Increasing the throughput of variant classification with multiplexed prime editing and single cell long-read technologies

About This Grant

PROJECT SUMMARY In genetic medicine, variants of uncertain significance (VUS) have posed a significant challenge. These variants, identified through genetic testing, are alterations in one's DNA sequence that cannot be definitively classified as benign or pathogenic. Approximately 80% of missense variants in the ClinVar database are classified as VUS. In clinical care, a VUS result frequently prevents clinicians from providing definitive diagnoses, limits treatment options, and causes uncertainty for patients and their families. Resolving VUS is thus essential to improve patient care in genetic medicine. Multiplexed Assays of Variant Effect (MAVEs) offer a high-throughput solution to VUS resolution by assessing variant effect for thousands of variants in vitro simultaneously. However, there are two limitations to realizing the potential of MAVEs to resolve VUS at scale. First, it is challenging to introduce a comprehensive set of variants into cells at scale, and second, MAVEs have historically relied on a small set of function assays, such as proliferation or survival-based assays, limiting the number of genes that can be investigated. This proposal aims to address both of these limitations, using CHD2 as a model gene, but developing techniques that could be applied to hundreds of clinically relevant genes. We will first advance high-throughput endogenous genome editing through the development of a suite of computational and experimental tools for prime editing. While prime editing offers a precise and versatile approach, it is plagued by low editing efficiencies. To address these shortcomings, we will develop a computational pipeline to design prime editing guide RNA (pegRNA) libraries with high predicted editing efficiency, an optimized cell line for editing, and a FACS-based approach to enrich for edited cells. We expect that such tools will boost prime editing efficiency for multiplexed cell libraries and thereby increase the efficiency of MAVEs. We will also address the limited set of functional assays for MAVEs. Here we propose scAVER-seq: a widely applicable and scalable single-cell Assay of Variant Effect using RNA-sequencing. This technique will combine transcript capture and long-read technology in single cells to identify each cell’s introduced variant and link it to its corresponding transcriptome in a single-tube workflow. By using the transcriptome as a functional readout, we will revolutionize variant classification in disorders associated with robust transcriptional effects, such as those caused by pathogenic variants in regulators of gene expression. This approach could help resolve the over 500,000 VUS that exist in gene expression regulators, which comprise 31% of all VUS in ClinVar. By developing generalizable methods to characterize VUS, we aim to contribute to the NHGRI's mission of understanding the impact of genomic variation on human health and simultaneously help deliver answers to families facing genetic testing results reporting VUS.