A transcriptome-based association study of growth, wood quality, and oleoresin traits in a slash pine breeding population.

Slash pine (Pinus elliottii Engelm.) is an important timber and resin species in the United States, China, Brazil and other countries. Understanding the genetic basis of these traits will accelerate its breeding progress. We carried out a genome-wide association study (GWAS), transcriptome-wide association study (TWAS) and weighted gene co-expression network analysis (WGCNA) for growth, wood quality, and oleoresin traits using 240 unrelated individuals from a Chinese slash pine breeding population. We developed high quality 53,229 single nucleotide polymorphisms (SNPs). Our analysis reveals three main results: (1) the Chinese breeding population can be divided into three genetic groups with a mean inbreeding coefficient of 0.137; (2) 32 SNPs significantly were associated with growth and oleoresin traits, accounting for the phenotypic variance ranging from 12.3% to 21.8% and from 10.6% to 16.7%, respectively; and (3) six genes encoding PeTLP, PeAP2/ERF, PePUP9, PeSLP, PeHSP, and PeOCT1 proteins were identified and validated by quantitative real time polymerase chain reaction for their association with growth and oleoresin traits. These results could be useful for tree breeding and functional studies in advanced slash pine breeding program.

Genetic analysis and elite tree selection of the main resin components of slash pine.

Pine resin, as a natural material, has been widely used in food, pharmaceutical, and chemical industries. Slash pine (Pinus elliottii Engelm var. elliottii) is the primary tree species for resin tapping due to its high resin yield, low resin crystallization rate, and high turpentine content. Current researches focuse on the targeted improvement of several significant components to meet industrial needs rather than just resin yield. The objective of this study was to examine the genetic variation and correlation of genetic and phenotype for four main resin components (α pinene, ß pinene, abietic acid, and levoprimaric acid) of 219 half-sib progenies from 59 families. The results showed that the levopimaric acid had the largest content (mean value = 21.63%), while the ß pinene content had the largest variation coefficient (CV = 0.42). The α pinene content has the highest heritability (h2 = 0.67), while levopimaric acid has the lowest heritability (h2 = 0.51). There was a significant negative correlation between α pinene and the other three components and a significant positive correlation between ß pinene and the two diterpenes. The family ranking and genetic gain suggested that it is possible to improve the contents of main resin components of slash pine through genetic breeding selection.

Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy.

Pine resin is one of the best known and most exploited non-wood products. Resin is a complex mixture of terpenes produced by specialized cells that are dedicated to tree defense. Chemical defenses are plastic properties, and concentrations of chemical defenses can be adjusted based on environmental factors, such as resource availability. The slope orientation (south/sunny or north/shady) and the altitude of the plantation site have significant effects on the soil nutrient and the plant performance, whereas little is known about how the slope affects the pine resin yield and its components. In total, 1180 slash pines in 18 plots at different slope positions were established to determine the effects on the α- and ß-pinene content and resin production of the slash pine. The near-infrared spectroscopy (NIR) technique was developed to rapidly and economically predict the turpentine content for each sample. The results showed that the best partial least squares regression (PLS) models for α- and ß-pinene content prediction were established via the combined treatment of multiplicative scatter correction-significant multivariate correlation (MSC-sMC). The prediction models based on sMC spectra for α- and ß-pinene content have an R2 of 0.82 and 0.85 and an RMSE of 0.96 and 0.82, respectively, and they were successfully implemented in turpentine prediction in this research. The results also showed that a barren slope position (especially mid-slope) could improve the α-pinene and ß-pinene content and resin productivity of slash pine, and the ß-pinene content in the resin had more variances in this research.