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.

Summary• The metabolome of a plant comprises all small molecule metabolites, which are produced during cellular processes. The genetic basis for metabolites in nonmodel plants is unknown, despite frequently observed correlations between metabolite concentrations and stress responses. A quantitative genetic analysis of metabolites in a nonmodel plant species is thus warranted.• Here, we use standard association genetic methods to correlate 3563 single nucleotide polymorphisms (SNPs) to concentrations of 292 metabolites measured in a single loblolly pine (Pinus taeda) association population.• A total of 28 single locus associations were detected, representing 24 and 20 unique SNPs and metabolites, respectively. Multilocus Bayesian mixed linear models identified 2998 additional associations for a total of 1617 unique SNPs associated to 255 metabolites. These SNPs explained sizeable fractions of metabolite heritabilities when considered jointly (56.6% on average) and had lower minor allele frequencies and magnitudes of population structure as compared with random SNPs.• Modest sets of SNPs (n = 1-23) explained sizeable portions of genetic effects for many metabolites, thus highlighting the importance of multi-SNP models to association mapping, and exhibited patterns of polymorphism consistent with being linked to targets of natural selection. The implications for association mapping in forest trees are discussed.

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A primary goal of evolutionary genetics is to discover and explain the genetic basis of fitness-related traits and how this genetic basis evolves within natural populations. Unprecedented technological advances have fueled the discovery of genetic variants associated with ecologically relevant phenotypes in many different life forms, as well as the ability to scan genomes for deviations from selectively neutral models of evolution. Theoretically, the degree of overlap between lists of genomic regions identified using each approach is related to the genetic architecture of fitness-related traits and the strength and type of natural selection molding variation at these traits within natural populations. Here we address for the first time in a plant the degree of overlap between these lists, using patterns of nucleotide diversity and divergence for .7000 unique amplicons described from the extensive expressed sequence tag libraries generated for loblolly pine (Pinus taeda L.) in combination with the .1000 published genetic associations. We show that loci associated with phenotypic traits are distinct with regard to neutral expectations. Phenotypes measured at the whole plant level (e.g., disease resistance) exhibit an approximately twofold increase in the proportion of adaptive nonsynonymous substitutions over the genome-wide average. As expected for polygenic traits, these signals were apparent only when loci were considered at the level of functional sets. The ramifications of this result are discussed in light of the continued efforts to dissect the genetic basis of quantitative traits.A primary goal of population and quantitative genetics is to understand the genetic architecture of ecologically relevant traits (Stinchcombe and Hoekstra 2008; Barrett and Hoekstra 2011;Neale and Kremer 2011). A primary step on the path to this goal is to link genetic with phenotypic variation, either through linkage mapping of quantitative trait loci using pedigrees or through linkage disequilibrium mapping in natural populations (Lander and Schork 1994), with the latter currently being the most utilized. A multitude of studies ranging across a diverse set of taxa have discovered myriad genotype-phenotype correlations (Hindorff et al. 2009;Ingvarsson and Street 2011;Neale and Kremer 2011). Each discovered variant, however, often explains only a small fraction of the heritable phenotypic variance, thus being consistent with a polygenic model for the genetic architecture of complex traits (Lynch and Walsh 1998). Concomitant with the discovery of these associations are population genomic scans documenting deviations from expectations derived using the neutral theory (Nielsen 2005). Such scans have also discovered large numbers of loci putatively underlying phenotypic traits in many different taxa (e.g., Pollinger et al. 2005;Pritchard et al. 2010;Hufford et al. 2012), but in this case the link between genotype and phenotype is not explicit. A natural question thus arises about how much overlap exists between the lists gen...

Gene expression analyses using native populations can contribute to the understanding of plant development and adaptation in multiple ways. These include the identification of candidate genes and genetic polymorphisms affecting expression and phenotypic traits and characterization of transcriptional networks. We analyzed the expression of 111 genes with probable roles in xylem/wood development in a population of loblolly pine (Pinus taeda L.) covering much of the natural range. Loblolly pine is one of the most commercially important forest tree species in the United States, and the discovery of genes and alleles contributing to desirable wood properties would be valuable. Of the 111 genes analyzed using quantitative reverse transcriptionpolymerase chain reaction, there were significant differences in gene expression between clones for 106 genes.Genes encoding lignin biosynthetic enzymes and arabinogalactan proteins were more variable than those encoding cellulose synthases or those involved in signal transduction. Several groups of genes with related functions form clusters. A network analysis identified transcription factors that may be key regulators of xylem development in pine. Secondary wall-associated NAC domain protein 1 (SND1) in particular appears to be involved in the regulation of many other genes. The cluster analysis using clones did not result in discrete populations but did identify some expression differences between regions. In the future, the gene expression data will be used for association analyses and promoter studies to understand how these gene expression differences are associated with specific genetic polymorphisms in other genes and promoters.