Data Release 2015
The GEnetic Factors for OSteoporosis (GEFOS) Consortium is a large international collaboration comprising numerous research groups. Osteoporosis is a common age-related complex disease with a strong genetic component. Osteoporotic fractures account for considerable disease burden and costs. The GEFOS.seq project used meta-analysis of whole genome sequencing, whole exome sequencing and deep imputation of genotype data to identify low-frequency and rare variants associated with risk of osteoporosis. Three (3) DXA-derived traits are included in this data release: Femoral Neck bone mineral density (BMD) (FN-BMD), Lumbar Spine BMD (LS-BMD), and Forearm BMD (FA-BMD).
This release includes the summary data from our 2015 meta-analyses of whole-genome sequencing, whole-exome sequencing, and deep imputation of genotype data. These files include summary statistics for approximately 10 million SNPs.
Data file description
Each file contains the following information:Marker chromosome Marker position (bp) Marker ID Effect allele Non effect allele effect allele frequency Overall beta value for meta-analysis standard error Lower 95% CI for BETA Upper 95% CL for BETA Z-score Meta-analysis p-value Absolute value of logarithm of meta-analysis p-value to the base of 10 Cochran's heterogeneity statistic Cochran's heterogeneity statistic's p-value Heterogeneity index I2 by Higgins et al 2003 Number of studies with marker present Number of samples with marker present (will be NA if marker is present in any input file where N column is not present) Summary of effect directions ('+' - positive effect of reference allele, '-' - negative effect of reference allele, '0' - no effect (or non-significant) effect of reference allele, '?' - missing data)
For further information on this format please refer to GWAMA by visiting http://www.well.ox.ac.uk/gwama/
Zheng HF, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 2015.
The UK10K, AOGC and GEFOS-seq Consortia require appropriate attribution for use of data in a product, service, or publication by citing the article generating this set of results (Zheng et al, Nature 2015) and acknowledging its use.