<p>Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocytes and other cardiometabolic traits, we developed a pipeline using CRISPR/Cas9, non-invasive, semi-automated fluorescence imaging and deep learning-based image analysis in live zebrafish larvae. Results from a dietary intervention show that 5 days of overfeeding is sufficient to increase the odds of lipid accumulation in adipocytes by 10 days post-fertilization (dpf, <i>n</i> = 275). However, subsequent experiments show that across 12 to 16 established obesity genes, 10 dpf is too early to detect an effect of CRISPR/Cas9-induced mutations on lipid accumulation in adipocytes (<i>n</i> = 1014), and effects on food intake at 8 dpf (<i>n</i> = 1127) are inconsistent with earlier results from mammals. Despite this, we observe effects of CRISPR/Cas9-induced mutations on ectopic accumulation of lipids in the vasculature (<i>sh2b1</i> and <i>sim1b</i>) and liver (<i>bdnf</i>); as well as on body size (<i>pcsk1</i>, <i>pomca</i>, <i>irs1</i>); whole-body LDLc and/or total cholesterol content (<i>irs2b</i> and <i>sh2b1</i>); and pancreatic beta cell traits and/or glucose content (<i>pcsk1</i>, <i>pomca</i>, and <i>sim1a</i>). Taken together, our results illustrate that CRISPR/Cas9- and image-based experiments in zebrafish larvae can highlight direct effects of obesity genes on cardiometabolic traits, unconfounded by their – not yet apparent – effect on excess adiposity.</p>