Analysis, Validation and Resource Creation for Genome Sequencing of Complex Diseases

 描述(申请人提供)在过去10年中，人类遗传学研究取得了空前数量的发现，将基因组变异与常见疾病联系起来。虽然毫无疑问，我们从迄今为止的发现中学到了很大的东西，但我们也必须承认，关于影响常见人类疾病风险的基因组变异的识别和特征--这些变异占公共卫生保健支出的绝大多数--我们的发现并没有产生几乎我们预期的疾病病因学知识，因为这些发现的数量如此之多。这些观察结果的结合，再加上测序成本的大幅降低，正在推动对常见疾病进行全基因组测序研究。我们将我们的应用重点放在了全基因组序列数据的方法开发和分析的三个关键挑战上。虽然在分析外显子组测序的方法开发方面已经取得了实质性的进展，基于基因的测试对稀有编码变体的影响方向以及基因稀有变体对表型的影响的比例相对稳健，但显然，整合常见和稀有变异分析以及功能基因组数据的方法将是适当分析全基因组序列数据的关键。因此，在具体目标1中，我们建议开发新的方法、软件和分析管道，用于对任何给定常见疾病的50,000-100,000个人的全基因组序列进行分析的常见和罕见变异的集成分析，并在特定目标2中开发和应用新的方法，用于使用BioVU来优先处理这些大规模测序研究的结果，BioVU是Vanderbilt的200,000名成员的生物库，与30年的高质量电子健康记录相关，并且在具体目标3中开发可查询的结果数据库和全面的网络门户，为内部测序界和更广泛的科学界提供我们关于序列数据的研究结果。

 DESCRIPTION (provided by applicant) Over the past 10 years human genetics studies made unprecedented numbers of discoveries relating genome variation to common diseases. While it is unquestionably true that we have learned substantially from the discoveries made to date, we must also acknowledge that with respect to the identification and characterization of the genome variation affecting risk of common human diseases - those accounting for the overwhelming majority of public health care expenditures - our discoveries have not generated nearly the knowledge of disease etiology that we would have expected given the sheer number of these discoveries. The combination of these observations coupled with the dramatic reduction in the cost of sequencing is driving the case for moving to whole genome sequencing studies for common disease. We have focused our application on three critical challenges for methods development and analysis of whole genome sequence data. While there has been substantial progress in methods development for analysis of exome sequencing, with gene-based tests that are relatively robust to the direction of effects of rare coding variants as well as the proportionof the gene's rare variants contributing to phenotype, it is clear that methods integrating the analysis of common and rare variation as well as functional genomics data will be essential for appropriate analysis of whole genome sequence data. Thus, we propose in Specific Aim 1 to develop novel methods, software and analysis pipelines for integrated analysis of common and rare variants for the analysis of whole genome sequence on 50,000- 100,000 individuals for any given common disease, and in Specific Aim 2 to develop and apply novel approaches for prioritizing results from these large-scale sequencing studies using BioVU, the 200,000 member biobank at Vanderbilt that is associated with 30 years of high quality electronic health records, and in Specific Aim 3 to develop queriable results databases and a comprehensive web portal to serve results of our studies on the sequence data for both the internal sequencing community and for the broader scientific community.