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At least 19 recordsLinked to original sources
PubMed2026

Functional divergence of two soybean cytosolic serine hydroxymethyltransferases in development and defense against soybean cyst nematode.

Serine hydroxymethyltransferase (SHMT) is an enzyme essential for one-carbon metabolism. In higher plants, multiple SHMT genes code for isoforms that function in the cytosol, nucleus, mitochondria, and chloroplasts. The soybean genome contains two cytosolic SHMTs, GmSHMT05 and GmSHMT08, sharing high sequence identity and similar expression throughout soybean development. In certain soybean genotypes, two amino acid substitutions negatively impact GmSHMT08's ability to bind to tetrahydrofolate (THF), leading to a gain-of-function in resistance to the soybean cyst nematode (SCN). Whether this perturbation to the enzyme has other functional consequences for soybean growth and development remains unknown. Here, we investigated the roles of cytosolic GmSHMTs in soybean growth and development. We determined that the 3D structure and folate-binding affinity of GmSHMT05 are highly similar to the version of GmSHMT08 found in susceptible soybeans. We further measured phenotypic traits of two ethyl methanesulfonate-derived Gmshmt08 mutant plants in an SCN-resistant soybean background. Aboveground soybean growth and development were similar, except the Gmshmt08 mutant plants showed a significant increase in pods/plant in field phenotyping trials. Belowground analyses revealed a significant increase in lateral root and total root length in mutant plants, and CRISPR-Cas9 editing demonstrated an essential role of cytosolic SHMTs in root growth. Taken together, our results indicate that GmSHMT05 sustains overall soybean growth and development in the absence of GmSHMT08; however, GmSHMT08's gain-of-function in SCN resistance negatively influences pod and root growth, highlighting a potential trade-off between soybean defense and development that may impact yield when breeding with GmSHMT08 to develop SCN-resistant varieties.

1-C folate metabolism↗
PubMed2022

Expansive and Diverse Phenotypic Landscape of Field Aedes aegypti (Diptera: Culicidae) Larvae with Differential Susceptibility to Temephos: Beyond Metabolic Detoxification.

Arboviruses including dengue, Zika, and chikungunya are amongst the most significant public health concerns worldwide. Arbovirus control relies on the use of insecticides to control the vector mosquito Aedes aegypti (Linnaeus), the success of which is threatened by widespread insecticide resistance. The work presented here profiled the gene expression of Ae. aegypti larvae from field populations of Ae. aegypti with differential susceptibility to temephos originating from two Colombian urban locations, Bello and Cúcuta, previously reported to have distinctive disease incidence, socioeconomics, and climate. We demonstrated that an exclusive field-to-lab (Ae. aegypti strain New Orleans) comparison generates an over estimation of differential gene expression (DGE) and that the inclusion of a geographically relevant field control yields a more discrete, and likely, more specific set of genes. The composition of the obtained DGE profiles is varied, with commonly reported resistance associated genes including detoxifying enzymes having only a small representation. We identify cuticle biosynthesis, ion exchange homeostasis, an extensive number of long noncoding RNAs, and chromatin modelling among the differentially expressed genes in field resistant Ae. aegypti larvae. It was also shown that temephos resistant larvae undertake further gene expression responses when temporarily exposed to temephos. The results from the sampling triangulation approach here contribute a discrete DGE profiling with reduced noise that permitted the observation of a greater gene diversity, increasing the number of potential targets for the control of insecticide resistant mosquitoes and widening our knowledge base on the complex phenotypic network of the Ae. aegypti response to insecticides.

Aedes↗
PubMed2026

Genetic basis of escape-related locomotor performance in a wild-introgressed sheep population.

Rapid running and jumping are core components of escape responses in prey animals and provide measurable traits for studying locomotor performance in large mammals. The genetic basis of these escape-related locomotor traits remains poorly understood in large mammals, partly because repeated, standardized phenotyping under field conditions is challenging. Here, we leveraged a sheep population carrying argali-introgressed genetic components to map genetic variations associated with running speed and jumping height. Through controlled field experiments, automated high-resolution phenotyping, whole-genome analysis, and gene-edited mouse models, we identified two loci associated with escape-related locomotor traits: one in ABCC4 (Chr10:71,849,347; p = 5.03 × 10-7) linked to maximum running speed and another in GRID2 (Chr6:32,120,477; p = 1.30 × 10-9) associated with jumping height. Functional assays in knockout mice reveal that disruption of Grid2 reduces jumping ability, whereas Abcc4 knockout and knockdown increase running speed through enhanced heart contractility under stress. These results elucidated the genetic bases of wild-derived variations in affecting locomotor performance.

Animals↗
PubMed1979

Nature of Rous sarcoma virus-specific RNA in transformed and revertant field vole cells.

Cytoplasmic and polyribosomal RNAs from Rous sarcoma virus-transformed and phenotypically reverted field vole cells were fractionated by rate-zonal sedimentation and hybridized with a (3)H-labeled complementary DNA viral probe to determine the size classes of virus-specific RNA present in these cell types. In contrast to Rous sarcoma virus-infected permissive avian cells, only two of three discrete species of virus-specific RNA were detected in the cytoplasm of these vole cells. These included genome-length 35S RNA and a 21S RNA. However, viral 28S RNA, routinely detected in the cytoplasm of productively infected avian cells, could not be found in cytoplasmic RNA from vole cells. In addition, a low-molecular-weight viral RNA sedimenting less than 16S was detected in both infected avian and vole cells. Because of its heterogeneity this latter species is most likely generated from the intracellular degradation of the larger viral RNAs. Both the viral 35S and 21S RNA were also found to be associated with total polyribosomes from these vole cells. Studies were also performed to determine the distribution of both total viral genomic and sarcoma-specific RNA sequences among the size classes of fractionated total polyribosomes. In both vole cell types the majority of cytoplasmic viral RNA sequences were also associated with polyribosomes and were similarly distributed among the size classes of total polyribosomes. Sarcoma-specific sequences were present on both the 35S and 21S RNA species. These data suggest that the expression of the viral transforming gene in revertant field vole cells may be controlled at some stage subsequent to translation of the viral RNA.

Animals↗
PubMed2025

From aerial drone to quantitative trait locus: leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa.

In recent years, accurate and low-cost variant calling has enabled the genotyping of large diversity panels for genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. This has created a strong need for high-throughput, accurate, and low-cost in-field phenotyping. Here, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera. Our high-throughput phenotyping approach integrates an RGB- and MSP camera to measure the color and height of lettuce in this large-scale field experiment. We used the mean and other summary statistics, such as median, quantiles, skewness, kurtosis, minimum, and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using these summary statistics as traits for GWAS, we confirm several previously described genetic associations, now under field conditions, and identify additional novel associations for color and height traits in lettuce.

Lactuca↗
PubMed2026

A transcriptome-wide approach for rapid pathotype discrimination of Puccinia striiformis f. sp. tritici in north-western India.

Stripe rust of wheat caused by Puccinia striiformis f. sp. tritici (Pst) remains a major constraint to wheat production in India due to the rapid evolution and frequent emergence of virulent pathotypes. Rapid and reliable discrimination of Pst pathotypes is essential for effective resistance deployment and surveillance. In the present study, transcriptome-wide simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) were exploited to develop and validate molecular markers for pathotype-specific detection of Pst pathotypes prevalent in North India (110S119, 238S119, 46S119, 110S84 and 78S84). Microsatellite mining from 6103 core orthologous clusters comprising 51,127 transcripts mined 14,634 SSR loci, from which 93 primer pairs were synthesized. However, only three SSR markers exhibited polymorphism indicating limited discrimination potential of expressed sequence-derived (EST) SSRs for pathotype differentiation. In contrast, SNP discovery through stringent variant calling and filtration yielded 186 pathotype-specific homokaryotic SNPs, of which 56 high-confidence loci were selected for Kompetitive Allele-Specific PCR (KASP) assay development. A total of 48 KASP markers were synthesized and 14 demonstrated clear pathotype- or cluster-specific polymorphism representing substantially higher resolution than SSR markers. The high SNP-to-KASP conversion efficiency (~ 95%) and reproducible fluorescence-based clustering emphasize the robustness of KASP assay. Comparative evaluation revealed that SNP-based KASP markers provide superior discriminatory capacity for closely related Pst pathotypes and represent a promising complementary molecular approach for rapid identification of predominant Indian Pst pathotypes. The validated marker panel developed in this study can complement conventional virulence phenotyping and field pathogenomics approaches for surveillance of currently known pathotypes, while continued refinement may accommodate future changes in pathogen populations.

India↗
PubMed2025

Inactivation of β-1,3-glucan synthase-like 5 confers broad-spectrum resistance to Plasmodiophora brassicae pathotypes in cruciferous plants.

Clubroot disease, caused by the obligate intracellular rhizarian protist Plasmodiophora brassicae, is devastating to cruciferous crops worldwide. Widespread field P. brassicae pathotypes frequently overcome the pathotype-specific resistance of modern varieties, posing a challenge for durable control of this disease. Here a genome-wide association study of 3 years of data comprising field clubroot phenotyping of 244 genome-resequenced Brassica napus accessions identified a strong association of β-1,3-glucan synthase-like 5 (GSL5) with clubroot susceptibility. GSL5 was evolutionarily conserved, and inactivation of GSL5 by genome editing in Arabidopsis, B. napus, Brassica rapa and Brassica oleracea conferred broad-spectrum, high-level resistance to P. brassicae pathotypes without yield penalties in B. napus. GSL5 inactivation derepressed the jasmonic acid-mediated immunity during P. brassicae secondary infection, and this immune repression was possibly reinforced through stabilization of GSL5 by a P. brassicae effector, facilitating clubroot susceptibility. Our study provides durable resistance resources for cruciferous clubroot disease control and insights into plant resistance against intracellular eukaryotic phytopathogens.

Disease Resistance↗
PubMed2026

Genome-wide association study combined with multi-assay phenotyping identifies a novel anthracnose resistance locus in apple.

BACKGROUND: Apple anthracnose, a disease complex that includes Glomerella leaf spot (GLS) and bitter rot caused by Colletotrichum species, is a major disease affecting apple production worldwide. In this study, we combined multi-year field evaluations with controlled inoculation assays to identify genomic regions associated with anthracnose resistance in apple. RESULTS: A total of 440 apple genotypes, including 411 F₁ progenies derived from six parental crosses and 29 cultivars, were evaluated under natural orchard conditions and through artificial fruit and leaf inoculation assays using wound and non-wound methods. Disease severity varied substantially between years, particularly under contrasting environmental conditions, indicating strong genotype-by-environment interactions. Genome-wide association analysis (GWAS) using field-derived disease severity scores from 2019 identified a significant quantitative trait locus (QTL) on chromosome 15 (~ 31.8 Mb) associated with reduced anthracnose severity. This locus was distinct from the previously reported Rgls/MdTNL1 region on chromosome 15 (~ 2-5 Mb), suggesting the presence of a novel resistance-associated locus. In contrast, no genome-wide significant associations were detected from artificial inoculation datasets. CONCLUSIONS: These findings demonstrate the importance of field-based, multi-environment phenotyping for detecting field-relevant resistance loci and improving understanding of the genetic architecture underlying anthracnose resistance in apple.

Malus↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗
PubMed1978

HLA antigens-risk factors for primary open-angle glaucoma?

The comparison of the HLA A, B, and C phenotypes of 70 patients with primary open-angle glaucoma (POAG) with the phenotypes of 450 healthy control individuals and the rather discordant data published by other investigators show that there is no genetical influence of HLA A, HLA B, or HLA C genes on POAG and on the clinical complications associated with this disease.

Adult↗
PubMed2024

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

causal inference↗
PubMed2026

Deep soil layers show the most pronounced genetic variation in wheat root length.

Wheat is one of the most important cereals worldwide, yet significant gaps remain in our understanding of genetic variability in root traits, especially those associated with deeper rooting that support resource acquisition in challenging environments. Root traits are typically controlled by many genes with small effects and often display low heritability. Our aim was to develop a statistical approach to analyse root variation across soil depth and to determine where genetic differences in root intensity are most detectable. An experiment was conducted at the RadiMax semi-field facility, which is designed to measure deep root systems. Five years of phenotypic data recorded each June produced observations from 1500 rows. Each row captured root intensity across the soil profile from 0.6 m to 2.6 m, enabling detailed analysis of vertical root distribution. Across the five years, 513 winter wheat cultivars were grown in the facility, and among those 409 were genotyped with SNP chips. Depth-resolved regression models with random coefficients were used to quantify genetic and non-genetic variation in root intensity across soil depths, while accounting for spatial variation between rows. Random variation within rows was found to be constant across depths. The models showed that genetic variance for cumulative root intensity increased substantially below 1.1 m, with the deepest layers exhibiting the largest differences between wheat lines. Narrow-sense heritability of point measurements peaked at approximately 1.5 m ([Formula: see text]).

Genetic variability↗
PubMed2026

Adaptation to Plant Defence in an Agricultural Insect Pest: Integrating Genome Scans and Gene Expression in the Soybean Aphid Reveals Multi-Genic Pathways.

In agroecosystems, intense selection pressures cause species to adapt and spread, often leading to the evolution and persistence of pests. Understanding how pests rapidly adapt can help develop sustainable strategies for their management and improve agroecosystem health. Pest adaptation involves stable variations in DNA sequence, as well as dynamic shifts in gene expression, often mediated by non-coding regulatory elements. We examined adaptation to plant defences in the soybean aphid, Aphis glycines, in which virulent aphids have overcome plant defences and avirulent aphids have not. Previous data with laboratory colonies suggested that virulent aphids have higher overall gene expression, including transposable elements, some of which influence gene regulation. However, we lack information on how genetic variation in natural populations impacts adaptation and potentially gene regulation. We integrated population genome scans of field-collected, soybean aphid populations with gene expression profiles of virulent and avirulent laboratory colonies to uncover connections between genetic differentiation and gene regulation for virulence. Genome scan methods found 2144 single nucleotide polymorphisms (SNPs) with significant genetic differentiation (i.e., outliers) in field-collected populations. These SNPs were near 1004 genes, representing 5.16% of the effective number of genes. Based on previous RNA-Seq data with laboratory colonies, we found 3160 genes and 147 long non-coding RNAs (lncRNAs) with differential expression among virulent and avirulent biotypes. By integrating both data sets, we identified 16 genes and 5 long non-coding RNAs with differential expression and that were associated with an outlier SNP (within 10 kbp). We validated SNPs with additional field collected aphids and found an aphid clone with stronger virulence than our laboratory virulent colony, surviving on 2 different aphid-resistant soybean varieties. This new virulent clone had fixed allele differences at 9 SNPs compared to our avirulent and other virulent colony. Field collected soybean aphids matching the phenotype of this new virulent clone had significant genetic differentiation with 3 outlier SNPs near genes related to zinc transport and lachesin compared to field collected avirulent aphids. Our entire data reinforced the importance of a potential multi-genetic response to overcome plant defence and generates new insights into complex genetic and regulatory mechanisms involved in insect-plant interactions.

Animals↗
PubMed1977

A study of X chromosome linkage with field dependence and spatial visualization.

The purposes of this report are to describe a design for the study of X linkage, to illustrate its application using cognitive test scores, and to offer a linkage hypothesis suggested by these data. Sixty-seven three-son families were examined for two X chromosome marker variables--red-green color vision and Xg(a) blood groups--and given a battery of cognitive tests of field dependence and spatial visualization abilities. Evidence was found to suggest that brothers who are identical in Xg(a) phenotype are more similar to each other in extent of field dependence than brothers who are different in Xg(a) phenotype. This result is tentative because of the small number of informative cases and the many linkage associations examined. If cross-validated, such a finding would be consistent with the proposition that an X chromosome gene contributes to the field dependence cognitive style.

Blood Group Antigens↗
PubMed2025

Genome wide association study unveils the genetic basis of Orobanche crenata resistance in pea.

GWAS using DArTseq markers identified novel resistance sources against parasitic broomrape in pea, elucidating candidate genes for marker-selected breeding as leverage for cultivar development and efficient disease control to enhance food security. Crenate broomrape (Orobanche crenata) is an important obligate root parasitic weed that causes severe yield losses in pea (Pisum sativum) production. O. crenata is difficult to eradicate in pea fields due to its high resilience and prolific seed boom capable of hibernating in soils for decades. Existing control strategies are not cost effective in low input legumes like pea. The most efficient ecofriendly mode of control is using resistant cultivars. Quantitative trait loci (QTL) studies based on bi-parental mapping has guided O. crenata resistance discovery, albeit their deployment in pea breeding is hindered by low marker resolution and large genetic distance. This study presents the first genome-wide association study (GWAS) on O. crenata resistance in pea, utilizing 324 diverse accessions and 26,045 diversity array technology sequence (DArTseq) markers. Phenotyping was performed over four seasons under field conditions using alpha lattice design. Results showed a strong phenotypic variation with an environmental influence on O. crenata infection. Novel resistance sources were identified mainly within the wild Pisum fulvum and P. sativum subsp. elatius. GWAS with two models yielded a total of 73 marker-trait associations with Chromosome 5 as major hotspot. Interestingly, some linked markers were detected in close proximity to four previous O. crenata resistance QTL. DArTseq markers identified 24 putative candidate genes participating in different cellular processes, including vesicle trafficking and transports, deoxyribonucleic acid transcription regulation, and defense including some leucine rich repeat receptor-like kinases. These results provide a valuable genetic resource for O. crenata resistance and a step toward its effective sustainable management-to enhance genetic diversity and cultivar improvement for food security.

Pisum sativum↗
PubMed2026

Penicillium melinii promotes root growth through subtle host reprogramming across model and crop species.

Root development is highly responsive to microbial interactions, yet the mechanisms by which beneficial fungi promote root growth remain incompletely understood. Here, we identified Penicillium melinii 'isolate 2' through a screen of endophytic fungi isolated from Arabidopsis and characterized it as a promoter of root development in both Arabidopsis and crop species. We combined phenotyping in vitro, rhizotron, greenhouse and field assays with reporter and mutant analyses, transcriptomics, phytohormone profiling and sequencing and annotation of the fungal genome to investigate the basis of this interaction. P. melinii consistently stimulated root growth and modified root architecture across experimental systems and host species. These effects were associated with subtle but reproducible host transcriptional reprogramming, supporting a model in which the fungus fine-tunes endogenous developmental programmes rather than broadly perturbing stress or growth pathways. Genetic and reporter analyses further suggested that this interaction modulates root branching through localized developmental reprogramming. Genomic analysis provided a framework for understanding the fungal traits associated with this beneficial interaction. The conservation of the response across model and crop species supports the relevance of P. melinii as both a useful experimental system to study beneficial plant-fungus interactions and a promising candidate for improving root traits and crop performance.

Penicillium melinii↗
PubMed2025

Root growth promotion by Penicillium melinii : mechanistic insights and agricultural applications.

This study characterizes Penicillium melinii , an endophytic fungus isolated from Arabidopsis thaliana roots, as a plant growth-promoting fungus with potential use as a model to study root development and as a biostimulant for sustainable agriculture. Although endophytes are known to promote plant growth, the underlying molecular mechanisms often remain poorly understood. Here, we aimed to elucidate how P. melinii enhances root system development and to assess its applicability across different crops. Phenotypic assays were conducted in Arabidopsis, quinoa and tomato under in vitro , greenhouse and field conditions. Root architecture and biomass were quantified using image-based phenotyping. Transcriptomic and phytohormone profiling assessed plant responses, and fungal genome sequencing coupled with secretome analysis was used to identify candidate effectors and metabolic traits. P. melinii consistently promoted root growth and increased plant biomass across species and environments, both in vitro and in the greenhouse. In tomato field trials, this translated into a significant increase in yield. The fungus colonized root surfaces without vascular penetration and triggered a mild transcriptomic response: early activation of stress-response genes followed by their attenuation and sustained upregulation of auxin-related pathways. Notably, the interaction modulates the SLR-ARF-LBD pathway and the number of pre-branch sites probably through increased auxin signalling in the oscillation zone. Additional hormonal changes were limited and mainly associated with the attenuation of the plant response to microorganisms. P. melinii enhances lateral root formation through a subtle molecular and metabolic dialogue with the host plant, underscoring its relevance as a model for studying root developmental plasticity. Its strong and reproducible growth-promoting effect, demonstrated with different fungal strains and under controlled and field conditions, supports its potential as a biostimulant for sustainable crop production.

Journal Article↗
PubMed2025

Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.

Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resistance alleles, and cereal genomic resources are well advanced due to large-scale sequencing and genotyping efforts. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique to initially discover novel disease resistance loci or alleles in these diverse collections. Traditional disease resistance phenotyping methods are reliant on visual estimation of disease symptom severity and have successfully supported genetic mapping studies either via GWAS or QTL mapping in biparental populations facilitating both marker development and gene cloning efforts. Due to foliar pathogens having a high capacity to evolve, there is a need to pyramid disease resistance genes with diverse mechanisms for durable control. Resistance expressed as a quantitative trait, known as quantitative resistance (QR), is hypothesized to be more durable, unlike major R-gene resistance that is race-specific and can be vulnerable to breaking down without gene stewardship. However, assessing QR visually is challenging, particularly when complicated by complex genotype × environment (G × E) effects in the field. High-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS. In this review, we discuss image-based disease phenotyping based on macroscopic (visible symptoms) and microscopic features during the host-pathogen interaction. Quantitative image analysis approaches using conventional and artificial intelligence (AI) algorithms are also discussed.

Disease Resistance↗