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505 results for “genome-wide association”
Natural selection drives genome-wide evolution via chance genetic associations
<p>Understanding selection's impact on the genome is a major theme in biology. Functionally-neutral genetic regions can be affected indirectly by natural selection, via their statistical association with genes under direct selection. The genomic extent of such indirect selection, particularly across loci not physically linked to those under direct selection, remains poorly understood, as does the time scale at which indirect selection occurs. Here we use field experiments and genomic data in stick insects, deer mice and stickleback fish to show that widespread statistical associations with genes known to affect fitness cause many genetic loci across the genome to be impacted indirectly by selection. This includes regions physically distant from those directly under selection. Then, focusing on the stick insect system, we show that statistical associations between SNPs and other unknown, causal variants result in additional indirect selection in general and specifically within genomic regions of physically linked loci. This widespread indirect selection necessarily makes aspects of evolution more predictable. Thus, natural selection combines with chance genetic associations to affect genome-wide evolution across linked and unlinked loci and even in modest-sized populations. This process has implications for the application of evolutionary principles in basic and applied science.</p>
Large scale across-breed genome-wide association study reveals a variant in HMGA2 associated with inguinal cryptorchidism risk in dogs
<p class="MsoNormal"><span>Cryptorchidism is the most common congenital sex development disorder in dogs. Despite this, little progress has been made in understanding its genetic background. Extensive genetic testing of dogs through consumer and veterinary channels using a high-density SNP genotyping microarray coupled with links to clinical records presents the opportunity for a large-scale genome-wide association study to elucidate the molecular risk factors associated with cryptorchidism in dogs. Using an inter-breed genome-wide association study approach, a significant statistical association on canine chromosome 10 was identified, with the top SNP pinpointing a variant of <em>HMGA2 </em>previously associated with adult weight variance. In further analysis we show that incidence of cryptorchidism is skewed towards smaller dogs in concordance with the identified variant's previous association with adult weight. This study represents the first putative variant to be associated with cryptorchidism in dogs.</span></p>
Illumina HD genotypes for 3,092 cattle from Burkina Faso, Ghana, Nigeria and Tanzania for: "Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle"
<p>Raw HD data for Riggio et al. 2022: Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle</p> <p>This repository contains the raw Illumina HD genotypes (i.e., 777,962 SNPs) mapped to the bovine UMD3.1 genome assembly for 3,092 animals from four African countries (namely Burkina Faso, Ghana, Nigeria and Tanzania). </p>
Improving genome-wide association discovery and genomic prediction accuracy in biobank data
<p>Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ<sup>2</sup> value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.</p>
Leveraging omics data to boost the power of genome-wide association studies
<p>Summary-level GWAS data for 8 traits generated by models M0, M1 and M2 as presented in:</p> <p>Lin, Z., Knutson, K. A., & Pan, W. (2022). Leveraging omic data to boost the power of genome-wide association studies. <em>Human Genetics and Genomics Advances</em>, 100144.</p>
Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
<p>Genome-wide association study summary statistics.</p>
Dataset for: Identification of genomic regions of wheat associated with grain Fe and Zn content under drought and heat stress using genome-wide association study
<p>The study material in the GWAS panel with 282 advanced breeding lines of bread wheat genotypes from IARI stress breeding program was selected to map the genomic regions responsible for grain iron and Zinc content under drought and heat stress treatments.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m) under Irrigated (IR), Restricted Irrigated (RI) and Late sown (LS) treatment conditions with augmented RCBD design. Data was collected on Grain Iron and Grain zinc content along with thousand-grain weight. Around 20 g of grain sample from each of 282 genotypes from the GWAS panel under all three conditions were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 10546 high-quality SNPs was used in GWAS analysis.</p>
Supplementary information for: Redundancy analysis, genome-wide association studies, and the pigmentation of brown trout (Salmo trutta L.)
<p><span>The association of molecular variants to phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multi-trait GWAS to explore the molecular basis of pigmentation variation in brown trout (<em>Salmo</em> <em>trutta</em>) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multi-trait GWAS allowed us to extract 337 independent "colour patterning loci" (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (<em>i</em>) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (<em>ii</em>) were found associated with</span><span> 218 </span><span>candidate genes, including 197 genes </span><span>formerly mentioned in the literature dealing with skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (<em>Onchorhynchus</em> <em>mykiss</em>)</span><span>, and (<em>iii</em>) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.</span></p>
Genome-wide association results: strictly-defined atrial fibrillation vs. all controls
<p>Genome-wide association study summary-level results from an analysis of broadly-defined atrial fibrillation vs controls. The columns are as follows:</p> <ul> <li>SNP: rs identifier</li> <li>CHR: chromosome, build hg19</li> <li>BP: basepair position, build hg19</li> <li>GENPOS: genetic position</li> <li>ALLELE1: tested (first) allele</li> <li>ALLELE0: other allele</li> <li>A1FREQ: frequency of ALLELE1</li> <li>F_MISS: missingness at that SNP</li> <li>BETA: beta (effect size) from the linear mixed model in BOLT-LMM</li> <li>SE: standard error from the linear mixed model in BOLT-LMM</li> <li>P_BOLT_LMM_INF: p-value from the linear mixed model (infinitesimal model) in BOLT-LMM</li> </ul>
Genome-wide association results: broadly-defined atrial fibrillation vs. all controls
<p>Genome-wide association study summary-level results from an analysis of broadly-defined atrial fibrillation vs controls. The columns are as follows:</p> <ul> <li>SNP: rs identifier</li> <li>CHR: chromosome, build hg19</li> <li>BP: basepair position, build hg19</li> <li>GENPOS: genetic position</li> <li>ALLELE1: tested (first) allele</li> <li>ALLELE0: other allele</li> <li>A1FREQ: frequency of ALLELE1</li> <li>F_MISS: missingness at that SNP</li> <li>BETA: beta (effect size) from the linear mixed model in BOLT-LMM</li> <li>SE: standard error from the linear mixed model in BOLT-LMM</li> <li>P_BOLT_LMM_INF: p-value from the linear mixed model (infinitesimal model) in BOLT-LMM</li> </ul> <p> </p>
Genome-wide association results from Phase 1 data comparing uveitis-JIA cases to non-uveitis JIA samples
<p>Summary-level GWAS results for Phase 1 data affiliated with the manuscript "An amino acid motif in HLA-DRβ1 distinguishes patients with uveitis in juvenile idiopathic arthritis."</p> <p>Columns are:</p> <p> 1. CHR: chromosome</p> <p> 2. SNP: SNP identifier</p> <p> 3. BP: basepair position (hg19)</p> <p> 4. A1: minor allele and tested allele</p> <p> 5. A2: the other allele (major allele)</p> <p> 6. FRQ: frequency of the A1 allele</p> <p> 7. INFO: imputation info score</p> <p> 8. EFFECT: beta/effect size of the SNP</p> <p> 9. SE: standard error of the SNP</p> <p> 10. P: p-value at that SNP</p> <p> </p>
Heritability and genome-wide association study of vaccine-induced immune response in Beagles: A pilot study
<p>Both genetic and non-genetic factors contribute to individual variation in the immune response to vaccination. Understanding how genetic background influences variation in both the magnitude and persistence of vaccine-induced immunity is vital for improving vaccine development and identifying possible causes of vaccine failure. Dogs provide a relevant biomedical model for investigating mammalian vaccine genetics; canine breed structure and long linkage disequilibrium simplify genetic studies in this species compared to humans. The objective of this study was to estimate the heritability of the antibody response to vaccination against viral and bacterial pathogens and to identify genes driving variation of the immune response to vaccination in Beagles. Sixty puppies were immunized following a standard vaccination schedule with an attenuated combination vaccine containing antigens for canine adenovirus type 2, canine distemper virus, canine parainfluenza virus, canine parvovirus, and four strains of <em>Leptospira</em> bacteria. Serum antibody measurements for each viral and bacterial component were measured at multiple time points. Heritability estimations and GWAS were conducted using SNP genotypes at 279,902 markers together with serum antibody titer phenotypes. The heritability estimates were: (1) to <em>Leptospira</em> antigens, ranging from 0.178 to 0.628; and (2) to viral antigens, ranging from 0.199 to 0.588. There was not a significant difference between the overall heritability of vaccine-induced immune response to <em>Leptospira</em> antigens compared to viral antigens. Genetic architecture indicates that SNPs of low to high effect contribute to immune response to vaccination. GWAS identified two genetic markers associated with vaccine-induced immune response phenotypes. Collectively, these findings indicate that genetic regulation of the immune response to vaccination is antigen-specific and influenced by multiple genes of small effect.</p>
Genome-wide association study identifies genomic regions associated with key reproductive traits in Korean Hanwoo cows
<p><strong>Background</strong></p> <p>Conducting genome-wide association studies (GWAS) for reproductive traits in Hanwoo cattle, including age at first calving (AFC), calving interval (CI), gestation length (GL), and number of artificial inseminations per conception (NAIPC), is of paramount significance. These analyses provided a thorough exploration of the genetic basis of these traits, facilitating the identification of key markers for targeted trait improvement. Breeders can optimize their selection strategies, leading to more efficient and sustainable breeding programs, by incorporating genetic insights. This impact extends beyond individual traits and contributes to the overall productivity and profitability of the Hanwoo beef cattle industry. Ultimately, GWAS is essential in ensuring the long-term genetic resilience and adaptability of Hanwoo cattle populations. The primary goal of this study was to identify significant single nucleotide polymorphisms (SNPs) or quantitative trait loci (QTLs) associated with the studied reproductive traits and subsequently map the underlying genes that hold promise for trait improvement.</p> <p><strong>Results</strong></p> <p>A genome-wide association study of reproductive traits identified 68 significant single nucleotide polymorphisms (SNPs) distributed across 29 <em>Bos taurus</em> autosomes (BTA). Among them, BTA14 exhibited the highest number of identified SNPs (25), whereas BTA6, BTA7, BTA8, BTA10, BTA13, BTA17, and BTA20 exhibited 8, 5, 5, 3, 8, 2, and 12 significant SNPs, respectively. Annotation of candidate genes within a 500 kb region surrounding the significant SNPs led to the identification of ten candidate genes relevant to age at first calving. These genes were: <em>FANCG</em>, <em>UNC13B</em>, <em>TESK1</em>, <em>TLN1</em>, and <em>CREB3</em> on BTA8; <em>FAM110B</em>, <em>UBXN2B</em>, <em>SDCBP</em>, and <em>TOX</em> on BTA14; and <em>MAP3K1</em> on BTA20. Additionally, <em>APBA3</em>, <em>TCF12</em>, and <em>ZFR2</em>, located on BTA7 and BTA10, were associated with the calving interval; <em>PAX1</em>, <em>SGCD</em>, and <em>HAND1</em>, located on BTA7 and BTA13, were linked to gestation length; and <em>RBM47</em>, <em>UBE2K</em>, and <em>GPX8</em>, located on BTA6 and BTA20, were linked to the number of artificial inseminations per conception in Hanwoo cows.</p> <p><strong>Conclusions</strong></p> <p>The findings of this study enhance our knowledge of the genetic factors that influence reproductive traits in Hanwoo cattle populations and provide a foundation for future breeding strategies focused on improving desirable traits in beef cattle. This research offers new evidence and insights into the genetic variants and genome regions associated with reproductive traits and contributes valuable information to guide future efforts in cattle breeding.</p>
Genome-wide association summary statistics for G4 and G6
<p>This repository contains GWAS summary statistics for G4 (g4_GWAS_Sumstats_Cleaned.txt) and G6 (g6_GWAS_Sumstats_Cleaned.txt), which are part of the paper titled "Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants".</p> <p>For methodological details check the paper at: <span><a href="https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41591-024-02960-5&data=05%7C02%7Cshafiqur.rahman%40mrc-bsu.cam.ac.uk%7Ceba3de1db9174976852b08dc6f7001a0%7C513def5bdf174107b5523dba009e5990%7C0%7C0%7C638507774058203135%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=c8Nc%2B%2BqUFnfIoFkLqM7S6FS6PbXk8kKvIE8QvvOb2Zw%3D&reserved=0">https://www.nature.com/articles/s41591-024-02960-5</a></span></p> <p>The columns are as follows:</p> <ul> <li>SNP: rs identifier for the SNP</li> <li>CHR: chromosome (GRCh37 build) </li> <li>BP: base pair (GRCh37 build) </li> <li>A1: effect allele </li> <li>A2: reference allele </li> <li>FREQ: effect allele frequency </li> <li>INFO: imputation information</li> <li>P: p-value </li> <li>BETA: effect size of effect allele</li> <li>SE: standard error </li> <li>N: sample size</li> </ul>
Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case)
<p>Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case). The first GWAS meta-analysis combines GWAS summary statistics of invasive melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The second GWAS meta-analysis combines GWAS summary statistics of in situ melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The third GWAS meta-analysis combines GWAS summary statistics of in situ vs invasive (case-case; in situ code 0, invasive code 1) melanoma from UK Biobank (as of August 2022), QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>Columns</p> <p>CHR Chromosome</p> <p>SNP rsid</p> <p>POS Base position HG Build 37</p> <p>A1 effect allele</p> <p>A2 Non-effect allele</p> <p>A1FREQ Allele frequency of effect allele</p> <p>BETA effect estimate of effect allele</p> <p>SE standard error of effect estimate</p> <p>PVAL two-tailed p value</p> <p>DIRECTION the Direction of effect of the SNP in each cohort ( in the order UKBB, FINNGEN, QSKIN, QMEGA 610k, QMEGA OMNI)</p> <p>N sample size</p> <p>See </p>
Multi-ancestry genome-wide association statistics of anxiety
<p><strong>Multi-Ancestry Genome-Wide Association Statistics of Anxiety</strong></p> <p><strong>Citation</strong>: Friligkou E, Lokhammer S, Cabrera Mendoza B, Shen J, He J, Deiana G, Zanoaga MD, Asgel Z, Pilcher A, Di Lascio L, Makharashvili A, Koller D, Tylee D, Pathak GA, Polimanti R. Gene Discovery and Biological Insights into Anxiety Disorders from a Large-Scale Multi-Ancestry Genome-wide Association Study. Nat Genet. doi: 10.1038/s41588-024-01908-2.</p> <p> </p> <p> </p> <p> </p>
PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity". <em>Nature Communications </em>volume 9, Article number: 2904 (2018) doi: 10.1038/s41467-018-04766-9 </p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: https://www.nature.com/articles/s41467-018-04766-9. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>The file PR_interval_July2018_summary_results.txt is gzipped and contains the PR interval GWAS meta-analysis summary results of 92,000 European samples. The imputation reference panel used for most studies was HapMap2 (please note: hg18, build36). </p> <ul> <li><em>SNP</em> - variantID (rsID).</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg18 / build36.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA_FIXED</em> - beta from the fixed-effects model.</li> <li><em>SE_FIXED </em>- standard error from the fixed-effects model.</li> <li><em>P_FIXED </em>- P-value from the fixed-effects model.</li> </ul>
Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz goat
<p>Illumina Caprine 53K SNPchip genotypes of 228 Iranian goats used for coat color and mohair traits GWAS</p>
Genome-wide association study for circulating metabolites in 619,372 individuals
<h2>Genome-wide association study for circulating metabolites in 619,372 individuals</h2> <p>Preprint: <a href="https://doi.org/10.1101/2024.10.15.24315557">https://doi.org/10.1101/2024.10.15.24315557</a></p> <p><br>meta_ALL - meta-analysis across Estonian Biobank and six genetic ancestry groups of the UK Biobank</p> <p>meta_EUR - meta-analysis across Estonian Biobank and EUR genetic acestry group of the UK Biobank<br><br>*_all_lead_varaints.tsv - all genome-wide significant lead variants detected for each of the 249 tested metabolites</p> <p>*_independent_lead_variants.tsv - indepedendent lead variants that were in high LD with each other (r2 > 0.8).</p> <p> </p>
RatXcan: A framework for cross-species integration of genome-wide association and gene expression data
<p>Data for paper</p> <p><span>RatXcan: A framework for cross-species integration of genome-wide association and gene expression data</span></p> <p><span>Natasha Santhanam</span><span><span>1</span></span><span><span>†</span></span><span>, Sandra Sanchez-Roige</span><span><span>2,3,4</span></span><span><span>†</span></span><span>, Sabrina Mi</span><span><span>2</span></span><span>, Yanyu Liang</span><span><span>1</span></span><span>, Apurva S. Chitre</span><span><span>2</span></span><span>, Daniel Munro</span><span><span>2</span></span><span>, Denghui Chen</span><span><span>2</span></span><span>, Riyan Cheng</span><span><span>2</span></span><span>, Jianjun Gao</span><span><span>2</span></span><span>, Angel Garcia-Martinez</span><span><span>6</span></span><span>, Anthony M. George</span><span><span>5</span></span><span>, Alexander F. Gileta</span><span><span>2</span></span><span>, Wenyan Han</span><span><span>6</span></span><span>, Katie Holl</span><span><span>7</span></span><span>, Alesa Hughson</span><span><span>8</span></span><span>, Christopher P. King</span><span><span>9</span></span><span>, Alexander C. Lamparelli</span><span><span>9</span></span><span>, Connor D. Martin</span><span><span>5</span></span><span>, Festus Nyasimi</span><span><span>1</span></span><span>, Celine L. St. Pierre</span><span><span>2</span></span><span>, Sarah Sumner</span><span><span>1</span></span><span>, Jordan Tripi</span><span><span>9</span></span><span>, Tengfei Wang</span><span><span>6</span></span><span>, Hao Chen</span><span><span>6</span></span><span>, Shelly Flagel</span><span><span>8</span></span><span>, Keita Ishiwari</span><span><span>5,10</span></span><span>, Paul Meyer</span><span><span>5,9</span></span><span>, Oksana Polesskaya</span><span><span>2</span></span><span>, Laura Saba</span><span><span>11</span></span><span>, Leah C. Solberg Woods</span><span><span>12</span></span><span>, Abraham A. Palmer</span><span><span>2,3</span></span><span>*, Hae Kyung Im</span><span><span>1</span></span><span>*</span></p> <p><span> </span></p> <p><span>[1] Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA</span></p> <p><span>[2] Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[3] Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[4] Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA</span></p> <p><span>[5] University at Buffalo, Clinical and Research Institute on Addictions University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[6] University of Tennessee Health Science Center, Department of Pharmacology, Addiction Science and Toxicology, Memphis, TN, 38120, USA</span></p> <p><span>[7] Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, 53226, USA</span></p> <p><span>[8] University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA</span></p> <p><span>[9] University at Buffalo, Department of Psychology, Buffalo, NY, 14260, USA</span></p> <p><span>[10] University at Buffalo, Pharmacology and Toxicology University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[11] University of Colorado Anschutz Medical Campus, Department of Pharmaceutical Sciences, Aurora, CO 80045, USA</span></p> <p><span>[12] Wake Forest University School of Medicine, Department of Internal Medicine, Winston-Salem, NC, 27157, USA</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.