Andrew J. Eckert

Patterns of Population Structure and Environmental Associations to Aridity Across the Range of Loblolly Pine (Pinus taeda L., Pinaceae)

Natural populations of forest trees exhibit striking phenotypic adaptations to diverse environmental gradients, thereby making them appealing subjects for the study of genes underlying ecologically relevant phenotypes. Here, we use a genome-wide data set of single nucleotide polymorphisms genotyped across 3059 functional genes to study patterns of population structure and identify loci associated with aridity across the natural range of loblolly pine (Pinus taeda L.). Overall patterns of population structure, as inferred using principal components and Bayesian cluster analyses, were consistent with three genetic clusters likely resulting from expansions out of Pleistocene refugia located in Mexico and Florida. A novel application of association analysis, which removes the confounding effects of shared ancestry on correlations between genetic and environmental variation, identified five loci correlated with aridity. These loci were primarily involved with abiotic stress response to temperature and drought. A unique set of 24 loci was identified as FST outliers on the basis of the genetic clusters identified previously and after accounting for expansions out of Pleistocene refugia. These loci were involved with a diversity of physiological processes. Identification of nonoverlapping sets of loci highlights the fundamental differences implicit in the use of either method and suggests a pluralistic, yet complementary, approach to the identification of genes underlying ecologically relevant phenotypes.

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

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.

Association Genetics of Coastal Douglas Fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-Hardiness Related Traits

Adaptation to cold is one of the greatest challenges to forest trees. This process is highly synchronized with environmental cues relating to photoperiod and temperature. Here, we use a candidate gene-based approach to search for genetic associations between 384 single-nucleotide polymorphism (SNP) markers from 117 candidate genes and 21 cold-hardiness related traits. A general linear model approach, including population structure estimates as covariates, was implemented for each marker–trait pair. We discovered 30 highly significant genetic associations [false discovery rate (FDR) Q < 0.10] across 12 candidate genes and 10 of the 21 traits. We also detected a set of 7 markers that had elevated levels of differentiation between sampling sites situated across the Cascade crest in northeastern Washington. Marker effects were small (r2 < 0.05) and within the range of those published previously for forest trees. The derived SNP allele, as measured by a comparison to a recently diverged sister species, typically affected the phenotype in a way consistent with cold hardiness. The majority of markers were characterized as having largely nonadditive modes of gene action, especially underdominance in the case of cold-tolerance related phenotypes. We place these results in the context of trade-offs between the abilities to grow longer and to avoid fall cold damage, as well as putative epigenetic effects. These associations provide insight into the genetic components of complex traits in coastal Douglas fir, as well as highlight the need for landscape genetic approaches to the detection of adaptive genetic diversity.

Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate

The Forest ecosystem genomics Research: supporTing Transatlantic Cooperation project (FoResTTraC, http://www.foresttrac.eu/) sponsored a workshop in August 2010 to evaluate the potential for using a landscape genomics approach for studying plant adaptation to the environment and the potential of local populations for coping with changing climate. This paper summarizes our discussions and articulates a vision of how we believe forest trees offer an unparalleled opportunity to address fundamental biological questions, as well as how the application of landscape genomic methods complement to traditional forest genetic approaches that provide critical information needed for natural resource management. In this paper, we will cover four topics. First, we begin by defining landscape genomics and briefly reviewing the unique situation for tree species in the application of this approach toward understanding plant adaptation to the environment. Second, we review traditional approaches in forest genetics for studying local adaptation and identifying loci underlying locally adapted phenotypes. Third, we present existing and emerging methods available for landscape genomic analyses. Finally, we briefly touch on how these approaches can aid in understanding practical topics such as management of tree populations facing climate change.

A practical guide to environmental association analysis in landscape genomics

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next‐generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced‐representation sequencing, candidate‐gene approaches, linearity of allele–environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.

Does gene flow destroy phylogenetic signal? The performance of three methods for estimating species phylogenies in the presence of gene flow.

Incomplete lineage sorting has been documented across a diverse set of taxa ranging from song birds to conifers. Such patterns are expected theoretically for species characterized by certain life history characteristics (e.g. long generation times) and those influenced by certain historical demographic events (e.g. recent divergences). A number of methods to estimate the underlying species phylogeny from a set of gene trees have been proposed and shown to be effective when incomplete lineage sorting has occurred. The further effects of gene flow on those methods, however, remain to be investigated. Here, we focus on the performance of three methods of species tree inference, ESP-COAL, minimizing deep coalescence (MDC), and concatenation, when incomplete lineage sorting and gene flow jointly confound the relationship between gene and species trees. Performance was investigated using Monte Carlo coalescent simulations under four models (n-island, stepping stone, parapatric, and allopatric) and three magnitudes of gene flow (N(e)m=0.01, 0.10, 1.00). Although results varied by the model and magnitude of gene flow, methods incorporating aspects of the coalescent process (ESP-COAL and MDC) performed well, with probabilities of identifying the correct species tree topology typically increasing to greater than 0.75 when five more loci are sampled. The only exceptions to that pattern included gene flow at moderate to high magnitudes under the n-island and stepping stone models. Concatenation performs poorly relative to the other methods. We extend these results to a discussion of the importance of species and population phylogenies to the fields of molecular systematics and phylogeography using an empirical example from Rhododendron.

Association genetics of traits controlling lignin and cellulose biosynthesis in black cottonwood (Populus trichocarpa, Salicaceae) secondary xylem

• An association genetics approach was used to examine individual genes and alleles at the loci responsible for complex traits controlling lignocellulosic biosynthesis in black cottonwood (Populus trichocarpa). Recent interest in poplars as a source of renewable energy, combined with the vast genomic resources available, has enabled further examination of their genetic diversity. • Forty candidate genes were resequenced in a panel of 15 unrelated individuals to identify single nucleotide polymorphisms (SNPs). Eight hundred and seventy-six SNPs were successfully genotyped in a clonally replicated population (448 clones). The association population (average of 2.4 ramets per clone) was phenotyped using pyrolysis molecular beam mass spectrometry. Both single-marker and haplotype-based association tests were implemented to identify associations for composite traits representing lignin content, syringyl : guaiacyl ratio and C6 sugars. • Twenty-seven highly significant, unique, single-marker associations (false discovery rate Q < 0.10) were identified across 40 candidate genes in three composite traits. Twenty-three significant haplotypes within 11 genes were discovered in two composite traits. • Given the rapid decay of within-gene linkage disequilibrium and the high coverage of amplicons across each gene, it is likely that the numerous polymorphisms identified are in close proximity to the causative SNPs and the haplotype associations reflect information present in the associations between markers.

Disentangling the Roles of History and Local Selection in Shaping Clinal Variation of Allele Frequencies and Gene Expression in Norway Spruce (Picea abies)

Understanding the genetic basis of local adaptation is challenging due to the subtle balance among conflicting evolutionary forces that are involved in its establishment and maintenance. One system with which to tease apart these difficulties is clines in adaptive characters. Here we analyzed genetic and phenotypic variation in bud set, a highly heritable and adaptive trait, among 18 populations of Norway spruce (Picea abies), arrayed along a latitudinal gradient ranging from 47°N to 68°N. We confirmed that variation in bud set is strongly clinal, using a subset of five populations. Genotypes for 137 single-nucleotide polymorphisms (SNPs) chosen from 18 candidate genes putatively affecting bud set and 308 control SNPs chosen from 264 random genes were analyzed for patterns of genetic structure and correlation to environment. Population genetic structure was low (FST = 0.05), but latitudinal patterns were apparent among Scandinavian populations. Hence, part of the observed clinal variation should be attributable to population demography. Conditional on patterns of genetic structure, there was enrichment of SNPs within candidate genes for correlations with latitude. Twenty-nine SNPs were also outliers with respect to FST. The enrichment for clinal variation at SNPs within candidate genes (i.e., SNPs in PaGI, PaPhyP, PaPhyN, PaPRR7, and PaFTL2) indicated that local selection in the 18 populations, and/or selection in the ancestral populations from which they were recently derived, shaped the observed cline. Validation of these genes using expression studies also revealed that PaFTL2 expression is significantly associated with latitude, thereby confirming the central role played by this gene in the control of phenology in plants.

Multilocus Patterns of Nucleotide Diversity and Divergence Reveal Positive Selection at Candidate Genes Related to Cold-hardiness in Coastal Douglas-fir (Pseudotsuga menziesii var. menziesii)

Forest trees exhibit remarkable adaptations to their environments. The genetic basis for phenotypic adaptation to climatic gradients has been established through a long history of common garden, provenance, and genecological studies. The identities of genes underlying these traits, however, have remained elusive and thus so have the patterns of adaptive molecular diversity in forest tree genomes. Here, we report an analysis of diversity and divergence for a set of 121 cold-hardiness candidate genes in coastal Douglas fir (Pseudotsuga menziesii var. menziesii). Application of several different tests for neutrality, including those that incorporated demographic models, revealed signatures of selection consistent with selective sweeps at three to eight loci, depending upon the severity of a bottleneck event and the method used to detect selection. Given the high levels of recombination, these candidate genes are likely to be closely linked to the target of selection if not the genes themselves. Putative homologs in Arabidopsis act primarily to stabilize the plasma membrane and protect against denaturation of proteins at freezing temperatures. These results indicate that surveys of nucleotide diversity and divergence, when framed within the context of further association mapping experiments, will come full circle with respect to their utility in the dissection of complex phenotypic traits into their genetic components.

Association Mapping of Quantitative Disease Resistance in a Natural Population of Loblolly Pine (Pinus taeda L.)

Genetic resistance to disease incited by necrotrophic pathogens is not well understood in plants. Whereas resistance is often quantitative, there is limited information on the genes that underpin quantitative variation in disease resistance. We used a population genomic approach to identify genes in loblolly pine (Pinus taeda) that are associated with resistance to pitch canker, a disease incited by the necrotrophic pathogen Fusarium circinatum. A set of 498 largely unrelated, clonally propagated genotypes were inoculated with F. circinatum microconidia and lesion length, a measure of disease resistance, data were collected 4, 8, and 12 weeks after inoculation. Best linear unbiased prediction was used to adjust for imbalance in number of observations and to identify highly susceptible and highly resistant genotypes (“tails”). The tails were reinoculated to validate the results of the full population screen. Significant associations were detected in 10 single nucleotide polymorphisms (SNPs) (out of 3938 tested). As hypothesized for genes involved in quantitative resistance, the 10 SNPs had small effects and proposed roles in basal resistance, direct defense, and signal transduction. We also discovered associated genes with unknown function, which would have remained undetected in a candidate gene approach constrained by annotation for disease resistance or stress response.