Analysis and interpretation of DNA Sequence Data in Association Studies

摘要 为疾病生成大量DNA序列数据正变得可行 协会研究。这对人类来说既是挑战也是机遇 遗传学。也许最重要的是，对于大规模的重新排序数据，它将是 有可能开始识别导致疾病的罕见变异基因 敏感度。在这里，我们建议创建一些分析工具，这些工具将 分析和解释即将公布的数据所需的信息。我们的前两个目标主要集中在 使用一些第一个全基因组重测序数据来更好地注释非编码 可能正常运行的站点。我们的第三个目标是开发统计方法 分析疾病关联研究中出现的数据，以识别罕见的基因 导致疾病的变异。统计方法将使用注解 我们将在前两个部分开发的方法旨在根据以下内容确定变体的优先级 它们可能具有生物功能的可能性。简要叙述小结 该项目的目的是开发用于分析和解释数据的新工具 来自DNA重测序研究。我们将开发使用重新测序数据的方法 以确定可能具有功能的位点，特别是在非编码区。使用，在 部分，我们对潜在功能站点的改进的功能注释，我们还将 开发新的统计方法来识别与疾病表型有关的基因 通过许多稀有变种的作用。

ABSTRACT It is becoming feasible to generate massive quantities of DNA sequence data for disease association studies. This presents both challenges and opportunities for human genetics. Perhaps most importantly, with large scale resequencing data, it will be possible to start identifying genes at which rare variants contribute to disease susceptibility. Here we propose to create a number of the analytical tools that will be needed for analyzing and interpreting the forthcoming data. Our first two aims focus on using some of the first genome-wide resequencing data to better annotate noncoding sites that are likely to be functional. Our third aim develops statistical methods for analyzing data that emerge from disease association studies to identify genes with rare variants that contribute to disease. The statistical methods will use the annotation approaches that we will develop in the first two aims to prioritize variants according to the likelihood that they might have biological function. BRIEF NARRATIVE SUMMARY The purpose of this project is to develop new tools for analyzing and interpreting data from DNA resequencing studies. We will develop methods that use resequencing data to identify sites that are likely to be functional, especially in noncoding regions. Using, in part, our improved functional annotation of potentially functional sites, we will also develop new statistical methods to identify genes that contribute to disease phenotypes through the action of many rare variants.