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Dataset results
161 results for “genetic susceptibility”
Genetic Polymorphism Associated With Coronary Heart Disease Susceptibility and Variability of Clopidogrel Response
ClinicalTrials.gov study NCT03373552. IPD Sharing: NO. Countries: 1. Publications: 1.
ScreenIng of Genetic Susceptibility Genes for Breast Cancer Patients in CHinese communiTies
ClinicalTrials.gov study NCT04265937. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical Utility Of Genetic Screening For HLA-B*5701, On Susceptibility To Abacavir Hypersensitivity
ClinicalTrials.gov study NCT00340080. IPD Sharing: Not stated. Countries: 19. Publications: 1.
Genetic Susceptibility for Bronchopulmonary Dysplasia in Preterm Infants
ClinicalTrials.gov study NCT00904774. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Host Genetic Susceptibility to Avian Influenza A/H5N1
ClinicalTrials.gov study NCT01074736. IPD Sharing: Not stated. Countries: 1. Publications: 56.
Genetic Susceptibility to Predict Weight Loss After Bariatric Surgery
ClinicalTrials.gov study NCT06226194. IPD Sharing: UNDECIDED. Countries: 1. Publications: 26.
Study of Factors of Genetic Susceptibility Associated to Severe Caries Phenotype
ClinicalTrials.gov study NCT00541060. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Molecular Genetics Study of Nasopharyngeal Carcinoma: Characterization of NCP Susceptibility Gene(s)
ClinicalTrials.gov study NCT00341146. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Genetic Susceptibility in MAlignant Pleural Mesothelioma: Clinical Implication of GermliNE VariaTionS
ClinicalTrials.gov study NCT06886672. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Genetic Susceptibility to Childhood Respiratory IIlness in Mexico City
ClinicalTrials.gov study NCT00339521. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Relating Genetic and Environmental Risk Scores to Multiple Sclerosis Susceptibility
ClinicalTrials.gov study NCT01617395. IPD Sharing: NO. Countries: 1. Publications: 3.
Multi-ancestry meta-analysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture
Open the record for dataset details and reuse information.
Genetic basis of growth, spring phenology and susceptibility to biotic stressors in maritime pine
Open the record for dataset details and reuse information.
Data from: Genome-wide exon-capture approach identifies genetic variants of Norway spruce genes associated with susceptibility to Heterobasidion parviporum infection
Open the record for dataset details and reuse information.
Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
Standing genetic variation in laboratory populations of insecticide-susceptible Phlebotomus papatasi and Lutzomyia longipalpis (Diptera: Psychodidae: Phlebotominae) for the evolution of resistance
<p>Insecticides can exert strong selection on insect pest species, including those that vector diseases, and have led to rapid evolution of resistance. Despite such rapid evolution, relatively little is known about standing genetic variation for resistance in insecticide-susceptible populations of many species. To help fill this knowledge gap, we generated genotyping-by-sequencing data from insecticide-susceptible Phlebotomus papatasi and Lutzomyia longipalpis sand flies that survived or died from a sub-diagnostic exposure to either permethrin or malathion using a modified version of the Centers for Disease Control and Prevention bottle bioassay. Multi-locus genome-wide association mapping methods were used to quantify standing genetic variation for insecticide resistance in these populations and to identify specific alleles associated with insecticide survival. For each insecticide treatment, we estimated the proportion of the variation in survival explained by the genetic data (i.e. 'chip' heritability) and the number and contribution of individual loci with measurable effects. For all treatments, survival to an insecticide exposure was heritable with a polygenic architecture. Both P. papatasi and L. longipalpis had alleles for survival that resided within many genes throughout their genomes. The implications for resistance conferred by many alleles, as well as inferences made about the utility of laboratory insecticide resistance association studies compared to field observations, are discussed in the manuscript that accompanies this data.</p>
Data from: Common genetic variation in ETV6 is associated with colorectal cancer susceptibility
Genome-wide association studies (GWAS) have identified multiple susceptibility loci for colorectal cancer, but much of heritability remains unexplained. To identify additional susceptibility loci for colorectal cancer, here we perform a GWAS in 1,023 cases and 1,306 controls and replicate the findings in seven independent samples from China, comprising 5,317 cases and 6,887 controls. We find a variant at 12p13.2 associated with colorectal cancer risk (rs2238126 in ETV6, P = 2.67 × 10-10). We replicate this association in an additional 1,046 cases and 1,076 controls of European ancestry (P = 0.034). The G allele of rs2238126 confers earlier age at onset of colorectal cancer (P = 1.98 × 10-6) and reduces the binding affinity of transcriptional enhancer MAX. The mRNA level of ETV6 is significantly lower in colorectal tumors than in paired normal tissues. Our findings highlight the potential importance of genetic variation in ETV6 conferring susceptibility to colorectal cancer.
Data from: Keeping pace with the Red Queen: identifying the genetic basis of susceptibility to infectious disease
Genome-wide association studies are widely used to identify "disease genes" conferring resistance/susceptibility to infectious diseases. Using a combination of mathematical models and simulations we demonstrate that genetic interactions between hosts and parasites (GxG interactions) can drastically affect the results of these association scans and hamper our ability to detect genetic variation in susceptibility. When hosts and parasites coevolve, these GxG interactions often make Genome-wide association studies unrepeatable over time or across host populations. Reanalyzing previously published data on Daphnia magna susceptibility to infection by Pastueria ramosa, we identify genomic regions consistent with GxG interactions. We conclude by outlining possible avenues for designing more powerful and more repeatable association studies.
Data from: Genetic susceptibility to infectious disease in East African Shorthorn Zebu: a genome-wide analysis of the effect of heterozygosity and exotic introgression
[No abstract entered]
Susceptibility to a sexually transmitted disease in a wild koala population shows heritable genetic variance but no inbreeding depression
<p><span>The koala, one of the most iconic Australian wildlife species, is facing several concomitant threats that are driving population declines. Some threats are well known and have clear methods of prevention (e.g., habitat loss can be reduced with stronger land-clearing control), whereas others are less easily addressed. One of the major current threats to koalas is chlamydial disease, which can have major impacts on individual survival and reproduction rates and can translate into population declines. Effective management strategies for the disease in the wild are currently lacking, and to date we know little about the determinants of individual susceptibility to disease. Here we investigated the genetic basis of variation in susceptibility to chlamydia using one of the most intensively studied wild koala populations. We combined data from veterinary examinations, chlamydia testing, genetic sampling and movement monitoring. Out of our sample of </span><span>342 wild koalas, 60 were found to have chlamydia.</span><span> Using genotype information on 5007 SNPs to investigate the role of genetic variation in determining disease status, we found no evidence of inbreeding depression, but a heritability of 0.11 (95%CI = 0.05 – 0.23) for the probability that koalas had chlamydia. Heritability of susceptibility to chlamydia could be relevant for future disease management, as it suggests adaptive potential for the population.</span></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)
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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.