Robust Methods for the Efficient Analysis and Integration of DNA Sequence Data

人类遗传学研究正处于如何捕获遗传变异的重大转变的尖端-从一个 从基于标记的方法到基于个体基因组的完整表征的方法， 测序这是一个令人兴奋的前景，但并非没有挑战。即将生产的大型 大量的序列数据提出了关于如何最好地使用这些数据的几个问题。例如：针对 由于数据规模庞大，将序列变异与人类疾病联系起来的统计方法需要 在统计和计算上都是有效的。此外，最近的大多数遗传关联实验 术语将不仅仅依赖于序列数据，而是将具有具有序列数据个体的子样本 而其余样品将保持未测序，但将包含基因型信息。可选择地， 序列数据可以在单独的外部样品上获得。因此，重要的是要发展统计 可以适当地将这些不同类型的数据集成到统一的推理框架中的方法。 这个研究项目将通过提出开发一类新的序列来解决这些问题- 基于单倍型共享统计，利用DNA序列进化在测试中的意义 变异/疾病关联（具体目标1）。此外，我们建议制定一个统计框架， 这允许DNA序列和基因型数据的统一分析（具体目标2）。在整个我们 将利用我们以前的工作，开发强大的单倍型推断方法，以开发计算 和统计学上有效的程序，保持强大的人口遗传假设。分层分析 将强调这种方法，以允许调整由于人群分层引起的混杂因素。高效 蒙特卡罗程序将被提出来考虑大量的序列变异的调查。 我们将开发一套软件工具，完全实现所开发的方法， 向一般研究界免费提供（具体目标3）。利用这些工具，我们 将分析一个公开的DNA序列数据集，目的是更好地定位疾病- 相关序列变体（具体目标4）。 通过这一建议制定的方法代表了一个统一的和严格的统计框架 用于开发强大的测试，利用DNA序列之间的进化关系，同时允许 将不同的数据类型纳入统一的分析。这些程序将为研究人员提供 更精细地定位疾病相关序列变异的工具，允许变异更好地优先考虑 通过功能研究进行后续调查。人类遗传学研究正处于如何捕获遗传变异的重大转变的尖端-从一个 从基于标记的方法到基于通过测序对个体基因组进行完整表征的方法。 然而，即将产生的大量测序数据导致了关于其生物学特性的问题。 统计分析和纳入更大的实验。我们通过提出一个 一个统一的、统计上严格的框架，用于开发利用进化关系的强大测试 这使得不同的数据类型可以被合并到一个统一的分析中。

Human genetics research is on the cusp of a major transformation in how genetic variation is captured-from a marker-based approach to one based on a complete characterization of an individual's genome by sequencing. This is an exciting prospect but not without its challenges. The imminent production of large amounts of sequence data raises several issues on how best to use these data. For example, because of the sheer scale of the data, statistical approaches for associating sequence variants with human disease need to be efficient, both statistically and computationally. In addition, most genetic association experiments in the near term will not rely solely on sequence data but instead will have sub-samples of individuals with sequence data while the rest of the sample will remain unsequenced but will contain genotype information. Alternatively, sequence data may be available on a separate, external sample. Thus it will be important to develop statistical methods that can appropriately integrate these various types of data into a unified inferential framework. This research project will address these issues by proposing to develop a novel class of sequence- based haplotype sharing statistics that exploit the implications of DNA sequence evolution in testing for variant/disease association (specific aim 1). Further, we propose to develop a statistical framework that allows for the unified analysis of DNA sequence and genotype data (specific aim 2). Throughout we will leverage our previous work developing robust methods for haplotype inference to develop computationally and statistically efficient procedures that remain robust to population genetic assumptions. A stratified analytic approach will be emphasized to allow for adjustment for confounding due to population stratification. Efficient Monte Carlo procedures will be proposed to account for the large number of sequence variants investigated. We will develop a suite of software tools that fully implement the methodology developed and make them freely available to the general research community (specific aim 3). Finally, using these tools, we will analyze a publicly available DNA sequence dataset with the goal of better localizing disease- associated sequence variants (specific aim 4). The methods developed through this proposal represent a unified and statistically rigorous framework for developing powerful tests that exploit evolutionary relationships between DNA sequences while allowing for disparate data types to be incorporated into a unified analysis. These procedures will give researchers the tools to more finely localize disease-associated sequence variants, allowing variants to be better prioritized for subsequent investigation via functional studies. Human genetics research is on the cusp of a major transformation in how genetic variation is captured-from a marker based approach to one based on a complete characterization of an individual's genome by sequencing. The imminent production of large amounts of sequencing data, however, leads to questions concerning their statistical analysis and incorporation into the larger experiment. We address these questions by proposing a unified and statistically rigorous framework for developing powerful tests that exploit evolutionary relationships between DNA sequences and that allow for disparate data types to be incorporated into a unified analysis.