Sequence-resolved structural variation of human genomes

人类基因组的序列解析结构变异

• 批准号: 10202688
• 负责人: Evan Eichler
• 金额: $ 63万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-06 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10202688
• 关键词: Algorithms; Alleles; Base Pairing; Complex; Copy Number Polymorphism; DNA; Data; Data Set; Disease; Future; Generations; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genotype; Goals; Graph; Haplotypes; Heritability; Human; Human Genetics; Human Genome; Link; Methods; Mutation; Nucleotides; Phase; Polyploidy; Population; Population Genetics; Property; Research; Resolution; Sampling; Shotguns; Structure; Technology; Time; Variant; Work; base; genetic variant; genome sequencing; genome-wide; human disease; improved; insertion/deletion mutation; insight; novel; paralogous gene; population based; reference genome; single molecule; single molecule real time sequencing; whole genome

Understanding the genetic basis of human disease requires a comprehensive assessment of the full spectrum of human genetic variation. Genome structural variation, including larger deletions, insertions, and inversions (>50 bp), has been more difficult to characterize due to the association with repetitive DNA. The majority of structural variation, including common structural variants or SVs, has not yet been discovered using short-read whole-genome datasets and standard SV callers. Advances in sequencing technology over the last three years, however, have made the systematic discovery of this variation possible for the first time. This proposal focuses on the discovery, sequence resolution, and genotyping of the most complex and under-ascertained forms of human genetic variation, including multi-copy number variants (mCNVs), inversions, and intermediate- size insertions and deletions. We target a diversity panel of 34 human genomes and partition long-read single- molecule, real-time sequencing data using 10X linked reads and Strand-seq data in order to fully phase and sequence-resolve SVs on each human haplotype. Using these long-read sequence data, we further develop a computational graph-based approach to distinguish and assemble distinct copies underlying large mCNVs mapping to high-identity segmental duplications. Finally, we take advantage of the sequence structure, including breakpoints and sequence differences among the copies, to more accurately genotype these variants in a diversity panel of >2,800 human genomes where short-read whole-genome sequence data are already available. The work will develop new methods to characterize more complex forms of human genetic variation and provide fundamental insight into their diversity, mechanism of origin, and mutational properties. This research has the additional benefit that it will improve genome assembly, characterize new human genome sequence, identify a large class of missing genetic variation, and provide us with the ability to systematically explore this form of human genetic variation as part of disease-association studies.

了解人类疾病的遗传基础需要对全谱进行全面评估 人类基因变异的证据基因组结构变异，包括较大的缺失、插入和倒位 （>50 bp），由于与重复DNA的关联，更难以表征。大多数 结构变异，包括常见的结构变异体或SV，尚未使用短读 全基因组数据集和标准SV调用程序。过去三年测序技术的进展 然而，多年来，第一次有可能系统地发现这种变异。这项建议 重点是发现，序列解析，和基因分型的最复杂的和未确定的 人类遗传变异的形式，包括多拷贝数变异（mCNVs）、倒位和中间变异。 调整插入和删除大小。我们针对34个人类基因组的多样性小组，并划分长读单- 分子，使用10 X连接的读数和Strand-seq数据的实时测序数据，以便完全定相， 每个人类单倍型上的序列解析SV。使用这些长读段序列数据，我们进一步开发了一个 一种基于计算图的方法来区分和组装大mCNVs下的不同拷贝 映射到高同一性片段重复。最后，我们利用序列结构， 包括断裂点和拷贝之间的序列差异，以更准确地对这些变异体进行基因分型 在一个超过2,800个人类基因组的多样性小组中， available.这项工作将开发新的方法来表征更复杂形式的人类遗传变异 并提供基本的洞察其多样性，起源机制和突变特性。这 这项研究还有一个额外的好处，那就是它将改善基因组组装， 序列，识别一大类缺失的遗传变异，并为我们提供系统地 探索这种形式的人类遗传变异作为疾病关联研究的一部分。