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788 results for “genotypic data”
Patients data for meta-analysis of genotype-phenotype associations in Bardet-Biedl Syndrome
<p>Data used for metaanalysis of the genotype-phenotype relationship in Bardet Biedl Syndrome.</p> <p>File "EV table 1 literature.xlsx" describes studies that were included in the metaanalysis. File "EV table 2 dataset.xlsx" contains individual patient data. Each row corresponds to a patient. If the same patient was reported in more than 1 study, their data were merged into one row. The columns are as follows:</p> <p>* source - a citation to the study the patient originated in</p> <p>* FamilyID - randomly generated ID of a family (unique over the dataset), two persons with the same FamilyID are related.</p> <p>* source case n. - A unique identifier of the patient within the study</p> <p>* gene - A gene carrying the principal BBSome related mutation</p> <p>* nucleotide change (allele 1,2) - description of the mutations in DNA individual alleles of the gene, in HGVS nomenclature</p> <p>* protein change (allele 1,2) - description of how the mutations in DNA change the resulting protein, in HGVS nomenclature</p> <p>* type of mut allele 1,2 - whether the given mutation is considered missense (MS) or large truncation (trunc)</p> <p>* mut/mut - combination of mutations for both alleles</p> <p>* additional mutations - mutations in other BBSome-related genes. Format is "gene: DNA mutation, protein mutation"</p> <p>* sex - "F" or "M" (where reported)</p> <p>* age group - age group (where reported)</p> <p>* age - age in years. Contains fractions, decimal values and "5 month"</p> <p>* RD, OBE, PD, CI, REP, REN, HEART, LIV, DD - presense or absence of phenotypes, if reported. RD – retinal dystrophy, OBE – obesity, PD – polydactyly, CI – cognitive impairment , REP – reproductive system anomalies, REN – renal anomalies, HRT – heart disease, LIV – liver anomalies, DD - Developmental delay. Values are "" (not reported), "0" (no phenotype), "1" (phenotype present), "1!" conflicting reports of phenotype in multiple studies (some patients were involved in multiple studies)</p> <p>* ethnicity - ethnicity of the patient, if reported</p> <p>* ethinc group - grouping of the ethnicities into 8 larger groups (see paper for details)</p> <p>* note - miscellanous text, in particular contains notes on patients merged from multiple studies</p> <p>====</p> <p>The protocol for this meta-analysis was pre-registered with PROSPERO (CRD42018096099).</p> <p>PubMed and Google Scholar databases were searched in May 2018 for the following keywords: [bardet-biedl syndrome AND (genotype phenotype OR cohort)]. Other suitable records were identified by snowball searching, in particular, by retrieving relevant articles from the references of the studied full-texts. In addition, all the references included in the publicly available Euro-Wabb database (<a href="https://lovd.euro-wabb.org/home.php">https://lovd.euro-wabb.org/home.php</a>) were covered. Our search was limited to the literature published in English language and covered the period from the inception of each database to the 21st of May 2018.</p>
Data From: Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping
<p>This is the data set from all figures and tables from the manuscript "Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping", which is posted to the ChemRxiv preprint server (10.26434/chemrxiv-2024-5bqm7) and currently in consideration for peer-reviewed publication elsewhere.</p>
Phenotype, genotype and fitness data related to genetic analysis of praziquantel response in schistosome parasites.
<p>These data are related to the study of the Genetic analysis of praziquantel response in schistosome parasites implicates a Transient Receptor Potential channel.</p> <p>Mass treatment with praziquantel (PZQ) monotherapy is the mainstay for schistosomiasis treatment. This drug shows imperfect cure rates in the field and parasites showing reduced PZQ response can be selected in the laboratory, but the extent of resistance in <em>Schistosoma mansoni</em> populations is unknown. We examined the genetic basis of variation in PZQ response in a <em>S. mansoni</em> population (SmLE-PZQ-R) selected with PZQ in the laboratory: 35% of these worms survive high dose (73 µg/mL) PZQ treatment. We used genome wide association to map loci underlying PZQ response. The major chr. 3 peak contains a transient receptor potential (Sm.TRPM_PZQ) channel (Smp_246790), activated by nanomolar concentrations of PZQ. PZQ response shows recessive inheritance and marker-assisted selection of parasites at a single Sm.TRPM_PZQ SNP enriched populations of PZQ-resistant (PZQ-ER) and sensitive (PZQ-ES) parasites showing >377 fold difference in PZQ response. The PZQ-ER parasites survived treatment in rodents better than PZQ-ES. Resistant parasites show 2.25-fold lower expression of Sm.TRPM_PZQ than sensitive parasites. Specific chemical blockers of Sm.TRPM_PZQ enhanced PZQ resistance, while Sm.TRPM_PZQ activators increased sensitivity. A single SNP in Sm.TRPM_PZQ differentiated PZQ-ER and PZQ-ES lines, but mutagenesis showed this was not involved in PZQ response, suggesting linked regulatory changes. We surveyed Sm.TRPM_PZQ sequence variation in 259 parasites from the New and Old World revealing one nonsense mutation that results in a truncated protein with no PZQ-binding site. Our results demonstrate that Sm.TRPM_PZQ underlies variation in PZQ response in <em>S. mansoni</em> and provides an approach for monitoring emerging PZQ-resistance alleles in schistosome elimination programs.</p> <p>This dataset is divided in 3 folders. Each folder has a readme detailing its content.</p> <p><strong>1-Phenotyping_data</strong></p> <p>This folder includes the data tables related to the phenotyping of the worms performed during this study. The phenotype measured was the viability of worms following PZQ treatment (i.e., PZQ response). This viability was assessed microscopically or using worm lactate production released in culture media.</p> <p>The data correspond to the following experiments:</p> <ul> <li>PZQ response of single adult male worms from SmLE and SmLE-PZQ-R populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>Lactate production from single SmLE-PZQ-R adult male worms and correlation with visual observation. This was a proof-of-principle that lactate production can be used to efficiently and unbiasedly phenotype schistosome adult male worms in response to PZQ drug.</li> <li>PZQ response of single SmLE-PZQ-R adult male worms. These worms were then divided in low and high producer in response to PZQ and used to perform a genome-wide association study.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations in presence of Sm.TRPM_PZQ blocker (MB2) and activator (MV1) with and without PZQ drug.</li> <li>In vivo PZQ response of schistosome worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations.</li> </ul> <p><strong>2-Genotyping_data</strong></p> <p>This folder includes the data tables related to the genotyping of the worms performed during this study. Worms were genotyping using PCR-RFLP (genotyping of single nucleotide polymorphisms (SNPs) on chr2 and chr3 QTLs) or using qPCR (genotyping of a copy number variation (CNV) on chr3 QTL).</p> <p>The data correspond to the following experiment:</p> <ul> <li>Association between PZQ response of single adult male worms from SmLE-PZQ-R population and their respective genotype on chromosome 2 (SNP) and chromosome 3 (SNP and CNV) loci.</li> </ul> <p><strong>3-Fitness_data</strong></p> <p>This folder includes the data tables related to the fitness of the parasite populations. We collected data regarding:</p> <ul> <li>The number of surviving and infected snails after exposure to SmLE-PZQ-ER or SmLE-PZQ-ES miracidia.</li> <li>The number of adult worms recovered from golden Syrian female hamsters exposed to SmLE-PZQ-ER or SmLE-PZQ-ES cercariae.</li> </ul> <p>All the data were collected during 12 generations of parasites and are used to evaluate a potential impact of PZQ resistance on the parasite fitness.</p>
Data from: "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat"
<p><em><strong>Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".</strong></em></p> <p>The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".</p> <p>The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.</p> <p>Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.</p> <p>The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).</p> <p>The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.</p>
Data for: Intergenerational genotypic interactions drive collective behavioural cycles in a social insect
<p>Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological, and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely but more surprisingly, indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).</p>
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Genotype and genetic diversity data for: Contrasts in riverscape patterns of intraspecific genetic variation in a diverse Neotropical fish community of high conservation value
<p><span>Spatial patterns in genetic variation compared across species provide information about the predictability of genetic diversity of natural populations and areas requiring conservation measures. Due to their remarkable fish diversity, rivers in Neotropical regions are ideal systems to confront theory with observations and would benefit greatly from such approaches given their increasing vulnerability to anthropogenic pressures. We used SNP data from 18 fish species with contrasting life-history traits, co-sampled across 12 sites in the Maroni – a major river system from the Guiana Shield – to compare patterns of intraspecific genetic variation and identify their underlying drivers. Analyses of covariance revealed a decrease in genetic diversity as distance from the river outlet increased for 5 of the 18 species, illustrating a pattern commonly observed in riverscapes for species with low-to-medium dispersal abilities. However, mean within-site genetic diversity was lowest in the two easternmost tributaries of the Upper Maroni and around an urbanized location downstream, indicating the need to address the potential influence of local pressures in these areas, such as goldmining or fishing. Finally, the relative influence of isolation by stream distance, isolation by discontinuous river flow and isolation by spatial heterogeneity in effective size on pairwise genetic differentiation varied across species. Species with similar dispersal and reproductive guilds did not necessarily display shared patterns of population structure. Increasing the knowledge of specific life history traits and ecological requirements of fish species in these remote areas should help further understand factors that influence their current patterns of genetic variation.</span></p>
Data for: Single-gene resolution of diversity-driven overyielding in plant genotype mixtures
<p>In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural <em>Arabidopsis </em><em>thaliana</em> genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the <em>AtSUC8</em> locus are strongly associated with mixture overyielding. <em>AtSUC8</em> encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in <em>AtSUC8</em> affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying such genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performing crop variety mixtures.</p>
Data for: Selective elimination of enterovirus genotypes by activated sludge and chlorination
<p>Raw data underlying the journal article "Selective elimination of enterovirus genotypes by activated sludge and chlorination" by Larivé et al., <em>Environmental Science: Water Research and Technology</em>, 2023 (doi: 10.1039/d3ew00050h)</p> <p>One CSV file for each of Figures 2-6 of the main manuscript $</p> <p>One CSV file for each of Figures S5, S6 and S7 of the Supplementary information. The data for Figures S2, S3 and S4 are summarized in a single CSV file.</p>
Linkage maps and genotype data of strawberry produced with skim-sequencing data
<p>The following set of files contain the results and scripts to produce those results, described in Chapter 5 of the PhD thesis of Alejandro Thérèse Navarro, entitled "How to map a million markers: linkage mapping of skim-sequencing data in strawberry". In this study, a large dataset of markers produced by whole genome resequecning of a strawberry (<em>Fragaria </em>x <em>ananassa</em>) biparental population are used to generate linkage maps. To that end the software <a href="https://github.com/Alethere/SmoothDescent">Smooth Descent</a> is used, since it is oriented to obtaining linkage maps in usin low quality (error-prone) genotype data. With this methodology we were able to produce a linkage map of 27 out of 28 chromosomes of strawberry which containing 1.85M markers in ~2400 unique genetic mpositions. We also compare this map with a linkage map produced using SNP array data and with the genome sequence assembly "Camarosa".</p>
Data from: Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve
<div class="section abstract"> <p>Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayer's graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.</p> </div>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></p>
ddRAD genotyping data
<p>This dataset includes raw sequences and metadata (barcodes) used for ddRAD genotyping in the publication:</p> <blockquote> <p>Stelzer, C.P., M. Pichler, P. Stadler, Genome streamlining and clonal erosion in nutrient-limited environments: a test using genome-size variable populations, <em>Evolution</em>, Volume 77, Issue 11, November 2023, Pages 2378–2391, <a href="https://doi.org/10.1093/evolut/qpad144">https://doi.org/10.1093/evolut/qpad144</a></p> </blockquote> <p>Please cite this study if you use the data.</p>
Data from: Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome
<p>Ejaculate proteins are key mediators of post-mating sexual selection and sexual conflict, as they can influence both male fertilization success and female reproductive physiology. However, the extent and sources of genetic variation and condition dependence of the ejaculate proteome are largely unknown. Such knowledge could reveal the targets and mechanisms of post-mating selection and inform about the relative costs and allocation of different ejaculate components, each with its own potential fitness consequences. Here, we used liquid chromatography coupled with tandem mass spectrometry to characterize the whole-ejaculate protein composition across twelve isogenic lines of Drosophila melanogaster that were reared on a high- or low-quality diet. We discovered new proteins in the transferred ejaculate and inferred their origin in the male reproductive system. We further found that the ejaculate composition was mainly determined by genotype identity and genotype-specific responses to larval diet, with no clear overall diet effect. Nutrient restriction increased proteolytic protein activity and shifted the balance between reproductive function and RNA metabolism. Our results open new avenues for exploring the intricate role of genotypes and their environment in shaping ejaculate composition, or for studying the functional dynamics and evolutionary potential of the ejaculate in its multivariate complexity.</p>
Genotype, phenotype and linkage data for Mimulus parishii x M. cardinalis hybrid incompatibility study
<p>The evolution of genomic incompatibilities causing postzygotic barriers to hybridization is a key step in species divergence. Incompatibilities take two general forms – structural divergence between chromosomes leading to severe hybrid sterility in F<sub>1</sub> hybrids and epistatic interactions between genes causing reduced fitness of hybrid gametes or zygotes (Dobzhansky-Muller incompatibilities). Despite substantial recent progress in understanding the molecular mechanisms and evolutionary origins of both types of incompatibility, how each behaves across multiple generations of hybridization remains relatively unexplored. Here, we use genetic mapping in F<sub>2</sub> and RIL hybrid populations between the phenotypically divergent but naturally hybridizing monkeyflowers <em>Mimulus cardinalis</em> and <em>M. parishii</em> to characterize the genetic basis of hybrid incompatibility and examine its changing effects over multiple generations of experimental hybridization. In F<sub>2</sub>s, we found severe hybrid pollen inviability (< 50% reduction vs. parental genotypes) and pseudolinkage caused by a reciprocal translocation between Chromosomes 6 and 7 in the parental species. RILs retained excess heterozygosity around the translocation breakpoints, which caused substantial pollen inviability when interstitial crossovers had not created compatible heterokaryotypic configurations. Strong transmission ratio distortion and inter-chromosomal linkage disequilibrium in both F<sub>2</sub>s and RILs identified a novel two-locus genic incompatibility causing sex-independent gametophytic (haploid) lethality. The latter interaction eliminated three of the expected nine F<sub>2</sub> genotypic classes via F<sub>1</sub> gamete loss without detectable effects on the pollen number or viability of F<sub>2</sub> double heterozygotes. Along with the mapping of numerous milder incompatibilities, these key findings illuminate the complex genetics of plant hybrid breakdown and are an important step toward understanding the genomic consequences of natural hybridization in this model system.</p>
Data used in: Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested
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Data from: Do biological control agents adapt to local pest genotypes? A multi-year test across geographic scales
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Data from: The structure of an ancient genotype-phenotype map shaped the functional evolution of a protein family
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Data for: Range and niche expansion through multiple interspecific hybridization - a genotyping by sequencing analysis of Cherleria (Caryophyllaceae)
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Data from: Multiple genotypes of Phelipanche ramosa indicate repeated introductions to the Americas: Sequence alignments and phylogenetic trees
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ScienceDex guides
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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.