Risk models in genetic epidemiology

Advances in the identification and treatment of genetically transmitted dis eases have lead to an increased need for reliable estimates of genetic susc eptibility risk. These estimates are used in clinic settings to identify in dividuals at increased risk of being a carrier of a disease susceptibility allele as well as to define the probability of developing a particular dise ase given one is a carrier. Accurate assessment of these probabilities is e xtremely important given the implications for medical decision malting incl uding the identification of patients who might benefit from genetic counsel ling or from entry into clinical trials. A wide range of risk models has be en proposed including those that utilize logistic regression, Cox proportio nal hazards regression, log-incidence models, and Bayesian modelling. The s pecific data used to create the various risk models varies by disease and m ay include molecular, epidemiologic, and clinical information although, in general, family history remains the primary variable of interest, particula rly for those diseases for which a susceptibility allele(s) has yet to be i dentified. When permitted by sample size, researchers also attempt to measu re the effect of any gene-environment interaction. In this paper we give an overview of the various definitions of risk as well as several of the more frequently used methods of risk estimation in genetic epidemiology at pres ent. In addition, the means by which different methods are able to provide a measure of error or uncertainty associated with a given risk estimate wil l be discussed. Applications to risk modelling for breast cancer are given the disease for which risk assessment has probably been most extensively de fined.