Unlike the deterministic genetic tests for fully penetrant Mendelian disorders, genetic predictions for complex traits will be probabilistic and the value may only be incremental in clinical decision making. The value of genetic risk prediction may be at a group level rather than individual level. For example, from a risk predictor for type 1 diabetes (T1D), created from risk variants known up to 2011, a risk group comprising the top ranked 18% of individuals would need to be monitored in order to capture 80% of future cases, yet because T1D is not common (prevalence 0.4%) the probability of disease for individuals in this risk group is still less than 2%14. Nonetheless, cost-effective public health strategies could result from use of genetic predictors to identify high-risk strata where disease prevention interventions should be focussed15, 16. In agriculture, genetic risk prediction is geared mostly towards selection of breeding stock based on estimates of additive genetic values (‘estimated breeding values’) in the parent generation with the aim of eliciting changes in the phenotype of the of the offspring generation on average. That is, the impact of genetic prediction is naturally at the level of a group rather than an individual.
