A natural extension of considering genome-wide risk prediction is the theoretical accuracy one might achieve if the genetic architecture of a disease were completely described. Some diseases might be difficult to predict due to poor current understanding of the underlying genetics, whereas others might never be tractable to genetic prediction. Epidemiological estimates of heritability (23) can be used to create such theoretically complete risk models. Three models have been proposed (33), each of which corresponds to a different assumption about the distribution of disease probability in the population. One of these models (the log model) is an analytically tractable but relatively unrealistic, assuming that probabilities are log-normally distributed, which can create disease probabilities greater than 1. The other two models are more complex but also more realistic, with each making different but apparently equally valid assumptions. The logit (or logistic) model assumes that odds ratios are log-normally distributed, and as a result has similar properties to, and is easy to integrate with, the logistic regression techniques used in GWAS. The probit (or liability threshold) model is a generalization of the
