Insights into drought adaptation of two European oak species revealed by nucleotide diversity of candidate genes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-10

AUTHORS

Andreas Homolka, Silvio Schueler, Kornel Burg, Silvia Fluch, Antoine Kremer

ABSTRACT

Nucleotide diversity, correlations to environmental conditions, and deviations from standard neutral models were examined in eight candidate genes for drought response of European oaks Quercus petraea and Quercus robur. Baseline genetic parameters were calculated and deviation from neutrality was tested using Tajima’s D, and Fu and Li’s D and F statistics. Population structure was investigated using differentiation indices and Bayesian clustering. Similar values of total nucleotide diversity were found in both species with significant higher diversity at non-synonymous sites in Q. robur while the number of haplotypes was significantly higher in Q. petraea. Significant population differentiation was found for three genes in Q. petraea and for one gene in Q. robur. Within four genes, strong correlations were found between the local temperature–precipitation regime and the allele frequencies of six alleles, of which three were private to Q. petraea. Using various population genetic and Bayesian tests for neutrality, four outlier single nucleotide polymorphisms (SNPs) under putative selection were detected in two of the analyzed genes. Significant differentiation and strong allelic correlation to environmental conditions support preceding gene expression profiling experiments, where functional impact of candidate genes in drought response has been revealed. Q. petraea populations were found to be more differentiated as compared to Q. robur—this could be linked to the higher adaptive potential of this species under arid conditions. More... »

PAGES

1179-1192

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11295-013-0627-7

    DOI

    http://dx.doi.org/10.1007/s11295-013-0627-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1003188951


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