New York Center for Collaborative Research in Common Disease Genomics

纽约常见疾病基因组学合作研究中心

• 批准号: 9923502
• 负责人: THOMAS P MANIATIS
• 金额: $ 829.45万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-30 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9923502
• 关键词: Address; Admixture; Affect; Algorithms; Alleles; Bipolar Disorder; Cell Line; Child; Clinical Data; Code; Collaborations; Collection; Communities; Complex; Computing Methodologies; DNA Sequence; Data; Data Aggregation; Data Set; Databases; Disease; Fostering; Foundations; Funding; Genes; Genetic; Genetic Diseases; Genetic Risk; Genetic Variation; Genome; Genomics; Goals; Healthcare; Heterogeneity; Human; Human Genome; Individual; Industry; Inherited; Joints; Link; Medical Research; Methods; Mutation; New York; Nucleotides; Pathogenicity; Pathway interactions; Patients; Phase; Phenotype; Physicians; Population; Population Genetics; Population Heterogeneity; Population Study; Publishing; RNA; Research; Research Design; Resolution; Risk; Sampling; Schizophrenia; Statistical Models; Structure; Techniques; Technology; Trans-Omics for Precision Medicine; United States; Untranslated RNA; Variant; Work; autism spectrum disorder; base; clinically actionable; cohort; collaborative environment; computerized data processing; data access; de novo mutation; deep sequencing; disorder prevention; drug development; epigenomics; ethnic diversity; falls; frontier; gene interaction; genetic approach; genetic variant; genome analysis; genome sequencing; insertion/deletion mutation; insight; large datasets; member; next generation; novel; protective allele; rare variant; reference genome; sequencing platform; software development; synergism; tool; transcriptome sequencing; transmission process; whole genome; Address; Admixture; Affect; Algorithms; Alleles; Bipolar Disorder; Cell Line; Child; Clinical Data; Code; Collaborations; Collection; Communities; Complex; Computing Methodologies; DNA Sequence; Data; Data Aggregation; Data Set; Databases; Disease; Fostering; Foundations; Funding; Genes; Genetic; Genetic Diseases; Genetic Risk; Genetic Variation; Genome; Genomics; Goals; Healthcare; Heterogeneity; Human; Human Genome; Individual; Industry; Inherited; Joints; Link; Medical Research; Methods; Mutation; New York; Nucleotides; Pathogenicity; Pathway interactions; Patients; Phase; Phenotype; Physicians; Population; Population Genetics; Population Heterogeneity; Population Study; Publishing; RNA; Research; Research Design; Resolution; Risk; Sampling; Schizophrenia; Statistical Models; Structure; Techniques; Technology; Trans-Omics for Precision Medicine; United States; Untranslated RNA; Variant; Work; autism spectrum disorder; base; clinically actionable; cohort; collaborative environment; computerized data processing; data access; de novo mutation; deep sequencing; disorder prevention; drug development; epigenomics; ethnic diversity; falls; frontier; gene interaction; genetic approach; genetic variant; genome analysis; genome sequencing; insertion/deletion mutation; insight; large datasets; member; next generation; novel; protective allele; rare variant; reference genome; sequencing platform; software development; synergism; tool; transcriptome sequencing; transmission process; whole genome

In this proposal, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease- associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort will be put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants. We will explore a wide spectrum of human variation using the WGS platform, including rare variants of modest to large effect, de novo variants of large effect, and common variants of small effect. We will combine available RNA and epigenomic datasets to predict modes of action of risk and identify protective alleles. These results, combined with the integration of environmental and clinical data, will enhance our understanding of genetic risk for common disease and lay the groundwork for utilization of personal genomics in disease prevention and treatment, including the delineation of pathways for drug development. Many of the population cohorts proposed for study are from New York, which harbors the most diverse population in the world. Analyzing diverse populations is a critical component of comprehensive common disease analysis, as effect sizes of individual alleles are believed to vary in different populations due to gene- gene interactions. Using the genetic admixture present in different populations from NY and throughout the United States, we will conduct the first systematic study of these interaction effects in many phenotypes. These aims will be accomplished through widespread collaborations, with genomicists, physicians, and patients, organized through a focused team at NYGC. They will be enriched by the collaboration and support from independent Foundations and from Industry.

在此提案中，我们解决了巨大的挑战常见复杂疾病对基因组的构成 分析和覆盖他们提供医疗保健提供的巨大机会。共同 据信这里提出的研究遗传疾病具有极端的基因座异质性，需要 分析大量样品，以全面识别它们的基因组变体。我们 提出，深入人群研究和SNP，Indels和结构的联合分析的结合 编码和非编码区域中的变体将提供对共同遗传的新水平 疾病。整个基因组测序（WGS）对于这种基因组学的下一代方法至关重要 复杂疾病。 WGS将需要伴随着生成和处理的技术能力 大型数据集，NYGC的特定重点和强度。 WGS还需要伴随新的 统计工具和算法将由致力于该建议的强大核心小组开发。 该提案的总体目标是利用WGS的能力，是确定疾病 - 在单个核苷酸水平上的相关变体。在许多情况下，致病突变属于非编码 基因组的区域，只能用WGS进行有效的探索。将付出重大努力 新的计算策略，以便在功能上注释非编码转录序列并构建新的计算策略 数据集以实现此类策略，为与疾病相关的调节的理解开放新的前沿 变体。我们将使用WGS平台探索各种各样的人类变异，包括稀有变体 适度至大效应，从头效应的变体和效果小的常见变体。我们将 将可用的RNA和表观基因组数据集结合在一起，以预测风险的作用模式并识别保护性 等位基因。这些结果，结合了环境和临床数据的整合，将增强我们的 了解常见疾病的遗传风险，并为利用个人基因组学奠定基础 在疾病预防和治疗中，包括划定药物开发途径。 许多人群进行研究的人群来自纽约，这是最多样化的 世界上的人口。分析多样化人群是综合共同的关键组成部分 疾病分析，由于基因的效果大小在不同种群中的效果大小会有所不同。 基因相互作用。使用来自纽约州不同人群中的遗传混合物 美国，我们将在许多表型中对这些相互作用效应进行首次系统研究。 这些目标将通过与基因组学家，医生以及 通过NYGC的一个专注团队组织的患者。他们将得到协作和支持的丰富 来自独立基础和工业。