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Back to nature: ecological genomics of loblolly pine (Pinus taeda, Pinaceae)

Authors

  • ANDREW J. ECKERT,

    1. Section of Evolution and Ecology, University of California—Davis, Davis, CA 95616, USA
    2. Center for Population Biology, University of California—Davis, Davis, CA 95616, USA
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  • ANDREW D. BOWER,

    1. USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR 97331, USA
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  • SANTIAGO C. GONZÁLEZ-MARTÍNEZ,

    1. Department of Forest Systems and Resources, Forest Research Institute, CIFOR-INIA, Madrid 28040, Spain
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  • JILL L. WEGRZYN,

    1. Department of Plant Sciences, University of California—Davis, Davis, CA 95616, USA
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  • GRAHAM COOP,

    1. Section of Evolution and Ecology, University of California—Davis, Davis, CA 95616, USA
    2. Center for Population Biology, University of California—Davis, Davis, CA 95616, USA
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  • DAVID B. NEALE

    1. Center for Population Biology, University of California—Davis, Davis, CA 95616, USA
    2. Department of Plant Sciences, University of California—Davis, Davis, CA 95616, USA
    3. Institute of Forest Genetics, USDA Forest Service, Davis, CA 95616, USA
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David B. Neale, Fax: +1 530 754 9366; E-mail: dbneale@ucdavis.edu

Abstract

Genetic variation is often arrayed in latitudinal or altitudinal clines, reflecting either adaptation along environmental gradients, migratory routes, or both. For forest trees, climate is one of the most important drivers of adaptive phenotypic traits. Correlations of single and multilocus genotypes with environmental gradients have been identified for a variety of forest trees. These correlations are interpreted normally as evidence of natural selection. Here, we use a genome-wide dataset of single nucleotide polymorphisms (SNPs) typed from 1730 loci in 682 loblolly pine (Pinus taeda L.) trees sampled from 54 local populations covering the full-range of the species to examine allelic correlations to five multivariate measures of climate. Applications of a Bayesian generalized linear mixed model, where the climate variable was a fixed effect and an estimated variance–covariance matrix controlled random effects due to shared population history, identified several well-supported SNPs associating to principal components corresponding to geography, temperature, growing degree-days, precipitation and aridity. Functional annotation of those genes with putative orthologs in Arabidopsis revealed a diverse set of abiotic stress response genes ranging from transmembrane proteins to proteins involved in sugar metabolism. Many of these SNPs also had large allele frequency differences among populations (FST = 0.10–0.35). These results illustrate a first step towards a ecosystem perspective of population genomics for non-model organisms, but also highlight the need for further integration of the methodologies employed in spatial statistics, population genetics and climate modeling during scans for signatures of natural selection from genomic data.

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