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

复杂疾病基因组测序的分析、验证和资源创建

基本信息

批准号：
10116927
负责人：
Nancy J Cox
金额：
$ 86.42万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10116927
关键词：
Accounting Affect Automobile Driving Code Communities Complex Computer software Computing Methodologies Coupled DNA Data Data Analyses Data Set Databases Disease Electronic Health Record Etiology Face Generations Genes Genetic Genetic Predisposition to Disease Genetic study Genome Government Health Expenditures Human Human Genetics Individual Knowledge Large-Scale Sequencing Link Machine Learning Methods National Human Genome Research Institute Phenotype Policies Public Health Regulator Genes Reporting Research Resources Risk Science Statistical Methods Statistical Models Technology Time Tissues Untranslated RNA Validation Variant analysis pipeline base biobank cost design disease phenotype exome exome sequencing expectation experience experimental study functional genomics genetic architecture genetic testing genetic variant genome sciences genome sequencing genome wide association study genomic data human disease human genomics improved insight member method development novel novel strategies personalized medicine pleiotropism public health relevance rare variant success web portal whole genome

项目摘要

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.

在过去的10年里，人类遗传学研究取得了前所未有的数量的发现，这些发现将基因组变异与常见疾病联系起来。毫无疑问，我们从迄今为止的发现中学到了很多东西，我们还必须承认，在确定和描述影响人类常见疾病风险的基因组变异方面-这些疾病占公共卫生保健支出的绝大部分-我们的发现并没有产生我们所期望的疾病病因学知识，因为这些发现的数量很大。这些观察结果与测序成本的大幅降低相结合，正在推动对常见疾病进行全基因组测序研究。我们将我们的应用集中在全基因组序列数据的方法开发和分析的三个关键挑战上。虽然在外显子组测序分析方法的开发方面取得了实质性进展，但基于基因的测试对罕见编码变体的影响方向以及基因罕见变体对表型的贡献比例相对稳健，显然，整合常见和罕见变异分析以及功能基因组学数据的方法对于全基因组的适当分析是必不可少的。序列数据因此，我们在具体目标1中提出开发新的方法、软件和分析管道，用于对常见和罕见变异进行综合分析，以分析任何给定常见疾病的50，000 - 100，000个体的全基因组序列，并在具体目标2中提出开发和应用新的方法，用于对使用BioVU、200，我们在范德比尔特建立了一个拥有30年高质量电子健康记录的生物库，并在Specific Aim 3中开发了可查询的结果数据库和一个综合性门户网站，为内部测序社区和更广泛的科学社区提供我们对序列数据的研究结果。