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Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

Nature Genetics (2024)Cite this article

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Abstract

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

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Fig. 1: Activity-normalized base editing screening pipeline.
Fig. 2: BEAN models variant effects from activity-normalized base editing screens.
Fig. 3: BEAN improves variant impact estimation from the LDL-C GWAS library screen.
Fig. 4: Functional characterization of LDL-C GWAS variants.
Fig. 5: Dissection of LDLR variant effects through BEAN modeling of a saturation tiled base editing screen.
Fig. 6: Deleterious variants in LDLR class B repeats weaken hydrophobic interactions.

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Data availability

The processed data used in this study have been deposited at Zenodo (https://doi.org/10.5281/zenodo.10139794), and primary sequencing data are available at the Sequence Read Archive under accession PRJNA1042659. Controlled access, patient-level data from the UKB may be requested at https://ams.ukbiobank.ac.uk/ams/. Source data are provided with this paper.

Code availability

Bean source code is available at https://github.com/pinellolab/crispr-bean. The scripts used to generate the figures and analyses presented in the study have been deposited at https://github.com/pinellolab/bean_manuscript and Zenodo110. The version (0.2.9) of ‘bean’ used for the analyses presented in this paper has been deposited at Zenodo74.

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Acknowledgements

We thank G. Losyev, A. James, Q. Qin, C. Smith, L. Blaine, K. Clement, Z. Patel, S. Yang and H. Boen for technical assistance. Funding for this work was obtained from UM1HG012010 (R.I.S. and L.P.), 1R01HL164409 (C.A.C., R.I.S. and L.P.), 1R01GM143249 (R.I.S.), R01HG010372 (C.A.C. and T.Y.), the American Cancer Society (R.I.S.), the American Heart Association (R.I.S.), the National Organization for Rare Diseases (R.I.S.), 1R35HG010717-01 (L.P.), the National Health and Medical Research Council of Australia (GNT1174405; D.B.A. and Y.Z.), and the Victorian Government’s Operational Infrastructure Support Program (Y.Z. and D.B.A.). We are indebted to the UKB and its participants (UKB application 41250 and IRB protocol 2020P002093).

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Authors and Affiliations

  1. Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA

    Jayoung Ryu, Zhijian Li, Manuel Tognon & Luca Pinello

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Jayoung Ryu

  3. Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Jayoung Ryu, Martin Jankowiak, Zhijian Li, Manuel Tognon & Luca Pinello

  4. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Sam Barkal, Tian Yu, Matthew Francoeur, Quang Vinh Phan, Lara Brown, Vineel Bhat, Christopher A. Cassa & Richard I. Sherwood

  5. School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia

    Yunzhuo Zhou & David B. Ascher

  6. Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Yunzhuo Zhou & David B. Ascher

  7. Computer Science Department, University of Verona, Verona, Italy

    Manuel Tognon

  8. Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

  9. Montreal Heart Institute, Montréal, Quebec, Canada

    Guillaume Lettre

  10. Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada

    Guillaume Lettre

  11. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Luca Pinello

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Contributions

R.I.S. conceived the experimental design, and J.R. and L.P. conceptualized BEAN. S.B. collected screen data. J.R. developed BEAN, and M.J., M.I.L. and L.P. advised on design and implementation of BEAN. J.R. and T.Y. processed and analyzed data. T.Y. performed BE-Hive and FUSE analysis. M.F., Q.V.P. and R.I.S. performed downstream characterization of LDL-C GWAS variants. T.Y., L.B., V.B. and C.A.C. obtained and analyzed UKB data. Y.Z. led structural analysis of LDLR variants with J.R. and D.B.A. J.R. and Z.L. benchmarked classification performance. M.T. and L.P. performed analysis of variant impact on transcription factor binding. G.L. advised on library design. J.R. and R.I.S. drafted the manuscript. R.I.S., L.P. and C.A.C. provided guidance and supervised this project. All the authors wrote and approved the final manuscript.

Corresponding authors

Correspondence to Christopher A. Cassa, Richard I. Sherwood or Luca Pinello.

Ethics declarations

Competing interests

L.P. has financial interests in Edilytics and SeQure Dx. L.P.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict-of-interest policies. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Andrew Wood and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Base editor editing preference profile and context specificity.

Deamination motif and PAM-dependent editing preference of AID-BE5-SpRY from 7294 gRNAs and AID-BE5-Cas9NG from 7299 gRNAs with more than 9 read counts across any replicates of bulk samples. a) Context specificity of AID-BE5-SpRY are represented as sequence logos. The height of each base represents the relative editing efficiency with each base. b) Mean editing efficiency of AID-BE5-SpRY by protospacer position and PAM sequence. c) Context specificity of AID-BE5-CasNG is represented as sequence logos. The height of each base represents the relative editing efficiency with each base. d) Mean editing efficiency of AID-BE5-CasNG by protospacer position and PAM sequence.

Source data

Extended Data Fig. 2 Nucleotide-level editing comparison of reporter and endogenous locus.

a) Scatterplots comparing of per-nucleotide-level editing efficiencies between the reporter and endogenous target sites. All edits introduced by each of 49 gRNAs across four loci across 3 experimental replicates were plotted. Points are colored by the identity of nucleotide edit and gRNA. b) The same plot colored by gRNA strand. R; Pearson correlation coefficient. n; number of plotted editing rates.

Source data

Extended Data Fig. 3 BEAN plate diagrams.

Plate diagrams of a) BEAN b) BEAN-Reporter, c) BEAN-Uniform. Xb and all parameters with superscript b is not used for benchmark analyses.

Extended Data Fig. 4 LDL-C GWAS library classification task benchmark.

a) AUPRC plot for classifying positive splicing control variants against negative control variants. Metrics for all 5 replicates are shown as markers and metrics of 15 two-replicate subsamples among the 5 replicates are shown as box plots. Boxplot was plotted as described in the statistical note of the Methods section. b) Precision-Recall curve for classifying all positive control splice sites of against negative controls for all replicates with no failing samples. c) Precision-Recall curve for classifying splice sites of LDLR/MYLIP against negative controls for all replicates with no failing samples. d) Precision-Recall curve for classifying all positive control splice sites of against negative controls for 2-replicate subsample of the data. Mean Precision value for a recall across 15 subsample runs are plotted as solid line. e) Precision-Recall curve for classifying splice sites of LDLR/MYLIP against negative controls for 2-replicate subsample of the data. Mean Precision value for a recall across 15 subsample runs are plotted as solid line.

Source data

Extended Data Fig. 5 Comparison of inferred effect sizes of individually transfected LDL-C GWAS library gRNAs.

Scatterplot and Pearson correlation coefficients (R) of effect size estimates and the log fold change (LFC) of fluorescence signal following individual transfection of 22 gRNAs. R; Spearman correlation coefficient.

Source data

Extended Data Fig. 6 BEAN accurately estimates variant effect confidence from per-variant evidence in input data.

a-c) Scatterplot of 2,182 LDLR tiling library variants comparing a) nnorm and effective edit rates, b) effective edit rates and BEAN σμ, and c) nnorm and BEAN σμ. d) Histogram of effective edit rates of 76 LDLR tiling library variants with UKB LDL-C levels. Quartile bin cutoffs used to categorize variants are shown as dotted lines. e) Scatterplots of BEAN z-scores and statin-adjusted UKB LDL-C measurements for variants in each effective edit rate quartile bin. r and rho shows the Pearson and Sparman correlation coefficients, respectively.

Source data

Extended Data Fig. 7 LDLR tiling library classification task benchmark.

a) AUPRC of classifying Pathogenic/Likely Pathogenic from Benign/Likely Benign variants when using 4 replicates without failing samples and 6 2-replicates combinations among the replicates. Bounds and the center of the boxes are the interquartile ranges. Boxplot was plotted as described in the statistical note of the Methods section. b-e) Precision-recall curve of classifying b, d) Pathogenic/Likely Pathogenic c, e) Pathogenic from Benign/Likely Benign variants. Top panels (b, c) show classification when used 4 replicates without failing samples. Bottom panels (d, e) show when used 6 2-replicates combinations among 4 replicates without failing samples.

Source data

Extended Data Fig. 8 Comparison of functional impact and conservation within conserved LDLR domains.

Repeat domain alignments shown with BEAN z-score for a) LDLR class A repeat domain, b) LDLR class B repeat domain, c) EGF-like domains aligned with the Pfam profile HMM logo by Skylign, where the height of each position show its information content and letter heights show the total height scaled by relative frequencies of the letters in the position. For a), conserved cysteine residue position is highlighted and for b-c), consensus positions from Clustal Omega alignment output are highlighted in grey.

Source data

Extended Data Fig. 9 Expanded LDLR missense variant pathogenicity estimates with FUSE.

a) Scatterplot of all considered UKB variant mean statin-adjusted LDL-C level against imputed BEAN-FUSE score. b) Prediction outcome of unobserved variants with XGBoost model trained on observed UKB variants and mean statin-adjusted LDL levels. c-d) Correlation coefficients and root mean squared error (RMSE) for predicted and true UKB mean statin-adjusted LDL-C level for XGBoost model with FUSE score, PhastCons PhyloP conservation score, and both as the input in predicting LDL-C levels. c) Boxplot of metrics for prediction of observed variants with 10-fold cross validation (n = 10) d) Barplot of metrics for prediction of unobserved variants with model trained on observed variants (n = 1). r, ρ; Pearson, Spearman correlation coefficient, RMSE; Root mean squared error.

Source data

Extended Data Fig. 10 Local atomic interaction in wild type and mutated structure for selected variants in LDLR class B repeat domain.

a–k, Residues with interaction with the variant position are shown. Variant positions and interacting residues are colored by the reference amino acid and atomic elements (O: red, N: blue, S: yellow). Ref AA; reference amino acid.

Supplementary information

Supplementary Information

Supplementary Figs. 1–20 and Notes 1–8.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–11.

Supplementary Data 1

Annotated base editor plasmid sequences.

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Ryu, J., Barkal, S., Yu, T. et al. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01726-6

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