Analyzing the functional effects of DNA variants with gene editing

Abstract

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Keywords: CP: biotechnology; CP: genetics.

Figure 1

CRISPR-based gene editing methods to analyze endogenous variant function (A) Schematic of a multiplexed assay of variant effect (MAVE) to analyze endogenous variants. These experiments have four main considerations: first, the method of installing the variant using gene editing technologies, which has a bearing on the cell model selected; second, the application, which includes the means of selecting cells based on the phenotype of interest and should be disease relevant; third, the readout for the variant function, which can be proliferation based or include higher-content readouts, such as scRNA-seq; fourth, the outcomes of the variant-function map, which include the potential value of variant interpretation for patients with genetic diseases, including disease association, predicting drug resistance, and patient stratification for appropriate therapies. (B) Outline of the gene editing methods that can be used to install endogenous variants into the genome. The relative advantages and disadvantages of SGE, base editing, and prime editing are highlighted in blue and red, respectively.

Figure 2

Evaluation of cancer gene essentiality in HAP1 and alternative cell models 85 CGC genes are essential in HAP1 cells. There is a horizontal dashed line at scaled Bayes factor = 0.485, above which genes have a 90% posterior probability of being essential and can be screened with current SGE essential/growth phenotyping. 355 of 658 genes that are not essential in HAP1 cells are essential in at least one DepMap cell model, suggesting that essential/growth phenotyping would be possible. 303 of 658 genes would require alternative phenotyping methods downstream of SGE. Data displayed were taken from Hart et al.

Figure 3

Genotyping strategies for endogenous MAVEs Genotyping of edits in endogenous MAVEs can be performed using sensor libraries or sequencing of the endogenous DNA locus. For sensor libraries, the target sequence is inserted into the guide RNA expression construct, including short regions of flanking DNA context. The construct is incorporated into the genome, and the editing outcomes for all targets are readout as a single, pooled PCR. For endogenous sequencing, multiple primer pairs are required for each locus. Multiplexed PCRs are possible but may require optimization. The advantages of both strategies are highlighted on the right.