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

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 variancecovariance 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 (F ST = 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.

Genome scanning for detecting adaptive genes along environmental gradients in the Japanese conifer, Cryptomeria japonica

“…This proportion of candidate loci is similar to that found in a previous study (2.7%) in which 148 cleaved amplified polymorphic sequence loci were examined (Tsumura et al, 2007). Eckert et al (2010) identified 22 candidate loci (BF log 10 42.0) associated with climate variables from 1730 loci in 682 loblolly pine tree samples covering the full range of the species. Prunier et al (2011) also identified 10 candidate loci above 99% confidence interval in black spruce.…”

“…Although LD in forest trees decays rapidly within several thousand basepairs (Neale and Savolainen, 2004), Eckert et al (2010) have observed high LD in the loblolly pine, affecting the set of five SNPs located on LG 8. The LD of forest trees has to date only been estimated in coding regions; it is possible that the LD values of non-coding regions might be higher than those for coding regions, as is the case in angiosperms (Gaut et al, 2007;Moritsuka et al, 2012).…”

“…Genome scanning is an effective and useful method for detecting genetic differentiation and candidate genes associated with specific phenotypes or environmental variables in forest tree species (Eveno et al, 2008;Namroud et al, 2008;Eckert et al, 2010;Holliday et al, 2010;Neale and Kremer, 2011;Prunier et al, 2011).…”

The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: The case of two white pines

“…These climatic variables could be important drivers of local adaptation as phenotypic traits such as timing of budburst, timing of budset (or growth initiation and cessation), and cold hardiness vary significantly among populations of both species (Joyce & Rehfeldt, 2013; Joyce & Sinclair, 2002; Li et al., 1997; Lu et al., 2003a,b; Rehfeldt et al., 1984). The putative ortholog (i.e., gene amplified using the same primers as those used in this study) was also detected as an F ST outlier among environmental groups defined based on DD5 in June and precipitation in December in Larix decidua (Mosca et al., 2013), and associated with spring–fall precipitation and aridity in P. taeda (Eckert et al., 2010). Thus, this gene may have evolved in response to climatic constraints in multiple tree species.…”

Common garden experiments in the genomic era: new perspectives and opportunities

“…Despite the advantages of common garden experiments, the study of local adaptation in non-model species during the past decade has been strongly driven by the study of genetic markers in natural populations (Luikart et al, 2003). Typically, evolutionary biologists go to natural populations, sample tissue from the individuals and genotype them with high-throughput methods and then proceed with a genome scan analysis of selection (see, for example, Eckert et al, 2010;Bourret et al, 2013;Fischer et al, 2013). Although this method can be quite powerful, it has some limitations (for example, false positives, no information on the adaptive phenotype).…”