~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

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(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.

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 is a cross-trait genome-wide association meta-analysis of perceived youthfulness in the UK Biobank (UKBB) split by sex. The analysis was performed using Multi-trait Analysis of GWAS (MTAG) software (DOI: 10.1038/s41588-017-0009-4). Traits were included in the analysis if they had a geentic correlation greater than 10% and with a P value < 2.3e-5. For full details see published manuscript.

Females - UKBB perceived facial aging; Yengo et al., 2018, BMI GWAS (DOI: 10.1093/hmg/ddy271); UKBB Vitamin E supplement use; UKBB Vitamin C supplement use; UKBB Zinc supplement use; UKBB age at first period; UKBB contraceptive pill use.

Males - UKBB perceived facial aging; Yengo et al., 2018, BMI GWAS (DOI: 10.1093/hmg/ddy271); Liu et al., 2019 Cigarettes per day (doi: 10.1038/s41588-018-0307-5); UKBB smoking status; UKBB male-pattern baldness four point scale; UKBB Diabetes status; UKBB Hypertension status; UKBB Age at first facial hair;

Combined sex - UKBB perceived facial aging; Yengo et al., 2018, BMI GWAS (DOI: 10.1093/hmg/ddy271); Liu et al., 2019 Cigarettes per day (doi: 10.1038/s41588-018-0307-5); UKBB smoking status; UKBB liking of taking stairs; UKBB falls in last year; UKBB use of false teeth; UKBB Hypertension status; UKBB Vitamin E supplement use; UKBB Zinc supplement use.

The columns are:

SNP -rsid/Single Nucleotide Polymorphism identifier

CHR - chromsome

BP -Base position (Build 37)

A1 - Effect allele

A2 - Non -effect allele

Z - Z score

N - Sample Size

FRQ - Frequency

mtag_beta - effect estimate from MTAG of the effect allele

mtag_se - standard error of the effect estimate from MTAG

mtag_z - mtag_beta/mtag_se

mtag_pval - p value of the effect estimate

Baseline characteristics of the UKBB participants included in this study.

We conducted a benchmarking analysis of 16 summary-level data-based MR methods for causal inference with five real-world genetic datasets, focusing on three key aspects: type I error control, the accuracy of causal effect estimates, replicability, and power.

The datasets used in the MR benchmarking study can be downloaded here:

1. "dataset-GWASATLAS-negativecontrol.zip": the GWASATLAS dataset for evaluation of type I error control in confounding scenario (a): Population stratification
2. "dataset-NealeLab-negativecontrol.zip": the Neale Lab dataset for evaluation of type I error control in confounding scenario (a): Population stratification;
3. "dataset-PanUKBB-negativecontrol.zip": the Pan UKBB dataset for evaluation of type I error control in confounding scenario (a): Population stratification;
4. "dataset-Pleiotropy-negativecontrol": the dataset used for evaluation of type I error control in confounding scenario (b): Pleiotropy;
5. "dataset-familylevelconf-negativecontrol.zip": the dataset used for evaluation of type I error control in confounding scenario (c): Family-level confounders;
6. "dataset_ukb-ukb.zip": the dataset used for evaluation of the accuracy of causal effect estimates;
7. "dataset-LDL-CAD_clumped.zip": the dataset used for evaluation of replicability and power;

Each of the datasets contains the following files:

1. "Tested Trait pairs": the exposure-outcome trait pairs to be analyzed;
2. "MRdat" refers to the summary statistics after performing IV selection (p-value < 5e-05) and PLINK LD clumping with a clumping window size of 1000kb and an r^2 threshold of 0.001.
3. "bg_paras" are the estimated background parameters "Omega" and "C" which will be used for MR estimation in MR-APSS.

Note:

1. Supplemental Tables S1-S7.xlxs provide the download link for the original GWAS summary-level data for the traits used as exposures or outcomes.
2. The formatted dataset after quality control can be accessible at our GitHub website (https://github.com/YangLabHKUST/MRbenchmarking).
3. The details on quality control of GWAS summary statistics, formatting GWASs, and LD clumping for IV selection can be found on the MR-APSS software tutorial on the MR-APSS website (https://github.com/YangLabHKUST/MR-APSS).
4. R code for running MR methods is also available at https://github.com/YangLabHKUST/MRbenchmarking.

Table of association between sets of associated phenotype–gene pairs after FDR correction (gene level association performed using SKAT test with variants weighted by their CADD score).

Explore the historical Whois records related to xn--aklforex-ukbb.com (Domain). Get insights into ownership history and changes over time.

Supplementary data set containing UKBB case and control overlap, and ρ estimates.

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.

Additional file 1: Table S1A. Detected CFTR variants across UKBB populations. Table S1B. CFTR2 CF-causing variants excluded/included in our analysis. Table S2. Unique CF-causing variants. Table S3. CF-causing CFTR variants. Table S4. Variable clinical consequence variants detected across UKBB ancestries. Table S5. Unique CF-causing variants. Table S6. Common varying clinical consequences (VCC) variants. Table S7. CFTR variants annotated as having High Impact. Table S8. Uncharacterized high impact variants. Table S9. High impact and CF-Causing variants. Table S10. Dated variants within the CFTR gene locus.

AimRare genetic variants in the CUBN gene encoding the main albumin-transporter in the proximal tubule of the kidneys have previously been associated with microalbuminuria and higher urine albumin levels, also in diabetes. Sequencing studies in isolated proteinuria suggest that these variants might not affect kidney function, despite proteinuria. However, the relation of these CUBN missense variants to the estimated glomerular filtration rate (eGFR) is largely unexplored. We hereby broadly examine the associations between four CUBN missense variants and eGFRcreatinine in Europeans with Type 1 (T1D) and Type 2 Diabetes (T2D). Furthermore, we sought to deepen our understanding of these variants in a range of single- and aggregate- variant analyses of other kidney-related traits in individuals with and without diabetes mellitus.MethodsWe carried out a genetic association-based linear regression analysis between four CUBN missense variants (rs141640975, rs144360241, rs45551835, rs1801239) and eGFRcreatinine (ml/min/1.73 m2, CKD-EPIcreatinine(2012), natural log-transformed) in populations with T1D (n ~ 3,588) or T2D (n ~ 31,155) from multiple European studies and in individuals without diabetes from UK Biobank (UKBB, n ~ 370,061) with replication in deCODE (n = 127,090). Summary results of the diabetes-group were meta-analyzed using the fixed-effect inverse-variance method.ResultsAlbeit we did not observe associations between eGFRcreatinine and CUBN in the diabetes-group, we found significant positive associations between the minor alleles of all four variants and eGFRcreatinine in the UKBB individuals without diabetes with rs141640975 being the strongest (Effect=0.02, PeGFR_creatinine=2.2 × 10-9). We replicated the findings for rs141640975 in the Icelandic non-diabetes population (Effect=0.026, PeGFR_creatinine=7.7 × 10-4). For rs141640975, the eGFRcreatinine-association showed significant interaction with albuminuria levels (normo-, micro-, and macroalbuminuria; p = 0.03). An aggregated genetic risk score (GRS) was associated with higher urine albumin levels and eGFRcreatinine. The rs141640975 variant was also associated with higher levels of eGFRcreatinine-cystatin C (ml/min/1.73 m2, CKD-EPI2021, natural log-transformed) and lower circulating cystatin C levels.ConclusionsThe positive associations between the four CUBN missense variants and eGFR in a large population without diabetes suggests a pleiotropic role of CUBN as a novel eGFR-locus in addition to it being a known albuminuria-locus. Additional associations with diverse renal function measures (lower cystatin C and higher eGFRcreatinine-cystatin C levels) and a CUBN-focused GRS further suggests an important role of CUBN in the future personalization of chronic kidney disease management in people without diabetes.

ObjectivesNon-alcoholic fatty liver disease (NAFLD) has been linked to an increased risk of kidney stones in prior observational studies, However, the results are inconsistent, and the causality remains to be established. We aimed to investigate the potential causal relationship between NAFLD and kidney stones using two-sample Mendelian randomization (MR).MethodsGenetic instruments were used as proxies for NAFLD. Summary-level data for the associations of exposure-associated SNPs with kidney stones were obtained from the UK Biobank study (6536 cases and 388,508 controls) and the FinnGen consortium (9713 cases and 366,693 non-cases). MR methods were conducted, including inverse variance weighted method (IVW), MR-Egger, weighted median, and MR-PRESSO. MR-Egger Regression Intercept and Cochran’s Q test were used to assess the directional pleiotropy and heterogeneity.ResultscALT-associated NAFLD did not exhibit an association with kidney stones in the Inverse variance weighted (IVW) methods, in both the FinnGen consortium (OR: 1.02, 95%CI: 0.94-1.11, p = 0.632) and the UKBB study (OR: 1.000, 95%CI: 0.998-1.002, p = 0.852). The results were consistent in European ancestry (FinnGen OR: 1.05, 95%CI: 0.98-1.14, p = 0.144, UKBB OR: 1.000, 95%CI: 0.998-1.002, p = 0.859). IVW MR analysis also did not reveal a significant causal relationship between NAFLD and the risk of kidney stone for the other three NAFLD-related traits, including imaging-based, biopsy-confirmed NAFLD, and more stringent biopsy-confirmed NAFLD. The results remained consistent and robust in the sensitivity analysis.ConclusionsThe MR study did not provide sufficient evidence to support the causal associations of NAFLD with kidney stones.

Fig A. Funnel plots for the effect of never smoking status on risk of self-reported fatigue for each single-nucleotide polymorphism (SNP), the resulting Mendelian randomization (MR) estimate is plotted against the inverse of the standard error of the MR estimate. Symmetry noted in this plot provides evidence against the presence of directional horizontal pleiotropy. The inverse-variance weighted and MR Egger causal estimates are represented by a red and blue line respectively. Fig B. Funnel plots for the effect of current smoking status on risk of self-reported fatigue for each single-nucleotide polymorphism (SNP), the resulting Mendelian randomization (MR) estimate is plotted against the inverse of the standard error of the MR estimate. Symmetry noted in this plot provides evidence against the presence of directional horizontal pleiotropy. The inverse-variance weighted and MR Egger causal estimates are represented by a red and blue line respectively. Fig C. Funnel plots for the effect of alcohol intake frequency on risk of self-reported fatigue for each single-nucleotide polymorphism (SNP), the resulting Mendelian randomization (MR) estimate is plotted against the inverse of the standard error of the MR estimate. Symmetry noted in this plot provides evidence against the presence of directional horizontal pleiotropy. The inverse-variance weighted and MR Egger causal estimates are represented by a red and blue line respectively. (ZIP)

Aims: This study was aimed to apply a Mendelian randomization design to explore the causal association between coronavirus disease 2019 (COVID-19) and three cardio-cerebrovascular diseases, including atrial fibrillation, ischemic stroke, and coronary artery disease.Methods: Two-sample Mendelian randomization was used to determine the following: 1) the causal effect of COVID-19 on atrial fibrillation (55,114 case participants vs 482,295 control participants), coronary artery disease (34,541 case participants vs 261,984 control participants), and ischemic stroke (34,217 case participants vs 40,611 control participants), which were obtained from the European Bioinformatics Institute, and 2) the causal effect of three cardio-cerebrovascular diseases on COVID-19. The single-nucleotide polymorphisms (SNPs) of COVID-19 were selected from the summary-level genome-wide association study data of COVID-19-hg genome-wide association study (GWAS) meta-analyses (round 5) based on the COVID-19 Host Genetics Initiative for participants with European ancestry. The random-effects inverse-variance weighted method was conducted for the main analyses, with a complementary analysis of the weighted median and Mendelian randomization (MR)-Egger approaches.Results: Genetically predicted hospitalized COVID-19 was suggestively associated with ischemic stroke, with an odds ratio (OR) of 1.049 [95% confidence interval (CI) 1.003–1.098; p = 0.037] in the COVID-19 Host Genetics Initiative GWAS. When excluding the UK Biobank (UKBB) data, our analysis revealed a similar odds ratio of 1.041 (95% CI 1.001–1.082; p = 0.044). Genetically predicted coronary artery disease was associated with critical COVID-19, with an OR of 0.860 (95% CI 0.760–0.973; p = 0.017) in the GWAS meta-analysis and an OR of 0.820 (95% CI 0.722–0.931; p = 0.002) when excluding the UKBB data, separately. Limited evidence of causal associations was observed between critical or hospitalized COVID-19 and other cardio-cerebrovascular diseases included in our study.Conclusion: Our findings provide suggestive evidence about the causal association between hospitalized COVID-19 and an increased risk of ischemic stroke. Besides, other factors potentially contribute to the risk of coronary artery disease in patients with COVID-19, but not genetics.

BackgroundPsoriasis is a chronic inflammatory skin disease. Dyslipidemia may be a risk factor of psoriasis. But the causal relationship between psoriasis and blood lipid still remains uncertain.MethodsThe two data of blood lipid were obtained from UK Biobank (UKBB) and Global Lipid Genetics Consortium Results (GLGC). The primary and secondary database were from large publicly available genome-wide association study (GWAS) with more than 400,000 and 170,000 subjects of European ancestry, respectively. The psoriasis from Finnish biobanks of FinnGen research project for psoriasis, consisting of 6,995 cases and 299,128 controls. The single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR) were used to assess the total and direct effects of blood lipid on psoriasis risk.ResultsSVMR estimates in primary data of blood lipid showed low-density lipoprotein cholesterol (LDL-C) (odds ratio (OR): 1.11, 95%, confidence interval (CI): 0.99−1.25, p = 0.082 in stage 1; OR: 1.15, 95% CI: 1.05−1.26, p = 0.002 in stage 2; OR: 1.15, 95% CI: 1.04−1.26, p = 0.006 in stage 3) and triglycerides (TG) (OR: 1.22, 95% CI: 1.10−1.35, p = 1.17E-04 in stage 1; OR: 1.15, 95% CI: 1.06−1.24, p = 0.001 in stage 2; OR: 1.14, 95% CI: 1.05−1.24, p = 0.002 in stage 3) had a highly robust causal relationship on the risk of psoriasis. However, there were no robust causal associations between HDL-C and psoriasis. The SVMR results in secondary data of blood lipid were consistent with the primary data. Reverse MR analysis showed a causal association between psoriasis and LDL-C (beta: -0.009, 95% CI: -0.016− -0.002, p = 0.009) and HDL-C (beta: -0.011, 95% CI: -0.021− -0.002, p = 0.016). The reverse causation analyses results between psoriasis and TG did not reach significance. In MVMR of primary data of blood lipid, the LDL-C (OR: 1.05, 95% CI: 0.99–1.25, p = 0.396 in stage 1; OR: 1.07, 95% CI: 1.01–1.14, p = 0.017 in stage 2; OR: 1.08, 95% CI: 1.02–1.15, p = 0.012 in stage 3) and TG (OR: 1.11, 95% CI: 1.01–1.22, p = 0.036 in stage 1; OR: 1.09, 95% CI: 1.03–1.15, p = 0.002 in stage 2; OR: 1.07, 95% CI: 1.01–1.13 p = 0.015 in stage 3) positively correlated with psoriasis, and there had no correlation between HDL-C and psoriasis. The results of the secondary analysis were consistent with the results of primary analysis.ConclusionsMendelian randomization (MR) findings provide genetic evidence for causal link between psoriasis and blood lipid. It may be meaningful to monitor and control blood lipid level for a management of psoriasis patients in clinic.

BackgroundBlood counts and biochemical markers are among the most common tests performed in hospitals and most readily accepted by patients, and are widely regarded as reliable biomarkers in the literature. The aim of this study was to assess the causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension (PAH).MethodsA two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between blood counts and biochemical indicators with PAH. The genome-wide association study (GWAS) for blood counts and biochemical indicators were obtained from the UK Biobank (UKBB), while the GWAS for PAH were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was used as the primary analysis method, supplemented by three sensitivity analyses to assess the robustness of the results. And we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 2003–2018 to verify the relationship.ResultsThe MR analysis primarily using the IVW method revealed genetic variants of platelet count (OR=2.51, 95% CI 1.56-4.22, P

The percentage of pairs for which a nominally significant (p ≤ 0.05) test statistic was computed given by number of cases and MHC inclusion status, and the median genetic correlation estimate in each group. ‘No. of cases’ gives the smaller number of disease cases in each pair of case-control UKBB GWAS. ‘Median ’ denotes the median genetic correlation estimate obtained from the exemplary data sets for each group of pairs.

BackgroundMitochondrial DNA (mtDNA) plays an important role in autoimmune diseases (AD), yet the relationship between mitochondria and autoimmune disease is controversial. This study employed bidirectional Mendelian randomization (MR) to explore the causal relationship between mtDNA copy number and 13 ADs (including ankylosing spondylitis [AS], Crohn’s disease [CD], juvenile rheumatoid arthritis [JRA], polymyalgia rheumatica [PMR], psoriasis [PSO], rheumatoid arthritis [RA], Sjogren’s syndrome [SS], systemic lupus erythematosus [SLE], thyrotoxicosis, type 1 diabetes mellitus [T1DM], ulcerative colitis [UC], and vitiligo)MethodsA two-sample MR analysis was performed to assess the causal relationship between mtDNA copy number and AD. Genome-wide association study (GWAS) for mtDNA copy number were obtained from the UK Biobank (UKBB), while those associated with AD were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was the primary analysis method, complemented by three sensitivity analyses (MR-Egger, weighted median, weighted mode) to validate the results.ResultsIVW MR analysis identified significant associations between mtDNA copy number and CD (OR=2.51, 95% CI 1.56-4.22, P

BackgroundAcute appendicitis is one of the most common abdominal emergencies worldwide. Both environmental and genetic factors contribute to the disease. C-reactive protein (CRP) is an important biomarker in the diagnosis of acute appendicitis. CRP concentrations are significantly affected by genetic variation. However, whether such genetic variation is causally related to appendicitis risk remains unclear. In this study, the causal relationship between single-nucleotide polymorphisms (SNPs) associated with circulating CRP concentrations and the risk and severity of acute appendicitis was investigated.MethodsCRP concentrations in serum of appendicitis patients (n = 325) were measured. Appendicitis was categorized as complicated/uncomplicated and gangrenous/non-gangrenous. Imputed SNP data (n = 287) were generated. A genome-wide association study (GWAS) on CRP concentrations and appendicitis severity was performed. Intersection and colocalization of the GWAS results were performed with appendicitis and CRP-associated loci from the Pan-UKBB cohort. A functional-genomics approach to prioritize genes was employed.ResultsThirteen percent of significant CRP quantitative trait loci (QTLs) that were previously identified in a large cohort of healthy individuals were replicated in our small patient cohort. Significant enrichment of CRP-QTLs in association with appendicitis was observed. Among these shared loci, the two top loci at chromosomes 1q41 and 8p23.1 were characterized. The top SNP at chromosome 1q41 is located within the promoter of H2.0 Like Homeobox (HLX) gene, which is involved in blood cell differentiation, and liver and gut organogeneses. The expression of HLX is increased in the appendix of appendicitis patients compared to controls. The locus at 8p23.1 contains multiple genes, including cathepsin B (CTSB), which is overexpressed in appendix tissue from appendicitis patients. The risk allele of the top SNP in this locus also increases CTSB expression in the sigmoid colon of healthy individuals. CTSB is involved in collagen degradation, MHC class II antigen presentation, and neutrophil degranulation.ConclusionsThe results of this study prioritize HLX and CTSB as potential causal genes for appendicitis and suggest a shared genetic mechanism between appendicitis and CRP concentrations.