New York Center for Collaborative Research in Common Disease Genomics

在这项提案中，我们解决了常见的复杂疾病对基因组的巨大挑战。 分析和巨大的机会，超越他们提供了推进医疗保健。共同 本文提出研究的遗传性疾病被认为具有极端的基因座异质性，需要 分析大量样本，以全面识别潜在的基因组变异。我们 建议将深入的人群研究与SNP、indels和结构分析的联合分析相结合， 编码区和非编码区的变异将提供对共同遗传学的下一个层次的理解。 紊乱全基因组测序（WGS）将是下一代基因组学方法的关键 复杂的疾病。WGS将需要伴随着技术能力，以产生和处理非常 大型数据集，NYGC的一个特别关注和优势。WGS还需要配备新的 统计工具和算法，将由致力于这一提案的强大核心小组开发。 这项提案的首要目标是利用WGS的力量，识别疾病- 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突变， 基因组的区域，这只能用WGS进行富有成效的探索。将大力建设 新的计算策略，功能注释非编码转录序列，并建立新的 数据集，以实现这些战略，开辟新的前沿理解疾病相关的监管 变体。我们将使用WGS平台探索广泛的人类变异，包括罕见变异 中到大的影响，从头变异的大的影响，和常见的变异的小的影响。我们将 联合收割机结合可用的RNA和表观基因组数据集，以预测风险的作用模式， 等位基因这些结果，结合环境和临床数据的整合，将提高我们的 了解常见疾病的遗传风险，并为个人基因组学的利用奠定基础 在疾病预防和治疗方面，包括划定药物开发的途径。 许多被提议进行研究的人群来自纽约，那里拥有最多样化的 世界人口。分析不同的人群是全面的共同 疾病分析，因为个体等位基因的效应大小被认为在不同人群中因基因而异， 基因相互作用使用来自纽约和整个纽约的不同人群中存在的遗传混合物， 在美国，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、医生和 患者，通过NYGC的专注团队组织。他们将通过合作和支持而丰富 独立的基金会。

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.