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

通过高通量测序数据检测大量样本中的结构变异

• 批准号: 10707270
• 负责人: Xin Zhou
• 金额: $ 38.79万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707270
• 关键词: Algorithms; Area; Base Pairing; Complex; Copy Number Polymorphism; DNA sequencing; Data; Data Set; Detection; Development; Diploidy; Disease; Etiology; Generations; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genomics; Haplotypes; Hereditary Disease; High-Throughput Nucleotide Sequencing; Human Genome; Individual; Link; Linkage Disequilibrium Mapping; Malignant Neoplasms; Maps; Methods; Patients; Pattern; Phase; Phenotype; Population; Population Control; Research; Risk; Sampling; Technology; Third Generation Sequencing; Variant; computerized tools; design; genome wide association study; genome-wide; improved; insight; single-cell RNA sequencing; tool; transcriptome sequencing; whole genome

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.

项目概要 人类基因组图谱和全基因组关联研究为我们提供了深刻的见解。 了解遗传性疾病的遗传病因学；然而，关键差距依然存在。遗传的一种 基因组研究中难以检测到的变异包括结构变异（SV）、破坏 涉及50多个碱基对。 SV 与许多遗传性疾病和癌症有关，但 对于传统的 DNA 测序方法来说，它们的检测仍然具有挑战性。第三方面的发展 一代测序（链接读长测序）和单细胞 RNA 测序 (scRNA-seq) 提供了极大改进 SV 和拷贝数变异 (CNV) 检测的机会，其中之一 常见的 SV 类型。然而，现有的计算工具并没有充分利用潜力和 这些技术提供的机会。在这个项目中，利用我们在这一快速发展领域的独特专业知识 不断发展的领域，我们建议开发新一代工具，这将大大改善检测 以及从大量样本中对 SV 进行定相。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群体的数据和 患者来表征赋予任何特定疾病风险的 SV。我们将进一步设计一个单倍型—— 基于全基因组范围连锁不平衡（LD）作图方法来识别独特的共享 单倍型模式并为复杂疾病研究提供新视角。组合检测 SV 小变异将进一步使我们能够解释复杂疾病的病因学。我们还将开发 从 scRNA-seq 数据集中检测 CNV 的算法，可应用于癌症研究。成功的 该项目的完成将在揭示复杂的遗传原因方面迈出重要一步 疾病和癌症。