Implications of human genetic variation in CRISPR-based therapeutic genome editing

Abstract

CRISPR-Cas genome-editing methods hold immense potential as therapeutic tools to fix disease-causing mutations at the level of DNA. In contrast to typical drug development strategies aimed at targets that are highly conserved among individual patients, treatment at the genomic level must contend with substantial inter-individual natural genetic variation. Here we analyze the recently released ExAC and 1000 Genomes data sets to determine how human genetic variation impacts target choice for Cas endonucleases in the context of therapeutic genome editing. We find that this genetic variation confounds the target sites of certain Cas endonucleases more than others, and we provide a compendium of guide RNAs predicted to have high efficacy in diverse patient populations. For further analysis, we focus on 12 therapeutically relevant genes and consider how genetic variation affects off-target candidates for these loci. Our analysis suggests that, in large populations of individuals, most candidate off-target sites will be rare, underscoring the need for prescreening of patients through whole-genome sequencing to ensure safety. This information can be integrated with empirical methods for guide RNA selection into a framework for designing CRISPR-based therapeutics that maximizes efficacy and safety across patient populations.

Figure 1. Human genetic variation significantly impacts the efficacy of RNA-guided endonucleases

a, Schematic illustrating the genomic target (consisting of spacer and adjacent PAM element), RNA guide, and target variation. b, Fraction of residues for individual nucleotides containing variation in the ExAC dataset. c, Fraction of 2-nt PAM motifs altered by variants in the ExAC dataset. d, Percent of target variants at different allele frequencies for each CRISPR endonuclease. e, Cumulative percent of targets containing variants for each CRISPR endonuclease. f, Fraction of targets containing homozygous variants at different allele frequencies. The Mean and SEM for 5 CRISPR endonucleases is shown.

Figure 2. Selection of platinum targets maximizes population efficacy

a, Schematic showing target variation within exon 2 of PCSK9, with regions containing high coverage in the ExAC dataset indicated by black lines below exons. Variants for a short region of PCSK9 exon 2 are highlighted along with 5 targets for SpCas9-WT. Top strand targets (PAM on the right) are shown above the region of PCSK9 exon 2, and bottom strand targets (PAM on the left) are shown below. The maximum frequency variant in the ExAC dataset intersecting each target is indicated colorimetrically and used as the “target variation frequency”. Variants that do not affect the target recognition by the endonuclease (such as a high frequency variant intersecting the ‘N’ in the NGG PAM of target 2) do not affect targeting efficiency and are excluded. Targets 2 and 4 show the lowest variation of the 5 targets shown. b, Frequency of target variation plotted by cut site position for targets spanning the start of PCSK9-001 exon 2, with the 5 targets shown in (a) indicated by arrows. The horizontal line at 0.01% separates platinum targets (grey) from targets with high variation (red). The classification for each target is depicted below for each enzyme (grey or red boxes). c, Classification of targets for each enzyme spanning exons 2 – 5 of PCSK9-001.

Figure 3. Human genetic variation significantly impacts CRISPR endonuclease therapeutic safety

a, Schematic illustrating off-target candidates arising due to multiple different haplotypes. MM, mismatch. b, Number of off-target candidates present in the 1000 Genomes dataset for each CRISPR endonuclease for 12 therapeutically relevant genes at different allele frequencies. c, Distribution of the number of off-target candidates per platinum target for each CRISPR endonuclease. d, Distribution of the number of off-target candidates per enzyme for the 4 CRISPR endonucleases studied here.

Figure 4. Gene- and population-specific variation informs therapeutic design

a, Distribution of the number of off-target candidates per platinum target for the 12 therapeutically relevant genes studied here. b, Total off-target candidates for platinum targets spanning exons 2 – 5 of PCSK9-001 are shown for each CRISPR endonuclease. c, Principal component analysis (PCA) separating 1000 Genomes individuals into superpopulations based on patient-specific off-target profiles for platinum targets spanning 12 therapeutically relevant genes. PC2 and PC3 are shown. AFR, African; AMR, Ad mixed American; EAS, East Asian; EUR, European; SAS, South Asian. d, Proposed framework for identifying therapeutic guides that maximize efficacy and safety.