Polygenic risk scores (PGS) are being used more frequently with the emergence of new genome wide association study (GWAS) findings, opening new possibilities for understanding how genes associate with health and socioeconomic traits. However, much of the research using PGS may be underpowered, resulting in a failure to identify associations with traits or even spurious associations. Previous work conducted in similar projects has explored differences in the predictive power of a PGS when modelled using various strategies such as a phenotype measured at single or multiple occasions. We wish to extend this work in three ways. Firstly, as a proof of principle, we will compare the predictive power of different PGS using single and repeated measure phenotypes including mood (depression), substance use (cannabis use) and anthropometric measures (blood pressure and height). Secondly, we will extend upon previous Mendelian Randomisation analyses to determine the statistical benefits of modelling an exposure and/or an outcome in a repeat measure framework. Finally, we will investigate the potential for increasing power in GWAS’ by using a repeated measures framework to determine if power can be maximised by using repeated measures rather than increased sample size. To meet these ends, we require phenotypic and genomic data at multiple occasions for mood, substance use and anthropometric measures. ALSPAC is the perfect resource for conducting this research given its impressive archive of data and sample.