Published July 21, 2025 | Version v2
Dataset Open

GWAS summary statistics for self-reported high cholesterol (UK Biobank Data-Field 20002; Coding 1473)

  • 1. ROR icon University of Chicago
  • 2. ROR icon University of Toronto

Description

GWAS summary statistics for self-reported high cholesterol in the UK Biobank (Data-Field 20002; Coding 1473), output by PLINK2 software. 

This includes:

  • P-values for each SNP (three rows per SNP, ordered by additive test TEST = "ADD"; dominance test TEST = "DOMDEV" and the 2-df joint test TEST = "GENO_2DF"), obtained from the genotypic model where both additive and dominance effects are included
    (file: bri_result_high_chol.self_reported_cholesterol.glm.logistic.hybrid). and
  • P-values for each SNP from the traditional additive test, where the model is additive-only
    (file: bri_result_high_chol_additive.self_reported_cholesterol.glm.logistic.hybrid).

Please note that for the traditional additive GWAS result, one should refer to the second file, not to the first file (where TEST = "ADD").

Each variable (column) in the PLINK2 summary is interpreted as the following:

  • #CHROM: The chromosome number.
  • POS: GRCh37 position.
  • ID: The SNP ID.
  • REF: Reference Allele.
  • ALT: Alternate Allele.
  • A1: The Effect Allele.
  • FIRTH?: Whether to use the Firth implementation for the logistic regression.
  • TEST: Indication of the type of hypothesis test to perform ("GENO_2DF": joint test; "DOMDEV": dominance test; "ADD": additive test)
  • OBS_CT: Number of samples included in the analysis.
  • OR: Odds ratio.
  • LOG(OR)_SE: Standard error of the log odds ratio.
  • (first file) Z_OR_F_STAT: Wald Statistics for the 1-df test (TEST = "DOMDEV" or "ADD") and the 2-df test (TEST = "GENO_2DF").
  • (second file) Z_STAT: Z Statistics for the test.
  • P: P-value for the test.
  • ERRCODE: Error code ("." means no error).

Files

Files (196.2 MB)

Additional details

References

  • Zhang Z, Lawless JF, Paterson AD, Sun L. Detecting latent gene-environment interaction when analyzing binary traits. bioRxiv. 2024.07.10.602954. doi: 10.1101/2024.07.10.602954