时间变化的功能特征（如血压、胆固醇水平或生长速度）的遗传结构越来越感兴趣。然而，在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。本研究的目标是发展通用的和更强大的统计方法来绘制功能数量遗传性状。更具体地说，在第一步，我们提出了一个非参数排列测试，通过检查家族聚集功能性状的整体遗传效应。当有基因贡献的证据时，第二步自然是估计这种整体的多基因效应。然后，我们发展了基于混合效应模型的估计方法，并使用功能主成分分析来总结多基因效应的主要时间变化。当整体遗传效应相当强时，研究兴趣在于定位基因组上有影响的基因。第三步，我们提出了通用功能方差成分模型，利用全基因组连锁研究中的标记基因型数据来检验和估计数量性状位点（QTL）的遗传效应。当前的特设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时间相关遗传效应的参数形式。我们提出了一个家族的基础系统来捕捉遗传效应和估计年龄特异性QTL遗传力。这种基础系统的灵活性允许识别任何形状的时间趋势。在这个功能图谱框架内，我们可以回答诸如QTL效应何时表达影响性状、基因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，并在软件包中实现它们。Framingham心脏研究是一项针对心血管疾病的大型前瞻性研究，旨在探讨心血管疾病的危险因素和遗传结构。GAW13仿真数据是在Framingham研究的基础上生成的，为方法评价和比较提供了现实和有价值的资源。将开发的方法应用于Framingham数据可以增强我们对心血管疾病相关特征的遗传结构的理解。开发的软件将向所有调查人员免费公开。公共卫生相关性：由于其病因的复杂性，解剖复杂的时变功能特征（如血压、胆固醇水平或生长速度）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导致复杂功能性状的遗传变异，并将方法应用于弗雷明汉心脏研究数据。该研究将为功能性定量遗传性状的定位提供通用和更有力的分析方法，并回答诸如基因何时表达影响性状、遗传效应持续多长时间以及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.