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KEY MESSAGE: Integrating disease screening data and genomic data for host and pathogen populations into prediction models provides breeders and pathologists with a unified framework to develop disease resistance. Developing disease resistance in crops typically consists of exposing breeding populations to a virulent strain of the pathogen that is causing disease. While including a diverse set of pathogens in the experiments would be desirable for developing broad and durable disease resistance, it is logistically complex and uncommon, and limits our capacity to implement dual (host-by-pathogen)-genome prediction models. Data from an alternative disease screening system that challenges a diverse sweet corn population with a diverse set of pathogen isolates are provided to demonstrate the changes in genetic parameter estimates that result from using genomic data to provide connectivity across sparsely tested experimental treatments. An inflation in genetic variance estimates was observed when among isolate relatedness estimates were included in prediction models, which was moderated when host-by-pathogen interaction effects were incorporated into models. The complete model that included genomic similarity matrices for host, pathogen, and interaction effects indicated that the proportion of phenotypic variation in lesion size that is attributable to host, pathogen, and interaction effects was similar. Estimates of the stability of lesion size predictions for host varieties inoculated with different isolates and the stability of isolates used to inoculate different hosts were also similar. In this pathosystem, genetic parameter estimates indicate that host, pathogen, and host-by-pathogen interaction predictions may be used to identify crop varieties that are resistant to specific virulence mechanisms and to guide the deployment of these sources of resistance into pathogen populations where they will be more effective.

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Modern diagnostic techniques based on DNA sequence similarity are currently the gold standard for the detection of existing and emerging pathogens. Whilst individual assays are inexpensive to use, assay development is costly and carries risks of not being sensitive or specific enough to capture an increasingly diverse range of targets. Sequencing can provide the entire nucleic acid content of a sample and may be used to identify all pathogens present in the sample when the depth of coverage is sufficient. Targeted enrichment techniques have been used to increase sequence coverage and improve the sensitivity of detection within virus samples, specifically, to capture sequences for a range of different viruses or increase the number of reads from low-titre virus infections. Vertebrate viruses have been well characterised using in-solution hybridisation capture to target diverse virus families. The use of probes for genotyping and strain identification has been limited in plants, and uncertainty around sensitivity is an impediment to the development of a large-scale virus panel to use within regulatory settings and diagnostic pipelines. This review aims to compare significant studies that have used targeted enrichment of viruses to identify approaches to probe design and potential for use in plant virus detection and characterisation.

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We report a draft genome assembly of Trichoderma longibrachiatum isolate GEV 3550, obtained from Florida, United States of America.

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BACKGROUND: Rapid and accurate pathogen identification is required for disease management. Compared to sequencing entire genomes, targeted sequencing may be used to direct sequencing resources to genes of interest for microbe identification and mitigate the low resolution that single-locus molecular identification provides. This work describes a broad-spectrum fungal identification tool developed to focus high-throughput Nanopore sequencing on genes commonly employed for disease diagnostics and phylogenetic inference. RESULTS: Orthologs of targeted genes were extracted from 386 reference genomes of fungal species spanning six phyla to identify homologous regions that were used to design the baits used for enrichment. To reduce the cost of producing probes without diminishing the phylogenetic power, DNA sequences were first clustered, and then consensus sequences within each cluster were identified to produce 26,000 probes that targeted 114 genes. To test the efficacy of our probes, we applied the technique to three species representing Ascomycota and Basidiomycota fungi. The efficiency of enrichment, quantified as mean target coverage over the mean genome-wide coverage, ranged from 200 to 300. Furthermore, enrichment of long reads increased the depth of coverage across the targeted genes and into non-coding flanking sequence. The assemblies generated from enriched samples provided well-resolved phylogenetic trees for taxonomic assignment and molecular identification. CONCLUSIONS: Our work provides data to support the utility of targeted Nanopore sequencing for fungal identification and provides a platform that may be extended for use with other phytopathogens.

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Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

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Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP ("HBLUP") analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the "Green Triangle" region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.

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The largest family of disease resistance genes in plants are nucleotide-binding site leucine-rich repeat genes (NLRs). The products of these genes are responsible for recognizing avirulence proteins (Avr) of phytopathogens and triggering specific defense responses. Identifying NLRs in plant genomes with standard gene annotation software is challenging due to their multidomain nature, sequence diversity, and clustered genomic distribution. We present the results of a genome-wide scan and comparative analysis of NLR loci in three coffee species (Coffea canephora, Coffea eugenioides and their interspecific hybrid Coffea arabica). A total of 1311 non-redundant NLR loci were identified in C. arabica, 927 in C. canephora, and 1079 in C. eugenioides, of which 809, 562, and 695 are complete loci, respectively. The NLR-Annotator tool used in this study showed extremely high sensitivities and specificities (over 99%) and increased the detection of putative NLRs in the reference coffee genomes. The NLRs loci in coffee are distributed among all chromosomes and are organized mostly in clusters. The C. arabica genome presented a smaller number of NLR loci when compared to the sum of the parental genomes (C. canephora, and C. eugenioides). There are orthologous NLRs (orthogroups) shared between coffee, tomato, potato, and reference NLRs and those that are shared only among coffee species, which provides clues about the functionality and evolutionary history of these orthogroups. Phylogenetic analysis demonstrated orthologous NLRs shared between C. arabica and the parental genomes and those that were possibly lost. The NLR family members in coffee are subdivided into two main groups: TIR-NLR (TNL) and non-TNL. The non-TNLs seem to represent a repertoire of resistance genes that are important in coffee. These results will support functional studies and contribute to a more precise use of these genes for breeding disease-resistant coffee cultivars.

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Pitch canker, caused by the fungal pathogen Fusarium circinatum, is a global disease affecting many Pinus spp. Often fatal, this disease causes significant mortality in both commercially grown and natural pine forests and is an issue of current and growing concern. F. circinatum isolates collected from three locations in the U.S. state of Florida were shown to be virulent on both slash and loblolly pine, with two of the isolates causing equivalent and significantly larger lesions than those caused by the third isolate during pathogenicity trials. In addition, significant genetic variation in lesion length in the pedigreed slash pine population was evident and rankings of parents for lesion length were similar across isolates. Experimental data demonstrate that both host and pathogen genetics contribute to disease severity. High-quality genomic assemblies of all three isolates were created and compared for structural differences and gene content. No major structural differences were observed among the isolates; however, missing or altered genes do contribute to genomic variation in the pathogen population. This work evaluates in planta virulence among three isolates of F. circinatum, provides genomic resources to facilitate study of this organism, and details comparative genomic methods that may be used to explore the pathogen's contribution to disease development.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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We report a draft genome assembly of the causal agent of tomato vascular wilt, Fusarium oxysporum f. sp. lycopersici isolate 59, obtained from the Andean region in Colombia.

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Resistance to fusiform rust disease in loblolly pine (Pinus taeda) is a classic gene-for-gene system. Early resistance gene mapping in the P. taeda family 10-5 identified RAPD markers for a major fusiform rust resistance gene, Fr1. More recently, single nucleotide polymorphism (SNP) markers associated with resistance were mapped to a full-length gene model in the loblolly pine genome encoding for a nucleotide-binding site leucine-rich repeat (NLR) protein. NLR genes are one of the most abundant gene families in plant genomes and are involved in effector-triggered immunity. Inter- and intraspecies studies of NLR gene diversity and expression have resulted in improved disease resistance. To characterize NLR gene diversity and discover potential resistance genes, we assembled de novo transcriptomes from 92 loblolly genotypes from across the natural range of the species. In these transcriptomes, we identified novel NLR transcripts that are not present in the loblolly pine reference genome and found significant geographic diversity of NLR genes providing evidence of gene family evolution. We designed capture probes for these NLRs to identify and map SNPs that stably cosegregate with resistance to the SC20-21 isolate of Cronartium quercuum f.sp. fusiforme (Cqf) in half-sib progeny of the 10-5 family. We identified 10 SNPs and 2 quantitative trait loci associated with resistance to SC20-21 Cqf. The geographic diversity of NLR genes provides evidence of NLR gene family evolution in loblolly pine. The SNPs associated with rust resistance provide a resource to enhance breeding and deployment of resistant pine seedlings.

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Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.

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Here, we announce the draft genome sequences of three Fusarium circinatum isolates that were used to inoculate slash pines (Pinus elliottii) at the U.S. Forest Service Resistance Screening Center in Asheville, North Carolina. The genomes of these isolates were similar to other publicly available genomes, with average nucleotide identity values of >0.98.

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The extent to which spatial structuring of host resistance in wild plant populations reflects direct pathogen-imposed selection is a subject of debate. To examine this issue, genetic susceptibilities to an exotic and a coevolved native fungal pathogen were compared using two Australian host tree species. Damage to common host germplasm of Corymbia citriodora ssp. variegata (CCV) and Eucalyptus globulus, caused by recently introduced (Austropuccinia psidii) and native (Quambalaria pitereka and Teratosphaeria sp.) pathogens was evaluated in common-garden experiments. There was significant additive genetic variation within host species for susceptibility to both the exotic and native pathogens. However, susceptibility to A. psidii was not genetically correlated with susceptibility to either native pathogen, providing support for pathogen-specific rather than general mechanisms of resistance. Population differentiation (QST ) for susceptibility to the native pathogens was greater than neutral expectations (molecular FST ), arguing for divergent selection. Coupled with lower native, but not exotic, pathogen susceptibility in host populations from areas climatically more prone to fungal proliferation, these findings suggest that pathogen-imposed selection has contributed directly to a geographic mosaic of host resistance to native pathogens.

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Wood is an important biological resource which contributes to nutrient and hydrology cycles through ecosystems, and provides structural support at the plant level. Thousands of genes are involved in wood development, yet their effects on phenotype are not well understood. We have exploited the low genomic linkage disequilibrium (LD) and abundant phenotypic variation of forest trees to explore allelic diversity underlying wood traits in an association study. Candidate gene allelic diversity was modelled against quantitative variation to identify SNPs influencing wood properties, growth and disease resistance across three populations of Corymbia citriodora subsp. variegata, a forest tree of eastern Australia. Nine single nucleotide polymorphism (SNP) associations from six genes were identified in a discovery population (833 individuals). Associations were subsequently tested in two smaller populations (130-160 individuals), 'validating' our findings in three cases for actin 7 (ACT7) and COP1 interacting protein 7 (CIP7). The results imply a functional role for these genes in mediating wood chemical composition and growth, respectively. A flip in the effect of ACT7 on pulp yield between populations suggests gene by environment interactions are at play. Existing evidence of gene function lends strength to the observed associations, and in the case of CIP7 supports a role in cortical photosynthesis.

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