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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>
A protocol to assess the risk of dementia among patients with coronary artery diseases using CAIDE score-Extended Data
<p>The contents of this extended data file are-<br> 01. Consent form (English & Bengali Version)<br> 02. Interview Questionnaire (English & Bengali Version)<br> These contents will help to address the objectives of the study that attempted to identify the risk of long-term dementia among coronary artery disease patients in Bangladesh.</p>
Dataset Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study
<p>This dataset is related to "Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study".</p> <p>Vanessa Kraege*, Janko Fabecic*, Pedro Marques Vidal, Gérard Waeber and Marie Méan</p> <p>*Contributed equally; co-first authors</p>
Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry
<p>These are a collection of EWAS summary statistics for the following publication:</p> <p>Deng Wei Q, Cawte Nathan, Campbell Natalie, Azab Sandi M, de Souza Russell J, Lamri Amel, Morrison Katherine M, Atkinson Stephanie A, Subbarao Padmaja, Turvey Stuart E, Moraes Theo J, Teo Koon K, Mandhane Piush, Azad Meghan B, Simons Elinor, Pare Guillaume, Anand Sonia S (2024) Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry eLife 13:RP93260, https://doi.org/10.7554/eLife.93260.3</p> <p>1. CHILD_450K_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in CHILD (HM450K array).</p> <p>2. CHILD_450K_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in CHILD (HM450K array).</p> <p>3. CHILD_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in CHILD (HM450K array).</p> <p>4. FAMILY_EPIC_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in FAMILY (customized EPIC array).</p> <p>5. FAMILY_EPIC_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in FAMILY (customized EPIC array).</p> <p>6. FAMILY_EPIC_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in FAMILY (customized EPIC array).</p> <p>7. START_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in START (HM450K array).</p> <p>8. mateversmk_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using ever definition.</p> <p>9. matsmoke_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using current smoking definition.</p> <p>10. mblsmkexp_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking exposure (hours per week).</p>
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>
Social Risk Score, Clinical Decision Support Tool and Closed Loop Referral for Social Risk Screen and Referral
ClinicalTrials.gov study NCT05574699. IPD Sharing: NO. Countries: 1. Publications: 2.
Comparing Non-invasive Diabetes Risk Scores for Detecting Patients in Clinical Practice
<p>The Cork and Kerry Diabetes and Heart Disease Study (Phase II – Mitchelstown Cohort) was a single centre study conducted between 2010 and 2011. A random sample was recruited from a large primary care centre in Mitchelstown, County Cork, Ireland. The Livinghealth Clinic serves a population of approximately 20,000 Caucasian-European subjects, with a mix of urban and rural residents. Stratified sampling was employed to recruit equal numbers of men and women from all registered attending patients in the 46–73-year age group. In total, 3,807 potential participants were selected from the practice list. Following the exclusion of duplicates, deaths and subjects incapable of consenting or attending appointment, 3,051 were invited to participate in the study and of these, 2,047 (49% male) completed the questionnaire and physical examination components of the baseline assessment (response rate: 67%). Individuals with pre-existing cardiovascular disease or T2DM were not excluded from the cohort.</p>
Predictive scoring for risk of complications in pediatric dengue infection
<p class="MsoNormal"><strong><span>Background: </span></strong><span>Dengue infection has been a worrisome cause of mortality and morbidity in children. Though numerous scoring systems have been developed, they are in the adult population or are too complicated for use in children. Pediatric dengue infection has a wide spectrum from a mild illness to severe complications and an unpredictable course. Hence the need for a predictive scoring system where the possibility of complications can be identified which can contribute to reduction in mortality and morbidity of dengue by prompt referrals and anticipatory management. </span></p> <p class="MsoNormal"><span><strong>Methodology:</strong> Prospective case cohort study of children with confirmed dengue. </span></p> <p class="MsoNormal"><span><strong>Results</strong>: 303 children were included and divided into two groups – the dengue fever group and the complicated dengue group based on the WHO clinical classification. The clinical and laboratory parameters were analysed individually, cut offs identified by ROC curves and compared for significance between the two groups. The parameters that emerged were hypotension, PCV ≥ 42%, platelet count ≤ 75000 cells/cumm, WBC ≥ 7000 cells/cumm, and ALT ≥ 70U/L.</span> <span>Using the adjusted odd's Ratio, and coefficient, individual predictive scores were tabulated ranging from 0 to 3, with a total score of 0 to 7. A cut-off score of 2 was then identified based upon the sensitivity(84.13%) and specificity(72.50%) as the ideal score to predict complicated dengue. Internal validation of the score was done where the area under the curve for predicting complicated dengue was 0.86(95% CI 0.8-0.92) with a P value of <0.001.</span><strong><span> </span></strong></p> <p class="MsoNormal"><strong><span>Conclusion</span></strong><span>: </span><span>Our dengue predictive scoring system has been developed using five indicators, with a score of 2 and above out of 7, suggesting increased risk of developing complications. This has been validated internally and can be used to predict complicated dengue among children.</span></p>
Metabolic syndrome severity score and associated cardiovascular risk in postmenopausal women of Bangladesh
<p>This data help to construct metabolic syndrome severity score and seek its association with absolute cardiovascular risk.</p>
Cross-sectional and prospective data on Framingham risk score, allostatic load, and ankle brachial index among Puerto Rican adults from the Boston Puerto Rican Health Study
<p><strong>Background</strong><br> Puerto Ricans have higher odds of peripheral artery disease (PAD) compared with Mexican Americans. Limited studies have examined relationships between clinical risk assessment scores with PAD assessments.</p> <p><strong>Methods</strong><br> Using 2004-2015 data from the Boston Puerto Rican Health Study (BPRHS) (n = 370-583), cross-sectional, 5-y change and patterns of change in Framingham Risk Score (FRS) and allostatic load (AL) with ankle brachial index (ABI) at 5-y follow-up was assessed among Puerto Rican adults (45-75 y). Analysis were conducted in 2020. FRS and AL were calculated at baseline, 2-y and 5-y follow-up. Multivariable linear regression models examined cross-sectional and 5-y changes in FRS and AL with ABI at 5-y. Latent growth mixture modeling identified trajectories of FRS and AL over 5-y, and multivariable linear regression models were used to test associations between trajectory groups at 5-y.</p> <p><strong>Results</strong><br> Greater FRS at 5-y and increases in FRS from baseline were associated with lower ABI at 5-y (β = -0.149, p = 0.010; β = -0.171, p = 0.038, respectively). AL was not associated with ABI in cross-sectional or change analyses. Participants in low-ascending (vs. no change) FRS trajectory, and participants in moderate-ascending (vs. low-ascending) AL trajectory, had lower 5-y ABI (β = -0.025, p = 0.044; β = -0.016, p = 0.023, respectively).</p> <p><strong>Conclusions</strong><br> FRS was a better overall predictor of ABI, compared with AL. FRS may be a clinically feasible measure of PAD risk in Puerto Ricans, an understudied population. Additional research examining relationships between FRS and AL and development of PAD is warranted.</p>
Thyroid cancer polygenic risk score improves classification of thyroid nodules as benign or malignant.
<p>Supplementary Data for the manuscript: Thyroid cancer polygenic risk score improves classification of thyroid nodules as benign or malignant. Nikita Pozdeyev, MD, PhD (ORCiD ID: 0000-0001-8574-1972), Manjiri Dighe, MD, Martin Barrio, MD, MS, Christopher Raeburn, MD, Harry Smith, MS, Matthew Fisher, MS, Sameer Chavan, MS, Nicholas Rafaels, MS, Jonathan A. Shortt, PhD, Meng Lin, PhD, Michael G. Leu, MD, Toshimasa Clark, MD, Carrie Marshall, MD, Bryan R. Haugen, MD, Devika Subramanian, PhD, Regeneron Genetics Center, Kristy Crooks, PhD, Christopher Gignoux, PhD, Trevor Cohen, MBChB, PhD, FACMI</p>
Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse
<p class="MsoNormal"><strong>Introduction</strong>: Spontaneous rupture of tendons and ligaments is common in several species including humans. In horses, degenerative suspensory ligament desmitis (DSLD) is an important acquired idiopathic disease of a major energy-storing tendon-like structure. DSLD risk is increased in several breeds, including the Peruvian Horse. Affected horses have often been used for breeding before the disease is apparent. Breed predisposition suggests a substantial genetic contribution, but heritability and genetic architecture of DSLD have not been determined.</p> <p class="MsoNormal"><strong>Methods</strong>: To identify genomic regions associated with DSLD, we recruited a reference population of 183 Peruvian Horses, phenotyped as DSLD cases or controls, and undertook a genome-wide association study (GWAS), a regional window variance analysis using local genomic partitioning, a signatures of selection (SOS) analysis, and polygenic risk score (PRS) prediction of DSLD risk. We also estimated trait heritability from pedigrees.</p> <p class="MsoNormal"><strong>Results</strong>: Heritability was estimated in a population of 1,927 Peruvian horses at 0.22±0.08. After establishing a permutation-based threshold for genome-wide significance, 151 DSLD risk single nucleotide polymorphisms (SNPs) were identified by GWAS. Multiple regions of enriched local heritability were identified across the genome, with strong enrichment signals on chromosomes 1, 2, 6, 10, 13, 16, 18, 22, and the X chromosome. With SOS analysis, there were 66 genes with a selection signature in DSLD cases that was not present in the control group that included the <em>TGFB3</em> gene. Pathways enriched in DSLD cases included proteoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways that included the hedgehog signaling pathway. The best PRS predictive performance was obtained when we fitted 1% of top SNPs using a Bayesian Ridge Regression model which achieved the highest mean of R<sup>2</sup> on both the probit and logit liability scales, indicating a strong predictive performance.</p> <p><strong>Discussion</strong>: We conclude that within-breed GWAS of DSLD in the Peruvian Horse has further confirmed that moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations in novel tendinopathy candidate genes influencing disease risk. Pathways enriched with DSLD risk variants include ones that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways.</p>
A Clinical Risk Score for Early Management of TB in Uganda
ClinicalTrials.gov study NCT05122624. IPD Sharing: NO. Countries: 1. Publications: 1.
Incorporating the Venous Excess Ultrasound Score (VExUS Score) Into Contemporary Haemodynamic Risk Assessment in Pulmonary Arterial Hypertension: The INVEXUS-PAH Study
ClinicalTrials.gov study NCT07266012. IPD Sharing: YES. Countries: 1. Publications: 3.
A Study of Eltrombopag or Placebo in Combination With Azacitidine in Subjects With International Prognostic Scoring System (IPSS) Intermediate-1, Intermediate-2 or High-risk Myelodysplastic Syndromes
ClinicalTrials.gov study NCT02158936. IPD Sharing: Not stated. Countries: 30. Publications: 1.
Prospective Evaluation of a RIsk Score for Postoperative Pulmonary COmPlications in Europe
ClinicalTrials.gov study NCT01346709. IPD Sharing: Not stated. Countries: 21. Publications: 5.
Thromboembolic Risk Stratification by TRiP(Cast) Score to Guide Physicians in Preventive Treatment Prescriptions for Patients With Lower Limb Trauma Requiring Brace or castING.
ClinicalTrials.gov study NCT04064489. IPD Sharing: NO. Countries: 2. Publications: 2.
Impact on Management of the HEART Risk Score in Chest Pain Patients
ClinicalTrials.gov study NCT01756846. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Prospective Validation of the Emergency Heart Failure Mortality Risk Grade (EHMRG) Score in Patients With Acute Heart Failure in the Emergency Department
ClinicalTrials.gov study NCT05710302. IPD Sharing: YES. Countries: 1. Publications: 4.
Study to Evaluate Imetelstat (GRN163L) in Participants With International Prognostic Scoring System (IPSS) Low or Intermediate-1 Risk Myelodysplastic Syndrome (MDS)
ClinicalTrials.gov study NCT02598661. IPD Sharing: NO. Countries: 17. Publications: 3.
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.