Base‐editing technologies enable the introduction of point mutations at targeted genomic sites in mammalian cells, with higher efficiency and precision than traditional genome‐editing methods that use DNA double‐strand breaks, such as zinc finger nucleases (ZFNs), transcription‐activator‐like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR‐associated protein 9 (CRISPR‐Cas9) system. This allows the generation of single‐nucleotide‐variant isogenic cell lines (i.e., cell lines whose genomic sequences differ from each other only at a single, edited nucleotide) in a more time‐ and resource‐effective manner. These single‐nucleotide‐variant clonal cell lines represent a powerful tool with which to assess the functional role of genetic variants in a native cellular context. Base editing can therefore facilitate genotype‐to‐phenotype studies in a controlled laboratory setting, with applications in both basic research and clinical applications. Here, we provide optimized protocols (including experimental design, methods, and analyses) to design base‐editing constructs, transfect adherent cells, quantify base‐editing efficiencies in bulk, and generate single‐nucleotide‐variant clonal cell lines. © 2020 Wiley Periodicals LLC.
Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.
Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.
Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (
- Award ID(s):
- 1822330
- PAR ID:
- 10370540
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Genome Biology
- Volume:
- 23
- Issue:
- 1
- ISSN:
- 1474-760X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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