| Title: | Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification |
| Journal: | Nature Genetics |
| Published: | 24 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38658794/ |
| DOI: | https://doi.org/10.1038/s41588-024-01726-6 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508837 |
| Citations: | 2 (2 in last 2 years) as of 8 Aug 2024 |
Publication 9842
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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.</p>
9 Keywords
- Bayes Theorem
- CRISPR-Cas Systems
- Gene Editing
- Genotype
- HEK293 Cells
- Humans
- Phenotype
- RNA, Guide, CRISPR-Cas Systems
- Receptors, LDL
17 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 41250 | Prioritizing Mendelian variants using GWAS and exome sequencing data |
Enabling scientific discoveries that improve human health