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.

Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration.

The quality of Chinese quince fruit is a significant factor for medicinal materials, influencing the quality of the medicine. However, it is difficult to distinguish different types of Chinese quince fruit. The main objective of this work was to use near-infrared (NIR) spectroscopy, which is a rapid and non-destructive analysis method, to classify the varieties of Chinese quince fruits. Raw spectra in the range of 1000 to 2500 nm were combined with linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machines (SVMs) for classification. The first three principal component analysis (PCA) scores were used as input variables to build LDA, QDA, and SVM discriminant models. The results indicate that all three of these methods are effective for distinguishing the different types of Chinese quince fruit. The classification accuracies for LDA, QDA, and SVM are 94, 96, and 98 %, respectively. QDA led to high-level classification accuracy of Chinese quince fruit.