Scalable detection and interpretation of structural variation in human genomes

项目总结 结构变异(SV)是一类多样的基因组变异，包括拷贝数变异(CNV) 如删除和重复，以及平衡重排，如颠倒和互换 易位。一个典型的人类基因组含有大于300bp的4000个SVS，并且它们的大小增加 删除或复制基因、破坏染色质结构和改变表达的可能性。尽管他们 SVS的患病率和潜在的表型后果仍然是出了名的难以检测和分型 精确度很高。这种困难很大程度上是由DNA序列比对“信号”这一事实驱动的 SVS比单核苷酸和插入缺失变异体复杂得多。与SNP对齐不同 仅在等位基因状态上变化，支持SVS的比对在状态上变化(支持或不支持替代结构) 对齐位置和类型。因此，SV发现的准确性远远低于SNPs和SNPs Indels。此外，SV管道的伸缩性很差，难以运行。这些挑战是 单基因组分析和对家族的研究必须投入大量精力来消除虚假的海洋 积极的一面。对于大规模的测序工作，这些问题变得更加尖锐，例如 TOPmed、常见病遗传学中心和我们所有人计划。软件效率是关键 这类项目的可伸缩性。然而，同样重要的是全面、准确的发现。 建立在十多年软件开发经验的基础上，分析各种不同的SV 在疾病的背景下，我们已经投入了大量的努力来了解准确性不足的原因 用于SV发现。这些努力，加上我们在这一领域的研发经验，给了我们 在提高SV发现的准确性和可扩展性方面具有独特的见解。我们的目标是缩小精确度 SNP/Indel变异与结构变异发现之间的差距。这些发展将使 在不同的背景下研究人类基因组，因此将产生广泛的影响。我们的目标是： 1.建立深度学习模型，校正层序深度的系统变化。这台新机器 学习模型将纠正DNA序列深度中的系统偏差，并显著提高 发现缺失和重复。 2.提高SV检测和基因分型的速度、可扩展性和准确性。使用新的算法， 我们将使SV检测的准确性更接近SNP和Indel发现，并允许 精确的SV发现将大规模部署。 3.从群体规模的基因组分析中创建了SV的基因组约束图谱。我们将部署 我们的新方法可以检测数万个人类基因组中的结构变异并进行基因分型。 所得到的SV图将使SV基因组约束模型的创建成为可能，并使新的 预测有害的SVS的软件，特别是在非编码基因组中。

PROJECT SUMMARY Structural variation (SV), is a diverse class of genome variation that includes copy number variants (CNVs) such as deletions and duplications, as well as balanced rearrangements, such as inversions and reciprocal translocations. A typical human genome harbors >4,000 SVs larger than 300bp and their large size increases the potential to delete or duplicate genes, disrupt chromatin structure, and alter expression. Despite their prevalence and potential for phenotypic consequence, SVs remain notoriously difficult to detect and genotype with high accuracy. Much of this difficulty is driven by the fact DNA sequence alignment “signals” indicating SVs are far more complex than for single-nucleotide and insertion deletion variants. Unlike SNP alignments that vary only in allele state, alignments supporting SVs vary in state (supports an alternate structure or not) alignment location, and type. Consequently, the accuracy of SV discovery is much lower than that of SNPs and INDELs. Furthermore, SV pipelines scale poorly and are difficult to run. These challenges are a barrier for single genome analysis and studies of families must invest substantial effort into eliminating a sea of false positives. These problems become exponentially more acute for large-scale sequencing efforts such as TOPmed, the Centers for Common Disease Genetics, and the All of Us program. Software efficiency is key to scalability for such projects. However, of equal importance is comprehensive, accurate discovery. Building upon more than a decade of software development experience and analyzing SV in diverse disease contexts, we have invested significant effort into understanding the causes of the insufficient accuracy for SV discovery. These efforts, together with our research and development experience in this area, give us unique insight into improving the accuracy and scalability of SV discovery. Our goal is to narrow the accuracy gap between SNP/INDEL variation and structural variation discovery. These developments will empower studies of human genomes in diverse contexts and will therefore have broad impact. Our goals are to: 1. Develop a deep learning model to correct systematic variation in sequence depth. This new machine learning model will correct systematic biases in DNA sequence depth and dramatically improve the discovery of deletions and duplications. 2. Improve the speed, scalability, and accuracy of SV detection and genotyping. Using new algorithms, we will bring the accuracy of SV detection much closer to that of SNP and INDEL discovery and allow accurate SV discovery to be deployed at scale. 3. Create a map of genomic constraint for SV from population-scale genome analysis. We will deploy our new methods to detect and genotype structural variation among tens of thousands of human genomes. The resulting SV map will empower the creation of a model of genomic constraint for SV and enable new software to predict deleterious SVs, especially in the noncoding genome.