The offspring of parents with mental illness represent a major lost opportunity for early-intervention. Having a parent with mental illness is the most common potent risk factor for early-onset depression. Early-onset depression (by age 24) is difficult to treat and is associated with a recurrent/chronic course and poor outcomes. Early-intervention strategies are effective when targeted at this group. However, we know that very few access support. There is also substantial variation in absolute levels of risk meaning that all offspring of parents with mental illness are unlikely to require early-intervention, and service constraints make this infeasible. Early detection and intervention that is personalized dependent on absolute level of risk is therefore needed to improve access to early-intervention for those at ultra-high-risk of developing early-onset depression. We will harness big-data to stratify risk for early-onset depression. We will validate the risk-stratification tool in an independent data set and test response to early-intervention in an already funded trial. Throughout, we will collaborate with lived experience experts and clinicians to ensure the risk-stratification tool is acceptable and develop implementation guidance together. The tool will help to promote timely, personalized access to early intervention for those at ultra-high-risk. Keywords: early-intervention, depression, risk, stratification