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.
32
datasets available to search
ShareScore release 0.9.0
Dataset results
32 results for “Statistical genetics”
Data from: Performance of partial statistics in individual-based landscape genetics
Individual-based landscape genetic methods have become increasingly popular for quantifying fine-scale landscape influences on gene flow. One complication for individual-based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance-based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual-based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine-scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.
Data from: Spatially structured statistical network models for landscape genetics
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape-genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non-binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape-genetic data. We further explore a set of spatial-statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape-genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
GWAS summary statistics for The genetic architecture and evolutionary consequences of the human pelvic form
Open the record for dataset details and reuse information.
Phenotypic and marker data for statistical genetic analysis of Minnesota maize lines
<p>Publicly available maize lines are a resource for studying quantitative genetic variation in plants. This dataset includes cluster or subpopulation information, phenotypic data, and single nucleotide polymorphism (SNP) data for 272 maize lines developed by the University of Minnesota, other universities, and seed companies. The lines were evaluated at multiple locations in Minnesota in 2011 and 2012 for different phenological, morphological, and kernel composition traits. The lines were genotyped for 56,110 SNP loci on the Maize SNP50 Beadchip (Illumina). Filtering for SNP data quality led to data for 28,626 SNP loci for the 272 maize lines. Population structure analysis showed that the maize lines clustered into five subpopulations (A321 or Minnesota 13, B73, Mo17, Oh43, and PH207 or Iodent) that represented most of the U.S. Corn Belt diversity (Schaefer and Bernardo 2013a, Crop Science 53:1529-1536). Genomewide association mapping revealed marker-trait associations for male flowering date, female flowering date, plant height, ear height, and kernel concentration for oil, protein, and starch (Schaefer and Bernardo 2013b, Crop Science 53:2518-2529). The covariance between relatives is a tenet in quantitative genetics, but such classical theory has ignored any resemblance between individuals unrelated by pedigree. Analysis of this maize dataset showed that a covariance between nonrelatives in maize is not ubiquitous but it is sometimes rpesent for specific traits and for certain groups of unrelated individuals (Bernardo 2022, Heredity).</p>
Data from: Spatially explicit summary statistics for historical population genetic inference
Open the record for dataset details and reuse information.
Phenotypic and marker data for statistical genetic analysis of Minnesota maize lines
Open the record for dataset details and reuse information.
Data from: Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines
Open the record for dataset details and reuse information.
Data from: Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood
Open the record for dataset details and reuse information.
Data from: Spatially structured statistical network models for landscape genetics
Open the record for dataset details and reuse information.
Data from: Performance of partial statistics in individual-based landscape genetics
Open the record for dataset details and reuse information.
Summary statistics for "Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke"
<p>Summary statistics of the GWA studies presented in <a href="https://doi.org/10.1101/718221">https://doi.org/10.1101/718221</a></p> <p>These are the summary statistics of all imputed SNPs, minus those that showed big differences in allele frequency between different populations (see manuscript for details).</p> <p>Header description:</p> <p>"rs_id" = the rs-id for the variant</p> <p>"chromosome" = the chromosome of the variant</p> <p>"base_pair_location" = the location in bases, according to human reference genome hg19</p> <p>"effect_allele" = the allele that is tested for association with the phenotype</p> <p>"other_allele" = the other allele</p> <p>"effect_allele_frequency" = the allele frequency of the effect allele (not necessarily the minor allele frequency)</p> <p>"IMPUTE2_info" = the imputation quality score as computed by IMPUTE2</p> <p>"beta" = the beta-coefficient from BOLT-LMM</p> <p>"standard_error" = the standard error of beta</p> <p>"p-value" = the p-value for association from BOLT-LMM</p> <p>To convert beta and standard error to odds ratios, you need to know the fraction of cases of the respective GWAS. These can be found in the supplementary material of the manuscript. Use the following formula with the case fraction μ: log OR = β / (μ * (1 - μ)). See the manuscript for more explanation.</p>
Summary statistics of enQTL from "Genetic determinants of gene expression noise and its role in complex trait variation"
<p><span>Here, we established an atlas of gene expression noise from 1.23 million human peripheral blood cells of 981 individuals, identifying its age- and gender-dependent pattern. We then identified 10,770 independent expression noise quantitative trait loci (enQTLs) for 6,743 unique enGenes (genetically driven gene expression noise) across 7 immune cell types.</span></p>
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.