Polygenic risk scores (PRSs) aggregate genetic data across the genome and summarise these data into a single number that quantifies an individual’s genetically defined disease risk. PRSs can also be used to predict standard outcomes that are relevant to health, such as height or weight. Genomics plc has built a range of tools to generate the most accurate PRS library, and combine these PRSs with other non-genetic factors into integrated risk tools (IRT). However, typical PRS and IRT evaluation focuses on adulthood. As a result, we have a limited understanding of how these tools perform across the life course, especially at which age genetically driven differences become apparent. Similarly, little is known of how the combination of PRS and early life data can jointly predict adult outcomes, which is key for preventative opportunities that need to occur at an early age.
ALSPAC has generated unique data across the life course of individuals. These data provide an opportunity to understand the interplay between childhood and adult data together with genetics. We therefore are proposing to use the ALSPAC data to understand the role of PRSs in the context of an individual life’s course, with a focus not only on prediction accuracy but also on how the accuracy of these predictions varies with age and when they have the greatest utility. We will further explore whether childhood information, together with immutable genetic data, can be combined to accurately predict traits in adulthood, thus providing an effective lever for triggering health interventions that are most useful during childhood.