This project develops and evaluates a voxel-wise imaging–genetic framework for anatomically resolved adiposity phenotyping using large-scale whole-body magnetic resonance imaging (MRI) data from the UK Biobank. The work is motivated by the principle that adiposity and metabolic dysfunction are spatially heterogeneous and cannot be adequately characterised using anthropometric measures such as body mass index alone. The analytical framework is organised around a shared whole-body anatomical coordinate system established through deformable image registration and voxel-wise analysis. The project consists of three analytically linked components. First, voxel-wise whole-body MRI is used to construct anatomically resolved phenotypic atlases of type 2 diabetes mellitus using quantitative fat fraction and a deformation-derived relative local volume metric. Second, genetically informed adiposity phenotypes are evaluated through voxel-wise genotype–phenotype correspondence analyses. Third, imaging-based recognition models assess whether genetically anchored anatomical programmes can be recovered from imaging data alone within a constrained non-causal framework. The project uses whole-body MRI data from the UK Biobank, including voxel-wise quantitative imaging measures and associated phenotypic variables. Analyses are conducted in sex-stratified frameworks using large-scale voxel-wise statistical modelling, permutation-based inference, and threshold-free cluster enhancement. Each participant is represented as a three-dimensional anatomical volume within a common coordinate system, enabling statistical comparison at homologous anatomical locations across the body. The computational workload requires high-performance computing infrastructure because analyses involve millions of voxel-wise statistical models, large-scale permutation testing, deformable image registration, and high-dimensional imaging data processing. All analyses are performed within secure computing environments due to the sensitive nature of UK Biobank imaging and health-related data. The project aims to establish a biologically informed and anatomically standardised framework for spatial phenotyping and imaging–genetic correspondence analysis in metabolic disease research. Main supervisor: Joel Kullberg, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden