• assessed in 170,000 participants;† assessed in 50,000 participants;‡measured in one heel for 170,000 participants and in both heels for 320,000 participants;¶ measured in 170,000 participants;§ measured in 100,000 participantsData collected at the baseline assessment.

This dataset contains GWAS summary statistics for Standing Height in the UK Biobank.

The GWAS study used data from "White British" samples (N = 337225), which were randomly divided into 5 folds for the purposes of cross-validation. The upload contains, for each fold, GWAS summary statistics for the training and test set. The test summary statistics can be used to evaluate PRS models via pseudo-validation methods. Association testing was done with plink2.

The structure of the data is as follows:

• train
  • fold_1
    • chr_1.PHENO1.glm.linear
    • chr_2.PHENO1.glm.linear
    • ...
  • fold_2
  • fold_3
  • ...
• test
  • fold_1
  • fold_2
  • fold_3
  • ...

For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication:

Zabad, S., Gravel, S., & Li, Y. (2023). Fast and accurate Bayesian polygenic risk modeling with variational inference. The American Journal of Human Genetics, 110(5), 741–761. https://doi.org/10.1016/j.ajhg.2023.03.009

If you use this data in your work, please cite the publication above.

• No qualifications, State school examinations at 16 years of age (‘O’ levels), at 18 years (‘A’ levels) or University/other professional qualification+: Number of eyes;++ model adjusted for eye laterality, gender, age (continuous), educational qualification, accommodation tenure, ethnicity and test centre.Association of all myopia and myopia (low, moderate or high), by key socio-demographic factors.

Quantile regression (QR) GWAS summary statistics from the study "Genome-wide discovery for biomarkers using quantile regression at biobank scale". The preprint is available at https://doi.org/10.1101/2023.06.05.543699.

List of traits

A comma-delimited text file, QRGWAS.Traits_n39.csv, includes the list of 39 quantitative traits from the UK Biobank reported in the QR GWAS analyses above.

Summary statistics

The tab-delimited text files are QR GWAS summary statistics, which are bgzip compressed (.tsv.gz files) and tabix indexed (.tbi files).

• Column "CHR": chromosome
• Column "POS": based pair position
• Column "ID": variant ID
• Column "REF": non-effect allele
• Column "ALT": effect allele tested in GWAS
• Column "EAF": frequency of the effect allele
• Column "N": sample size
• Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.
• Column "P_LR": p-value of the linear regression (LR) association statistic
• Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).

• No qualifications, State school examinations at 16 years of age (‘O’ levels), at 18 years (‘A’ levels) or University/other professional qualificationMissing data: educational qualification: 1,466 (1.4%), accommodation tenure: 2,102 (2.0%), ethnicity: 779 (0.7%)Distribution of refractive errors by key socio-demographic factors.

Abstract

Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.

Data Description

This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.

As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.

Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.

Paper & Code

The original paper for this article can be accessed here:

• https://ieeexplore.ieee.org/abstract/document/10196736

To access the codes relevant for this project, please access the project GitHub Repos:

• https://github.com/AndreiRoibu/AgeMapper

If using this work, please cite it based on the above paper, or using the following BibTex:

@inproceedings{roibu2023brain,
 title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes},
 author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J},
 booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)},
 pages={17--25},
 year={2023},
 organization={IEEE},
 doi={10.1109/SDS57534.2023.00010}
}

Data Access

The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi

Funding

ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

• No qualifications, State school examinations at 16 years of age (‘O’ levels), at 18 years (‘A’ levels) or University/other professional qualification+: Number of eyes;++ model adjusted for eye laterality, gender, age (continuous), educational qualification, accommodation tenure, ethnicity and test centre.Association of all hypermetropia and hypermetropia (low or moderate/high), by key socio-demographic factors.

This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on unrelated British samples of the UK-Biobank (n = 306604). It is intended to be used as an input to the GhostKnockoffGWAS pipeline.

• This is the output of applying solveblock executable directly on 306,604 unrelated British samples of the UK-Biobank.
• Quasi-independent blocks are computed by applying the snp_ldsplit function with parameters thr_r2=0.01, max_r2=0.3, min_size = 500, and max_size = {1000, 1500, 3000, 6000, 10000}.
• SNPs with minor allele frequency less than 0.01 or Hardy-Weinburg equilibrium p-value less than 1e-6 are removed.
• Only HG19 coordinates are available.
• Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl
• The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS.

Note: We previously released another set of EUR LD files. This set of LD files should be preferred over the previous one. The main difference with this entry is that the previous entry used quasi-independent blocks from LDetect computed on the 1000 genomes project. Here we compute the independent blocks using snp_ldsplit directly on the UK-Biobank British samples.

Statistically significant signals from the metaUSAT analysis are shown in the left-hand column. The central column shows the association p-values for those SNPs in the six original GWAS analyses, with the direction of effect indicated by a + or–sign. Candidate genes are those selected from the prioritised genes (using the four mapping strategies described previously for all GWAS-discovered loci) or genes in proximity as identified within the UCSC genome browser.

This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on the EUR cohort of Pan-UKB LD data. It is intended to be used as an input to the GhostKnockoffGWAS pipeline.

• We restricted our attention to the EUR panel
• We filtered the original HailBlockMatrix LD panel to genotypes that are typed (i.e. imputed SNPs were removed)
• Coordinates in both hg19 and hg38 are available. Conversion from hg19 to hg38 were achieved by the R package liftOver.
• Downloading and processing of the original HailBlockMatrix formatted data is accomplished by the EasyLD.jl software: https://biona001.github.io/EasyLD.jl
• Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl
• The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS.

• PGS-effects.csv.gz: vectors of effect sizes for 215 polygenic scores (PGS)- pred-cor: partial correlations of these PGS with the corresponding phenotypes, in eight ancestry groups from the UK Biobank- phenotype-description.xlsx: description of all phenotypes used in the study (30 were discarded due to very low prediction)-> these report the best prediction from penalized regression and LDpred2.We also provide these files separately for penalized regression (PLR) and LDpred2-auto (without using the test set).The effect size file for penalized regression is very small because vectors of effects are very sparse.Those are based on the UK Biobank data only.

We present a database of representative left and right ventricular meshes constructed from patient-specific models based on a large cohort of ~55k participants from UK Biobank. It comprises 1423 representative tetrahedral finite element meshes across sex (male, female), body mass index (range: 16 - 42 kg/m²) and age (range: 49 - 80 years).

For each mesh, it also includes:

• a realistic biventricular myocardial fibre structure
• a morphological coordinate system which describes the positions within ventricles based on (1) the apical-basal (Z), (2) transmural (ρ) (from endocardium to epicardium), (3) rotational (Φ) (anterior, anteroseptal, inferior, inferolateral, anterolateral) and (4) chamber-wise (left ventricle and right ventricle) coordinates.

We also present trained network weights and nnUNet plan and hyperparameter selection files for cine MR segmentation models trained separately for the following views: 2 chamber, 3 chamber, 4 chamber and short axis. These are supplied as a zip of relevant nnUNet files for each view: Dataset101_UKBB_LAX_2Ch.zip, Dataset102_UKBB_LAX_3Ch.zip, Dataset103_UKBB_LAX_4Ch.zip, Dataset100_UKBB_Petersen_SAX.zip.

Unique Data Identifier (UDI) codes (DOCX)

This project contains datasets related to:

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes
Rachel M. Petersen, Christopher M. Vockley, Amanda J. Lea

A preprint of this work can be found here: https://www.biorxiv.org/content/10.1101/2024.08.23.609412v1

Specifically, the data provided here are:

1) replicateinfo.txt contains metadata for each mSTARR-seq replicate, including replicate number, pool number, sample type (DNA vs RNA) and methylation status

2) rnadnacounts_400bpwin.txt contains a count matrix with the number of DNA and RNA reads falling within each 400 bp genomic window for each replicate. Columns are replicate names, rows are genomic windows.

3) Joint_genotyping.vcf contains results from joint genotyping analysis using DNA sequences generated in the current study from 25 individuals accessed through the 1000 Genomes Project.

4) ASE_data.zip contains

• ASE_totalcounts.txt: counts matrix of the total number of DNA and RNA reads in each replicate for each variant
• ASE_refcounts.txt: counts matrix of the number of DNA and RNA reads for the reference allele in each replicate for each variant
• ASE_mashr_inputsites.txt: sites that were tested for methylation-dependent allele-specific expression using mashr
• WASP_ASE_sites.txt: variant sites that were retained after using the WASP mappability pipeline (Van De Geijn et al. 2015)

5) model_results.zip contains

• model1_methonly_results.txt: results from linear modeling to identify windows with regulatory function in the methylated condition
• model1_unmethonly_results.txt: results from linear modeling to identify windows with regulatory function in the methylated condition
• model2_mashr_results.txt: results from mashr analysis to identify windows with methylation-dependent regulatory function
• ASE_meth_results.txt: results from allele specific expression analysis to identify ASE in the methylated condition
• ASE_unmeth_results.txt: results from allele specific expression analysis to identify ASE in the unmethylated condition
• ASE_mashr_results.txt: results from mashr analysis to identify sites with methylation-dependent ASE

6) Comparison_datasets.zip contains

• Johnston_eLife_mSTARR_counts_K562.txt: counts matrix from Johnston et al. 2024, adapted to use 200 bp windows. Original dataset can be found here: https://zenodo.org/records/7949036#.ZGZ5UnbMJq9
• Lea_eLife_mSTARR_counts.txt: counts matrix from Lea et al. 2018

7) GWAS_EWAS_overlap_files.zip contains

• GWAShits_siteformat.txt: GWAS associations accessed through the NHGRI-EBI catalog in March 2024, formatted for use in R
• EWAS_Atlas_associations.tsv: EWAS associations accessed through the EWAS Open Platform Data Hub in March 2024
• EWAS_Atlas_probe_annotations.tsv: genomic locations of EWAS probes
• ASE_mashr_GWASOverlap.bed: methylation-dependent genetic effect sites that are located within 400 bp of a GWAS hit (results of bedtools intersect)
• ASE_mashr_EWASOverlap.bed: methylation-dependent genetic effect sites that are located within 400 bp of a EWAS hit (results of bedtools intersect)
• blood_gwas_overlaps.rds: methylation-dependent genetic effects sites that are located within 400 bp of a GWAS hit for 20 quantitative immune-related blood traits from Pan-UK Biobank.

GWAS summary statistics for major depressive disorder from the PGC MDD2 (Wray et al.) excluding 23andMe and UK Biobank.
Cite Wray et al 2018 (source of cohort summary stastics) and Howard et al 2019 (source of UKB/PGC overlap resolution).

Update 2022/03/07

• v3 Apply QC step to remove duplicate CPIDs, to match v1
• v2 Correct doubly counted Nca/Nco tallies for a subset of SNPs.

This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on Caribbean samples of the UK-Biobank (n = 4517). It is intended to be used as an input to the GhostKnockoffGWAS pipeline.

• This is the output of applying solveblock executable directly on 4517 Caribbean samples of the UK-Biobank.
• Quasi-independent blocks are computed by applying the snp_ldsplit function with parameters thr_r2=0.01, max_r2=0.3, min_size = 500, and max_size = {1000, 1500, 3000, 6000, 10000}.
• SNPs with minor allele frequency less than 0.01 or Hardy-Weinburg equilibrium p-value less than 1e-6 are removed.
• Only HG19 coordinates are available.
• Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl
• The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS.

This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on Chinese samples of the UK-Biobank (n = 1574). It is intended to be used as an input to the GhostKnockoffGWAS pipeline.

• This is the output of applying solveblock executable directly on 1574 Chinese samples of the UK-Biobank.
• Quasi-independent blocks are computed by applying the snp_ldsplit function with parameters thr_r2=0.01, max_r2=0.3, min_size = 500, and max_size = {1000, 1500, 3000, 6000, 10000}.
• SNPs with minor allele frequency less than 0.01 or Hardy-Weinburg equilibrium p-value less than 1e-6 are removed.
• Only HG19 coordinates are available.
• Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl
• The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS.

This dataset contains all model weights and corresponding datasets generated by Betti et al. in the manuscript Genetically regulated enhancer RNA expression predicts enhancer-promoter contact frequency and reveals genetic mechanisms at complex trait-associated loci. The following are the contents of the sub-directories in this dataset:

• coloc: Colocalization results for genome-wide significant (p < 5 x 10^-8) GWAS associations in the UK Biobank with eRNA and canonical gene eQTLs (Supplementary Tables 11 and 12).
• contact_model_training: Input datasets from whole blood and brain, respectively, that were used to train the neural network-based models of contact frequency.
• eqtl_mapping: eQTLs mapped across 49 cell and tissue types for both eRNAs and canonical genes.
• scz_mr: Inputs and results for Mendelian randomization analysis of eRNA and canonical gene-based TWAS of schizophrenia.
• scz_twas: eRNA and canonical gene-based TWAS results of schizophrenia.
• trained_models: Model weights and SNP covariance matrices for genetically regulated eRNA expression (GReX) across 49 cell and tissue types.
• uk_biobank_twas: eRNA-based TWAS summary statistics for 4,671 UK Biobank traits across 49 cell and tissue types.

Please cite:

Betti, M.J., Aldrich, M.C., Lin, P., & Gamazon, E.R. (2024). Genetically regulated enhancer RNA expression predicts enhancer-promoter contact frequency and reveals genetic mechanisms at complex trait-associated loci. Preprint.

Betti, M.J., Aldrich, M.C., Lin, P., & Gamazon, E.R. (2024). eRNA GReX (Version 1.0). Zenodo. 10.5281/zenodo.11212496

These are the genome-wide association study (GWAS) statistics in the UK Biobank and Source Data files for our paper Chen ZJ, Das SS, Kar A, Lee SHT, Abuhanna KD, Alvarez M, Sukhatme MG, Wang Z, Gelev KZ, Heffel MG, Zhang Y, Avram O, Rahmani E, Sankararaman S, Heinonen S, Peltoniemi H, Halperin E, Pietiläinen KH, Luo C, Pajukanta P. Single-cell DNA methylome and 3D genome atlas of human subcutaneous adipose tissue.
Further details of these analyses can be found in the Methods and Results part of this paper.

Repository contents

GWAS summary statistics in the UK Biobank for C-reactive protein (CRP), body mass index (BMI), metabolic-dysfunction associated steatotic liver disease (MASLD), and waist-to-hip ratio adjusted for BMI (WHRadjBMI):

• GWAS.zip

Figure source data:

• Figure2.zip
• Figure3.zip
• Figure4.zip
• Figure5.zip
• Figure6.zip
• ExtendedDataFigure1.zip
• ExtendedDataFigure2.zip
• ExtendedDataFigure3.zip
• ExtendedDataFigure4.zip
• ExtendedDataFigure5.zip
• ExtendedDataFigure6.zip
• ExtendedDataFigure7.zip
• ExtendedDataFigure8.zip
• ExtendedDataFigure9.zip
• ExtendedDataFigure10.zip
• SupplementaryFigure1.zip
• SupplementaryFigure2.zip
• SupplementaryFigure3.zip
• SupplementaryFigure4.zip
• SupplementaryFigure5.zip
• SupplementaryFigure6.zip
• SupplementaryFigure7.zip

This dataset contains all model weights and corresponding datasets generated by Betti et al. in the manuscript Genetically regulated enhancer RNA expression predicts enhancer-promoter contact frequency and reveals genetic mechanisms at complex trait-associated loci. The following are the contents of the sub-directories in this dataset:

• coloc: Colocalization results for genome-wide significant (p < 5 x 10^-8) GWAS associations in the UK Biobank with eRNA and canonical gene eQTLs (Supplementary Tables 14 and 15).
• contact_model_training: Input datasets from whole blood and brain, respectively, that were used to train the neural network-based models of contact frequency.
• eqtl_mapping: eQTLs mapped across 49 cell and tissue types for both eRNAs and canonical genes.
• scz_mr: Inputs and results for Mendelian randomization analysis of eRNA and canonical gene-based TWAS of schizophrenia.
• scz_twas: eRNA and canonical gene-based TWAS results of schizophrenia.
• trained_models: Model weights and SNP covariance matrices for genetically regulated eRNA expression (GReX) across 49 cell and tissue types.
• uk_biobank_twas: eRNA-based TWAS summary statistics for 4,671 UK Biobank traits across 49 cell and tissue types.

Please cite:

Betti, M.J., Aldrich, M.C., Lin, P., & Gamazon, E.R. (2024). Genetically regulated enhancer RNA expression predicts enhancer-promoter contact frequency and reveals genetic mechanisms at complex trait-associated loci. Preprint.

Betti, M.J., Aldrich, M.C., Lin, P., & Gamazon, E.R. (2024). eRNA GReX (Version 2.0). Zenodo. 10.5281/zenodo.14027849