This release containing Python code and data for comparing genome-wide association studies (GWAS) for bone marrow adiposity (BMA). This includes data from the following four studies: Kaufmann et al, eLife, 2024 (https://doi.org/10.7554/eLife.101499.1) - Data from Table S5. Xu et al, Nature Communications, 2025 (https://doi.org/10.1038/s41467-024-55422-4) – Data from Supplemnetary Data 13, 26, 27, 28 and 29. Wu et al, Nature Communications, 2025 (https://doi.org/10.1038/s41467-025-62826-3) – Data from Supplementary Data 8 and 19 Ahmed et al, J Obes, 2025 (https://doi.org/10.1155/jobe/7792701) – Data from Supplementary Figures 4H, 4I and 4J The Python code and associated csv files were used to generate the Venn Diagrams in Figure 1A of the following manuscript: Title: Deep learning for analysis of bone marrow adiposity: breakthroughs from recent large-scale analyses in the UK Biobank Authors: Wei Xu (1,2), Chengjia Wang (3,4), William P Cawthorn (2) Affiliations: Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK. Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK. School of Mathematics and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 1AS, UK. Journal: Current Osteoporosis Reports Year: 2026 DOI: not yet available as of 22/01/2026 (paper in press) Further details of the source data, shown in the csv files, are provided in the README file in the main directory for this repository (https://github.com/WillCawthorn/BMA_GWAS/). The repository may be updated with data and code for analysis of new BMA GWAS studies if/when these are published.