Current programs for identifying at risk children are constructed on evidence linking early life adversity, such as poverty or birth outcomes, and the risk for mental illness. These factors predict mental illness at the level of the population, but are inefficient at the level of the individual due to the considerable variability in outcomes: many children born early, small, or into poverty are healthy and productive. The goal of this project is to identify robust and predictive biological and psychological measurements that help to predict which children are at risk early on. In particular, integrating the ALSPAC data set with 3 other birth cohort data sets (Wisconsin Study of Families and Work (WSFW), Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and Growing Up Singapore Towards Healthy Outcomes (GUSTO)) we aim to isolate more generalizable mechanisms associated with specific psychiatric diagnoses and identify factors that differentiate those who do and do not succumb to childhood mental illness given the same set of known early life risk factors. This study will thus inform targeted, and personalized interventions aimed at both reducing the severity of onset of these disorders and the negative outcomes that accompany them, such as suicide and substance abuse.