The overall aim of this project is to increase the understanding of cardiometabolic disease by combined analysis of whole-body imaging data with other phenotype and genetic data. This by use of image analysis techniques based on both Imiomics and deep learning and applyying it to data from the UK Biobank study. Specific aims are 1) To create a whole-body human imaging atlas 2) To study the effect of aging on the whole-body MRI data 3) To study the association between cardiometabolic risk factors, like diabetes, hypertension, smoking, dyslipidemia, and the whole-body MRI data, controlling for effects of aging. 4) To perform genome-wide body-wide studies of body composition, to study how our genes determines differences in body composition. 5) To perform Mendelian Randomization studies of causal effects. Estimated storage need: (TB) Raw data: MRI(compressed) 5TB, Genetics (compressed), 1.4TB Expansions: MRI x10, Genetics, x10 Estimated total storage need: 70 TB (50TB MRI + 15TB Genetics) Estimated computation times: (core hours per month) MRI 30k, Genetics 20k Sensitive personal data: Human MRI data, genetics data, personal health records. Softwares: Python - In-house developed registration software (written in C++) Matlab SNPTEST PLINK2