The principal outputs of the GWAS revolution have been the new insights into the biological mechanisms of disease (4–6), but it is also possible to use the fruits of GWAS extend genetic prediction from single large factors to aggregations of individually weak effects. In order to explore post-GWAS risk prediction, we first discuss the relative merits of different statistical summaries of prediction. We next consider the state of prediction from current GWAS knowledge and consider possible insights from idealized models of prediction—as well as their potential pitfalls. Finally, we look forward towards possible future clinical implementation of genetic risk prediction.
