Characterization of indigenous populations of cannabis in Iran: a morphological and phenological study.

BACKGROUND: Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars. RESULTS: Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g- 1.day- 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants. CONCLUSIONS: Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes.

Performance analysis of conventional and AI-based variant callers using short and long reads.

BACKGROUND: The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use. RESULTS: In this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons. CONCLUSION: This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

Identification of fusarium head blight resistance markers in a genome-wide association study of CIMMYT spring synthetic hexaploid derived wheat lines.

Fusarium head blight (FHB), caused by Fusarium graminearum, is one of the most destructive wheat diseases worldwide. FHB infection can dramatically reduce grain yield and quality due to mycotoxins contamination. Wheat resistance to FHB is quantitatively inherited and many low-effect quantitative trait loci (QTL) have been mapped in the wheat genome. Synthetic hexaploid wheat (SHW) represents a novel source of FHB resistance derived from Aegilops tauschii and Triticum turgidum that can be transferred into common wheat (T. aestivum). In this study, a panel of 194 spring Synthetic Hexaploid Derived Wheat (SHDW) lines from the International Maize and Wheat Improvement Center (CIMMYT) was evaluated for FHB response under field conditions over three years (2017-2019). A significant phenotypic variation was found for disease incidence, severity, index, number of Fusarium Damaged Kernels (FDKs), and deoxynivalenol (DON) content. Further, 11 accessions displayed < 10 ppm DON in 2017 and 2019. Genotyping of the SHDW panel using a 90 K Single Nucleotide Polymorphism (SNP) chip array revealed 31 K polymorphic SNPs with a minor allele frequency (MAF) > 5%, which were used for a Genome-Wide Association Study (GWAS) of FHB resistance. A total of 52 significant marker-trait associations for FHB resistance were identified. These included 5 for DON content, 13 for the percentage of FDKs, 11 for the FHB index, 3 for disease incidence, and 20 for disease severity. A survey of genes associated with the markers identified 395 candidate genes that may be involved in FHB resistance. Collectively, our results strongly support the view that utilization of synthetic hexaploid wheat in wheat breeding would enhance diversity and introduce new sources of resistance against FHB into the common wheat gene pool. Further, validated SNP markers associated with FHB resistance may facilitate the screening of wheat populations for FHB resistance.

Genomics-based taxonomy to clarify cannabis classification.

In the 18th century, Carolus Linnaeus created a formalized system of classification of living organisms based on their anatomic relationships, which we know as taxonomic nomenclature. Historically, the genus Cannabis has been described three ways under this system: Cannabis sativa by C. Linnaeus in 1753, Cannabis indica by J.B. Lamarck in 1785, and Cannabis ruderalis by D.E. Janischewsky in 1924, with these taxonomic classifications having been derived from physical, morphological, chemical, and geographical data. Today, this confusing taxonomy has led to an ongoing debate about whether the genus Cannabis consists of a single species or multiple distinct species or subspecies. Recently, genome sequencing and bioinformatics have provided greater resolution of taxonomic assignments at the species level. As a result, some previously discussed classification frameworks have been brought into question. The aim of this review is to provide a historical context for the confusion surrounding the taxonomy of the genus Cannabis and highlight recent research on genomics-based taxonomical approaches to clarify the question of Cannabis taxonomy. We suggest that the latest evidence shifts away from the previous multiple species framework and points towards the genus Cannabis consisting of a highly diverse monotypic species.

Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology.

BACKGROUND: Structural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars. RESULTS: We used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE. CONCLUSIONS: We show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

Soybean-SCN Battle: Novel Insight into Soybean's Defense Strategies against Heterodera glycines.

Soybean cyst nematode (SCN, Heterodera glycines, Ichinohe) poses a significant threat to global soybean production, necessitating a comprehensive understanding of soybean plants' response to SCN to ensure effective management practices. In this study, we conducted dual RNA-seq analysis on SCN-resistant Plant Introduction (PI) 437654, 548402, and 88788 as well as a susceptible line (Lee 74) under exposure to SCN HG type 1.2.5.7. We aimed to elucidate resistant mechanisms in soybean and identify SCN virulence genes contributing to resistance breakdown. Transcriptomic and pathway analyses identified the phenylpropanoid, MAPK signaling, plant hormone signal transduction, and secondary metabolite pathways as key players in resistance mechanisms. Notably, PI 437654 exhibited complete resistance and displayed distinctive gene expression related to cell wall strengthening, oxidative enzymes, ROS scavengers, and Ca2+ sensors governing salicylic acid biosynthesis. Additionally, host studies with varying immunity levels and a susceptible line shed light on SCN pathogenesis and its modulation of virulence genes to evade host immunity. These novel findings provide insights into the molecular mechanisms underlying soybean-SCN interactions and offer potential targets for nematode disease management.

Transcriptomic Profiling of Embryogenic and Non-Embryogenic Callus Provides New Insight into the Nature of Recalcitrance in Cannabis.

Differential gene expression profiles of various cannabis calli including non-embryogenic and embryogenic (i.e., rooty and embryonic callus) were examined in this study to enhance our understanding of callus development in cannabis and facilitate the development of improved strategies for plant regeneration and biotechnological applications in this economically valuable crop. A total of 6118 genes displayed significant differential expression, with 1850 genes downregulated and 1873 genes upregulated in embryogenic callus compared to non-embryogenic callus. Notably, 196 phytohormone-related genes exhibited distinctly different expression patterns in the calli types, highlighting the crucial role of plant growth regulator (PGRs) signaling in callus development. Furthermore, 42 classes of transcription factors demonstrated differential expressions among the callus types, suggesting their involvement in the regulation of callus development. The evaluation of epigenetic-related genes revealed the differential expression of 247 genes in all callus types. Notably, histone deacetylases, chromatin remodeling factors, and EMBRYONIC FLOWER 2 emerged as key epigenetic-related genes, displaying upregulation in embryogenic calli compared to non-embryogenic calli. Their upregulation correlated with the repression of embryogenesis-related genes, including LEC2, AGL15, and BBM, presumably inhibiting the transition from embryogenic callus to somatic embryogenesis. These findings underscore the significance of epigenetic regulation in determining the developmental fate of cannabis callus. Generally, our results provide comprehensive insights into gene expression dynamics and molecular mechanisms underlying the development of diverse cannabis calli. The observed repression of auxin-dependent pathway-related genes may contribute to the recalcitrant nature of cannabis, shedding light on the challenges associated with efficient cannabis tissue culture and regeneration protocols.

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study.

KEY MESSAGE: Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.

Genetic analysis of sucrose concentration in soybean seeds using a historical soybean genomic panel.

KEY MESSAGE: Significant QTL for sucrose concentration have been identified using a historical soybean genomic panel, which could aid in the development of food-grade soybean cultivars. Soybean (Glycine max (L.) Merr) is a crop of global importance for both human and animal consumption, which was domesticated in China more than 6000 years ago. A concern about losing genetic diversity as a result of decades of breeding has been expressed by soybean researchers. In order to develop new cultivars, it is critical for breeders to understand the genetic variability present for traits of interest in their program germplasm. Sucrose concentration is becoming an increasingly important trait for the production of soy-food products. The objective of this study was to use a genome-wide association study (GWAS) to identify putative QTL for sucrose concentration in soybean seed. A GWAS panel consisting of 266 historic and current soybean accessions was genotyped with 76 k genotype-by-sequencing (GBS) SNP data and phenotyped in four field locations in Ontario (Canada) from 2015 to 2017. Seven putative QTL were identified on chromosomes 1, 6, 8, 9, 10, 13 and 14. A key gene related to sucrose synthase (Glyma.06g182700) was found to be associated with the QTL located on chromosome 6. This information will facilitate efforts to increase the available genetic variability for sucrose concentration in soybean breeding programs and develop new and improved high-sucrose soybean cultivars suitable for the soy-food industry.

Machine learning: its challenges and opportunities in plant system biology.

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

The pan-genome of the cultivated soybean (PanSoy) reveals an extraordinarily conserved gene content.

Studies on structural variation in plants have revealed the inadequacy of a single reference genome for an entire species and suggest that it is necessary to build a species-representative genome called a pan-genome to better capture the extent of both structural and nucleotide variation. Here, we present a pan-genome of cultivated soybean (Glycine max), termed PanSoy, constructed using the de novo genome assembly of 204 phylogenetically and geographically representative improved accessions selected from the larger GmHapMap collection. PanSoy uncovers 108 Mb (˜11%) of novel nonreference sequences encompassing 3621 protein-coding genes (including 1659 novel genes) absent from the soybean 'Williams 82' reference genome. Nonetheless, the core genome represents an exceptionally large proportion of the genome, with >90.6% of genes being shared by >99% of the accessions. A majority of PAVs encompassing genes could be confirmed with long-read sequencing on a subset of accessions. The PanSoy is a major step towards capturing the extent of genetic variation in cultivated soybean and provides a resource for soybean genomics research and breeding.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics.

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

DepthFinder: a tool to determine the optimal read depth for reduced-representation sequencing.

MOTIVATION: Identification of DNA sequence variations such as single nucleotide polymorphisms (SNPs) is a fundamental step toward genetic studies. Reduced-representation sequencing methods have been developed as alternatives to whole genome sequencing to reduce costs and enable the analysis of many more individual. Amongst these methods, restriction site associated sequencing (RSAS) methodologies have been widely used for rapid and cost-effective discovery of SNPs and for high-throughput genotyping in a wide range of species. Despite the extensive improvements of the RSAS methods in the last decade, the estimation of the number of reads (i.e. read depth) required per sample for an efficient and effective genotyping remains mostly based on trial and error. RESULTS: Herein we describe a bioinformatics tool, DepthFinder, designed to estimate the required read counts for RSAS methods. To illustrate its performance, we estimated required read counts in six different species (human, cattle, spruce budworm, salmon, barley and soybean) that cover a range of different biological (genome size, level of genome complexity, level of DNA methylation and ploidy) and technical (library preparation protocol and sequencing platform) factors. To assess the prediction accuracy of DepthFinder, we compared DepthFinder-derived results with independent datasets obtained from an RSAS experiment. This analysis yielded estimated accuracies of nearly 94%. Moreover, we present DepthFinder as a powerful tool to predict the most effective size selection interval in RSAS work. We conclude that DepthFinder constitutes an efficient, reliable and useful tool for a broad array of users in different research communities. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/jerlar73/DepthFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cannabis, the multibillion dollar plant that no genebank wanted.

Although cannabis is legalized and accepted as an agricultural commodity in many places around the world, a significant lack of public germplasm repositories remains an unresolved problem in the cannabis industry. The acquisition, preservation, and evaluation of germplasm, including landraces and ancestral populations, is key to unleashing the full potential of cannabis in the global marketplace. We argue here that accessible germplasm resources are crucial for long-term economic viability, preserving genetic diversity, breeding, innovation, and long-term sustainability of the crop. We believe that cannabis restrictions require a second look to allow genebanks to play a fuller and more effective role in conservation, sustainable use, and exchange of cannabis genetic resources.

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis.

For a long time, Cannabis sativa has been used for therapeutic and industrial purposes. Due to its increasing demand in medicine, recreation, and industry, there is a dire need to apply new biotechnological tools to introduce new genotypes with desirable traits and enhanced secondary metabolite production. Micropropagation, conservation, cell suspension culture, hairy root culture, polyploidy manipulation, and Agrobacterium-mediated gene transformation have been studied and used in cannabis. However, some obstacles such as the low rate of transgenic plant regeneration and low efficiency of secondary metabolite production in hairy root culture and cell suspension culture have restricted the application of these approaches in cannabis. In the current review, in vitro culture and genetic engineering methods in cannabis along with other promising techniques such as morphogenic genes, new computational approaches, clustered regularly interspaced short palindromic repeats (CRISPR), CRISPR/Cas9-equipped Agrobacterium-mediated genome editing, and hairy root culture, that can help improve gene transformation and plant regeneration, as well as enhance secondary metabolite production, have been highlighted and discussed.

Transcriptomics of Improved Fruit Retention by Hexanal in 'Honeycrisp' Reveals Hormonal Crosstalk and Reduced Cell Wall Degradation in the Fruit Abscission Zone.

Apples (Malus domestica Borkh) are prone to preharvest fruit drop, which is more pronounced in 'Honeycrisp'. Hexanal is known to improve fruit retention in several economically important crops. The effects of hexanal on the fruit retention of 'Honeycrisp' apples were assessed using physiological, biochemical, and transcriptomic approaches. Fruit retention and fruit firmness were significantly improved by hexanal, while sugars and fresh weight did not show a significant change in response to hexanal treatment. At commercial maturity, abscisic acid and melatonin levels were significantly lower in the treated fruit abscission zone (FAZ) compared to control. At this stage, a total of 726 differentially expressed genes (DEGs) were identified between treated and control FAZ. Functional classification of the DEGs showed that hexanal downregulated ethylene biosynthesis genes, such as S-adenosylmethionine synthase (SAM2) and 1-aminocyclopropane-1-carboxylic acid oxidases (ACO3, ACO4, and ACO4-like), while it upregulated the receptor genes ETR2 and ERS1. Genes related to ABA biosynthesis (FDPS and CLE25) were also downregulated. On the contrary, key genes involved in gibberellic acid biosynthesis (GA20OX-like and KO) were upregulated. Further, hexanal downregulated the expression of genes related to cell wall degrading enzymes, such as polygalacturonase (PG1), glucanases (endo-ß-1,4-glucanase), and expansins (EXPA1-like, EXPA6, EXPA8, EXPA10-like, EXPA16-like). Our findings reveal that hexanal reduced the sensitivity of FAZ cells to ethylene and ABA. Simultaneously, hexanal maintained the cell wall integrity of FAZ cells by regulating genes involved in cell wall modifications. Thus, delayed fruit abscission by hexanal is most likely achieved by minimizing ABA through an ethylene-dependent mechanism.

Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas.

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-mediated genome editing system has recently been used for haploid production in plants. Haploid induction using the CRISPR/Cas system represents an attractive approach in cannabis, an economically important industrial, recreational, and medicinal plant. However, the CRISPR system requires the design of precise (on-target) single-guide RNA (sgRNA). Therefore, it is essential to predict off-target activity of the designed sgRNAs to avoid unexpected outcomes. The current study is aimed to assess the predictive ability of three machine learning (ML) algorithms (radial basis function (RBF), support vector machine (SVM), and random forest (RF)) alongside the ensemble-bagging (E-B) strategy by synergizing MIT and cutting frequency determination (CFD) scores to predict sgRNA off-target activity through in silico targeting a histone H3-like centromeric protein, HTR12, in cannabis. The RF algorithm exhibited the highest precision, recall, and F-measure compared to all the tested individual algorithms with values of 0.61, 0.64, and 0.62, respectively. We then used the RF algorithm as a meta-classifier for the E-B method, which led to an increased precision with an F-measure of 0.62 and 0.66, respectively. The E-B algorithm had the highest area under the precision recall curves (AUC-PRC; 0.74) and area under the receiver operating characteristic (ROC) curves (AUC-ROC; 0.71), displaying the success of using E-B as one of the common ensemble strategies. This study constitutes a foundational resource of utilizing ML models to predict gRNA off-target activities in cannabis.

Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data.

BACKGROUND: Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations. RESULTS: In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5 M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm. A single gene (Glyma.01 g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele. CONCLUSION: These results suggest that the new region on chromosome 1 harbors a promising resistance QTL to SSR that can be used in soybean breeding program.