University of Manchester
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Summary statistics of UK Biobank blood pressure genome-wide association studies (GWAS) using 337,422 unrelated white European individuals

Version 2 2024-03-22, 09:13
Version 1 2023-12-21, 09:56
posted on 2024-03-22, 09:13 authored by Xiaoguang XuXiaoguang Xu

Three blood pressure traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP). Mean SBP and DBP values from automated values were calculated. After calculating blood pressure values, SBP and DBP were adjusted for medication use by adding 15 and 10 mm Hg to their values, respectively, for individuals reported to be taking blood pressure–lowering medication.

For the UK Biobank genome-wide association studies (GWAS), we performed linear mixed model (LMM) association testing under an additive genetic model of the three continuous, medication-adjusted blood pressure traits (SBP, DBP, PP) for all measured and imputed genetic variants (Data Field-22828) with minor allele frequency (MAF) >=1% and imputation score>=0.3 in dosage format using the BOLT-LMM (v2.4.1) software. Covariates were age, age2, sex, BMI, genotyping array and 10PCs. Genomic inflation was not applied to the GWAS summary statistics.

Sample QC was described below:

We included up to 337,422 individuals from UK Biobank for the purpose of this project. We followed UK Biobank sample-based quality control criteria (Nature 2018;562:203-209); excluded were samples/individuals based on the following criteria: (i) outliers in heterozygosity and missingness, (ii) self-reported gender not consistent with genetic data inferred gender (ii) sample call rate (computed using probesets internal to Affymetrix) <97% or the resolution of the distributions of intensity 'contrast' values <0·82, (iii) carriers of sex chromosomal abnormalities (configurations other than XX or XY), (iv) subjects who had cryptic relatedness with other individuals, or (v) individuals of non-white British genetic ancestry.

If you use this data, please cite the following paper: Xu, X., Khunsriraksakul, C., Eales, J.M. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 15, 2359 (2024).


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