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

项目摘要/摘要 即将到来的NHGRI常见病基因组学中心和孟德尔基因组学中心 (CMG)计划生成超过20万个个体的全基因组测序(WGS)数据。WGS将提供 跨编码和非编码变体的全面和完整的遗传数据，呈现出 在人类疾病的基因分析中发现前所未有的机会。然而，缺乏 充分认识到这些数据的潜力的强大分析工具已成为有效 将这些海量WGS数据中包含的丰富信息转化为对人类有意义的见解 疾病。迫切需要为WGS开发强大而健壮的分析方法，以 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。以我们在测序方面的广泛记录为基础 研究、统计遗传学、功能分析和计算生物学，我们将推动下一轮 通过(1)建立大量的WGS对照样本并开发方法来发现基因 将这些对照纳入对复杂和孟德尔疾病的研究；(2)创造更强大的 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)建立多种表型分析方法，以增强 联系并理解不同的表型在遗传上是如何联系的。这些方法将增强我们的 能够从孟德尔疾病的广泛遗传结构中识别新的关联 受复杂多基因性状的强大作用等位基因驱动。新奇的协会承诺为 对驱动疾病的生物机制有了新的见解，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，并 将开发的数据资源、工具和方法通过友好的开放与社会共享 源软件和教育模块。

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.