Prepared by Wei Gan: 10 August 2017

A zip file containing summary statistics is available for download here. This document is available in PDF format here.

This file contains genome-wide association summary statistics for estimated heel bone mineral density (eBMD) assessed by quantitative ultrasound (QUS) in 116,501 individuals from the UK Biobank study, published in Gan et al. (2017).

In this study, for men and women separately, eBMD was regressed on age, age-squared, height, weight, genotyping array version and assessment centre and the residuals were transformed by the rank-inverse standard normal function. The normalized residuals were subsequently pooled together (between men and women) for genome-wide association analyses. SNVs with minor allele frequecny (MAF) =0.1% passing QC (imputation r 2 = 0.4, MAF = 0.001, missingness <0.1 and with a Hardy-Weinberg equilibrium p>1×10-6 ) were tested for eBMD association adjusting genotyping array version (UKBILEVE or UK BioBank). BOLT-LMM was used to perform linear mixed models, which adjusted for population structure and relatedness.

For each SNP, we have provided the following information:

  1. SNPID
  2. Chromosome and Position (build 37, base-pairs)
  3. ALLELE1: effect allele and ALLELE0: other allele (aligned to the forward strand)
  4. A1FREQ: effect allele frequency
  5. INFO: IMPUTE2 INFO score
  6. BETA: effect size from BOLT-LMM approximation to infinitesimal mixed model
  7. SE: standard error of effect size
  8. P_BOLT_LMM_INF: infinitesimal mixed model association test p-value
  9. P_BOLT_LMM: non-infinitesimal mixed model association test p-value

The precision of the statistics presented should preclude identification of any individual subject. However, in downloading these data, you undertake not to attempt to de- identify individual subjects.

Reference

Gan W, et al. (2017). Bone mineral density and risk of type 2 diabetes and coronary heart disease: a Mendelian randomization study. Wellcome Open Res (in press).

Please refer any queries to:

Mark McCarthy (mark.mccarthy@drl.ox.ac.uk)