title: Combining Genotype, Phenotype, and Environment to Infer Potential Candidate Genes.
abstract: Population genomic analysis can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species' range in the southeastern United States. We analyzed associations between phenotypic, molecular, and environmental variables from datasets of 3082 single nucleotide polymorphism (SNP) loci and 3 categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only 6 SNP loci that displayed potential signals of local adaptation. Five of the 6 identified SNPs are linked to gene expression traits for lignin development, and 1 is linked with whole-plant traits. We subsequently compared the 6 candidate genes with environmental variables and found a high correlation in only 3 of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes.
authors.affiliation: Department of Biology, University of Western Ontario, London, Ontario, Canada.
authors.forename: Benoit
authors.initials: B
authors.lastname: Talbot
authors.affiliation: J. F. Blumenbach Institute of Zoology and Anthropology, Georg-August-Universität Göttingen, Göttingen, Germany.
authors.forename: Ting-Wen
authors.initials: TW
authors.lastname: Chen
authors.affiliation: Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA.
authors.forename: Shawna
authors.initials: S
authors.lastname: Zimmerman
authors.affiliation: Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Joost
authors.affiliation: Department of Biology, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Eckert
authors.affiliation: Department of Ecosystem Science and Management, University of Wyoming, Laramie, WY, USA.
authors.forename: Taylor M
authors.initials: TM
authors.lastname: Crow
authors.affiliation: Department of Forest Genetics and Forest Tree Breeding, Georg-August-Universität Göttingen, Göttingen, Germany.
authors.forename: Devrim
authors.initials: D
authors.lastname: Semizer-Cuming
authors.affiliation: Center for Environmental Studies, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Chitra
authors.initials: C
authors.lastname: Seshadri
authors.affiliation: EPHE, PSL Research University, CNRS, UM, SupAgro, IRD, INRA, UMR, Montpellier, France.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Manel
mesh_terms: D000220:Adaptation, Biological; D004777:Environment; D059647:Gene-Environment Interaction; D017343:Genes, Plant; D056726:Genetic Association Studies; D005828:Genetics, Population; D023281:Genomics / Q000379:methods; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D041603:Pinus taeda / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D012641:Selection, Genetic; D014481:United States
keywords: Pinus taeda (Loblolly pine); environmental variation; genome scan; local adaptation; phenotypic traits; potential candidate gene