Analysis and interpretation of DNA Sequence Data in Association Studies

描述(申请人提供)：为疾病关联研究生成大量DNA序列数据变得可行。这对人类遗传学既带来了挑战，也带来了机遇。也许最重要的是，有了大规模的重新测序数据，就有可能开始识别那些罕见的变异导致疾病易感性的基因。在这里，我们建议创建一些分析工具，这些工具将是分析和解释即将到来的数据所需的。我们的前两个目标集中在使用一些第一批全基因组重新测序数据来更好地注释可能具有功能的非编码位点。我们的第三个目标是开发统计方法，用于分析疾病关联研究中出现的数据，以确定具有导致疾病的罕见变异的基因。统计方法将使用我们将在前两个目标中开发的注释方法，根据变体可能具有生物功能的可能性来确定它们的优先级。在一定程度上，利用我们对潜在功能位点的改进功能注释，我们还将开发新的统计方法，通过许多罕见变异的作用来识别与疾病表型有关的基因。

DESCRIPTION (provided by applicant): 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. 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.