Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing

Abstract

A major challenge in genetics is to identify genetic variants driving natural phenotypic variation. However, current methods of genetic mapping have limited resolution. To address this challenge, we developed a CRISPR-Cas9-based high-throughput genome editing approach that can introduce thousands of specific genetic variants in a single experiment. This enabled us to study the fitness consequences of 16,006 natural genetic variants in yeast. We identified 572 variants with significant fitness differences in glucose media; these are highly enriched in promoters, particularly in transcription factor binding sites, while only 19.2% affect amino acid sequences. Strikingly, nearby variants nearly always favor the same parent's alleles, suggesting that lineage-specific selection is often driven by multiple clustered variants. In sum, our genome editing approach reveals the genetic architecture of fitness variation at single-base resolution and could be adapted to measure the effects of genome-wide genetic variation in any screen for cell survival or cell-sortable markers.

Keywords: CRISPR; Cas9; QTL; evolution; fitness; genetic variation; genome editing; yeast.

Figure 1.. CRISPEY is highly efficient and precise.

(a) Schematic for generation of msDNA (black) as single-stranded oligodeoxynucleotide repair donor in vivo through reverse transcription of a hybrid retron-guide RNA molecule. The components shown are: retron scaffold from Ec86 retron (orange), donor template to be reverse-transcribed (blue), guide sequence targeting genomic loci (dark blue), sgRNA scaffold RNA for SpCas9 binding (red). (b) CRISPEY construct for generating retron-guide RNA in yeast. Retron-guide RNA sequence is flanked by two self-cleaving ribozymes for removing mRNA cap and poly(A) tail after pGAL7 driven Polymerase II transcription. (c) Southern blot analysis of msDNA species from galactose-induced yeast total RNA. Note size shift after RNase, indicating removal of retron RNA attached to msDNA. Band (2) indicates partial degradation of the retron-guide RNA. (d) Quantification of retron-mediated CRISPR/Cas9 editing in ADE2. Yeast were subjected to editing for 48 hours via induced expression of Cas9, RT, and the CRISPEY construct targeting ADE2. The fractions (±STD) of edited (ADE2 knockout phenotype expected from HDR repair) and non-edited (wildtype phenotype) are shown for combinations of ADE2 editing retron donor and ADE2-targeting guide (mark by +ADE2 Donor / +ADE2 Guide); or “non-functional” BFP retron donor and GFP-targeting guide (marked by − ADE2 Donor / − ADE2 Guide). (e) CRISPEY also allows insertion of a 765 bp sequence with low error rate. Top, schematic for long-insert editing of genomic DNA at the ADE1 locus. Bottom, diagram indicating outcome of editing, with 87.4% showing the intended edit, 4.6% showing 1 bp error within the insertion (possibly due to RT-induced error) and 8% have no edit. See also Table S1, Table S2, Table S3 and Table S4.

Figure 2.. CRISPEY screen for fitness effects of natural variants.

(a) Schematic for experiment to assay phenotypic effects of CRISPEY libraries. (b) Example of one edit increasing fitness (red), one decreasing fitness (blue), and random guides representing wildtype BY (black). Each guide/donor pair is shown with abundances from six biological replicate cultures. (c) Irreproducible discovery rate analysis, based on agreement between independently edited biological replicates. The greater number of positive effects (RM allele fitter) is likely due to asymmetric power. (d) Edits without a reproducible fitness effect (blue) show little reproducibility between independent guides, as expected if their fitness effect estimates are dominated by noise or random drift. In contrast, edits with reproducible effects (orange) are more highly correlated. (e) We selected 14 edits for validation: 12 with positive fitness effects, 1 with no effect, and 1 with negative effect. Our estimates of log₂ fold change per generation from pooled competition were slightly smaller than those from the validation experiments, suggesting that our fitness effects are not over-estimated due to the “winner’s curse”. Data are represented as mean ± SEM. See also Figure S1, Table S1, Table S2, Table S3, Table S4 and Table S5.

Figure 3.. Cas9 is sensitive to mismatches in the seed region and depends on the mismatched nucleotides.

(a) Each Cas9-induced double strand break (DSB) is potentially toxic, so edits that prevent repeated cutting—either of the genome or of the donor sequence on the plasmid—are expected to show higher fitness during the 48-hour editing phase of our experiment. We observed the lowest toxicity for edits in the PAM or seed region (positions −1 to −7 in the guide), consistent with previous results on Cas9 mismatch-tolerance(Doench et al., 2016; Fu et al., 2016). For comparison, “None” shows the fitness of random guides that are not expected to cut anywhere. Whiskers show the distribution range (excluding outliers) (b) Off-target cutting level as a function of the edit position in the guide and the nucleotides in the guide and in the off-target sense strand. Off-target cutting level is defined as the difference between the fitness of cells with specific guide/donor and the median fitness of strains with random DNA guide/donor (i.e. guide/donor that do not promote any cutting; “None” in (a)). Note the lower sensitivity to guanine to adenine mismatches in the seed relative to other mismatches in this region. (c) and (d) show agreement of the off target cutting level shown in (b) with in vitro measurements of dCas9 binding equilibrium (Pearson’s R=0.85 and 0.65, p-value = 1.7×10⁻⁸ and 2×10⁻⁴ for the guides marked by red and green respectively) and binding rate (Pearson’s R=−0.027 and −0.12, p-value = 0.89 and 0.52 for the guides marked by red and green respectively) from Boyle et al.(Boyle et al., 2017). See also Figure S2, Figure S3 and Table S5.

Figure 4.. Characterizing variants affecting fitness.

(a) Distribution of significant fitness effect variants throughout the genome. “Disrupting coding gene category” includes out-of-frame indels and gain or loss of stop codon. Positive = RM-fitter, negative = BY-fitter. (b) Quantile-quantile plot showing the extent of significant RM-fitter hits for the major annotation categories. Values larger than 60 were set to 60 and marked by triangles (see Figure S4c for non-truncated version). (c) Variants with strong fitness effects (RM-fitter alleles, IDR < 0.05) are enriched in promoters, and depleted in coding regions. (d) Locations of significant variants (RM-fitter alleles, IDR < 0.05) with respect to TSSs (all ORFs were scaled to the same length for visualization; 900 bp of flanking regions are also shown). (e) Variants in or near transcription factor binding sites are most likely to affect fitness. The blue line shows the result over all intergenic variants. (f) Missense variants with IDR < 0.05 are enriched for smaller BLOSUM62 scores, indicating less conservative amino acid changes. See also Figure S4 and S5.

Figure 5.. Detecting lineage-specific selection.

(a) Locations of significant variants (RM-fitter alleles, IDR < 0.05) with respect to TSSs for 25 cytoplasmic translation genes. Most RM-fitter variants are in the flanking noncoding regions. (b) Example of a ribosomal subunit with five significant variants clustered within its promoter; all five variants affect fitness in the same direction, and two (indicated by red dotted lines) are adjacent positions next to conserved Fhl1/Rap1 binding sites, both with validated fitness effects (Figure 2e). Larger dots indicate significant fitness effects. (c) Example of a divergent promoter with five significant variants clustered around Fhl1/Rap1 binding sites (Harbison et al., 2004); all five variants affect fitness in the same direction, and three (indicated by red dotted lines) have validated fitness effects (Figure 2e). (d) Variants at IDR < 0.05 favor the same parent 98% of the time when separated by up to 50 bp; the agreement decreases at larger distances, but is still greater than random (blue line) up to 1 kb. e. Illustration of selection within a locus leading to reinforcing variant effect directions and a resulting composite QTL. See also Figure S4 and Figure S5.