Identifying and Characterizing the Full Spectrum of Haplotype-resolved Structural Variation in Human Genomes

项目总结 结构变体(SVs)的识别，包括缺失、插入、复制和倒置 人类全基因组测序(WGS)数据对于基因组研究和精确医学是必不可少的。 然而，SV发现仍然是一个挑战，因为没有单一的测序技术或计算机算法 有效地捕获了SVS的全部频谱。这个项目的调查人员已经取得了实质性的进展 通过结合来自长读和短读测序平台的分析来实现全面的SV发现，例如 以及结合其他技术，如跳跃文库、链接阅读测序和染色体 链特异测序。这一方法在三个父亲-母亲-孩子三人组中的应用 识别出的SVS大约是仅使用标准短读WGS检测到的SVS的三倍。这 该项目建立在研究人员正在进行的开发优化、集成多技术计算的工作基础上 用于全面鉴定人类基因组中SVS的管道。在目标1中，计算方法将 被开发用于在使用所述多种基因组技术生成的WGS数据集中进行SV检测 以及计算方法的组合，从而产生最全面和最准确的SV调用集 将被建立为计算管道，将被打包以供广泛共享。这项工作将重点放在 来自1000基因组计划所有26个种群的家庭三人组和无关的个人。三重奏的使用也将 允许确定不同SV类别的SV变异率。AIM 2将开发新的SV呼叫 解决在短只读WGS数据集中检测SV的挑战性任务的方法。这项工作将集中于 关于由NHGRI资助的旨在识别相关遗传变异的大规模倡议的基因组测序 疾病，如常见病基因组学中心(CCDG)和孟德尔基因组学中心 (CMG)。对这些短读WGS数据集的分析将为全基因组SV数据集和 作为一种资源，可用于在更多的CCDG、CMG、 和其他短读的WGS数据集。该项目的实施将产生深度覆盖的WGS和多个 技术基因组数据集，以及新的SV呼叫集，适用于全球26个群体的个人。 这些数据将通过一个开放的ftp站点广泛提供。CCDG患者样本的SV数据集 和CMG将可通过DBGaP访问，并实现更全面的遗传变异关联 人类的疾病。所有计算管道将在一个可移植的框架中提供，以促进 被其他用户广泛采用。总体而言，该项目将建立跨越许多人的SV参考集 世界各地所有SVS(以及小的插入和缺失)都已被序列解决的群体 并沿着整个染色体的长度正确地相继出现。这将成为一个有价值的社区 在临床和基因组中跨WGS数据集对SV发现和基因分型进行基准测试的资源 研究领域。

PROJECT SUMMARY The identification of structural variants (SVs) including deletions, insertions, duplications, and inversions from human whole-genome sequencing (WGS) data is essential for genomic research and precision medicine. However, SV discovery remains a challenge because no single sequencing technology or computer algorithm effectively captures the full spectrum of SVs. The Investigators of this project have made substantial advances toward comprehensive SV discovery by combining analyses from long- and short-read sequencing platforms, as well as incorporating other technologies such as jumping libraries, linked-read sequencing, and chromosomal strand-specific sequencing. Application of this approach to the genomes of three father-mother-child trios identified approximately threefold more SVs than could be detected using standard short-read WGS alone. This project builds on the Investigators’ ongoing work to develop optimized, integrated multi-technology computational pipelines for the comprehensive identification of SVs in human genomes. In Aim 1, computational methods will be developed for SV detection in WGS datasets generated using the multiple genomic technologies described above, and the combination of computational methods yielding the most comprehensive and accurate SV callset will be established as computational pipelines that will be packaged for broad sharing. This work will focus on family trios and unrelated individuals from all 26 populations of the 1000 Genomes Project. Use of trios will also enable determination of SV mutation rates for the different SV classes. Aim 2 will develop novel SV calling methods that address the challenging task of SV detection in short-read-only WGS datasets. This work will focus on genomes sequenced by large-scale NHGRI-funded initiatives that aim to identify genetic variants associated with disease, such as the Centers for Common Disease Genomics (CCDG) and Centers for Mendelian Genomics (CMG). Analyses of these short-read WGS datasets will yield a gold standard for genome-wide SV datasets and serve as a resource that can be used to genotype common variants across the larger number of CCDG, CMG, and other short-read WGS datasets. Execution of this project will generate deep coverage WGS and multi- technology genomic datasets, as well as new SV callsets, for individuals across 26 populations around the world. This data will be made widely available through an open FTP site. SV datasets for patient samples from CCDG and CMG will be accessible through dbGaP and enable a more comprehensive association of genetic variants with human diseases. All computational pipelines will be made available in a portable framework to promote wide adoption by other users. Overall, this project will establish SV reference sets spanning many human populations around the world in which all SVs (and small insertions and deletions) have been sequence resolved and correctly phased along the entire length of the chromosomes. This will serve as a valuable community resource for benchmarking SV discovery and genotyping across WGS datasets in the clinical and genomic research domains.