Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “Quantitative resistance”
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato
<p>Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato (1135884)</p>
Quantitative disease resistance in wild Silene vulgaris to its endemic pathogen Microbotryum silenes-inflatae collection sites
<p>Details presented on the collections sites of <em>Silene vulgaris</em> seeds from natural populations, for use in the study, Quantitative Disease Resistance in wild <em>Silene vulgaris</em> to its Endemic Pathogen <em>Microbotryum silenes-inflatae</em>. In this study the seeds were collected a mixed half and full sibling families (seeds from the same maternal plants) and used to produce plants that were then cloned by vegetative cuttings. The infection rates of these clones, as well as of their F1 offspring, by the fungus Microbotryum silenes-inflatae are also presented.</p>
Quantitative plant resistance enhances pathogen adaptation to ecological stresses
Open the record for dataset details and reuse information.
Quantitative disease resistance in wild Silene vulgaris to its endemic pathogen Microbotryum silenes-inflatae collection sites
Open the record for dataset details and reuse information.
Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
<p>Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome-wide association mapping approach, this study aims to identify in <em>Arabidopsis thaliana</em> the genetic bases of robust resistance mechanisms to the devastating pathogen<em> Ralstonia solanacearum</em> under heat stress. A local mapping population was phenotyped against the <em>R. solanacearum</em> GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome-wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin <em>SOLO DANCERS</em> gene was validated by a reverse genetic approach as involved in QDR to <em>R. solanacearum </em>at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.</p>
Data from: Assessing the effects of quantitative host resistance on the life-history traits of sporulating parasites with growing lesions
Assessing life-history traits of parasites on resistant hosts is crucial in evolutionary ecology. In the particular case of sporulating pathogens with growing lesions, phenotyping is difficult because one needs to disentangle properly pathogen spread from sporulation. By considering Phytophthora infestans on potato, we use mathematical modelling to tackle this issue and refine the assessment pathogen response to quantitative host resistance. We elaborate a parsimonious leaf-scale model by convolving a lesion growth model and a sporulation function, after a latency period. This model is fitted to data obtained on two isolates inoculated on three cultivars with contrasted resistance level. Our results confirm a significant host-pathogen interaction on the various estimated traits, and a reduction of both pathogen spread and spore production, induced by host resistance. Most interestingly, we highlight that quantitative resistance also changes the sporulation function, whose mode is significantly time-lagged.This alteration of the infectious period distribution on resistant hosts may have strong impacts on the dynamics of parasite populations, and should be considered when assessing the durability of disease control tactics based on plant resistance management. This inter-disciplinary work also supports the relevance of mechanistic models for analysing phenotypic data of plant-pathogen interactions.
Complex adaptive architecture underlies adaptation to quantitative host resistance in a fungal plant pathogen
<p>Plant pathogens often adapt to plant genetic resistance so characterization of the architecture underlying such an adaptation is required to understand the adaptive potential of pathogen populations. Erosion of banana quantitative resistance to a major leaf disease caused by polygenic adaptation of the causal agent, the fungus <i>Pseudocercospora fijiensis,</i> was recently identified in the northern Caribbean region<i>. </i>Genome scan and quantitative genetics approaches were combined to investigate the adaptive architecture underlying this adaptation. Thirty-two genomic regions showing host selection footprints were identified by pool sequencing of isolates collected from seven plantation pairs of two cultivars with different levels of quantitative resistance. Individual sequencing and phenotyping of isolates from one pair revealed significant and variable levels of correlation between haplotypes in 17 of these regions with a quantitative trait of pathogenicity (the diseased leaf area). The multilocus pattern of haplotypes detected in the 17 regions was found to be highly variable across all the population pairs studied. These results suggest complex adaptive architecture underlying plant pathogen adaptation to quantitative resistance with a polygenic basis, redundancy, and a low level of parallel evolution between pathogen populations. Candidate genes involved in quantitative pathogenicity and host adaptation of <i>P. fijiensis </i>were identified in genomic regions by combining annotation analysis with available biological data.</p>
Dataset of Indication of Quantitative Multiple Disease Resistance to Foliar Pathogens in Pinus radiata D.Don in New Zealand
<p>The file contains the phenotypic data and pedigree information used to produce the manuscript <strong>Indication of quantitative cross-resistance to foliar pathogens in </strong><strong><em>Pinus radiata </em></strong><strong>D.Don</strong></p>
Data from: Quantitative genetic variation in, and environmental effects on, pathogen resistance and temperature-dependent disease severity in a wild trout
Open the record for dataset details and reuse information.
Data from: Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis
Open the record for dataset details and reuse information.
Quantitative trait locus analysis of parasitoid counteradaptation to symbiont-conferred resistance
Open the record for dataset details and reuse information.
Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
Open the record for dataset details and reuse information.
Data from: Assessing the effects of quantitative host resistance on the life-history traits of sporulating parasites with growing lesions
Open the record for dataset details and reuse information.
Complex adaptive architecture underlies adaptation to quantitative host resistance in a fungal plant pathogen
Open the record for dataset details and reuse information.
Data from: MHC-I provides both quantitative resistance and susceptibility to blood parasites in blue tits in the wild
Major histocompatibility complex (MHC) genes are central for the adaptive immune response against parasites. Here, we investigated potential associations among MHC-I alleles and blood parasite infections in a natural breeding population of a passerine bird, the blue tit Cyanistes caeruleus, in central Spain. We screened both infection status (presence/absence of infection) and infection intensity to the pathogenic blood parasites Haemoproteus and Leucocytozoon. Three MHC-I alleles (UA104, UA108 and UA117) were associated with higher or lower infection intensities by Leucocytozoon. Interestingly, these associations were dependent on age and were found both among young and adult birds. No MHC alleles were associated with infection intensity by Haemoproteus parasites. In addition, no significant relationships were detected between infection status by Haemoproteus and Leucocytozoon infections and MHC alleles. The very high prevalence of these two parasites in our study population (79–100%) poses challenges to identify associations with infection status and also suggests that clearance of infections may be rare. In conclusion, associations between specific MHC-I alleles and Leucocytozoon parasites were related to either high or low infection intensities, and hence increased susceptibility or resistance to infection.
Data from: Virus adaptation to quantitative plant resistance: erosion or breakdown?
Adaptation of populations to new environments is frequently costly due to trade-offs between life history traits, and consequently, parasites are expected to be locally adapted to sympatric hosts. Also, during adaptation to the host, an increase of parasite fitness could have direct consequences on its aggressiveness (i.e. the quantity of damages caused to the host by the virus). These two phenomena have been observed in the context of pathogen adaptation to host qualitative and monogenic resistances. However, the ability of pathogens to adapt to quantitative polygenic plant resistances and the consequences of these potential adaptations on other pathogen life history traits remain to be evaluated. Using Potato virus Y and two pepper genotypes (one susceptible and one with quantitative resistance), experimental evolutions showed that adaptation to a quantitative resistance was possible and resulted in resistance breakdown. This adaptation was associated to a fitness cost on the susceptible cultivar, but had no consequence neither in terms of aggressiveness, which could be explained by a high tolerance level, nor in terms of aphid transmission efficiency. It results that quantitative resistances are not necessarily durable but management strategies mixing susceptible and resistant cultivars in space and/or in time should be useful to preserve their efficiency.
Data from: Quantitative trait loci from the host genetic background modulate the durability of a resistance gene: a rational basis for sustainable resistance breeding in plants
The combination of major resistance genes with quantitative resistance factors is hypothesized as a promising breeding strategy to preserve the durability of resistant cultivar, as recently observed in different pathosystems. Using the pepper (Capsicum annuum)/Potato virus Y (PVY, genus Potyvirus) pathosystem, we aimed at identifying plant genetic factors directly affecting the frequency of virus adaptation to the major resistance gene pvr23 and at comparing them with genetic factors affecting quantitative resistance. The resistance breakdown frequency was a highly heritable trait (h²=0.87). Four loci including additive quantitative trait loci (QTLs) and epistatic interactions explained together 70% of the variance of pvr23 breakdown frequency. Three of the four QTLs controlling pvr23 breakdown frequency were also involved in quantitative resistance, strongly suggesting that QTLs controlling quantitative resistance have a pleiotropic effect on the durability of the major resistance gene. With the first mapping of QTLs directly affecting resistance durability, this study provides a rationale for sustainable resistance breeding. Surprisingly, a genetic trade-off was observed between the durability of PVY resistance controlled by pvr23 and the spectrum of the resistance against different potyviruses. This trade-off seemed to have been resolved by the combination of minor-effect durability QTLs under long term farmer selection.
Data from: MHC-I provides both quantitative resistance and susceptibility to blood parasites in blue tits in the wild
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.