A scalable, integrative, multi-omic analysis platform

项目摘要 尽管经过几十年的努力，目前只有一小部分遗传性疾病的遗传性可以解释。 对这一差距的两种解释是，潜在的遗传变异是罕见的，目前未知，我们 对我们确实拥有的变体的影响了解不多，特别是那些存在于 编码区。解决这些问题既需要更大的群体，也需要更多的全基因组功能。 在一些实施方案中，所述方法可以通过测定（例如RNA-seq、CHiP-seq、ATAC-seq等）来进行。为了表彰像共同发展中心这样的项目， 遗传性疾病（CCGD），精准医学的Trans-Omics（TOPMed）计划和ENCODE 在许多不同的个体和组织中收集大量的遗传数据。在 总的来说，这些数据将大大提高我们理解变异如何影响基因组的能力。 架构挑战在于，这些数据庞大、复杂、多维，而当前的方法 无法在这种规模下运作。 该提案通过将数据分为两种不同类型的数据，基因型， 和基因组注释，并开发优化的技术来存储和搜索每种类型 独立地。这两种高度可扩展的方法本身就非常有价值， 集成到一个单一的系统，使查询跨变异，基因表达和调控。为 例如，考虑这个问题，“是否有任何组织，在这种情况下，新生变异具有差异 与对照组相比，有什么不同？”这个问题被分解为一个基因型查询， 变体集：case中的de novos和control中的de novos。然后，这些集合用作到 在所有组织中的所有推定增强子上进行基因组注释搜索。 这个建议建立在我最近发表的基因型查询工具（GQT），一种方法， 通过直接在压缩的基因型索引上操作， 过去的研究和基因组算术算法的培训，我已经出版了多本小说 算法到目前为止，我一直专注于方法，因此，虽然该项目的K99阶段将包括 在发展过程中，它将明确侧重于对疾病群组的分析。这一额外培训将是 一个独立的研究计划的基础，将释放大规模基因组学的潜力， 功能数据集，提供表型，基因型和功能之间的快速和流体整合 数据

PROJECT SUMMARY Despite decades of effort, only a small portion of the heritability of genetic disorders can be currently explained. Two explanations for this gap are that the underlying genetic variants are rare and currently unknown, and, we have a poor understanding of the impact of the variants that we do have, in particular those residing outside of the coding regions. Addressing these issues requires both larger cohorts and more whole-genome functional assays (e.g RNA-seq, CHiP-seq, ATAC-seq, etc.). In recognition of projects like the Center for Common Genetic Disorders (CCGD), the Trans-Omics for Precision Medicine (TOPMed) Program and ENCODE are performing the gathering of massive amounts of genetic data across many different individuals and tissues. In aggregate, this data will dramatically improve our power to understanding how variation affects genomic architecture. The challenge is that these data are vast, complex, and multidimensional, and current methods cannot operate at this scale. This proposal addresses this challenge by splitting the data into two distinct types of data, genotypes and genome annotations, and developing technologies that are optimized to store and search each type independently. These two highly-scalable methods, which will be extremely valuable on their own, will then be integrated into a single system that enables queries across variation, gene expression, and regulation. For example, consider the question, “Are there any tissues where de novo variants in case have a differential enrichment versus those in controls?” This question is decomposed into a genotype query that produces two sets of variants: de novos in case and de novos in controls. The sets then serve as input queries into a genome annotation search across all putative enhancers in all tissues. This proposal builds upon both my recently published Genotype Query Tools (GQT), a method that achieved vast speedups over other methods by operating directly on a compressed genotype index, and my past research and training in genome arithmetic algorithms, for which I have published multiple novel algorithms. Up to now I have focused on methods, so while the K99 phase of this project will include development, it will have a distinct focus on the analysis of disease cohorts. This additional training will be the foundation of an independent research program that will unlock the potential of large-scale genomics and functional data sets, providing for the fast and fluid integration between phenotype, genotype, and functional data.