High-throughput PRIME-editing screens identify functional DNA variants in the human genome

Xingjie Ren; Han Yang; Jovia L Nierenberg; Yifan Sun; Jiawen Chen; Cooper Beaman; Thu Pham; Mai Nobuhara; Maya Asami Takagi; Vivek Narayan; Yun Li; Elad Ziv; Yin Shen

doi:10.1016/j.molcel.2023.11.021

High-throughput PRIME-editing screens identify functional DNA variants in the human genome

Mol Cell. 2023 Dec 21;83(24):4633-4645.e9. doi: 10.1016/j.molcel.2023.11.021.

Authors

Xingjie Ren¹, Han Yang¹, Jovia L Nierenberg², Yifan Sun¹, Jiawen Chen³, Cooper Beaman¹, Thu Pham⁴, Mai Nobuhara⁴, Maya Asami Takagi¹, Vivek Narayan¹, Yun Li⁵, Elad Ziv⁶, Yin Shen⁷

Affiliations

¹ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
² Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
³ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁴ Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
⁵ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC, USA; Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
⁶ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Division of General Internal Medicine, Department of Medicine, and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁷ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA. Electronic address: yin.shen@ucsf.edu.

Abstract

Despite tremendous progress in detecting DNA variants associated with human disease, interpreting their functional impact in a high-throughput and single-base resolution manner remains challenging. Here, we develop a pooled prime-editing screen method, PRIME, that can be applied to characterize thousands of coding and non-coding variants in a single experiment with high reproducibility. To showcase its applications, we first identified essential nucleotides for a 716 bp MYC enhancer via PRIME-mediated single-base resolution analysis. Next, we applied PRIME to functionally characterize 1,304 genome-wide association study (GWAS)-identified non-coding variants associated with breast cancer and 3,699 variants from ClinVar. We discovered that 103 non-coding variants and 156 variants of uncertain significance are functional via affecting cell fitness. Collectively, we demonstrate that PRIME is capable of characterizing genetic variants at single-base resolution and scale, advancing accurate genome annotation for disease risk prediction, diagnosis, and therapeutic target identification.

Keywords: disease variants; enhancer; high-throughput screens; prime editing; single-base resolution.

MeSH terms

CRISPR-Cas Systems
DNA
Gene Editing / methods
Genome, Human* / genetics
Genome-Wide Association Study*
Humans
Regulatory Sequences, Nucleic Acid
Reproducibility of Results

Substances

DNA

Abstract

MeSH terms

Substances

Grants and funding