The availability of large GWAS permits us to weight and to model the cumulative effect of hundreds or even thousands of small-effect variants into polygenic risk scores (PRS)85. There are a wide range of methods available to model the polygenicity of complex traits determining the genetic risk of an individual86 (or that part of the risk affected by available common variant information). Similarly, the strength of prediction between PRS and outcome can be measured with several goodness-of-fit metrics86, such as the effect size estimate, phenotypic variance explained, the area under the receiver–operator curve (AUC), and the P-value corresponding to a null hypothesis of no association. For SUDs, recent studies are applying these approaches mainly based on large-scale GWAS of traits related to alcohol, cannabis, and tobacco use and dependence. SUD PRS show limited predictive power on an individual basis — meaning they are not (yet) good at predicting disease risk in individuals — but are useful to understand genetic overlap of SUDs with psychiatric and behavioural traits. Due to the wide range of methods applied and SUD-related traits tested, it
