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Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize.

Guillaume P RamsteinEdward S Buckler
Published in: Genome biology (2022)
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 ( https://doi.org/10.25739/hybz-2957 ).
Keyphrases
  • copy number
  • genome wide
  • crispr cas
  • dna methylation
  • physical activity
  • high resolution
  • amino acid
  • electronic health record
  • big data
  • machine learning
  • deep learning