The Center for Actionable Variant Analysis; measuring variant function at scale

项目总结 与生命相适应的大约90亿个可能的单核苷酸变体几乎全部存在于78亿个 今天还活着的人。了解这些变异的影响，特别是在疾病相关蛋白中 编码基因，对于理解人类生物学和利用基因组序列信息指导 疾病的诊断和治疗。不幸的是，基因检测发现的大多数新变异都是 不确定意义的变体，这意味着没有足够的信息来明确地将该变体解释为 致病的或良性的。不确定意义的变量不能用于指导患者护理和反映 我们对变异效应的不完全理解。为了克服这一挑战，我们开发了饱和基因组 通过大规模并行测序编辑(SGE)和变异丰度(VAMP-SEQ)，多路分析 变异效应，可以制造和测量大量变异的功能效应。在SGE中，单人 核苷酸变异被直接编辑到基因组中，揭示了这些变异对细胞存活的影响 由于对剪接或蛋白质功能的影响，从而能够准确地识别致病和 良性变异。VAMP-SEQ测量错义变异对细胞内蛋白质丰度的影响，以及 可以识别高达80%的致病变异。SGE和VAMP-SEQ加在一起可以应用于至少40%的 基因以单核苷酸分辨率产生高质量、临床有用的功能数据。已经，变体 这些方法产生的功能数据正被临床医生用来解释基因变异。 我们建议的可操作变异分析中心(CAVA)将利用SGE和VAMP-SEQ来 贡献~32个临床病例中~200,000个变异的单核苷酸变异功能数据 对IGVF变异/元件/表型目录有影响的蛋白质编码基因。要做到这一点 变革性目标我们提出了四个目标。在目标1中，我们将选择目标基因并使用 使临床需求、临床影响和实用性最大化的框架。每个靶标/化验配对都将是 在投入生产前经过严格验证。我们将在第一年向财团提供资金，并 通过制定标准、共享试剂和启动协作项目。在AIM 2中，SGE和 将对~32个基因进行VAMP-SEQ，以使用定义明确的指标跟踪高质量标准。这些 包括分析动态范围和重复性、个体测量误差以及与现有 功能数据和黄金标准临床数据。与我们的LIMS集成的数据分析管道将确保 可重复性，并可仔细跟踪进度。在目标3中，我们将共享多路变体泛函 数据。严格定义的数据共享标准和元数据将确保可发现性、可计算性和 耐用性。我们将与财团合作达成共识，并将相应地修改我们的计划。在……里面 目标4，我们将使实验室能够快速建立SGE或VAMP-SEQ。我们将创建一个预测-评估-修订循环 这将利用我们将生成的数据并协同工作来生成数据，以改进建模工作。

PROJECT SUMMARY Nearly all of the ~9 billion possible single nucleotide variants compatible with life exist among the 7.8 billion individuals alive today. Understanding the effects of these variants, especially in disease-associated protein coding genes, is central to understanding human biology and to using genome sequence information to guide the diagnosis and treatment of disease. Unfortunately, most new variants revealed by genetic testing are variants of uncertain significance, meaning insufficient information exists to definitively interpret the variant as either pathogenic or benign. Variants of uncertain significance cannot be used to guide patient care and reflect our incomplete understanding of variant effects. To overcome this challenge, we developed saturation genome editing (SGE) and variant abundance by massively parallel sequencing (VAMP-seq), multiplexed assays of variant effect that can make and measure the functional effect of massive numbers of variants. In SGE, single nucleotide variants are edited directly into the genome, revealing the effect of these variants on cell survival due to effects on splicing or protein function, thereby enabling accurate identification of both pathogenic and benign variants. VAMP-seq measures the effects of missense variants on protein abundance inside cells, and can identify up to 80% of pathogenic variants. Together, SGE and VAMP-seq can be applied to at least 40% of genes to produce high quality, clinically useful functional data at single nucleotide resolution. Already, variant functional data produced by each of these methods are being used by clinicians to interpret genetic variants. Our proposed Center for Actionable Variant Analysis (CAVA) will harness SGE and VAMP-seq to contribute single nucleotide variant functional data for ~200,000 variants in ~32 of the most clinically impactful protein coding genes to the IGVF Variant/Element/Phenotype Catalog. To accomplish this transformative goal we propose four Aims. In Aim 1, we will choose target genes and assays using a framework that maximizes clinical need, clinical impact and practicality. Each target/assay pair will be rigorously validated prior to entering production. We will contribute to the Consortium during the first year and beyond by developing standards, sharing reagents and initiating collaborative projects. In Aim 2, SGE and VAMP-seq will be performed on ~32 genes to high quality standards tracked using well-defined metrics. These include assay dynamic range and reproducibility, individual measurement error, and concordance with existing functional data and gold standard clinical data. A data analysis pipeline, integrated with our LIMS, will ensure reproducibility and enable careful progress tracking. In Aim 3, we will share the multiplexed variant functional data. Rigorously defined data sharing standards and metadata will ensure discoverability, computability and durability. We will work with the Consortium to achieve consensus and we will revise our plans accordingly. In Aim 4, we will enable labs to quickly stand up SGE or VAMP-seq. We will create a predict-evaluate-revise cycle that leverages the data we will generate and work collaboratively to generate data to improve modeling efforts.