The identification of individuals at increased risk of developing rheumatoid arthritis (RA) would enable targeted interventions aimed at preventing disease or reducing its future severity. There is growing interest in developing statistical prediction models to perform this function by accurately stratifying populations into risk groups or predicting individual probabilities of incident disease. While many models use clinical and lifestyle risk factors as the basis for predicting RA, specific genetic risk scores can be also developed and, furthermore, genetic information can be included in models alongside clinical and lifestyle factors. As the number of identified genetic associations with RA increases, the potential predictive value of data from this source also increases and with it the potential benefit of incorporating genetic information into predictive modelling. This talk will consider the role of genetic information in models for predicting the development of RA, including the need to assess the predictive advantage gained over models based solely on more routinely available risk information.
Disclosure of Interest None declared