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PreprintCited by 32CRISPR and Genetic EngineeringGenomics and Rare DiseasesGenetic Associations and Epidemiology

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

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Ryu, Jayoung, Barkal, Sam, Yu, Tian, Jankowiak, Martin, Zhou, Yunzhuo, Francoeur, Matthew, Phan, Quang Vinh, Li, Zhijian, Tognon, Manuel, Brown, Lara, Love, Michael I., Lettre, Guillaume, Ascher, David B., Cassa, Christopher A., Sherwood, Richard I., Pinello, Luca·2023·Nature Genetics
DOI 10.1101/2023.09.08.23295253PMC PMC11669423

Preprint — published as 10.1038/s41588-024-01726-6

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Abstract

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR , we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.

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Metadata

Authors
Ryu, J., Barkal, S., Yu, T., Jankowiak, M., Zhou, Y., Francoeur, M., Phan, Q. V., Li, Z., Tognon, M., Brown, L., Love, M. I., Lettre, G., Ascher, D. B., Cassa, C. A., Sherwood, R. I., & Pinello, L.
Affiliations
  • Broad Institute (US)
  • Brigham and Women's Hospital (US)
  • Baker Heart and Diabetes Institute (AU)
  • University of North Carolina at Chapel Hill (US)
  • Montreal Heart Institute (CA)
Corresponding author
  • Cassa, Christopher A.
  • Sherwood, Richard I.
  • Pinello, Luca
ORCID
Venue
Nature Genetics
Publisher
openRxiv
Published
2023-09-10
Volume
56
Issue
5
Pages
925-937
ISSN
1061-4036 (ISSN-L)
DOI
10.1101/2023.09.08.23295253
PMC
PMC11669423
OpenAlex
W4395074959
Cited by
32
References
3
Citations by year
2026: 15 · 2025: 13 · 2024: 3 · 2023: 1
FWCI
4.21
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Nature Genetics (publishedVersion)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, other-oa)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • U.S. Department of Health and Human Services (1R35HG010717-01, 1R01HL164409, UM1HG012010, R01-HG010372, 1R01GM143249)
  • American Cancer Society
  • American Heart Association
  • National Organization for Rare Disorders
  • State Government of Victoria
  • Medical Research Council
  • National Health and Medical Research Council (1174405)
  • National Heart, Lung, and Blood Institute (R01HL164409)
  • National Human Genome Research Institute (R56HG012681, R35HG010717, R01HG010372, UM1HG012010)
  • National Institute of General Medical Sciences (R01GM143249)
  • National Institute of Environmental Health Sciences (P30ES010126)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
variant effect prediction, Bayesian networks, beans, chromatin accessibility, LDL uptake, LDL receptor, disease-associated variants, regulatory variants

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