This upload includes summary statistics from analysis of the fluid intelligence phenotype from the UK Biobank from the Neale lab as part of their 20170915 release. We cite the following as a reference for this data [last accessed July 31 2020]http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobankSpecifically included here are the following two filesfluid_intelligence.20016.assoc.tsv.gz MD5 = 685d4b5e2f35c82fe29d9d9ac6e35db4variants.tsv.gz MD5 = 1e622b87b19aa60aa18d8ac11d7b3a31We also include snapshots of the websites referenced in this post as additional filesThis data was obtained from the following linkshttps://www.dropbox.com/s/ehnp53rfqmp6xjg/variants.tsv?dl=0https://www.dropbox.com/s/shsiq0brkax886j/20016.assoc.tsv.gz?dl=0The general website hosting the release can be viewed at [last accessed July 31 2020]http://www.nealelab.is/uk-biobankA manifest of files released as part of the 20170915 release can be viewed at [last accessed July 31 2020]https://docs.google.com/spreadsheets/d/1b3oGI2lUt57BcuHttWaZotQcI0-mBRPyZihz87Ms_No/edit#gid=275725118A FAQ detailing usage rights and citations can be viewed at [last accessed July 31 2020]http://www.nealelab.is/uk-biobank/faqSpecifically it includes the following notes about usage and citations. We include the appropriate citation above, and release the data here as a by-product of the usage rights stating the results may be used without restriction.Usage: Q: What is the data use policy of the GWAS results? Do I need permission to download and explore them?A: No, they may be freely downloaded and used without restrictionCitation: Q: How do I cite these results?A: For the round 2 results [released 1st August 2018], please cite the the page http://www.nealelab.is/uk-biobank/For the round 1 results [released 20th September 2017], please cite the blogpost (http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank).

Levels of sociability are continuously distributed in the general population, and decreased sociability represents an early manifestation of several brain disorders. Here, we investigated the genetic underpinnings of sociability in the population.

Main question of our research: 1. Are there common genetic variants that are associated with sociability in the general population? 2. Are genetic variants that are associated with sociability also associated with neuropsychiatric disorders?

Type of data uploaded in this repository: The UK Biobank project (see https://www.ukbiobank.ac.uk/) is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. The raw data that this project is based on comes from the publically available UK Biobank set, which is very large and is therefore not provided here. Here we only provide the results from our analysis, that is also described here: https://www.biorxiv.org/content/10.1101/781195v2 and currently in revision in a scientific journal. In the dataset you will find the association of 9327396 genetic variants with the phenotype sociability. This dataset is not applicable to be opened with Excel, and can best be opened on a cluster computer or using specfic software.

Subjects The UK Biobank (UKBB) is a major population-based cohort from the United Kingdom that includes individuals aged between 37 and 73 years. We constructed a sociability measure based on the the aggregation of scores per participant on four questions from the UKBB database that link to sociability, including (1) a question about the frequency of friend/family visits, (2) a question on the number and type of social venues that are visited, (3) a question about worrying after social embarrassment and (4) a question about feeling lonely, leading to a sociability score ranging from 0-4. Participants were excluded if they had somatic problems that could be related to social withdrawal (BMI < 15 or BMI > 40, narcolepsy (all the time), stroke, severe tinnitus, deafness or brain-related cancers) or if they answered that they had “No friends/family outside household” or “Do not know” or “Prefer not to answer” to any of the questions.

SNP genotyping and quality control Details about the available genome-wide genotyping data for UKBB participants have been reported previously (PMID: 30305743). We used third-release genotyping data (see https://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=100319). Briefly, 49,950 participants were genotyped using the UK BiLEVE Axiom Array and 438,427 participants were genotyped using UK Biobank Axiom Array. Genotypes were imputed into the dataset using the Haplotype Reference Consortium (HRC), and the UK10K haplotype resource. To account for ethnicity, we included only those individuals that identified themselves as "white" by self-report and plotted the Principal Components (PC) provided by the UKBB, excluding individuals considered to be outliers according to PCs 1 and 2. Genetic relatedness calculated with KING kinship and provided by the UKBB (https://kenhanscombe.github.io/ukbtools/articles/explore-ukb-data.html ; http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf) was used to identify first and second-degree relatives. Subsequently ´families´ (i.e. clusters of related individuals above an IBD>0.125 threshold) were created and only one individual from each of these created ‘families’ was included in the analysis. If self-reported sex and SNP-based sex differed, individuals were excluded from further analysis. Single nucleotide polymorphisms (SNPs) with minor allele frequency <0.005, Hardy-Weinberg equilibrium test P value<1e−6, missing genotype rate >0.05, and imputation quality of INFO <0.8 were excluded. In the current study, all analyses are based on 342,461 participants of European ancestry for which both genotype data and sociability scores were available.

Genome-wide association analysis Genome-wide association analysis with the imputed marker dosages was performed in PLINK1.9, using a linear regression model with the sociability measure as the dependent variable and including sex, age, 10 first PCs, assessment center, and genotype batch as covariates. SNPs were considered significantly associated if they had p-value < 5e-8. Associated loci were considered independent of each other at r2 0.6 and lead SNPs were classified as the SNP with the smallest association p-value and at r2 0.1, using a 250kb window. The summary statistics come from the plink2 linear regression analysis.

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).

This folder contains the clumped summary statistics (in daner format) of GWAS performed on different definitions of depression in UKBiobank described in Cai N., et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nature Genetics (2020) doi:10.1038/s41588-020-0594-5.These files are used for assessing polygenic risk score (PRS) prediction of depression status in individual cohorts of Major Depressive Disorder Psychiatric Genomics Consortium from the PGC29 release, as described in the paper. GWAS summary statistics from this study, accessible through https://doi.org/10.6084/m9.figshare.11733753.v1, are first pruned for SNPs present in the PGC29 cohorts, with their effect alleles also aligned with those specified in PGC29 cohorts. They then undergo LD-clumping using the clump_nav3 function in the Ricopilli pipeline, with the following parameters: clump_nav3 --noindel \ --pfile $pheno_clumpOut.gz \ --hq_f 0.01 \ --hq_i 0.6 \ --outname $pheno_clumpOut \ --clu_p1 1.0 \ --clu_p2 1.0 \ --clu_window 500 \ --clu_r2 0.1 \ --refdir $refdir --popname eur \ --force1They are then used in PRS prediction of MDD status in PGC29 cohorts using the my_postimp_navi function in Ricopilli. Please note that these files do not contain SNPs filtered based on P value thresholds; they contain SNPs across the whole range of association P values in their respective GWAS, and P value thresholds were only imposed when calculating PRS prediction statistics. ====The phenotypes are described briefly below. For more details please see description in the paper and its supplemental methods. All phenotypes are case-control phenotypes (coded cases = 1, controls = 0), defined using criteria as described. Cases are those who have answered the relevant questions in UKBiobank and fulfil case criteria, controls are those who have answered the same questions but did not fulfil the case criteria.Please note there are overlaps between cases and controls between phenotypes, these are described in the paper and its supplemental methods.1. LifetimeMDD - Lifetime MDD derived with DSM-V symptom and impairment criteria using the PHQ-9 questionnaire in the Online Mental Health follow-up in UKBiobank2. MDDRecur - Lifetime MDD with recurrence derived with DSM-V symptom and impairment criteria using the PHQ-9 questionnaire in the Online Mental Health follow-up in UKBiobank, with additional criteria on recurrence3. GPpsy - Having gone to a General Practitioner (GP) for nerves, anxiety, tension or depression, answered in the touchscreen interview in UKBiobank. Please note this is the most similar phenotype to "Broad Depression" introduced in Howard et al 2018 Nature Communications (doi:10.1038/s41467-018-03819-3)4. Psypsy - Having gone to a psychiatrist for nerves, anxiety, tension or depression, answered in the touchscreen interview in UKBiobank5. DepAll - Having either of the two cardinal symptoms for MDD (low mood or anhedonia) for more than two weeks in addition to having gone to either the GP or psychiatrist for nerves, anxiety, tension or depression, answered in the touchscreen interview in UKBiobank. Please note phenotype is first introduced as "Probable Depression" in Smith et al 2013 PloS One (doi:10.1371/journal.pone.0075362.s001), and the most similar phenotype to "Probable Depression" introduced in Howard et al 2018 Nature Communications (doi:10.1038/s41467-018-03819-3)6. GPNoDep - Having gone to the GP for nerves, anxiety, tension or depression, but did not report having cardinal symptoms of MDD in the touchscreen interview in UKBiobank.7. SelfRepDep - Self-reported depression or depression symptoms described to a trained nurse during verbal interview of medical conditions, classified under "Non-cancer illness" in the UKBiobank dataset.8. ICD10Dep - Electronic health record indicating ICD-10 primary and secondary codes for depression in ICD-10 information linked to participants in UKBiobank

Data supporting paper 'Molecular genetic contributions to social deprivation and household income in UK Biobank'. Current Biology (2016). doi: 10.1016/j.cub.2016.09.035 ## Note re working with data ## Each of the three data files contains over seventeen million rows. Users will encounter difficulties if they attempt to view the content using Notepad++ or Microsoft Notepad. Microsoft Excel 2016 will not display all rows. These space-delimited text files contains seven columns, with a header row, which are listed in the readme file. ## Note re other copy ## The data files are identical to the files of the same name previously made available on the website of the Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE) http://www.ccace.ed.ac.uk/node/335 as the zip archive 'Hill_CB_2016.zip'.

Associations between the 27 phenotypes (Table 1) and four load scores computed for four different groups of coding variants stratified by derived allele frequency: 76,185 variants with DAF∈(0,0.05); 10,552 variants with DAF∈[0.05,0.3); 5,530 variants with DAF∈[0.3,0.7); and 3,587 variants with DAF∈[0.7,1). (XLSX)

We used the number of non-reference variants counted for each individual as an additional covariate in our linear regression model to test the association between load score and the 27 phenotypes (Table 1). (XLSX)

‘snp’: chromosome and position (based on GRCh37); ‘rsid’: rsID; ‘effect’: effect allele; ‘ref’: reference allele; ‘dist_min’: minimum distance from a genome-wide significant SNP (base pairs); ‘trait_code_UKB’: code of the UK biobank trait outcome (from GeneAtlas); ‘trait_description_UKB’: description of the UK biobank trait outcome (from GeneAtlas); ‘beta_UKB’: beta of the effect allele in UK Biobank (from GeneAtlas); ‘se_UKB’; standard error of ‘beta_UKB’; ‘p_UKB’; p-value based on ‘beta_UKB’ and ‘se_UKB’; ‘replication_attempted’: ‘True’ if replication attempted in FinnGen; ‘trait_code_FG’: code of the corresponding FinnGen trait outcome; ‘trait_description_FG’: description of the corresponding FinnGen trait outcome; ‘beta_FG’: beta of the effect allele in FinnGen; ‘se_FG’: standard error of ‘beta_FG’; ‘p_FG’: p-value based on ‘beta_FG’ and ‘se_FG’; ‘bonf_thresh’: significance threshold for replication in FinnGen (based on per-trait Bonferroni correction); ‘replication_successful’: ‘True’ if replication in FinnGen successful (based on per-trait Bonferroni correction); ‘lookup_attempted’: ‘True’ if lookup in GWAS Catalog attempted; ‘traits_Catalog’: genome-wide significant trait association/s in GWAS Catalog; ‘lookup_successful’; ‘True’ if SNP associated to corresponding disease trait in GWAS Catalog; Note: ‘dist_min’ is provided for those associations for which replication in FinnGen was attempted. The value of ‘dist_min’ was set to ‘inf’ if the novel disease-associated SNP was located on a different chromosome from all genome-wide significant SNPs. Replication of nine novel diabetes-associated loci was attempted in FinnGen for both Type 1 and Type 2 diabetes. The values of ‘beta_FG’, ‘se_FG’ and ‘p_FG’ for those nine loci correspond to the more significant association (smaller p-value) between Type 1 and Type 2 diabetes. (TXT)

This table shows the results of association tests between a set of potential confounders and each load score. Logistic regression was used for each category of population density and linear regression was used for the others. (XLSX)

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.

Associations between the 27 phenotypes (Table 1) and five load scores computed for five different groups of coding variants stratified by phyloP score: 54,840 variants with phyloP∈(0,2]; 19,414 variants with phyloP∈(2,4]; 10,865 variants with phyloP∈(4,6]; 7,582 variants with phyloP∈(6,8]; and 3,315 variants with phyloP∈(8,10]. (XLSX)

Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, identifying significantly associated loci remains a considerable challenge due to the concomitant multiple-testing burden of performing such analyses genome-wide. Here, we leverage the genetic associations of molecular traits – DNA CpG-site methylation status and RNA expression – to mitigate this problem. We encode their co-association across the genome using PinSage, a graph convolutional neural network-based recommender system previously deployed at Pinterest. We demonstrate, using this framework, that a model trained only on methylation quantitative trait locus (QTL) data could recapitulate over half (554,209/1,021,052) of possible SNP-RNA associations identified in a large expression QTL meta-analysis. Taking advantage of a recent ‘saturated’ map of height associations, we then show that height-associated loci predicted by a model trained on molecular-QTL data replicated comparably, following Bonferroni correction, to those that were genome-wide significant in UK Biobank (88% compared to 91%). On a set of 64 disease outcomes in UK Biobank, the same model identified 143 independent novel disease associations, with at least one additional association for 64% (41/64) of the disease outcomes examined. Excluding associations involving the MHC region, we achieve a total uplift of over 8% (128/1,548). We successfully replicated 38% (39/103) of the novel disease associations in an independent sample, with suggestive evidence for six additional associations from GWAS Catalog. Replicated associations included for instance that between rs10774625 (nearest gene: SH2B3/ATXN2) and coeliac disease, and that between rs12350420 (nearest gene: MVB12B) and glaucoma. For many GWAS, attaining such an enhancement by simply increasing sample size may be prohibitively expensive, or impossible depending on disease prevalence.

Association between coding load score and 27 traits.

MR, mendelian randomisation. (XLSX)

Population characteristics and distribution of symptoms, blood tests and primary care consultation patterns in CPRD and UK Biobank.

Multimodal MRI protocols for COVID-related neuroimaging with Siemens and GE 3T scanners.

Baseline characteristics of the UKBB participants included in this study.

‘snp’: chromosome and position (based on GRCh37); ‘rsid’: rsID; ‘effect’: effect allele; ‘ref’: reference allele; ‘beta_UKB’: beta of the effect allele in UK Biobank (from GeneAtlas); ‘se_UKB’; standard error of ‘beta_UKB’; ‘p_UKB’; p-value based on ‘beta_UKB’ and ‘se_UKB’; ‘replication_attempted’; ‘True’ if replication attempted in Yengo et al.; ‘beta_META’; beta of the effect allele in Yengo et al.; ‘se_META’; standard error of ‘beta_META’; ‘p_META’; p-value based ‘beta_META’ and ‘se_META’; ‘replication_successful’; ‘True’ if replication in Yengo et al. successful. Note: The significance threshold for successful replication in Yengo et al. was 0.05/26. (TXT)

List of traits belonging to each of the different disease categories considered when assessing network centrality. (TXT)

Details of the different outcome measures considered.