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

Jayoung Ryu; Sam Barkal; Tian Yu; Martin Jankowiak; Yunzhuo Zhou; Matthew Francoeur; Quang Vinh Phan; Zhijian Li; Manuel Tognon; Lara Brown; Michael I Love; Vineel Bhat; Guillaume Lettre; David B Ascher; Christopher A Cassa; Richard I Sherwood; Luca Pinello

doi:10.1038/s41588-024-01726-6

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

Nat Genet. 2024 Apr 24. doi: 10.1038/s41588-024-01726-6. Online ahead of print.

Authors

Jayoung Ryu^{1

2

3}, Sam Barkal⁴, Tian Yu⁴, Martin Jankowiak³, Yunzhuo Zhou^{5

6}, Matthew Francoeur⁴, Quang Vinh Phan⁴, Zhijian Li^{1

3}, Manuel Tognon^{1

3

7}, Lara Brown⁴, Michael I Love⁸, Vineel Bhat⁴, Guillaume Lettre^{9

10}, David B Ascher^{5

6}, Christopher A Cassa¹¹, Richard I Sherwood¹², Luca Pinello^{13

14

15}

Affiliations

¹ Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
³ Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁴ Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
⁵ School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
⁶ Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁷ Computer Science Department, University of Verona, Verona, Italy.
⁸ Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ Montreal Heart Institute, Montréal, Quebec, Canada.
¹⁰ Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada.
¹¹ Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ccassa@bwh.harvard.edu.
¹² Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. rsherwood@bwh.harvard.edu.
¹³ Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA. lpinello@mgh.harvard.edu.
¹⁴ Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA. lpinello@mgh.harvard.edu.
¹⁵ Department of Pathology, Harvard Medical School, Boston, MA, USA. lpinello@mgh.harvard.edu.

PMID: 38658794
DOI: 10.1038/s41588-024-01726-6

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.

Abstract

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