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PLoS One. 2014 Dec 31;9(12):e115499. doi: 10.1371/journal.pone.0115499. eCollection 2014.

Micro- and macro-geographic scale effect on the molecular imprint of selection and adaptation in Norway spruce.

Author information

1
Research and Innovation Centre, Fondazione Edmund Mach (FEM), S. Michele all'Adige, Trento, Italy.
2
National Research Council, Institute of Biosciences and Bioresources, Sesto Fiorentino, Firenze, Italy.
3
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland.
4
Research and Innovation Centre, Fondazione Edmund Mach (FEM), S. Michele all'Adige, Trento, Italy; Department of Plant Sciences, University of California Davis, Davis, CA, United States of America.

Abstract

Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based F(ST)-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the F(ST)-outlier methods detected together 11 F(ST)-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with F(ST)-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.

PMID:
25551624
PMCID:
PMC4281139
DOI:
10.1371/journal.pone.0115499
[Indexed for MEDLINE]
Free PMC Article

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