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298 results for “ancestry”
Phlorest phylogeny derived from Sagart et al. 2019 'Dated language phylogenies shed light on the ancestry of Sino-Tibetan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sagart L, Jacques G, Lai Y, Ryder RJ, Thouzeau V, Greenhill SJ, List J- M. 2019 Dated language phylogenies shed light on the ancestry of Sino-Tibetan. Proceedings of the National Academy of Sciences, 201817972.</p> </blockquote>
Phlorest phylogeny derived from Chang et al. 2015 'Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chang W, Cathcart C, Hall D, & Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis. Language, 91(1):194-244.</p> </blockquote>
Summary statistic of a Trans ancestry multi-trait GWAS
<p>Trans ancestry multi-trait GWAS by adapting the omnibus test to the trans ancestry setting. </p><p>Genome wide summary statistics for 19 blood count traits were retrieved from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel)</a></p><p>They curated using the JASS (Joint Analysis of Summary Statistics) pipeline https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline</p><p>See Troubat et al preprint for all details on the obtention of this dataset https://doi.org/10.1101/2023.06.23.546248</p>
MAGE: Multi-ancestry Analysis of Gene Expression
<p>MAGE comprises RNA-seq data from lymphoblastoid cell lines derived from 731 individuals from the <a href="https://doi.org/10.1038/nature15393" rel="nofollow">1000 Genomes Project (1KGP)</a>, representing 26 globally-distributed populations across five continental groups. These data offer a large, geographically diverse, open access resource to facilitate studies of the distribution, genetic underpinnings, and evolution of variation in human transcriptomes and include data from several ancestry groups that were poorly represented in previous studies.</p> <p>Briefly, this repo contains the following data:</p> <ol> <li>Sample metadata and sequencing metrics</li> <li>Gene expression and splicing matrices used for e/sQTL mapping and analyses of global trends of expression/splicing diversity</li> <li>cis-e/sQTL mapping results, including aFC estimates for cis-eQTLs</li> <li>Functional annotations of cis-e/sQTLs</li> <li>Results of colocalization analysis between MAGE e/sQTLs and complex trait GWAS from the <a href="https://doi.org/10.1038/s41586-019-1310-4" rel="nofollow">PAGE</a> study</li> <li>Results of analyses of global trends of expression/splicing diversity</li> <li>Jointly-generated top genotype PCs for samples in MAGE and other resources with paired WGS/RNA-seq data (Geuvadis, GTEx, AFGR)</li> </ol> <p>READMEs are provided for all data in the repo.</p>
An updated map of GRCh38 linkage disequilibrium blocks based on European ancestry data
<p>A map of approximately independent linkage disequilibrium (LD) blocks has many uses in statistical genetics. Current publicly available LD block maps are based on sparse recombination maps and are only available for GRCh37 (hg19) and prior genome assemblies. We generated LD blocks in GRCh38 for European (EUR) ancestry populations using a recent recombination map based on more than 115,000 individuals. This new map consists of 1,361 independent LD blocks across the 22 autosomal chromosomes and can be accessed at https://github.com/jmacdon/LDblocks_GRCh38</p>
Chromatin activity identifies differential gene regulation across human ancestries
<p>This repository contains data related to:</p> <p>Chromatin activity identifies differential gene regulation across human ancestries</p> <p>Kade P. Pettie, Maxwell Mumbach, Amanda J. Lea, Julien Ayroles, Howard Y. Chang, Maya Kasowski, Hunter B. Fraser</p> <p> </p>
Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p> </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
The PODER cross-ancestry long-read RNA-seq dataset
<p>Table legends:</p> <ul> <li>column_descriptions.docx: Extended description of columns in selected tables.</li> <li>00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.</li> <li>01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.</li> <li>02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.</li> <li>03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.</li> <li>04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.</li> <li>06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.</li> <li>07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.</li> <li>08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.</li> <li>09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.</li> <li>10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.</li> <li>11_astu: Allele-specific transcript usage results.</li> <li>12_ase: Allele-specific expression results.</li> <li>13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.</li> <li>14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.</li> <li>16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).</li> <li>17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.</li> <li>18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47</li> </ul>
Short tandem repeat expansions in LRP12 are absent in familial and sporadic amyotrophic lateral sclerosis patients of European ancestry
<p>In patients of Asian ancestry, a heterozygous CGG repeat expansion of >100 units in <i>LRP12</i> is the cause of oculopharyngodistal myopathy type 1 (OPDM1), and has been associated with amyotrophic lateral sclerosis (ALS) when repeat lengths are between 61-100 units, although with unusually long disease duration and without significant upper motor neuron involvement. This study investigated if <i>LRP12</i> CGG tandem repeats were expanded in ALS patients of European ancestry. We screened whole-genome sequencing data from 608 sporadic ALS patients, 35 familial ALS probands, and 4,703 neurologically normal controls for the <i>LRP12</i> CGG expansion using ExpansionHunter v4. All individuals had <i>LRP12 </i>CGG repeat lengths between 3-25 units. Our results suggest that <i>LRP12 </i>CGG repeat expansions may only be present in ALS patients of Asian ancestry with atypical clinical presentations.</p>
Selection shapes the genomic landscape of introgressed ancestry in a pair of sympatric sea urchin species
<p>A growing number of recent studies have demonstrated that introgression is common across the tree of life. However, we still have a limited understanding of the fate and fitness consequence of introgressed variation at the whole-genome scale across diverse taxonomic groups. Here, we implemented a phylogenetic hidden Markov model to identify and characterize introgressed genomic regions in a pair of well-diverged, non-sister sea urchin species: <em>Strongylocentrotus</em> <em>pallidus</em> and <em>S. droebachiensis</em>. Despite the old age of introgression, a sizable fraction of the genome (1% - 5%) exhibited introgressed ancestry, including numerous genes showing signals of historical positive selection that may represent cases of adaptive introgression. One striking result was the overrepresentation of hyalin genes in the identified introgressed regions despite observing considerable overall evidence of selection against introgression. There was a negative correlation between introgression and chromosome gene density, and two chromosomes were observed with considerably reduced introgression. Relative to the non-introgressed genome-wide background, introgressed regions had significantly reduced nucleotide divergence (<em>d</em><sub>XY</sub>) and overlapped fewer protein-coding genes, coding bases, and genes with a history of positive selection. Additionally, genes residing within introgressed regions showed slower rates of evolution (<em>d</em><sub>N</sub>, <em>d</em><sub>S</sub>, <em>d</em><sub>N</sub>/<em>d</em><sub>S</sub>) than random samples of genes without introgressed ancestry. Overall, our findings are consistent with widespread selection against introgressed ancestry across the genome and suggest that slowly evolving, low-divergence genomic regions are more likely to move between species and avoid negative selection following hybridization and introgression.</p>
The structure of simple satellite variation in the human genome and its correlation with centromere ancestry (Supplemental Data)
<p>Accompanying <a href="https://github.com/is-the-biologist/1KGP_SATS" target="_blank" rel="noopener">Github</a></p> <p><strong>Supplemental File 1.</strong> BLAST results of k-mer concatemers against T2T-CHM13-v2.0.</p> <p><strong>Supplemental File 2.</strong> Annotations of centromeres, and telomeres of T2T-CHM13-v2.20. Table of abundance of k-mers in annotated regions as numpy file from BLAST hits. Abundance of k-mers across genome in 100kb bins from BLAST hits as .npz files accessible by example:</p> <p> import numpy as np<br> dense = np.load("filename.npz")<br> dense["chr1"]<br> <br><strong>Supplemental File 3</strong>. Table of pairwise R2 between simple satellites and table of pairwise interspersion OR between simple satellites. Folder containing QQ plots of negative binomial fit of satellite copy number distribution used to qualitatively asses model fit.</p> <p><strong>Supplemental File 4. </strong>Materials and results of cenGRM analysis. Boundaries used for centromeric regions of each cenGRM, cenGRMs in GCTA format, and tables with the results of cenGRM GCTA runs. Also provide pdfs of the dendrograms/heatmaps produced from UPGMA clustering of each cenGRM. </p> <p><strong>Supplemental File 5</strong> Non-human significant BLAST hits from BLAST-ing k-mer concatamers to non-human sequences.</p> <p><strong>Supplemental Table 1.</strong> Copy number normalized to 1x depth given GC bias of 126 most abundant satellites analyzed in paper in each individual. Additional columns represent metadata of the individual:</p> <ul> <li>instrument: sequencer instrument name used to sequence library.</li> <li>run: sequencer run of the library.</li> <li>flow: flowcell ID of the ibrary.</li> <li>pop: 1,000 Genomes Project population ID.</li> <li>superpop: 1,000 Genomes Project superpopulation ID.</li> <li>reads: average autosomal read depth of the library.</li> </ul> <p><strong>Supplemental Table 2. </strong>Copy number normalized to 1x depth given GC bias of the top 126 most abundant satellites analyzed in paper in each individual of the 1KGP, plus estimates of the same satellites in CHM13 short-read libraries subsampled from 18x-0.5x, 18x depth simulated library of the T2T-CHM13v2.0 assembly analyzed using k-Seek, and Tandem Repeat Finder results of the T2T-CHM13v2.0 asembly <a href="https://doi.org/10.1126/science.abk3112" target="_blank" rel="noopener">Hoyt 2022</a>.</p> <p><strong>Supplemental Table 3.</strong> Copy number normalized to 1x depth given GC bias of all tandem repeats with k-mer <= 20 (6,309) found collectively in the CHM13 short-read libraries subsampled from 18x-0.5x, 18x depth simulated library of the T2T-CHM13v2.0 assembly analyzed using k-Seek, and Tandem Repeat Finder results of the T2T-CHM13v2.0 asembly <a href="https://doi.org/10.1126/science.abk3112" target="_blank" rel="noopener">Hoyt 2022</a>.</p>
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K in Riverscape genetics of the orangethroat darter complex
F I G U R E 4 Assigned ancestry plot using AIC criteria determining optimal K of 5. Each color corresponds to a distinct genetic cluster, and each bar represents an individual's assigned ancestry to each cluster. Names above chart correspond to river basin (HUC6). EB—E. burri, EP— E. pulchellum, ES—E. spectabile, and EU—E. uniporum. Glaciated and unglaciated correspond to areas that were/were not covered in ice during the last glacial maximum.
Common Ancestry of the Id Locus: Chromosomal Rearrangement and Polygenic Possibilities
<h2>The Id locus, with a potential polygenic nature, is inverted alongside ZARU1 at the distal end of the q-arm of chromosome Z, indicating a shared ancestry among BBC breeds.</h2>
"Do you want to know who you are?" The rise of genetic ancestry testing and the search for genealogies: an anonymized survey from Sweden
<p>Full, anonymized survey data on genetic genealogy, ancestry and identity conducted by the Swedish Genealogical Society as part of a research project funded by the HERA joint research program "Uses of the Past"</p>
Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries
<p><strong>Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions, and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Cross-tissue analyses indicated that over a third of effector genes most likely act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis, and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.</strong></p> <p><strong>The data submitted here are expression and methylation models with LD reference data for the transcriptome-wide (TWAS), methylome-wide (MWAS) and transcript isoform-wide association study (TIsWAS) as described in the manuscript "Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries". Details of the methods are presented in the method section and supplementary information file. </strong></p> <p><strong>TWAS analysis </strong></p> <p>Gene expression models for the six in-house expression datasets were generated using the PredictDB v7 pipeline for a total of 1,077 participants. Elastic net model building with 10-fold cross-validation was performed independently for each dataset. The elastic net models for GTEx v8 Colon Transverse were obtained from the PredictDB data repository (<a href="http://predictdb.org/">http://predictdb.org/</a>) and had been generated using the same pipeline. Models were computed using HapMap2 SNPs ±1Mb from each gene, together with covariate factors estimated using PEER32, clinical covariates when appropriate (age, sex and, where appropriate, case-control status, type of polyp and anatomic location in the colorectum), and three PCs from the individual dataset’s SNP genotype data.</p> <p>Transcript-based TWAS analyses (TIsWAS) were likewise performed by using transcript-level data from the SOCCS, BarcUVa-Seq and GTEx Colon Transverse datasets.</p> <p><strong>MWAS analysis </strong></p> <p>Methylation beta values were calculated based on the manufacturer’s standard, ranging from 0 to 1. Quality control and data normalization were performed in R using the ChAMP software pipeline for the EPIC and 450K arrays. Briefly, we filtered out failed probes with detection P > 0.02 in >5% of samples, probes with <3 reads in >5% of samples per probe and all non-CpG probes. Samples with failed probes >0.1 were also excluded from downstream analyses. We discarded all probes with SNPs within 10bp of the interrogated CpG (from 1,000 Genomes Project, CEU population)34, and probes that ambiguously mapped to multiple locations in the human genome with up to two mismatches33. We only considered probes mapping to autosomes and those overlapping between the EPIC and the 450K arrays. Normalization was achieved using the Beta MIxture Quantile (BMIQ) method. Per probe methylation models were created using the PredictDB pipeline on the normalized methylation matrix and the genotypes as per TWAS eQTL analysis. To optimize power, we restricted our analysis to 263,341-238,443 (for the 450K array) and 377,678 (for the EPIC array) probes annotated to Islands, Shores and Shelves, and discarded “Open Sea” regions. </p>
The gut microbiome reflects ancestry despite dietary shifts across a hybrid zone
<p>The microbiome is critical to an organism's phenotype, and its composition is shaped by, and a driver of, eco-evolutionary interactions. We investigated how host ancestry, habitat, and diet shape gut microbial composition in a mammalian hybrid zone that occurs across an ecotone between distinct vegetation communities. We found that habitat is the primary determinant of diet, while host genotype is the primary determinant of the gut microbiome—a finding further supported by intermediate microbiome composition in first generation hybrids. Despite these distinct primary drivers, microbial richness was correlated with diet richness, and individuals that maintained higher dietary richness had greater gut microbial community stability. Both relationships were stronger in the relative dietary generalist of the two parental species. Our findings show that host ancestry interacts with dietary habits to shape the microbiome, ultimately resulting in the organismal phenotypic plasticity that host-microbial interactions allow.</p>
Fig. 3 in Crinoid ancestry without blastozoans
Fig. 3. Line drawings showing oral regions of the derived blastozoan, Glyptocystella loeblichi (A) and crinoid, Hybocrinus nitidus, five wedgeshaped coelomic slots, and madreporic hydropore (inverted U below peristomial opening) (B). Modified from Sprinkle (1982a, b).
Fig. 1 in Crinoid ancestry without blastozoans
Fig. 1. Orals in early pentaradial echinoderms. A. Plesiomorphic template expressing the 2-1-2 pattern. B. Near pentameric symmetry, showing potential pentaradiality through shortening initial branches during ontogeny.
Fig. 8 in Crinoid ancestry without blastozoans
Fig. 8. Examples of derived blastozoans with superficial crinoid-like expressions. A. Eocrinoid Rhopalocystis destombesi Ubaghs, 1963 (PE 52747), Early Ordovician of Morocco; lateral view (A1), showing derived superficially camerate-like thecal plating, lacking continuity between "fixed brachial"-like plates and feeding appendages, "rays" tapering, not aligned with ambulacra tips above; oral surface (A2), showing typical biserial blastozoan brachiole facets. B. Diploporite-like blastozoan Eumorphocystis multiporata Branson and Peck, 1940 (TMM 1107TX2), Late Ordovician of Oklahoma, USA; with arm-like appendages superficially similar to crinoids. A ray feeding appendage stub near theca (B1), showing triserial facets, cross-sectional plate shapes and sub-central ovate canal; thecal summit in A-B interray view (B2), showing abrupt boundary between theca and feeding appendages, feeding appendages with thickplated triserial pattern, diplopore-bearing thecal plates; feeding appendage segment with pinnule-like structures (B3). C, D. Paracrinoid, Platycystites sp., Late Ordovician of Oklahoma, USA; oral surfaces of lenticular thecae, sub-ambulacral lumens revealed by natural spalling of brachiole-bearing ambulacrals, in continuity with the peristome. C. NPL 1113TX17, lumens short. D. NPL 1106TX13, lumens long. All Tremadoc crinoids are non-pinnulate.
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.