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Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material. [ABSTRACT FROM AUTHOR]

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant's cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids' qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ9-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances. [ABSTRACT FROM AUTHOR]

Potatoes are an important human food crop, but have a number of yield limiting factors, including disease susceptibility. Potato virus Y (PVY) is found worldwide, and is one of the main virus problems for potato growers. PVY is transmitted by aphids and mechanically by machinery, tools and people, and symptoms are variable across cultivars and strains, including being symptomless in some cultivars. Therefore, breeding resistant cultivars is the best way to control this virus. This study phenotypically screened 74 of the main commercial cultivars and a few other select cultivars grown in Australia, in order to identify sources of resistance to PVY. The cultivars were screened against PVYO and PVYNTN, with 23 out of 71 resistant to PVYO and 13 out of 74 resistant to PVYNTN, and all these 13 were resistant to both strains. When the phenotypic screening was compared to the results listed on the European Cultivated Potato Database, the majority of results were found to be consistent. We then evaluated three molecular markers RYSC3, M45, and STM0003 for the extreme resistance genes Ryadg and Rysto, to validate the usefulness of the markers for marker-assisted selection (MAS) on Australian germplasm. The degree of correlation between the resistance phenotypes and the RYSC3, M45, and STM0003 markers for Ryadg and Rysto conferred PVY resistance was determined. Three cultivars amplified the RYSC3 marker, while the M45 marker amplified the same 3 and an additional 9. Of the 12 cultivars, 11 phenotyped as resistant, but 1 was susceptible. The STM0003 marker was amplified from only 2 cultivars that both had resistant phenotypes. The RYSC3, M45, and STM0003 markers were therefore able to identify all the 13 cultivars that were resistant to both strains of PVY. Therefore, these markers will enable the identification of genotypes with resistance to PVY, and enable PVY resistant parents to be used for the development of superior progeny; these genetic markers can be used for MAS in the Australian potato breeding program. [ABSTRACT FROM AUTHOR]

RNA-Seq using second-generation sequencing technologies permits generation of a reference unigene set for a given species, in the absence of a well-annotated genome sequence, supporting functional genomics studies, gene characterisation and detailed expression analysis for specific morphophysiological or environmental stress response traits. A reference unigene set for lentil has been developed, consisting of 58,986 contigs and scaffolds with an N50 length of 1719 bp. Comparison to gene complements from related species, reference protein databases, previously published lentil transcriptomes and a draft genome sequence validated the current dataset in terms of degree of completeness and utility. A large proportion (98%) of unigenes were expressed in more than one tissue, at varying levels. Candidate genes associated with mechanisms of tolerance to both boron toxicity and time of flowering were identified, which can eventually be used for the development of gene-based markers. This study has provided a comprehensive, assembled and annotated reference gene set for lentil that can be used for multiple applications, permitting identification of genes for pathway-specific expression analysis, genetic modification approaches, development of resources for genotypic analysis, and assistance in the annotation of a future lentil genome sequence. [ABSTRACT FROM AUTHOR]

Detection of genetic diversity is important for characterisation of crop plant collections in order to detect the presence of valuable trait variation for use in breeding programs. A collection of faba bean (Vicia faba L.) genotypes was evaluated for intra- and inter-population diversity using a set of 768 genome-wide distributed single nucleotide polymorphism (SNP) markers, of which 657 obtained successful amplification and detected polymorphisms. Gene diversity and polymorphism information content (PIC) values varied between 0.022-0.500 and 0.023-1.00, with averages of 0.363 and 0.287, respectively. The genetic structure of the germplasm collection was analysed and a neighbour-joining (NJ) dendrogram was constructed. The faba bean accessions grouped into two major groups, with several additional smaller sub-groups, predominantly on the basis of geographical origin. These results were further supported by principal co-ordinate analysis (PCoA), deriving two major groupings which were differentiated on the basis of site of origin and pedigree relationships. In general, high levels of heterozygosity were observed, presumably due to the partially allogamous nature of the species. The results will facilitate targeted crossing strategies in future faba bean breeding programs in order to achieve genetic gain. [ABSTRACT FROM AUTHOR]

Advancements in high-throughput genotyping and sequencing technologies are enabling the development of a vast range of genomic tools and resources for a new revolution in plant breeding. Several genotyping-by-sequencing (GBS) methods including capture-based, genome complexity reduction and sequencing of cDNA (GBS-t) are available for application in trait dissection, association mapping, and genomic selection (GS) in crop plants. The aims of this study were to identify genomic regions conferring resistance to Ascochyta blight (AB) introgressed from the wild Cicer echinospernum into the domesticated C. arietinum, through a conventional recombinant inbred population genotyped using a variety of GBS methods. Evaluation of GBS methods revealed that capture-based approaches are robust and reproducible while GBS-t is rapid and flexible. A genetic linkage map consisting of 5886 polymorphic loci spanning 717.26 cM was generated. Using field phenotyping data from two years, a single genomic region on LG4 was identified with quantitative trait loci (QTL) mapping. Both GBS methods reported in this study are well suited for applications in genomics assisted plant breeding. Linked markers for AB resistance, identified in the current study, provide an important resource for the deployment into chickpea breeding programs for marker-assisted selection (MAS). [ABSTRACT FROM AUTHOR]

Potato is an important food crop worldwide and is grown in a large number of countries. As such, the crop is under disease pressures and the need for selecting disease resistance genes during breeding programs is essential. Of particular importance within Australia and other parts of the world is the potyvirus, Potato virus Y (PVY). In this paper, three commonly used PVY resistance markers, M45, RYSC3 and M6, were evaluated using existing genomic resources and phenotypic data from the Australian potato breeding program to identify a region where the PVY resistance gene, Ryadg may reside. A region of Chromosome XI was investigated, and a cluster of disease resistance genes was identified that the resistance gene Ryadg is suspected to reside within. Protein characterization was also performed on the putative resistant gene. A specific variant that had complete association with the resistance gene was identified and a single nucleotide polymorphism (SNP) assay was designed to avoid dissociation of marker and gene in future breeding programs. This SNP marker (SNP37279) was validated as a Kompetitive Allele-specific PCR (KASP) genotyping assay and was found to perform more accurately than all previously used markers for detecting Ryadg. [ABSTRACT FROM AUTHOR]

Soil salinity is a major abiotic stress in Australian lentil-producing areas. It is therefore imperative to identify genetic variation for salt tolerance in order to develop lentil varieties suitable for saline soils. Conventional screening methods include the manual assessment of stress symptoms, which can be very laborious, time-consuming, and error-prone. Recent advances in image-based high-throughput phenotyping (HTP) technologies have provided unparalleled opportunities to screen plants for a range of stresses, such as salt toxicity. The current study describes the development and application of an HTP method for salt toxicity screening in lentils. In a pilot study, six lentil genotypes were evaluated to determine the optimal salt level and the growth stage for distinguishing lentil genotypes using red–green–blue (RGB) images on a LemnaTec Scanalyzer 3D phenomics platform. The optimized protocol was then applied to screen 276 accessions that were also assessed earlier in a conventional phenotypic screen. Detailed phenotypic trait assessments, including plant growth and green/non-green color pixels, were made and correlated to the conventional screen (r = 0.55; p < 0.0001). These findings demonstrated the improved efficacy of an image-based phenotyping approach that is high-throughput, efficient, and better suited to modern breeding programs. [ABSTRACT FROM AUTHOR]

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding. [ABSTRACT FROM AUTHOR]

Soil salinity is a major abiotic stress, limiting lentil productivity worldwide. Understanding the genetic basis of salt tolerance is vital to develop tolerant varieties. A diversity panel consisting of 276 lentil accessions was screened in a previous study through traditional and image-based approaches to quantify growth under salt stress. Genotyping was performed using two contrasting methods, targeted (tGBS) and transcriptome (GBS-t) genotyping-by-sequencing, to evaluate the most appropriate methodology. tGBS revealed the highest number of single-base variants (SNPs) (c. 56,349), and markers were more evenly distributed across the genome compared to GBS-t. A genome-wide association study (GWAS) was conducted using a mixed linear model. Significant marker-trait associations were observed on Chromosome 2 as well as Chromosome 4, and a range of candidate genes was identified from the reference genome, the most plausible being potassium transporters, which are known to be involved in salt tolerance in related species. Detailed mineral composition performed on salt-treated and control plant tissues revealed the salt tolerance mechanism in lentil, in which tolerant accessions do not transport Na + ions around the plant instead localize within the root tissues. The pedigree analysis identified two parental accessions that could have been the key sources of tolerance in this dataset.