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: 10085285
• 负责人: XIHONG LIN
• 金额: $ 88.48万
• 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: 2020-03-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10085285
• 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.

项目概要/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 200,000 人的全基因组测序 (WGS) 数据。 WGS 将提供 跨编码和非编码变异的全面且完整的遗传数据，呈现 人类疾病遗传分析中发现的前所未有的机会。然而，缺乏 充分发挥这些数据潜力的强大分析工具已成为有效利用这些数据的瓶颈。 将这些海量 WGS 数据中包含的丰富信息转化为关于人类的有意义的见解 疾病。迫切需要开发强大而稳健的 WGS 分析方法 加速基因发现。为了满足这一需求，我们组建了一支跨学科团队 计算生物学家、遗传学家和统计学家。以我们在测序方面的丰富记录为基础 研究、统计遗传学、功能分析和计算生物学，我们将为下一轮提供动力 通过 (1) 构建大量 WGS 对照样本并开发方法来发现基因 将这些控制纳入复杂疾病和孟德尔疾病的研究中； （二）创造更强大的力量 通过结合功能和监管信息进行罕见变异分析的统计方法 和先进的统计工具； (3) 建立多种表型分析方法，增强分析能力 关联并了解不同表型如何在遗传上相关。这些方法将增强我们的 能够从孟德尔疾病中识别各种遗传结构中的新关联 由强作用等位基因驱动，形成复杂的多基因性状。新颖的协会有望奠定基础 获得对驱动疾病的生物机制的新见解，并成为精准预防的基石 和医学策略。我们将与基因组测序计划的研究人员合作，并且 将通过用户友好的开放方式与社区分享开发的数据资源、工具和方法 源软件和教育模块。