Mining Thousands of Genomes to Classify Somatic and Pathogenic Structural Variants

项目摘要 结构变异(SVS)与广泛的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。要确定是哪一个仍然是一个挑战 在癌症患者中观察到的SVS是体细胞的，而在罕见疾病患者中观察到的SVS是致病的。这个 与单核苷酸的最新进展相比，SV的解释差距尤其明显 变异(SNV)，这是由发布大规模人口等位基因频率估计从 GnomAD。考虑到导致癌症和罕见疾病的变异在普通人群中应该是罕见的， 来自125,000个样本的SNV等位基因频率是一个非常强大的指标。仅等位基因频率就可以 将潜在致病变异的数量减少两个数量级。不幸的是，没有 服务的等效资源。 有来自大型队列的高质量SV呼叫集(SV VCF)，但这些静态列表不能构成 良好的等位基因频率参考。SV检测涉及广泛的过滤以减少误报，以及 由于过滤从来不是完美的，真实的SV不可避免地会被移除，因此很难得出关于 存在于患者体内但不在VCF中的SVS。SV可能是罕见的，在人群中不存在，或者可能 被过滤掉了。 我们提出了一种新的方法(STIX)来描述SV，该方法动态地搜索原始数据 来自数千个基因组的比对，以寻找支持推定的SV的证据。通过这样的搜索，我们可以 结论是，在许多样本中有高水平证据的SV很可能是一种常见的变体，而且不太可能 是体细胞的或致病的。通过这种方法，我们发现许多已发表的体细胞和从头开始的SVS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……里面 事实上，在从肿瘤中移除生殖系SVS方面，STIX与使用来自匹配正常样本的呼叫一样有效 纸巾叫声。我们还表明，通过依赖原始信号，STIX从 队列比其相应的SV VCF更高。 除了大规模的SV搜索之外，我们还提出了一个稳健的统计框架来估计SV 等位基因频率和区域噪声。我们计划将搜索技术和静态数据免费提供给 近30,000个基因组通过公共网络接口并与Anvil集成。如果获得资金，这个项目将 通过利用数以万计的数据，提供准确估计SV种群频率的方法 基因组，这将极大地提高我们优先处理患者SVS的能力，并为 更广泛地将SVS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.