Empirical Evaluation of Inflorescences' Morphological Attributes for Yield Optimization of Medicinal Cannabis Cultivars.

In recent decades with the reacknowledgment of the medicinal properties of L. (cannabis) plants, there is an increased demand for high performing cultivars that can deliver quality products for various applications. However, scientific knowledge that can facilitate the generation of advanced cannabis cultivars is scarce.

Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs.

Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs. Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield.

The characterization of key physiological traits of medicinal cannabis (Cannabis sativa L.) as a tool for precision breeding.

Background: For millennia, drug-type cannabis strains were extensively used for various medicinal, ritual, and inebriant applications. However, cannabis prohibition during the last century led to cultivation and breeding activities being conducted under clandestine conditions, while scientific development of the crop ceased. Recently, the potential of medicinal cannabis has been reacknowledged and the now expanding industry requires optimal and scientifically characterized varieties.

Boosting Genetic Gain in Allogamous Crops Speed Breeding and Genomic Selection.

Breeding schemes that utilize modern breeding methods like genomic selection (GS) and speed breeding (SB) have the potential to accelerate genetic gain for different crops. We investigated through stochastic computer simulation the advantages and disadvantages of adopting both GS and SB (SpeedGS) into commercial breeding programs for allogamous crops. In addition, we studied the effect of omitting one or two selection stages from the conventional phenotypic scheme on GS accuracy, genetic gain, and inbreeding.

Using Sensors and Unmanned Aircraft Systems for High-Throughput Phenotyping of Biomass in Perennial Ryegrass Breeding Trials.

Increasing herbage biomass is the predominant objective for pasture plant breeding programs. Three types of field trials are commonly involved during forage plant breeding, i.e.

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations.

Genotyping-by-sequencing through transcriptomics: implementation in a range of crop species with varying reproductive habits and ploidy levels.

The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker-dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost-effectiveness and sample throughput.

Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass.

Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass () improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000.

Targeted genotyping-by-sequencing permits cost-effective identification and discrimination of pasture grass species and cultivars.

A targeted amplicon-based genotyping-by-sequencing approach has permitted cost-effective and accurate discrimination between ryegrass species (perennial, Italian and inter-species hybrid), and identification of cultivars based on bulked samples. Perennial ryegrass and Italian ryegrass are the most important temperate forage species for global agriculture, and are represented in the commercial pasture seed market by numerous cultivars each composed of multiple highly heterozygous individuals. Previous studies have identified difficulties in the use of morphophysiological criteria to discriminate between these two closely related taxa.

Design of an F1 hybrid breeding strategy for ryegrasses based on selection of self-incompatibility locus-specific alleles.

Relatively modest levels of genetic gain have been achieved in conventional ryegrass breeding when compared to cereal crops such as maize, current estimates indicating an annual improvement of 0.25-0.6% in dry matter production.

StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations.

Statistical Analysis of Mixed-Ploidy Populations (StAMPP) is a freely available R package for calculation of population structure and differentiation based on single nucleotide polymorphism (SNP) genotype data from populations of any ploidy level, and/or mixed-ploidy levels. StAMPP provides an advance on previous similar software packages, due to an ability to calculate pairwise FST values along with confidence intervals, Nei's genetic distance and genomic relationship matrixes from data sets of mixed-ploidy level. The software code is designed to efficiently handle analysis of large genotypic data sets that are typically generated by high-throughput genotyping platforms.

Transcriptome sequencing of field pea and faba bean for discovery and validation of SSR genetic markers.

Background: Field pea (Pisum sativum L.) and faba bean (Vicia faba L.) are cool-season grain legume species that provide rich sources of food for humans and fodder for livestock.

Transcriptome sequencing of lentil based on second-generation technology permits large-scale unigene assembly and SSR marker discovery.

Background: Lentil (Lens culinaris Medik.) is a cool-season grain legume which provides a rich source of protein for human consumption. In terms of genomic resources, lentil is relatively underdeveloped, in comparison to other Fabaceae species, with limited available data.