Supplementary summary statistic GWAS data for Meta-Analysis of Urinary Metabolite GWAS Studies Identifies Three Novel Genome-Wide Significant Loci

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General Information

Title: Meta-Analysis of Urinary Metabolite GWAS Studies Identifies Three Novel Genome-Wide Significant Loci
Authors: Jihan K. Zaki, Jakub Tomasik, Jade McCune, Oren Scherman, Sabine Bahn
DOI: [10.1101/2024.06.25.600593](https://doi.org/10.1101/2024.06.25.600593)
Data File DescriptionThis file contains the summary statistics from a genome-wide association meta-analysis focusing on urinary metabolites. Data fields include SNP identifiers, alleles, effect sizes, p-values, and sample sizes, among others.

Contents: Summary statistics for GWAS meta-analysis of urinary metabolitesFormat: CSVColumns:Phenotype_chr: Chromosome for the phenotypeposition: Genomic positionSNP: SNP identifierother_allele: Non-effect alleleeffect_allele: Effect alleleeffect_pval: P-value of the effectn: Sample sizeeaf: Effect allele frequencym-pval: Meta-analysis p-valuem-n: Meta-analysis sample sizestudies: Number of studies includedMethodology

Study Design: The study involved a meta-analysis of GWAS data to identify significant loci associated with urinary metabolites.Data Processing: Data was obtained through the EMBL-EBI GWAS Catalog, PubMed, and metabolomix.com, we employed a sample size-based meta-analytic approach to evaluate the significance of previously reported GWAS associations. Further details can be found in the manuscript.Usage Notes

Intended Use: These data are intended for use in academic and research settings. Users are encouraged to use the data to validate findings or for further analysis under the terms provided.Citation and Contact

Citation: Please cite this dataset using the DOI provided above and by referring to the main manuscript.Contact: For further inquiries, contact Jihan Zaki ([e-mail]), Oren Scherman ([e-mail]) or Sabine Bahn ([e-mail]).License

License Type: CC BYAcknowledgments

Funding and Support: This work was supported by the Stanley Medical Research Institute (grant number: O7R-1888) by grants to Sabine Bahn, and by the Oskar Huttunen Foundation grant to Jihan K. Zaki.