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Genetic diversity of Norway spruce ecotypes assessed by GBS-derived SNPs.

Jiří KoreckýJaroslav ČeplJan StejskalZuzana FaltinováJakub DvořákMilan LstibůrekYousry A El-Kassaby
Published in: Scientific reports (2021)
We investigated the genetic structure of three phenotypically distinct ecotypic groups of Norway spruce (Picea abies) belonging to three elevational classes; namely, low- (acuminata), medium- (europaea), and high-elevation (obovata) form, each represented by 150 trees. After rigorous filtering, we used 1916 Genotyping-by-Sequencing generated SNPs for analysis. Outputs from three multivariate analysis methods (Bayesian clustering algorithm implemented in STRUCTURE, Principal Component Analysis, and the Discriminant Analysis of Principal Components) indicated the presence of a distinct genetic cluster representing the high-elevation ecotypic group. Our findings bring a vital message to forestry practice affirming that artificial transfer of forest reproductive material, especially for stands under harsh climate conditions, should be considered with caution.
Keyphrases
  • genome wide
  • genetic diversity
  • climate change
  • primary care
  • single cell
  • machine learning
  • deep learning
  • high resolution
  • gene expression
  • rna seq