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21 results for “Polygenic disease”
An integrated polygenic tool substantially enhances coronary artery disease prediction
<p>Summary-level CAD GWAS data generated by Genomics plc as presented in:</p> <p>Riveros-Mckay F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circulation: Genomics and Precision Medicine (in press). </p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com</p> <p> </p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “Group I” in the published paper. Group I, “no PCE/QRISK3 available”, included 114,196 European-ancestry individuals with missing data that prevented PCE or QRISK3 calculation.</p> <p>CAD case phenotypes were defined as described in the “Phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events.</p> <p>All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p> </p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> cpra Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom Chromosome<br> pos Position in base pairs (b37, 1-based)<br> alt Alternative allele (effect allele)<br> beta Effect size (log odds ratio)<br> standard_error Standard error of beta <br> minus_log10_p Minus log(base 10) of P-value<br> ref Reference allele (non-effect allele)<br> ncase Number of cases<br> ncontrol Number of controls</p>
UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
<p>Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in:</p> <p>Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>)</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p><strong>NOTES</strong></p> <p>These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.</p> <p>Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows:</p> <table> <tbody> <tr> <td>Narrow CAD<br> (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095 in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD<br> (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> <p>Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke.</p> <p>All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.</p> <p>GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.</p> <p>The results are not adjusted for genomic control.</p> <p><strong>DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> <p><strong>DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> <p><strong>FILE NAMES</strong></p> <p>The following is a list of traits and their corresponding file names.</p> <p><em><strong>DISEASE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> <p><em><strong>QUANTITATIVE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>
Polygenic risk scores validated in patient-derived cells stratify for mitochondrial subtypes of Parkinson's disease
<p><strong>Background</strong> Parkinson’s disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better understand the genetic architecture and downstream cellular alterations underlying PD pathogenesis, in order to identify more focused therapeutic targets. While only ∼10% of PD cases can be clearly attributed to monogenic causes, there is mounting evidence that additional genetic factors could play a role in idiopathic PD (iPD). In particular, common variants with low to moderate effect size in multiple genes regulating key neuroprotective activities may act as risk factors for PD. In light of the well-established involvement of mitochondrial dysfunction in PD, we hypothesized that a fraction of iPD cases may harbour a pathogenic combination of common variants in nuclear-encoded mitochondrial genes, ultimately resulting in neurodegeneration.</p> <p><strong>Methods</strong> to capture this mitochondria-related “missing heritability”, we leveraged on existing data from previous genome-wide association studies (GWAS) – i.e., the large PD GWAS from Nalls and colleagues. We then used computational approaches based on mitochondria-specific polygenic risk scores (mitoPRSs) for imputing the genotype data obtained from different iPD case-control datasets worldwide, including the Luxembourg Parkinson’s Study (412 iPD patients and 576 healthy controls) and the COURAGE-PD cohorts (7270 iPD cases and 6819 healthy controls).</p> <p><strong>Results</strong> applying this approach to gene sets controlling mitochondrial pathways potentially relevant for neurodegeneration in PD, we demonstrated that common variants in genes regulating <em>Oxidative Phosphorylation (OXPHOS</em>-PRS<em>)</em> were significantly associated with a higher PD risk both in the Luxembourg Parkinson’s Study (odds ratio, OR=1.31[1.14-1.50], <em>p</em>=5.4e-04) and in COURAGE-PD (OR=1.23[1.18-1.27], <em>p</em>=1.5e-29). Functional analyses in primary skin fibroblasts and in the corresponding induced pluripotent stem cells-derived neuronal progenitor cells from Luxembourg Parkinson’s Study iPD patients stratified according to the <em>OXPHOS</em>-PRS, revealed significant differences in mitochondrial respiration between high and low risk groups (<em>p</em> < 0.05). Finally, we also demonstrated that iPD patients with high <em>OXPHOS</em>-PRS have a significantly earlier age at disease onset compared to low-risk patients.</p> <p><strong>Conclusions</strong> our findings suggest that OXPHOS-PRS may represent a promising strategy to stratify iPD patients into pathogenic subgroups – in which the underlying neurodegeneration is due to a genetically defined mitochondrial burden – potentially eligible for future, more tailored mitochondrially targeted treatments.</p>
Data from: Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk
<p>This dataset contains supporting data for the publication: </p> <p>Boudriot, E.<em> et al.</em> Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk. <em>Biological Psychiatry</em><span> </span><a href="https://doi.org/10.1016/j.biopsych.2024.04.014">https://doi.org/10.1016/j.biopsych.2024.04.014</a></p> <p> </p> <p>Files:</p> <ul> <li><em>clinical.csv </em>contains data from clinical assessment and polygenic risk scores for schizophrenia</li> <li><em>ophthalmic_examination.csv </em>contains data on spherical equivalent, intraocular pressure and visual acuity</li> <li><em>oct.csv </em>contains segmentation output from Iowa Reference Algorithms</li> <li><em>erg.csv </em>contains pre-processed ERG data for the four ERG conditions</li> <li><em>mri.csv </em>contains ICV-corrected MRI volumes</li> </ul>
Incorporating family history of disease improves polygenic risk scores in diverse populations
<p>Code relevant to Hujoel et al. "Incorporating family history of disease improves polygenic risk scores in diverse populations"</p>
Source data for manuscript "Polygenic burden of short tandem repeat expansions promote risk of Alzheimer's disease"
<p>Included are the source data and scripts used to make all plots for the mansucript "Polygenic burden of short tandem repeat expansions promote risk of Alzheimer's disease"</p>
Polygenic Risk Driven Pragmatic Statin Trial for Heart Disease Prevention
ClinicalTrials.gov study NCT06820086. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Effect of polygenic load on striatal dopaminergic deterioration in Parkinson's disease
Objective: To investigate the effect of polygenic load on the progression of striatal dopaminergic dysfunction in patients with Parkinson's disease (PD). Methods: Using data from 335 PD patients in the Parkinson's Progression Markers Initiative (PPMI) database, we investigated the longitudinal association of PD-associated polygenic load with changes in striatal dopaminergic activity as measured by 123I-N-3-fluoropropyl-2-beta-carboxymethoxy-3beta-(4-iodophenyl) nortropane (123I-FP-CIT) single photon emission computed tomography (SPECT) over 4 years. PD-associated polygenic load was estimated by calculating weighted genetic risk scores (GRS) using: i) all available 27 PD-risk single nucleotide polymorphisms (SNPs) in the PPMI database (GRS1); and ii) 23 SNPs with minor allele frequency > 0.05 (GRS2). Results: GRS1 and GRS2 were correlated with younger age-at-onset in PD patients (GRS1, Spearman's rho = -0.128, p = 0.019; GRS2, Spearman's rho = -0.109, p = 0.047). Although GRS1 did not show an association with changes in striatal 123I-FP-CIT availability, GRS2 was associated with a slower decline of striatal dopaminergic activity (interactions with disease duration in linear mixed model; caudate nucleus, estimate = 0.399, SE = 0.165, p = 0.028; putamen, estimate = 0.396, SE = 0.137, p = 0.016). Conclusions: Our results suggest that genetic factors for PD risk may have heterogeneous effects on the striatal dopaminergic degeneration, and some factors may be associated with a slower decline of dopaminergic activity. Composition of PD-progression specific GRS may be useful in predicting disease progression in patients.
Data from: Effect of polygenic load on striatal dopaminergic deterioration in Parkinson's disease
Open the record for dataset details and reuse information.
Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions (striatum LNDBB).
GEO Series GSE89706. Homo sapiens. 49 samples. Type: Methylation profiling by array.
Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions (cerebellum DBCBB).
GEO Series GSE89702. Homo sapiens. 33 samples. Type: Methylation profiling by array.
Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions (hippocampus LNDBB).
GEO Series GSE89703. Homo sapiens. 27 samples. Type: Methylation profiling by array.
Transcriptional Profiling of Human Liver Identifies Sex-Biased Genes Associated with Polygenic Dyslipidemia and Coronary Artery Disease
GEO Series GSE23766. Homo sapiens. 16 samples. Type: Expression profiling by array.
A polygenic score for acute vaso-occlusive pain in pediatric sickle cell disease
<p><a href="https://viz.stjude.cloud/communityData/PGS_snpLists.xlsx">Polygenic Risk Scores</a> is from the Pain study described below:</p> <p>Recurrent acute pain, or vaso-occlusive pain crisis (VOP), is the most common complication of sickle cell disease and correlates strongly with increased hospital visits and early mortality <a href="https://pubmed.ncbi.nlm.nih.gov/1710777/">PMID: 1710777</a>. While the genetics of VOP in sickle cell patients has been studied (PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/29205277/">29205277</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/27883292/">27883292</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/25102390/">25102390</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/30079801/">30079801</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/22925497/">22925497</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29531649/">29531649</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29620434/">29620434</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/19468207/">19468207</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/20172753/">20172753</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/22576309/">22576309</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/30031848/">30031848</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/24136375/">24136375</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/27603703/">27603703</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29559808/">29559808</a>), it is not fully understood.</p> <p>Using WGS, we interrogated the <em>a</em>-thalassemia deletion -<em>a</em>3.7 and 133 candidate risk single nucleotide polymorphisms (SNPs) across an additional 65 genes for association with VOP: 11 SNPs in 3 gene regions associated with fetal hemoglobin (HbF) and 122 additional SNPs in 62 genes previously reported to be associated with pain due to SCD and/or other etiologies. We then constructed unweighted polygenic risk scores (PGSs) by counting the total number of risk alleles per individual across the 11 HbF SNPs (PGSHbF) and the 5 SNPs in COMT (PGSCOMT), where COMT is the gene previously associated with SCD pain (PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/29559808/">29559808</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/215537663/">15537663</a>). We also defined a final PGS comprised of these 16 SNPs plus another 5 internally-validated candidate SNPs (PGSHbF+COMT+5snps), which was more strongly associated with acute VOP than any individual variant. Additionally, patients with the highest 5% of scores had 3-fold more pain events than the bottom 5% but were 5 times more likely to be on hydroxyurea, indicating that patients with high scores might benefit from a second drug.</p>
Assessing the Polygenic Burden of Rare Disruptive Mutations in Parkinson's Disease
ClinicalTrials.gov study NCT04620980. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Impact of Disclosing Coronary Artery Disease Polygenic Risk Score on Cardiovascular Health
ClinicalTrials.gov study NCT07087431. IPD Sharing: YES. Countries: 1. Publications: 0.
Early Detection of Coronary Artery Disease by Polygenic and Metabolic Risk Scoring
ClinicalTrials.gov study NCT04604353. IPD Sharing: NO. Countries: 1. Publications: 0.
Utility and Effectiveness of Polygenic Risk Scoring (PRS) for Coronary Artery Disease (CAD)
ClinicalTrials.gov study NCT06542432. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions (striatum DBCBB).
GEO Series GSE89705. Homo sapiens. 33 samples. Type: Methylation profiling by array.
Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions
GEO Series GSE89707. Homo sapiens. 142 samples. Type: Methylation profiling by array.
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.