Detecting structural variants in a large population of samples through high-throughput sequencing data

项目总结 人类基因组图谱和全基因组关联研究为我们的 对遗传性疾病的遗传病因学的理解；然而，关键的差距仍然存在。一种基因 在基因组研究中难以检测到的变异是结构变异(SVS)、干扰 涉及50多个碱基对。SVS与许多遗传性疾病和癌症有关，但 使用传统的DNA测序方法，它们的检测仍然具有挑战性。第三方面的发展- 世代测序(连读和长读测序)和单细胞RNA测序(scRNA-seq) 提供极大改进SVS和拷贝数变异(CNV)检测的机会，一 常见的SVS类型。然而，现有的计算工具没有充分利用潜力和 这些技术提供的机会。在这个项目中，借助我们在这方面的独特专业知识， 对于不断发展的领域，我们建议开发新一代工具，这些工具将极大地改进检测 以及从大量样本中分阶段进行SVS。我们将开发计算工具来生成 来自每个个体的高质量二倍体组装，并结合来自大量对照和 患者对任何特定疾病具有风险的SVS进行特征描述。我们将进一步设计一种单倍型- 基于全基因组水平的连锁不平衡(LD)作图方法识别独特共享 单倍型模式，为复杂疾病研究提供了新的视角。检测组合中的SVS 小的变异将进一步使我们能够解释复杂疾病的病因学。我们还将发展 从scRNA-seq数据集中检测CNV的算法，这在癌症研究中有应用。成功 该项目的完成将是在揭示复杂性遗传原因方面向前迈出的重要一步 疾病和癌症。

PROJECT SUMMARY The mapping of the human genome and genome wide association studies have provided great insights in our understanding of the genetic etiology of hereditary diseases; however, critical gaps remain. A type of genetic variations that has been difficult to detect in genomic studies has been Structural Variants (SVs), disruptions involving more than 50 base pairs. SVs have been implicated in a lot of inherited diseases and cancers, yet their detection remains challenging with conventional DNA sequencing methods. Developments in third- generation sequencing (linked-read and long-read sequencing) and single-cell RNA sequencing (scRNA-seq) provide an opportunity to greatly improve the detection of SVs and Copy Number Variations (CNVs), one common type of SVs. However, existing computational tools do not fully take advantage of the potential and the opportunities that these technologies offer. In this project, drawing from our unique expertise in this rapidly evolving area, we propose the development of a new generation of tools that will improve greatly the detection and phasing of SVs from a large population of samples. We will develop computational tools to generate a high-quality diploid assembly from each individual and to combine data from large populations of controls and patients to characterize SVs that confer risk for any particular disease. We will further design a haplotype- based linkage disequilibrium (LD) mapping approach at the whole genome scale to identify unique sharing haplotype patterns and provide a new perspective for complex disease studies. Detecting SVs in combination with small variants will further allow us to explain the etiology of complex diseases. We will also develop algorithms to detect CNVs from scRNA-seq datasets, which have application in cancer studies. Successful completion of this project will constitute a major step forward in uncovering the genetic cause of complex diseases and cancers.