The genome is particularly useful for assessing inherited disease risk and the modulation of drug response. To improve the precision of diagnosis and treatment for personalized medicine, multiple sources of information, including genomic information from high-throughput genomics technologies, will likely need to be combined.
In this commentary, Altman argues that this integration of personal genomic information into the clinic must also involve integration of a broader set of population-based data sources to be successful in personalized medicine. In particular, Bayesian reasoning will enable use to estimate the a posteriori probability of a clinical event as a function of the a priori probability of that event as well as information contained in any newly measured data.
Therefore, along with genomic data, population data may be used to help estimate the a posteriori probability of disease, drug response, surgical outcomes, etc and both are critical for the implementation of personalized medicine.