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Genetically improving constitutive resin canal development in Pinus stems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association with RCN in two growth rings and three environments in a complex pedigree of 520 Pinus taeda individuals (CCLONES). The map locations of associated genes were compared with RCN quantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottii pseudo‐backcross of 345 full‐sibs (BC1). Resin canal number was heritable (h² ˜ 0.12–0.21) and positively genetically correlated with xylem growth (rg ˜ 0.32–0.72) and oleoresin flow (rg ˜ 0.15–0.51). Sixteen well‐supported candidate regulators of RCN were discovered in CCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped to RCN QTLs in BC1. Breeding is predicted to increase RCN 11% in one generation and could be accelerated with genomic selection at accuracies of 0.45–0.52 across environments. There is significant genetic variation for RCN in loblolly pine, which can be exploited in breeding for elevated terpene content.

Quantitative genetic analyses aim to estimate genetic parameters and breeding values to select superior parents, families, and individuals. For these estimates a relationship matrix derived from the pedigree typically is used in a mixed model framework. However, breeding is a complex, multistep process and errors in the pedigree are common. Because errors reduce the accuracy of genetic parameter estimates and affect genetic gain, it is important to correct these errors. Here we show that a realized relationship matrix (RRM) derived from single nucleotide polymorphism markers based on the normality of the relationship coefficients can be used to correct pedigree errors. For a loblolly pine (Pinus taeda L.) breeding population, errors in the pedigree were detected and corrected with the RRM. With the corrected pedigree, best linear unbiased predictor (BLUP) models fit the data significantly better for 14 out of 15 traits evaluated, and the predictive ability of the genomic selection models using ridge regression BLUP increased for 13 traits. The corrected pedigree based on the normality of the relationship coefficients improves accuracy of traditional estimations of heritability and breeding values as well as genomic selection predictions. As more breeding programs begin to use genomic selection, we recommend first using the dense panel of markers to correct pedigree errors and then using the improved information to develop genomic selection prediction models.

• 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.

Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h−1) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in the southeastern United States. Additive genetic variation in oleoresin flow (h 2 ≈ 0.12–0.30) was strongly correlated between years in which precipitation varied (r a ≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231 SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites. SNPs in sequences similar to ethylene signaling proteins, ABC transporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5- to 2.4-fold in one generation.

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.

Fusiform rust resistance in loblolly pine, Pinus taeda L., is comprised of both polygenic and major gene effects. In this study, we applied a Bayesian approach to test for single nucleotide polymorphisms (SNPs) associated with resistance to fusiform rust, which is caused by Cronartium quercuum (Berk.) Miyabe ex Shirai f.sp. fusiforme. A population of 3810 clonal varieties from 100 full-sib families was produced through somatic embryogenesis and planted in replicated field experiments in the Southeastern United States. Rust phenotypes were measured after 6 growing seasons, and each of 863 clones from 35 full-sib families was genotyped with 3340 SNPs. We identified five associated SNPs with posterior probabilities of inclusion above 0.4 in the clonal population using a BayesCπ model. A second experiment was conducted with one of the identified SNPs (SNP2374) using seedlings from 2 full-sib families that were included in the first experiment. Resistant genotypes exhibited an overall infection rate of 11% compared to the susceptible genotype which had 57% infection. Using one of the full-sib families, we were able to map SNP2374 (CL1208Contig1-03-75) to linkage group 7. Confirmation of the SNP2374 association with rust resistance demonstrates the potential for selecting and screening genotypes to further reduce disease incidence in forest plantations of loblolly pine.

Eucalyptus amplifolia and Corymbia torelliana genetic improvement has been conducted in the lower southeastern USA by UF and collaborators since 1980. The collective accomplishments in genetic resources and potential commercial uses are summarized. For example, fast-growing, freeze-resilient E. amplifolia seeds are provided by 1st and 2nd generation seedling seed orchards (SSO) and a 2nd generation clonal seed orchard (CSO), while C. torelliana seed are available from 1st and 2nd generation SSOs. Breeding values (BV) have been developed for guiding the deployment of improved genotypes. Collaborative genetic improvement of these species is ongoing, including testing E. amplifolia in 11 countries and development of hybrid clones. Short Rotation Woody Crop (SRWC) systems may increase productivity and extend uses beyond conventional mulchwood to products such as medium density fiberboard (MDF), biochar, and energywood, while other possible applications include honey production, windbreaks, dendroremediation, and carbon sequestration. C. torelliana may be paired with E. grandis in two-row windbreaks to maximum windbreak effectiveness and may sequester as much carbon as E. grandis.

Southern pine genetic improvement programs have selected for faster early growth which has often increased yields over unimproved material, and some of this improvement is likely attributable to variation in growth phenology among genotypes. However, the genetics of shoot growth phenology traits are not well characterized. Loblolly pine cuttings and seedlings from parents originating in the Atlantic coastal plain (ACP) and Florida and grown on sites established in Palatka, FL and Cuthbert, GA were assessed for shoot phenology and growth traits during the second year and for growth in year 6. Individual-tree clonal repeatability in different growth and shoot phenology traits varied from 0.09 to 0.79 in cuttings, and was lower in Palatka than Cuthbert. Non-additive components of heritability were lower, with a few exceptions, than additive effects. Additive and genotypic correlations across sites were high (>0.6) for all traits measured in cuttings and for most seedling traits, suggesting low genotype × environment interactions between these two sites. Compared with progeny from crosses between ACP parents, progeny of Florida parents started growth earlier in the season and ended later. Strong genetic correlations were observed between second-year phenology traits and sixth-year height and diameter. This suggests some two-year traits could be useful for early selection of high-performing genotypes.

Several techniques to control for spatial heterogeneity in breeding trials were compared through the use of simulated data for a field site with 256 genotypes (i.e., treatments). Various experimental designs, error structures, and polynomial functions were modeled. The error structures studied included first-order autoregressive with and without measurement error (or nugget) and independent errors. Also, several nearest neighbor methods (Papadakis [PAP] and moving average [MA]) were used. The results indicated that, of models with independent errors, row-column designs gave the best correlation between the predicted and true treatment effects (CORR). Once the autoregressive error structure, with or without nugget, was incorporated, CORR values were even higher. Also, failing to incorporate the nugget produced bias in the correlation parameters of the error structure. Nearest neighbor technique were also among the best options, where some variants of the Papadakis method were almost as good as models that incorporated the error structure.

Field trials established with clones and seedlings from the same families provide an opportunity for comparing full-sib family performance across propagule types. More than 1200 different clones together with over 14 000 zygotic seedlings from the same 61 full-sib families of loblolly pine (Pinus taeda L.) were tested on multiple sites across Florida and Georgia. The genetic variance associated with several early growth traits partitioned differently depending on propagule type. Most of the genetic variance associated with growth in the clonal population was additive, while the estimate of dominance in the seedling population was greater than estimates of dominance in the clonal population, based on single-site analyses. Apparently, a lack of randomization of the seedlings prior to field establishment caused full-sib families to appear more different, inflating estimates of dominance genetic variance. Parental and full-sib family ranks were stable regardless of propagule type as indicated by type B genetic correlations. In the clonal population, little genotype x environment interaction was observed across sites at the parental, family, and clonal levels for all traits. The high genetic correlations between propagule types provide further assurance that selections made through traditional tree-improvement activities for recurrent selection for general combining ability in seedling trials can also be used successfully for breeding families to test in a clonal forestry program.