The success of many clinical trials depends on the accurate identification of participants with early disease or with a high-risk of disease. Correctly identifying these patients can be particularly challenging in diseases with few or expensive screening options or if symptoms do not present at an early stage. We have developed a comprehensive genetic risk prediction tool that quickly and cost-effectively identifies high-risk and early-stage breast, prostate, colorectal, ovarian, and pancreatic cancer. Our system also works to identify high-risk and early-stage neurodegenerative and cardiometabolic diseases. Using low-coverage, long-read whole genomic sequencing and proprietary algorithms, our solution provides an ancestry-adjusted method to identify and pre-screen patients for Phase 2 and 3 trials. Our algorithms combine penetrant gene variants, polygenic risk scores, clinical risk scores, and family history. We successfully identified 15% more qualified individuals who will develop a disease than screening done by monogenetic testing or clinical risk surveys. Our algorithms have been trained on some of Asia’s largest cardiometabolic and cancer cohorts and validated using data from the UK Biobank and more than 50,000 Singaporean samples. We are developing partnerships with US-based genetic labs to offer our prediction tools in the US.