Identifying robust genomic associations for drug response phenotypes presents specific challenges within EHR systems. Patients receiving a specific drug must first be identified and then the outcome of interest, within a predefined time window following drug administration, must then be identified. As a consequence, these datasets tend to be smaller and the algorithms more complicated than those used for disease genotype associations. Further, the requirement for ascertainment at multiple time points (baseline, on drug) can introduce bias since not all patients have complete follow up. Differences in a wide range of factors across patients exposed to a drug may account for variable responses and many of these may be inapparent or difficult to measure in the EHR. Whether patients actually take their medication is a source of variability in any drug response study, and EHR-based work is no exception. Seeing a change in a drug response metric (e.g. lowering LDL with a statin) might indicate compliance, but no change could be non-compliance, altered kinetics, or altered response. Measuring drug levels can help, and these data, if obtained, are available in the EHR.
