Mining Thousands of Genomes to Classify Somatic and Pathogenic Structural Variants

项目摘要 结构变异体（SV）与多种癌症和孟德尔疾病有关，但 与口译相关的复杂性减缓了它们的采用。要确定哪一个仍然是一个挑战， 在癌症患者中观察到的SV是体细胞的，而在罕见疾病患者中观察到的SV是致病的。的 与单核苷酸的最新进展相比，SV解释差距尤其明显 变异（SNVs），这是由大规模群体等位基因频率估计值的发布所驱动的， gnomAD。鉴于导致癌症和罕见疾病的变异在一般人群中应该是罕见的， 来自12.5万个样本的SNV等位基因频率是一个非常强大的指标。等位基因频率本身可以 将潜在致病变异的数量减少两个数量级。不幸的是， SV的等效资源。 有来自大型队列的高质量SV呼叫集（SV VCF），但这些静态列表并不能使 很好的等位基因频率参考。SV检测涉及广泛的过滤以减少假阳性，并且 因为滤波从来都不是完美的，所以真实的SV不可避免地被去除，使得难以得出关于 患者体内但不在VCF中的SV。SV可能是罕见的，在人群中不存在，或者可能有 被过滤了。 我们提出了一种新的方法（STIX）的SV表征，动态搜索的原始 来自数千个基因组的比对，以获得支持推定SV的证据。通过这样的搜索，我们可以 结论是，在许多样本中具有高水平证据的SV可能是一种常见的变异， 是躯体的还是致病的用这种方法，我们表明，许多已发表的体细胞和从头SV是 实际上存在于参考人群中，这意味着这些变异不太可能导致疾病。在 事实上，在从肿瘤中去除生殖系SV方面，STIX与使用来自匹配正常样本的调用一样有效。 组织呼叫。我们还表明，通过依赖于原始信号，STIX从一个 与其相应的SV VCF相比。 除了大规模的SV搜索，我们提出了一个强大的统计框架估计SV 等位基因频率和区域噪声。我们计划使搜索技术和静力学免费提供给 近30，000个基因组通过公共网络接口和集成AnVIL。如果获得资助，该项目将 通过利用成千上万的数据，提供准确估计SV群体频率的方法 这将大大提高我们优先考虑患者SV的能力，并为实现 将SV更广泛地纳入医学遗传学。

Project Summary Structural variants (SVs) have been associated with a wide range of cancers and Mendelian disorders, but complexities associated with interpretation have slowed their adoption. It is still a challenge to determine which SVs observed in a cancer patient are somatic and which SVs in a rare disease patient are pathogenetic. The SV interpretation gap is especially stark when compared to the recent progress made with single nucleotide variants (SNVs), which was driven by the release of large-scale population allele frequency estimates from gnomAD. Given that variants that lead to cancer and rare disease should be rare in the general population, the SNV allele frequency from 125 thousand samples is an extremely powerful metric. Allele frequency alone can reduce the number of potentially pathogenic variants by two orders of magnitude. Unfortunately, there is no equivalent resource for SV. There are high-quality SV call sets (SV VCFs) from large cohorts, but these static lists do not make good allele frequency references. SV detection involves extensive filtering to reduce false positives, and because filtering is never perfect, real SVs are inevitably removed making it difficult to draw a conclusion about SVs that are in patients but not in VCF. The SV could be rare and absent from the population or could have been filtered. We propose a new method (STIX) for SV characterization that dynamically searches the raw alignments from thousands of genomes for evidence supporting a putative SV. From such a search we can conclude that an SV with high-level evidence in many samples is likely to be a common variant and unlikely to be somatic or pathogenic. With this method we show that many published somatic and de novo SVs are actually present in reference populations, which implies that these variants are unlikely to cause disease. In fact, STIX is as effective as using calls from a matched-normal sample at removing germline SVs from tumor tissue calls. We also show that by relying on the raw signal, STIX recovers substantially more SVs from a cohort than its corresponding SV VCF. In addition to large-scale SV searching, we propose a robust statistical framework for estimating SV allele frequency and regional noise. We plan to make the searching technology and statics freely available for nearly 30,000 genomes through a public web interface and integration with AnVIL. If funded, this project will provide the means to accurately estimate SV population frequency by leveraging the data in tens of thousands of genomes, which will greatly increase our ability to prioritize SVs in patients and pave the way toward broader inclusion of SVs in medical genetics.