The meta-analyzed GWAS summary statistics for 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
The dataset contains meta-analyzed GWAS summary statistics for 35 biomarker traits described in the following preprint:
N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506
Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.
We provide the list of 35 biomarkers in "list_of_35_biomarkers.tsv". We used the "Phenotype_name" column in this table for the file names.
For each phenotype, we provide two compressed tab-delimited files,
named "[Phenotype_name].array.gz" and
"[Phenotype_name].imp.gz", which contain the summary statistics
for genetic variants on the genotyping array and the imputed dataset,
We used METAL for the meta-analysis for 4 populations (White British, non-British White, African, and South Asian) within UK Biobank. The files have the following columns:
- CHROM: the chromosome
- POS: the position
- MarkerName: the variant identifier
- REF: the reference allele
- ALT: the alternate allele
- Effect: the effect size (BETA) estimate
- StdErr: the standard error of effect size estimate
- P-value: the p-value of the association
- Direction: the direction of effect size
- HetISq, HetChiSq, HetDf, HetPVal: heterogeneity statistics from METAL
Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.
Please also check the METAL documentation (https://genome.sph.umich.edu/wiki/METAL_Documentation).
The summary statistic files are compressed with
bgzip and indexed with
.tbi files). One should be able to read those files with the standard
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASES
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