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Abstract BackgroundCrop 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. ResultsUsing 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. ConclusionsOur 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 (https://doi.org/10.25739/hybz-2957).
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A TILLING by sequencing approach to identify induced mutations in sunflower genes
Abstract The Targeting Induced Local Lesions in Genomes (TILLING) technology is a reverse genetic strategy broadly applicable to every kind of genome and represents an attractive tool for functional genomic and agronomic applications. It consists of chemical random mutagenesis followed by high-throughput screening of point mutations in targeted genomic regions. Although multiple methods for mutation discovery in amplicons have been described, next-generation sequencing (NGS) is the tool of choice for mutation detection because it quickly allows for the analysis of a large number of amplicons. The aim of the present work was to screen a previously generated sunflower TILLING population and identify alterations in genes involved in several important and complex physiological processes. Twenty-one candidate sunflower genes were chosen as targets for the screening. The TILLING by sequencing strategy allowed us to identify multiple mutations in selected genes and we subsequently validated 16 mutations in 11 different genes through Sanger sequencing. In addition to addressing challenges posed by outcrossing, our detection and validation of mutations in multiple regulatory loci highlights the importance of this sunflower population as a genetic resource.
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- Award ID(s):
- 1759942
- PAR ID:
- 10227129
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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