Genome-Wide Association Study Identifies Single Nucleotide Polymorphism Markers Associated with Mycelial Growth (at 15, 20, and 25°C), Mefenoxam Resistance, and Mating Type in Phytophthora infestans

Machine Learning-Based Identification of Mating Type and Metalaxyl Response in Phytophthora infestans Using SSR Markers

Phytophthora infestans is the causal agent of late blight in potato. The occurrence of P. infestans with both A1 and A2 mating types in the field may result in sexual reproduction and the generation of recombinant strains. Such strains with new combinations of traits can be highly aggressive, resistant to fungicides, and can make the disease difficult to control in the field. Metalaxyl-resistant isolates are now more prevalent in potato fields. Understanding the genetic structure and rapid identification of mating types and metalaxyl response of P. infestans in the field is a prerequisite for effective late blight disease monitoring and management. Molecular and phenotypic assays involving molecular and phenotypic markers such as mating types and metalaxyl response are typically conducted separately in the studies of the genotypic and phenotypic diversity of P. infestans. As a result, there is a pressing need to reduce the experimental workload and more efficiently assess the aggressiveness of different strains. We think that employing genetic markers to not only estimate genotypic diversity but also to identify the mating type and fungicide response using machine learning techniques can guide and speed up the decision-making process in late blight disease management, especially when the mating type and metalaxyl resistance data are not available. This technique can also be applied to determine these phenotypic traits for dead isolates. In this study, over 600 P. infestans isolates from different populations-Estonia, Pskov region, and Poland-were classified for mating types and metalaxyl response using machine learning techniques based on simple sequence repeat (SSR) markers. For both traits, random forest and the support vector machine demonstrated good accuracy of over 70%, compared to the decision tree and artificial neural network models whose accuracy was lower. There were also associations (p < 0.05) between the traits and some of the alleles detected, but machine learning prediction techniques based on multilocus SSR genotypes offered better prediction accuracy.

Phytophthora capsici Populations Are Structured by Host, Geography, and Fluopicolide Sensitivity

Phytophthora capsici epidemics are propelled by warm temperatures and wet conditions. With temperatures and inland flooding in many locations worldwide expected to rise as a result of global climate change, understanding of population structure can help to inform management of P. capsici in the field and prevent devastating epidemics. Thus, we investigated the effect of host crop, geographical origin, fungicide sensitivity, and mating type on shaping the population structure of P. capsici in the eastern United States. Our fungicide in vitro assays identified the emergence of insensitive isolates for fluopicolide and mefenoxam. A set of 12 microsatellite markers proved informative to assign 157 P. capsici isolates to five distinct genetic clusters. Implementation of Bayesian structure, population differentiation, genetic diversity statistics, and index of association analysis, allowed us to identify population structure by host with some correspondence with genetic clusters for cucumber and squash isolates. We found weak population structure by state for geographically close isolates. In this study, we discovered that North Carolina populations stratify by fluopicolide sensitivity with insensitive isolates experiencing nonrandom mating. Our findings highlight the need for careful monitoring of local field populations, improved selection of relevant isolates for breeding efforts, and hypervigilant surveillance of resistance to different fungicides.

Robust and efficient software for reference-free genomic diversity analysis of genotyping-by-sequencing data on diploid and polyploid species

Genotyping-by-sequencing (GBS) is a widely used and cost-effective technique for obtaining large numbers of genetic markers from populations by sequencing regions adjacent to restriction cut sites. Although a standard reference-based pipeline can be followed to analyse GBS reads, a reference genome is still not available for a large number of species. Hence, reference-free approaches are required to generate the genetic variability information that can be obtained from a GBS experiment. Unfortunately, available tools to perform de novo analysis of GBS reads face issues of usability, accuracy and performance. Furthermore, few available tools are suitable for analysing data sets from polyploid species. In this manuscript, we describe a novel algorithm to perform reference-free variant detection and genotyping from GBS reads. Nonexact searches on a dynamic hash table of consensus sequences allow for efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of-the-art variant detector already implemented in this tool. We performed benchmark experiments with three different empirical data sets of plants and animals with different population structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for many research groups conducting population genetic studies in a wide variety of species.

Non-Target Site Mechanisms of Fungicide Resistance in Crop Pathogens: A Review

The rapid emergence of resistance in plant pathogens to the limited number of chemical classes of fungicides challenges sustainability and profitability of crop production worldwide. Understanding mechanisms underlying fungicide resistance facilitates monitoring of resistant populations at large-scale, and can guide and accelerate the development of novel fungicides. A majority of modern fungicides act to disrupt a biochemical function via binding a specific target protein in the pathway. While target-site based mechanisms such as alternation and overexpression of target genes have been commonly found to confer resistance across many fungal species, it is not uncommon to encounter resistant phenotypes without altered or overexpressed target sites. However, such non-target site mechanisms are relatively understudied, due in part to the complexity of the fungal genome network. This type of resistance can oftentimes be transient and noninheritable, further hindering research efforts. In this review, we focused on crop pathogens and summarized reported mechanisms of resistance that are otherwise related to target-sites, including increased activity of efflux pumps, metabolic circumvention, detoxification, standing genetic variations, regulation of stress response pathways, and single nucleotide polymorphisms (SNPs) or mutations. In addition, novel mechanisms of drug resistance recently characterized in human pathogens are reviewed in the context of nontarget-directed resistance.

Fantastic Downy Mildew Pathogens and How to Find Them: Advances in Detection and Diagnostics

Downy mildews affect important crops and cause severe losses in production worldwide. Accurate identification and monitoring of these plant pathogens, especially at early stages of the disease, is fundamental in achieving effective disease control. The rapid development of molecular methods for diagnosis has provided more specific, fast, reliable, sensitive, and portable alternatives for plant pathogen detection and quantification than traditional approaches. In this review, we provide information on the use of molecular markers, serological techniques, and nucleic acid amplification technologies for downy mildew diagnosis, highlighting the benefits and disadvantages of the technologies and target selection. We emphasize the importance of incorporating information on pathogen variability in virulence and fungicide resistance for disease management and how the development and application of diagnostic assays based on standard and promising technologies, including high-throughput sequencing and genomics, are revolutionizing the development of species-specific assays suitable for in-field diagnosis. Our review provides an overview of molecular detection technologies and a practical guide for selecting the best approaches for diagnosis.

Genome-Wide Association Study in New York Phytophthora capsici Isolates Reveals Loci Involved in Mating Type and Mefenoxam Sensitivity

Phytophthora capsici is a soilborne oomycete plant pathogen that causes severe vegetable crop losses in New York (NY) state and worldwide. This pathogen is difficult to manage, in part due to its production of long-lasting sexual spores and its tendency to quickly evolve fungicide resistance. We single nucleotide polymorphism (SNP) genotyped 252 P. capsici isolates, predominantly from NY, in order to conduct a genome-wide association study for mating type and mefenoxam sensitivity. The population structure and extent of chromosomal copy number variation in this collection of isolates were also characterized. Population structure analyses showed isolates largely clustered by the field site where they were collected, with values of F_ST between pairs of fields ranging from 0.10 to 0.31. Thirty-three isolates were putative aneuploids, demonstrating evidence for having up to four linkage groups present in more than two copies, and an additional two isolates appeared to be genome-wide triploids. Mating type was mapped to a region on scaffold 4, consistent with previous findings, and mefenoxam sensitivity was associated with several SNP markers at a novel locus on scaffold 62. We identified several candidate genes for mefenoxam sensitivity, including a homolog of yeast ribosome synthesis factor Rrp5, but failed to locate near the scaffold 62 locus any subunits of RNA polymerase I, the hypothesized target site of phenylamide fungicides in oomycetes. This work expands our knowledge of the population biology of P. capsici and provides a foundation for functional validation of candidate genes associated with epidemiologically important phenotypes.