UK Biobank is a health resource with data from over 500,000 adults. The cognitive assessment in UK Biobank is brief and bespoke, and is administered without supervision on a touchscreen computer. Psychometric information on the UK Biobank cognitive tests are limited. Despite the non-standard nature of these tests and the limited psychometric information, the UK Biobank cognitive data have been used in numerous scientific publications. The present study examined the validity and short-term test-retest reliability of the UK Biobank cognitive tests. A sample of 160 participants (mean age = 62.59, SD = 10.24) was recruited who completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests (‘reference tests’). Fifty-two participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and reference tests. Two measures of general cognitive ability were created by entering scores on the UK Biobank cognitive tests, and scores on the reference tests, respectively, into separate principal component analyses and saving scores on the first principal component. Four-week test-retest correlations were calculated for UK Biobank tests. UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r = 0.53, range = 0.22 to 0.83, p≤.005). The measure of general cognitive ability based on the UK Biobank cognitive tests correlated at r = 0.83 (p < .001) with a measure of general cognitive ability created using the reference tests. Four-week test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r = 0.55, range = 0.40 to 0.89, p≤.003). Despite the brief, non-standard nature of the UK Biobank cognitive tests, some tests showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.

Results for 2,230 UK Biobank binary and continuous traits.

We applied the gene-based tests (Gene1D, Gene3D, GeneScan1D and GeneScan3D) to 1,403 UK Biobank binary phecodes and 827 continuous phenotypes (797 continuous traits + 30 biomarkers) using GWAS summary statistics on 28 million imputed variants.

The results are in 3 different zipped folders: 'GeneScan3D_UKBB_1403binary_results.zip', 'GeneScan3D_UKBB_797continuous_results.zip' and 'GeneScan3D_UKBB_30biomarkers_results.zip'. A list of all 2,230 binary and continuous phenotypes is available in excel file 'UKBB_phenotype_description.xlsx'.

Reference: Ma, S., Dalgleish, J. L ., Lee, J., Wang, C., Liu, L., Gill, R., Buxbaum, J. D., Chung, W., Aschard, H., Silverman, E. K., Cho, M. H., He, Z. and Ionita-Laza, I. "Improved gene-based testing by integrating long-range chromatin interactions and knockoff statistics", 2021

Results for 1,403 UK Biobank phecodes. We applied the gene-based tests (Gene1D, Gene3D, GeneScan1D and GeneScan3D) to 1,403 UK Biobank Phecode binary phenotypes using summary statistics on 28 million imputed variants, and report results in the zipped folder. A summary table of the significant genes for 1,403 UK Biobank phenotypes is also available. These results can be employed in conjunction with TWAS results to prioritize genes at GWAS loci of interest.

Reference: Shiyang Ma, James Dalgleish, Justin Lee, Chen Wang, Richard Gill, Edwin K. Silverman, Michael H. Cho, Zihuai He, Iuliana Ionita-Laza, "A unified knockoff framework for gene-based testing with joint analysis of coding and regulatory variation",(2020+)

Pearson correlations between UK Biobank tests and age, general tests, and reference tests (n = 154–160).

UK Biobank cognitive tests, general cognitive tests, and reference cognitive tests administered in the current study.

IntroductionThe UK Biobank cognitive assessment data has been a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population. Given the diverse nature of this data, researchers use different approaches – from the use of a single test to composing the general intelligence score, g, across the tests. We argue that both approaches are suboptimal - one being too specific and the other one too general – and suggest a novel multifactorial solution to represent cognitive abilities.MethodsUsing a combined Exploratory Factor (EFA) and Exploratory Structural Equation Modeling Analyses (ESEM) we developed a three-factor model to characterize an underlying structure of nine cognitive tests selected from the UK Biobank using a Cattell-Horn-Carroll framework. We first estimated a series of probable factor solutions using the maximum likelihood method of extraction. The best solution for the EFA-defined factor structure was then tested using the ESEM approach with the aim of confirming or disconfirming the decisions made.ResultsWe determined that a three-factor model fits the UK Biobank cognitive assessment data best. Two of the three factors can be assigned to fluid reasoning (Gf) with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as a processing speed (Gs) factor.DiscussionThis study characterizes cognitive assessment data in the UK Biobank and delivers an alternative view on its underlying structure, suggesting that the three factor model provides a more granular solution than g that can further be applied to study different facets of cognitive functioning in relation to health outcomes and to further progress examination of its biological underpinnings.

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.

Levels of sex differences for human body size and shape phenotypes are hypothesized to have adaptively reduced following the agricultural transition as part of an evolutionary response to relatively more equal divisions of labor and new technology adoption. In this study, we tested this hypothesis by studying genetic variants associated with five sexually differentiated human phenotypes: height, body mass, hip circumference, body fat percentage, and waist circumference. We first analyzed genome-wide association (GWAS) results for UK Biobank individuals (~197,000 females and ~167,000 males) to identify a total of 119,023 single nucleotide polymorphisms (SNPs) significantly associated with at least one of the studied phenotypes in females, males, or both sexes (P<5x10-8). From these loci we then identified 3,016 SNPs (2.5%) with significant differences in the strength of association between the female- and male-specific GWAS results at a low false-discovery rate (FDR<0.001). Genes w...

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).

Dataset "lin2024-sex_combined_interaction-association_signifincant_in_one_or_more_tests-summary.txt" is a minimal dataset to reproduce the figures and tables in the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".

To generate this dataset, see "https://github.com/BoxiLin/t2meta" Steps 0, 1.

This dataset is the input for Steps 2, 3, 4, 5 to generate Figures 1-3 and Table 2-3.

##### Column information ########################

The following columns are annotations on each variant in the GWAS, calculated across the analysis subset of 361,194 samples by the Neale lab:

code: Phenotype identifier in the form of "[UKB Data field]_raw"
variant: Unique variant identifier in the form "chr:pos:ref:alt", where "ref" is aligned to the forward strand.
chr: Chromosome of the variant.
pos: Position of the variant in GRCh37 coordinates.
rsid: rs ID
ref: Reference allele on the forward strand.
alt: Alternate allele (not necessarily minor allele).
p_hwe: Hardy-Weinberg p-value.
info: Imputation INFO score as provided by UK Biobank.

The following columns are sex-stratified test statistics calculated by the Neale lab:

minor_allele.x: Minor allele (AF < 0.5) in the female GWAS
minor_AF.x: Minor allele frequency in the female GWAS
beta.x: Estimated effect size of alt allele in the female GWAS
se.x: Estimated standard error of beta in the female GWAS
tstat.x: t-statistic of beta estimate (= beta/se) in the female GWAS
pval.x: p-value of beta significance test in the female GWAS

minor_allele.y: Minor allele (AF < 0.5) in the male GWAS
minor_AF.y: Minor allele frequency in the male GWAS
beta.y: Estimated effect size of alt allele in the male GWAS
se.y: Estimated standard error of beta in the male GWAS
tstat.y: t-statistic of beta estimate (= beta/se) in the male GWAS
pval.y: p-value of beta significance test in the male GWAS

The following columns are sex-combined test statistics calculated in our analysis:

T.I: test statsitic for interaction effect-only
p.T.I: p-value of the interaction effect-only test
TSG.L: test statsitic for inverse variance weighted meta-analysis
p.TSG.L: p-value of the inverse variance weighted meta-analysis
TSG.Q: test statsitic for the omnibus meta-analysis
p.TSG.Q: p-value for the omnibus meta-analysis

Sex-stratified GWAS can help shed light on sexual differences in genetic architecture. In Bernabeu et al (2021) we fit sex-stratified linear mixed models (using DISSECT) across a total of 530 phenotypes to assess the effects of sex on genetic effect estimates, and compared estimates between males and females in a search for genetic variants that presented significant differences in association to the traits considered. Here, the summary statistics of said efforts, pertaining to non-clinical binary traits, are included (note: includes UK Biobank cancer traits). Each file contains the results for a single non-clinical binary trait, as stated in the file name, using its corresponding UK Biobank trait code. Trait descriptions, including their respective UK Biobank codes, are stated in the 'trait_description.tsv' file. For each trait (each .gz file), GWAS summary statistics obtained for over 4 million genetic variants across the genome (both autosomal, and X chromosome, MAF 10% filtered) and circa 450K individuals, as well as the results of the t-test comparing genetic effect estimates between the sexes, are included.

UKBB_noPCsA GWAS for human height in the UK Biobank. Linear regression without any structure correction---with only genotype, age, sex and sequencing array as covariates (unrelated British ancestry individuals only). See the paper for the plink command and more details.UKBB_sib_gwasA GWAS for human height in the UK Biobank sibs. Family-based sib-pair analysis. See the paper for the plink command and more details.IRL-GBR allele frequency differencesLogistic regression using self-identified as "White British" or "White Irish" in the UK Biobank were compared with distinct phenotype labels. See paper for plink command line and more details.BvI.nocovar.Irish.glm.logistic.gzGBR-TSI allele frequency differencesIndividuals from the GBR and TSI populations from 1000G Phase 3 were assigned binary phenotype labels and a chi square test was performed for allele frequency differences. See paper for the plink command line and more details.gwas.hwe1e6.geno05.nocovar.chisq.British.assoc.gzUK Biobank...

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Summary-level data as presented in: "Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry." Pulit, SL et al. bioRxiv, 2018. https://www.biorxiv.org/content/early/2018/04/18/304030 **If you use these data, please cite the above preprint. If you have any questions or comments regarding these files, please contact me: Sara L Pulit spulit@well.ox.ac.uk or s.l.pulit@umcutrecht.nl ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ (1) Data files i. whradjbmi.giant-ukbb.meta-analysis.combined.23May2018.txt Meta-analysis of waist-to-hip ratio adjusted for body mass index (whradjbmi) in UK Biobank and GIANT data. Combined set of samples, max N = 694,649. ii. whradjbmi.giant-ukbb.meta-analysis.females.23May2018.txt Meta-analysis of whradjbmi in UK Biobank and GIANT data. Female samples only, max N = 379,501. iii. whradjbmi.giant-ukbb.meta-analysis.males.23May2018.txt Meta-analysis of whradjbmi in UK Biobank and GIANT data. Male samples only, max N = 315,284. iv. whr.giant-ukbb.meta-analysis.combined.23May2018.txt Meta-analysis of waist-to-hip ratio (whr) in UK Biobank and GIANT data. Combined set of samples, max N = 697,734. v. whr.giant-ukbb.meta-analysis.females.23May2018.txt Meta-analysis of whr in UK Biobank and GIANT data. Female samples only, max N = 381,152. vi. whr.giant-ukbb.meta-analysis.males.23May2018.txt Meta-analysis of whr in UK Biobank and GIANT data. Male samples only, max N = 316,772. vii. bmi.giant-ukbb.meta-analysis.combined.23May2018.txt Meta-analysis of body mass index (bmi) in UK Biobank and GIANT data. Combined set of samples, max N = 806,834. viii. bmi.giant-ukbb.meta-analysis.females.23May2018.txt Meta-analysis of bmi in UK Biobank and GIANT data. Female samples only, max N = 434,794. ix. bmi.giant-ukbb.meta-analysis.males.23May2018.txt Meta-analysis of bmi in UK Biobank and GIANT data. Male samples only, max N = 374,756. (2) Data file format CHR: Chromosome POS: Chromosomal position of the SNP, build hg19 SNP: the dbSNP151 identifier of the SNP, followed by the first allele and second allele of the SNP, delimited with a colon. A small number of SNPs (<9,000) from the GIANT data had no dbSNP151 identifier, and are left as just an rsID. Note that these SNPs are also missing chromosome and position information (not provided in the GIANT data). Tested_Allele: the allele for which all association statistics are reported Other_Allele: the other allele at the SNP Freq_Tested_Allele: frequency of the tested allele BETA: the effect size of the tested allele SE: the standard error of the beta P: the p-value of the SNP, as reported from the inverse variance-weighted fixed effects meta-analysis N: the total sample size for this SNP INFO: the imputation quality (info score) of the SNP, as reported by UK Biobank. A number between 0 and 1 indicating quality of imputation (0, poor quality; 1, high quality or genotyped). Note that the summary-level GIANT data does not report info score, so SNPs appearing only in the GIANT analysis do not have info scores.

Drug treatment for nociceptive musculoskeletal pain (NMP) follows a three-step analgesic ladder, starting from non-steroidal anti-inflammatory drugs (NSAIDs), followed by weak or strong opioids until the pain is under control. Here, we conducted a genome-wide association study (GWAS) of a binary phenotype comparing NSAID users and opioid users as a proxy of treatment response to NSAID using data from the UK Biobank. We aim to find the common genetic variants associated with pain treatment response in the general population.Type of data uploaded in this repositoryUK Biobank is a large-scale biomedical database and research resource containing in-depth genetic and health information from half a million UK participants (https://www.ukbiobank.ac.uk/). The database is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. As the raw data is quite large and only available upon application to UKB, we only provide the results from our analysis, which is also described here: medrxiv and currently in revision in a scientific journal. In the dataset, you will find the association of 9,435,994 SNPs genetic variants with the pain treatment response (PTR) phenotype. This dataset is not applicable to be opened with Excel and can best be opened on a cluster computer or using specific software.SubjectsThe UK Biobank is a general population cohort with over 0.5 million participants aged 40–69 recruited across the United Kingdom (UK). We derived a phenotype as a proxy for the pain treatment response to NSAIDs by using recently released primary care (general practitioners', GPs') data, which contains longitudinal structured diagnosis and prescription data. To define the PTR phenotype, we first extracted all nociceptive musculoskeletal pain (NMP) treatments and diagnoses from the GP data. NMP diagnosis was primarily selected from the chapters on musculoskeletal and connective tissue diseases and relevant symptoms or signs from other chapters in the Read codes (versions 2 and 3). See Supplementary data 1 on medrxiv for the diagnosis codes included in this study. Secondly, pain prescriptions (NSAID and opioid) were extracted from the GP data using the British national formulary (BNF), dictionary of medicines and devices (dmd), and Read code (version 2) for data extraction. An overview of the extracted medication codes is provided in Supplementary data 2 on medrxiv. Only participants with an NMP diagnosis record and a pain prescription record occurring on the same date were included for analysis to ensure that we would only include pain treatment for NMP.PhenotypeBased on the information of NMP and pain prescriptions from the UK biobank, a dichotomous score was used for the binary (case/control) PTR phenotype: NSAID users were defined as controls and opioid users as cases. Two additional quality control (QC) steps were applied. First, participants with only one treatment event were removed to safeguard the inclusion of only participants with relatively long-term treatment. Second, a chronological check was applied for the first prescription of each ladder to ensure that the treatment ladder was correctly followed, i.e., initial NSAID use was followed by weak or strong opioids. Participants that were not treated according to this order were removed.SNP genotyping and quality controlGenotyping procedures have been described in detail elsewhere [PMID: 30305743].The third-release genotyping data were used for analysis (see https://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=100319).Participants passing quality control were included for analysis. QC steps for the samples included removal of participants with (1) inconsistent self-reported and genetically determined sex, (2) missing individual genetic data with a frequency of more than 0.1, (3) putative sex-chromosome aneuploidy. Participants were also excluded from the analysis if they were considered outliers due to missing heterozygosity, not white British ancestry based on the genotype, and had missing covariate data. Note that when we fit the linear mixed model in GCTA, it reminded us that the number of closely related participants was low. Therefore, we didn't further remove the related individuals in the sample.Routine QC steps for genetic markers on autosomes included removal of single nucleotide polymorphisms (SNPs) with (1) an imputation quality score less than 0.8, (2) a minor allele frequency (MAF) less than 0.005, (3) a Hardy-Weinberg equilibrium (HWE) test P-value less than 1 × 10−6, and (4) a genotyping call rate less than 0.95.Genome-wide association analysisA GWAS for binary PTR phenotype was conducted using a linear function in GCTA [38] for markers on the autosomal chromosomes, adjusting for age, sex, BMI, depression history, smoking status, drinking frequency, assessment center, genotyping array, and the first ten principal components (PCs). The following variables from the UK Biobank data set...

Pearson correlations and age-adjusted Pearson correlations between two measures of general cognitive ability, created using the UK Biobank cognitive tests, and the general and reference tests (n = 151–160).

Sex-stratified GWAS can help shed light on sexual differences in genetic architecture. In Bernabeu et al (2021) we fit sex-stratified logistic mixed models (using REGENIE) across a total of 42 binary traits for which we'd found evidence of sexual differences for at least one genetic variant. Here, the results of said models, as well as the comparison statistics between male and female genetic effects, are included. Each file contains the results for a single binary trait, as stated in the file name, using its corresponding UK Biobank trait code. Trait descriptions, including their respective UK Biobank codes, are stated in the 'trait_description_binary.tsv' file. For each trait (each .gz file), GWAS summary statistics obtained for over 9 million genetic variants across the autosomal genome and circa 450K individuals, as well as the results of the t-test comparing genetic effect estimates between the sexes, are included.

UK Biobank includes 502,649 middle- and older-aged adults from the general population who have undergone detailed phenotypic assessment. The majority of participants completed tests of cognitive functioning, and on average four years later a sub-group of N = 20,346 participants repeated most of the assessment. These measures will be used in a range of future studies of health outcomes in this cohort. The format and content of the cognitive tasks were partly novel. The aim of the present study was to validate and characterize the cognitive data: to describe the inter-correlational structure of the cognitive variables at baseline assessment, and the degree of stability in scores across longitudinal assessment. Baseline cognitive data were used to examine the inter-correlational/factor-structure, using principal components analysis (PCA). We also assessed the degree of stability in cognitive scores in the subsample of participants with repeat data. The different tests of cognitive ability showed significant raw inter-correlations in the expected directions. PCA suggested a one-factor solution (eigenvalue = 1.60), which accounted for around 40% of the variance. Scores showed varying levels of stability across time-points (intraclass correlation range = 0.16 to 0.65). UK Biobank cognitive data has the potential to be a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population.

Background: Cardiorespiratory fitness may moderate the association between obesity and all‐cause mortality (ie, the “fat‐but‐fit” hypothesis), but unaddressed sources of bias are a concern. Methods and Results: Cardiorespiratory fitness was estimated as watts per kilogram from a submaximal bicycle test in 77 169 men and women from the UK Biobank cohort and combined with World Health Organization standard body mass index categories, yielding 9 unique fitness‐fatness combinations. We also formed fitness‐fatness combinations based on bioimpedance as a direct measure of body composition. All‐cause mortality was ascertained from death registries. Multivariable‐adjusted Cox regression models were used to estimate hazard ratios and 95% CIs. We examined the association between fitness‐fatness combinations and all‐cause mortality in models with progressively more conservative approaches for accounting for reverse causation, misclassification of body composition, and confounding. Over a median follow‐up of 7.7 years, 1731 participants died. In our base model, unfit men and women had higher risk of premature mortality irrespective of levels of adiposity, compared with the normal weight–fit reference. This pattern was attenuated but maintained with more conservative approaches in men, but not in women. In analysis stratified by sex and excluding individuals with prevalent major chronic disease and short follow‐up and using direct measures of body composition, mortality risk was 1.78 (95% CI, 1.17–2.71) times higher in unfit‐obese men but not higher in obese‐fit men (0.94 [95% CI, 0.60–1.48]). In contrast, there was no increased risk in obese‐unfit women (1.09 [95% CI, 0.44–1.05]) as compared with the reference. Conclusions: Cardiorespiratory fitness modified the association between obesity and mortality in men, but this pattern appeared susceptible to biases in women.

"ukbnmr_jan_8_most.STD.zst" file contains multivariate UK Biobank-based sex-combined GWAS summary statistics for the Nightingale panel of 249 circulating plasma metabolic markers presented in "Pleiotropic and sex-specific genetic architecture of circulating metabolic markers" [https://doi.org/10.1101/2024.07.30.24311254].

"interaction_ukbnmr_12_most.STD.zst" file contains multivariate UK Biobank-based sex by genetic variant interaction GWAS summary statistics for the Nightingale panel of 249 circulating plasma metabolic markers presented in "Pleiotropic and sex-specific genetic architecture of circulating metabolic markers" [https://doi.org/10.1101/2024.07.30.24311254].

Each file contains eight columns:

SNP: ID of the genetic marker;

CHR: chromosome code (GRCh37 genomic build);

BP: base-pair coordinate (GRCh37 genomic build);

PVAL: regression p-value;

A1: first allele;

A2: second allele;

N: sample size;

Z: unsigned z-score (effect direction is not available for the multivariate test).

The dataset contains results of a genome-wide association study of back pain. Two files contain association summary statistics for discovery GWAS based on the analysis of 350,000 white British individuals from the UK Biobank and meta-analysis GWAS based on the meta-analysis of the same 350,000 individuals and additional 103,862 individuals of European Ancestry from the UK biobank (total N = 453,862). The phenotype of back pain was defined by the answer provided by the UK biobank participants to the following question: "Pain type(s) experienced in last month". Those who reported “Back pain”, were considered as cases, all the rest were considered as controls. Individuals who did not reply or replied: "Prefer not to answer" or "Pain all over the body" were excluded. This dataset is also available for graphical exploration in the genomic context at http://gwasarchive.org.

The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.

When using downloaded data, please cite corresponding paper and this repository:

1. Insight into the genetic architecture of back pain and its risk factors from a study of 509,000 individuals. Freidin, Maxim; Tsepilov, Yakov; Palmer, Melody; Karssen, Lennart; Suri, Pradeep; Aulchenko, Yurii; Williams, Frances MK,# CHARGE Musculoskeletal Working Group. PAIN: February 06, 2019 - Volume Articles in Press - Issue - p
   doi: 10.1097/j.pain.0000000000001514
2. Maxim B Freidin, Yakov A Tsepilov, Melody Palmer, Lennart Karssen, CHARGE Musculoskeletal Working Group, Pradeep Suri, … Frances MK Williams. (2018). Genome-wide association summary statistics for back pain (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1319332

Funding:

This study was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736).
The research has been conducted using the UK Biobank Resource (project # 18219).

The development of software implementing SMR/HEIDI test and database for GWAS results was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Program”.

Dr. Suri’s time for this work was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development Service. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

Dr. Tsepilov’s time for this work was supported in part by the Russian Ministry of Science and Education under the 5-100 Excellence Program.

Column headers - discovery (350K)

1. CHR: chromosome
2. POS: position (GRCh37 build)
3. ID: SNP rsID
4. REF: reference allele (coded as "0")
5. ALT: effect allele (coded as "1")
6. CASE_ALLELE_CT: allele observation count in cases
7. CTRL_ALLELE_CT: allele observation count in controls
8. ALT_FREQ: effect allele frequency
9. MACH_R2: imputation quality
10. TEST: model of association test (additive)
11. OBS_CT: sample size
12. BETA: effect size of effect allele
13. SE: standard error of effect size
14. T_STAT: Z-value of effect allele
15. P: P-value of association (without GC correction)
16. MAF: minor allele frequency

Column headers - meta-analysis (450K)

1. MarkerName: SNP rsID
2. Allele1: effect allele (coded as "1")
3. Allele2: reference allele (coded as "0")
4. Freq1: effect allele frequency
5. FreqSE: standard error of effect allele frequency
6. Effect: effect size of effect allele
7. StdErr: standard error of effect size
8. P-value: P-value of association (without GC correction)
9. Direction: sign of effect in discovery and replication samples
10. n_total: Total sample size
11. CHR: chromosome
12. POS: position (GRCh37 build)
13. MACH_R2_discovery: imputation quality in discovery sample