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

可行变异分析中心；

• 批准号: 10687156
• 负责人: Douglas M Fowler
• 金额: $ 181.37万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-23 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687156
• 关键词: Benchmarking; Benign; Biological Assay; Catalogs; Cell Survival; Cells; Classification; Clinic; Clinical; Clinical Data; Code; Communities; Consensus; Data; Data Set; Diagnosis; Disease; Elements; Ensure; Genes; Genetic Variation; Genome; Goals; Human Biology; Human Genetics; Individual; Life; Massive Parallel Sequencing; Measurement; Measures; Metadata; Methods; Modeling; Nucleotides; Pathogenicity; Patient Care; Phenotype; Production; Proteins; Protocols documentation; RNA Splicing; Reagent; Reproducibility; Resolution; Resources; Single Nucleotide Polymorphism; Validation; Variant; Work; actionable mutation; data analysis pipeline; data sharing; design; empowerment; genetic information; genetic testing; genetic variant; genome editing; improved; multiplex assay; predictive modeling; protein function; tool; variant of unknown significance; web interface

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.

项目总结