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2,785 results for “genotype”
Dataset of Pedigree, genotypes, clinical and biochemical characteristics of families of Northeastern Mexico
<p>This dataset combines pedigree, genotypes, clinical and biochemical data of 37 families of Northeastern Mexico. Primary reference is the article:</p> <p>Gallardo‑Blanco, H.L., Villarreal‑Perez, J.Z., Cerda‑Flores, R.M., Figueroa, A., Sanchez‑Dominguez, C.N., Gutierrez‑Valverde, J.M. ... Martinez‑Garza, L.E. (2017). Genetic variants in KCNJ11, TCF7L2 and HNF4A are associated with type 2 diabetes, BMI and dyslipidemia in families of Northeastern Mexico: A pilot study. Experimental and Therapeutic Medicine, 13, 523-529. https://doi.org/10.3892/etm.2016.3990</p> <p><strong>If you use these data please cite the corresponding manuscript, which can be downloaded here:</strong></p> <p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348709/</p> <p>https://www.spandidos-publications.com/10.3892/etm.2016.3990</p> <p>This dataset contains genotypes for the following SNPs:</p> <p>rs2986742</p> <p>rs4846051</p> <p>rs1801131</p> <p>rs1801133</p> <p>rs6541030</p> <p>rs12130799</p> <p>rs11208654</p> <p>rs1137100</p> <p>rs12405556</p> <p>rs3118378</p> <p>rs3737576</p> <p>rs10923931</p> <p>rs7554936</p> <p>rs3737787</p> <p>rs2516839</p> <p>rs1040404</p> <p>rs4670767</p> <p>rs7578597</p> <p>rs13400937</p> <p>rs10496971</p> <p>rs2627037</p> <p>rs1801262</p> <p>rs1569175</p> <p>rs2975760</p> <p>rs3792267</p> <p>rs10510228</p> <p>rs1801282</p> <p>rs3856806</p> <p>rs4955316</p> <p>rs9809104</p> <p>rs4607103</p> <p>rs6548616</p> <p>rs734873</p> <p>rs5400</p> <p>rs2030763</p> <p>rs4402960</p> <p>rs1513181</p> <p>rs9291090</p> <p>rs10010131</p> <p>rs10007810</p> <p>rs385194</p> <p>rs1799883</p> <p>rs2504853</p> <p>rs7754840</p> <p>rs7745461</p> <p>rs1800750</p> <p>rs1800629</p> <p>rs361525</p> <p>rs12200998</p> <p>rs2397060</p> <p>rs192655</p> <p>rs1044498</p> <p>rs4463276</p> <p>rs731257</p> <p>rs864745</p> <p>rs32314</p> <p>rs2330442</p> <p>rs4717865</p> <p>rs3173798</p> <p>rs10954737</p> <p>rs854555</p> <p>rs3917542</p> <p>rs662</p> <p>rs705308</p> <p>rs3943253</p> <p>rs751141</p> <p>rs1471939</p> <p>rs12544346</p> <p>rs13266634</p> <p>rs7844723</p> <p>rs2242103</p> <p>rs1408801</p> <p>rs10811661</p> <p>rs10511828</p> <p>rs12779790</p> <p>rs3793791</p> <p>rs4746136</p> <p>rs1111875</p> <p>rs10885390</p> <p>rs11196175</p> <p>rs7903146</p> <p>rs10885406</p> <p>rs12255372</p> <p>rs290487</p> <p>rs4918842</p> <p>rs2237892</p> <p>rs10839880</p> <p>rs1837606</p> <p>rs5210</p> <p>rs5218</p> <p>rs5219</p> <p>rs2946788</p> <p>rs11227699</p> <p>rs7930460</p> <p>rs1800849</p> <p>rs1387153</p> <p>rs948028</p> <p>rs2270031</p> <p>rs2416791</p> <p>rs7961581</p> <p>rs2070586</p> <p>rs1503767</p> <p>rs2269793</p> <p>rs8050136</p> <p>rs818386</p> <p>rs2966849</p> <p>rs1879488</p> <p>rs757210</p> <p>rs2033111</p> <p>rs11652805</p> <p>rs10512572</p> <p>rs2125345</p> <p>rs12946618</p> <p>rs12946115</p> <p>rs12950541</p> <p>rs1885088</p> <p>rs3907047</p> <p>rs2071023</p> <p>rs2833479</p> <p>rs2833483</p> <p>rs2300386</p> <p>rs2835370</p> <p>rs1296819</p> <p>rs1892848</p> <p>rs4821004</p> <p> </p>
The Biomass and Plant Functional Traits of Leymus chinensis Affected by Genotypic Diversity and Soil Nitrogen Addition through a Two-year Experiment, Tianjin, China, 2021-2023
In order to investigate the effects of soil nitrogen addition on the genotypic diversity of Leymus chinensis, 12 genotypes of Leymus chinensis were used as plant material and a two-factor experimental design was carried out in this study. Factor one was genotypic diversity of L. chinensis, including three levels: mono-genotype (G1), three genotypes (G3), and six genotypes (G6). Factor two was the soil nitrogen addition level, which included four levels: no nitrogen addition (N0), 2.5 g N/(m²·a) nitrogen application (N2.5), 5 g N/(m²·a) nitrogen application (N5), and 10 g N/(m²·a) nitrogen application (N10). Each treatment had 12 combinations as replicates, and 12 genotypes of L. chinensis were used. The frequency of each genotype was standardized across all treatment levels of genotypic diversity × soil nitrogen addition. The experiment commenced in September 2021 and soil nitrogen was applied every 2 months. Plants were cultivated in the experimental field at Nankai University, but were moved to a greenhouse for overwintering from November to February each year. During the experiment, there were no stresses or disturbances such as shading, drought, or insect feeding; weeds were regularly removed.
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Formatting hemiclone Drosophila melanogaster genotype data for GWAS
<p>Data and code for generating filtering and formatting of Drosophila melanogaster genotype data, from the Sussex LHM hemiclone population sample.</p>
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
Genotyping-by-sequencing (GBS) dataset for genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))
<p>These vcf-files constitute underlying raw data material for the manuscript "Genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))". For more detailed information please consult the README file in the repository.</p>
Supplementary Material to the Publication Genotyping of Francisella tularensis subsp. holarctica from Hares in Germany
<p>Supplementary Material in Open Data Format to Publication Genotyping of Francisella tularensis subsp. holarctica from Hares in Germany</p>
Genotyping of the Chinese Spring x Renan mapping population with the TaBW280K SNP array
<p>The TaBW280K SNP array (Rimbert et al., PLoS ONE 2018) was used to genotype 430 Single Seed Descent (SSD) individuals<br> derived from a cross between Chinese Spring and Renan (CsRe; Choulet et al., Science 2014). Out of the 280,226 probesets, 85,276 were found to be polymorphic between the two parental lines and PHR on the population. Eventually, 83,721 (98.2%) SNPs were genetically mapped in 21 linkage groups corresponding to the 21 chromosomes of bread wheat, with no unlinked markers. This file contains the genotyping data of the 430 SSD lines.<br> </p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Liftover of Drosoophila melanogaster genotypes, DGRP sample, from dm3 to dm6 reference assembly.
<p>Genotype data for Drosophila melanogaster DGRP collection.<strong> </strong>Variant positions relative to reference genome version dm6.<strong> </strong>Variant identifiers are in NCBI format.<strong> </strong>Processing code (including original data, reference sequences, perl scripts, etc).</p> <p>The initial intention of performing this 'liftover' was to allow comparison with data from a different melanogaster sample which had been analysed against a dm6 reference assembly. At the time of writing I am not aware that a dataset like this one exists.</p> <p>I've uploaded it in case it is useful to anyone in the future - and also as a trial of zenodo.</p> <p>See README.txt and flylifter.sh for more info.</p> <p> </p>
SNP and indel discovery and genotyping in next-generation sequencing data
<p>Code, logs and data for discovery and genotyping of SNPs and indels, in the the D.melanogaster genome, using GATK HaplotypeCaller. Code is in the zipped folder named code.zip. Run logs for this code as in the zipped folder named logs.zip. The unfiltered vcf genotypes file is named lhm_rg_HC_2015-09-15.vcf.gz. The filtered vcf genotypes file is named f1.lhm_rg_HC_raw.vcf.gz. The vcf submitted to NCBI dbSNP (filtered, and with indels >50bp and variants with null alternate alleles both removed) is named dbSNP.lhm_rg_HC_raw.vcf.gz. The folder local_reference.zip contains the reference assembly files against which genotypes were called against, and includes the code used to format the data prior to use. Also included is genotypes data from the two in-house reference line samples sequenced (BDGP6+ISO1 mito/dm6, Bloomington <em>Drosophila</em> Stock Center no. 2057)</p> <p>Samples are 220 Sussex-LH<sub>M</sub> hemiclones, and 2 RG. The first run did not include chromosome 4 and the mitochondrial genome, so these were genotyped separately, and then added to the rest of the results.</p> <p>The link for the NCBI dbSNP record is currently https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461and the submitter handle is MORROW_EBE_SUSSEX.</p> <p>At the time of writting, the NCBI D.melanogaster build is still being updated, and therefore ss identifiers, but not rs identifers are available.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Structural variant discovery and genotyping in next-generation sequencing data
<p>Code, logs, data, and summaries for detection and genotyping of genomic structural variants in the D.melanogaster Sussex LHM hemiclones (and one in-house reference line individual), using Genomestrip/2.0</p> <p>The unfiltered CNV pipleline results are lhm_gs.cnvs.raw.vcf.gz</p> <p>Filtered CNV results (including removal of bad samples) are filtered.goodS.lhm_gs.cnvs.raw.vcf.gz</p> <p>The file uploaded to NCBI dbVAR (which comprises of the filtered CNVs and indels >50bp from the HaplotypeCaller method) is lhm_sx16.dbVAR.vcf.gz</p> <p>The NCBI dbVAR accession number is nstd134. Code, logs and summary data are in the zipped archives, named accordingly. The archive reference_data.zip contains additional input files required for Genomestrip, including a shell script for making some of them. The file gstrip_lhm_RG_bams.list is also an input for Genomestrip, indicating bam file names and paths.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p> <p> </p>
Neither alpha-synuclein-preformed fibrils derived from patients with GBA1 mutations nor the host murine genotype significantly influence seeding efficacy in the mouse olfactory bulb
<p>Data sets for;</p> <p>Neither alpha-synuclein-preformed fibrils derived from patients with <em>GBA1</em> mutations nor the host murine genotype significantly influence seeding efficacy in the mouse olfactory bulb</p>
2023 HYDRAS Proof of concept experiment with soybean genotypes
<h2>Description</h2> <p>This data sets contains metadata and data of the <strong>2023_POC experiment in the HYDRAS facility</strong>.</p> <p> The 2023 proof-of-concept study in hydras tested all standard field-phenotyping measurement types available in the infrastructure with 3 contrasting varieties of soybean + a control and a drought treatment using rain-out shelters. Start date: 23/5/2023, End dat: 4/10/2023. Location: Melle, Belgium. </p> <p>Data set contains: UAV sensor data, Electrical Resistivity Tomography data, soil point sensor data (water content, water potential and temperature), weather data, yield data and associated experimental information. </p> <h2>Content</h2> <ul> <li>2023_POC_metadata.xlsx contains all information about the experiment (goal, design, sensors, treatments, biological material, ...) and about the associated data files.</li> <li>Subfolder SPATIAL_INFO contains all spatial information about the experimental layout (location of field, plots, sensors, transects, ...) in GEOJSON files</li> <li>Other subfolders contain the actual data from various sources as .csv files. </li> </ul>
Sample accession list for "Malaria protection due to sickle haemoglobin depends on parasite genotype"
<p>This dataset contains a list of sample accessions and associated metadata for <em>P.falciparum</em><br> DNA samples sequenced for the analysis presented in the paper:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The data contains: i. a single tab-delimited text file containing accessions and sequence read quality control-related information related to the processing described in [1], and ii. a README file describing the contents of the data in markdown and HTML format. Please see the enclosed README file for full details.</p> <p>A full list of datasets which have been released with this manuscript can be found on the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a>.</p> <p> </p>
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
Microsatellite genotypes for «Genetic diversity and spatial genetic structure support the specialist‑generalist variation hypothesis in two sympatric woodpecker species»
<p>Species are often arranged along a continuum from “specialists” to “generalists”. Specialists typically use fewer resources, occur in more patchily distributed habitats and have overall smaller population sizes than generalists. Accordingly, the specialist-generalist variation hypothesis (SGVH) proposes that populations of habitat specialists have lower genetic diversity and are genetically more differentiated due to reduced gene flow compared to populations of generalists. Here, expectations of the SGVH were tested by examining genetic diversity, spatial genetic structure and contemporary gene flow in two sympatric woodpecker species differing in habitat specialization. Compared to the generalist great spotted woodpecker (<em>Dendrocopos major</em>), lower genetic diversity was found in the specialist middle spotted woodpecker (<em>Dendrocoptes medius</em>). Evidence for recent bottlenecks was revealed in some populations of the middle spotted woodpecker, but in none of the great spotted woodpecker. Substantial spatial genetic structure and a significant correlation between genetic and geographic distances were found in the middle spotted woodpecker, but only weak spatial genetic structure and no significant correlation between genetic and geographic distances in the great spotted woodpecker. Finally, estimated levels of contemporary gene flow did not differ between the two species. Results are consistent with all but one expectations of the SGVH. This study adds to the relatively few investigations addressing the SGVH in terrestrial vertebrates.</p>
Nuclear genotypes from Coryphaena hippurus
<p>This dataset comprises genotypes from nuclear microsatellites of Coryphaena hippurus samples. </p> <p>We amplified 14 nuclear microsatellites; five of which were designed by Chapmann (pers. comm) and obtained from GenBank accession numbers: AY135025-AY132028, and AY189832. The rest nine loci were designed by Bayona-Vásquez et al. (2015). Microsatellites loci were amplified from 311 tissue samples from Coryphaena hippurus commonly know as mahi-mahi. Samples were obtained across nine localities within the Tropical Eastern Pacific: Bahía Magdalena (BM); Punta Lobos (PL); Cabo San Lucas (CSL); Guaymas (GU), Mazatlan (MZ); Chiapas (PM); Ecuador (EC); Perú (PE); and ocean sample (OC). Genotypes were recorded in three and two digits. Missing data coded as -9.</p> <p>Geographic coordinates of sample localities:</p> <p>Bahía Magdalena (BM): 24.579118, -111.998283<br> Punta lobos (PL): 23.413503, -110.234702<br> Cabo San Lucas (CSL) : 22.869653, -109.898667<br> Guaymas, Sonora (GU): 27.825287, -110.933325<br> Oceanic sample (OC): 11.886755, -122.880087<br> Mazatlán, Sinaloa (MZ): 23.192460, -106.471719<br> Puerto Madero, Chiapas (PM):14.664185, -92.482619<br> Ecuador (EC):-1.484832, -81.457839<br> Perú (PE): -12.184145, -77.250531</p>
DATASET: Genotyping by sequencing of the common bean Spanish Diversity Panel
<p>Genotyping by sequencing of 308 common bean lines included in the Spanish Diversity Panel. The ApeKI restriction enzyme was used. The sequencing reads were aligned using the reference genome V2.1 (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Pvulgaris). A total of 11,763 SNP markers are included in this dataset after filtering for missing values (< 10%) and minor allele frequency (MAF> 0.05). </p>
AYUKA Genotyper Databases - Human Adenovirus (HAdV)
<p>Database files for several k-lenghts to be used in the AYUKA method to classify adenovirus genotypes</p>
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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.