Powering whole genome sequence-based genetic discovery for common human diseases

为常见人类疾病提供基于全基因组序列的基因发现

• 批准号: 10168752
• 负责人: XIHONG LIN
• 金额: $ 25万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-31 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10168752
• 关键词: Alleles; Automobile Driving; Base Sequence; Biological; Code; Communities; Complex; Computational Biology; Computer software; Computing Methodologies; Data; Data Set; Databases; Disease; Elements; Ensure; Etiology; Foundations; Genes; Genetic; Genetic Models; Genetic Structures; Genetic Variation; Genomics; Goals; Grouping; Heritability; Human; Human Genetics; Individual; Knowledge; Learning; Learning Module; Measures; Mediation; Mendelian disorder; Methods; Modeling; National Human Genome Research Institute; Non-Insulin-Dependent Diabetes Mellitus; Pathway interactions; Patient Care; Phenotype; Play; Polygenic Traits; Population Control; Randomized; Regulatory Element; Research Personnel; Resources; Risk; Role; Sampling; Scheme; Schizophrenia; Signal Transduction; Statistical Methods; Testing; Time; To specify; Translating; Untranslated RNA; Variant; Weight; analytical method; analytical tool; base; burden of illness; data resource; data sharing; disease phenotype; disorder prevention; epigenomics; genetic analysis; genetic architecture; genetic association; genome sequencing; human disease; improved; individualized prevention; insight; novel; novel strategies; open source; outcome forecast; phenotypic data; pleiotropism; population stratification; power analysis; precision medicine; programs; rare variant; scale up; tool; trait; user-friendly; whole genome

PROJECT SUMMARY/ABSTRACT The coming NHGRI Centers for Common Disease Genomics (CCDG) and Centers for Mendelian Genomics (CMG) plan to generate whole genome sequencing (WGS) data on over 200,000 individuals. WGS will provide comprehensive and complete genetic data across coding and non-coding variation, presenting an unprecedented opportunity for discovery in the genetic analysis of human diseases. However, a lack of powerful analytic tools that fully realize the potential of these data has emerged as a bottleneck for effectively translating rich information contained in these massive WGS data into meaningful insights about human diseases. There is a pressing need to develop powerful and robust analytic methods for WGS that can accelerate genetic discoveries. To meet this need, we have assembled an interdisciplinary team of computational biologists, geneticists, and statisticians. Building on our extensive track record in sequencing studies, statistical genetics, functional analysis and computational biology, we will power the next round of genetic discoveries by (1) building a massive WGS control sample and developing the methods for incorporating these controls in studies of complex and Mendelian diseases; (2) creating more powerful statistical methods for rare variant analysis through the incorporation of functional and regulatory information and advanced statistical tools; (3) establishing methods to analyze multiple phenotypes to boost the power for association and understand how different phenotypes relate genetically. These methods will enhance our ability to identify novel associations across a wide range of genetic architectures, from Mendelian diseases driven by a strong acting allele to complex polygenic traits. Novel associations promise to lay the foundation for gaining new insight into the biological mechanisms driving disease and be the bedrock for precision prevention and medicine strategies. We will collaborate with the investigators of the Genome Sequencing Program, and will share the developed data resources, tools and methods with the community through user-friendly open source software and educational modules.

项目总结/摘要 即将成立的NHGRI常见疾病基因组学中心（CCDG）和孟德尔基因组学中心 (CMG)计划生成超过20万人的全基因组测序（WGS）数据。WGS将提供 编码和非编码变异的全面和完整的遗传数据， 这是人类疾病基因分析中前所未有的发现机会。然而，缺乏 能够充分发挥这些数据潜力的强大分析工具已经成为有效解决这些问题的瓶颈。 将这些海量WGS数据中包含的丰富信息转化为对人类的有意义的见解 疾病迫切需要为WGS开发强大且强大的分析方法，以便 加速基因发现。为了满足这一需求，我们组建了一个跨学科的团队， 计算生物学家遗传学家和统计学家基于我们在测序方面的广泛记录， 研究，统计遗传学，功能分析和计算生物学，我们将为下一轮的 遗传发现通过（1）建立一个大规模的WGS对照样本，并开发方法， 将这些控制纳入复杂疾病和孟德尔疾病的研究中;（2）创建更强大的 通过结合功能和调控信息进行罕见变异分析的统计方法 和先进的统计工具;（3）建立分析多种表型的方法，以提高对 关联，并了解不同的表型如何在遗传上相关。这些方法将提高我们的 能够在广泛的遗传结构中识别新的关联，从孟德尔疾病 由一个强作用等位基因驱动形成复杂的多基因性状。新的协会承诺奠定基础， 获得对疾病驱动生物机制的新见解，并成为精确预防的基石 和医学策略。我们将与基因组测序计划的研究人员合作， 我们将通过用户友好的开放平台，与社会各界分享已开发的数据资源、工具和方法。 源软件和教育模块。