BACKGROUND: In observational studies, analyses of blood pressure (BP) typically require some correction for the use of antihypertensive medications by study participants. Different approaches to correcting for treatment have been compared, but the impact of pharmacogenetic interactions that influence the efficacy of antihypertensive treatments on estimates of genetic main effects has not been considered. This work demonstrates the potential influence of pharmacogenetic interactions in genetic analyses of BP. METHODS: A simulation study is conducted to test the influence of pharmacogenetic interactions on approaches to the analysis of BP. Results from three plausible scenarios are presented. RESULTS: Informative BP approaches (Fixed Treatment Effect, Non-parametric adjustment, Censored Normal Regression) perform well when there is no pharmacogenetic interaction, but yield biased estimates of the main effects of particular genetic variants when pharmacogenetic interactions exist. Substitution approaches (Binary Trait, Fixed Substitution, Random Substitution, Median Method) are unaffected by pharmacogenetic interactions, but consistently perform sub-optimally. CONCLUSIONS: We recommend that the Informative BP approaches remain the most appropriate methods to use in practice, but stress that caution is required in the interpretation of their results-especially when an interaction between treatment and a genetic variant of interest is suspected. We make some suggestions as to how to check for possible interactions and confirm the results from genetic analyses of BP, but warn that these should be reviewed when data on real pharmacogenetic interactions become available.
BACKGROUND: In observational studies, analyses of blood pressure (BP) typically require some correction for the use of antihypertensive medications by study participants. Different approaches to correcting for treatment have been compared, but the impact of pharmacogenetic interactions that influence the efficacy of antihypertensive treatments on estimates of genetic main effects has not been considered. This work demonstrates the potential influence of pharmacogenetic interactions in genetic analyses of BP. METHODS: A simulation study is conducted to test the influence of pharmacogenetic interactions on approaches to the analysis of BP. Results from three plausible scenarios are presented. RESULTS: Informative BP approaches (Fixed Treatment Effect, Non-parametric adjustment, Censored Normal Regression) perform well when there is no pharmacogenetic interaction, but yield biased estimates of the main effects of particular genetic variants when pharmacogenetic interactions exist. Substitution approaches (Binary Trait, Fixed Substitution, Random Substitution, Median Method) are unaffected by pharmacogenetic interactions, but consistently perform sub-optimally. CONCLUSIONS: We recommend that the Informative BP approaches remain the most appropriate methods to use in practice, but stress that caution is required in the interpretation of their results-especially when an interaction between treatment and a genetic variant of interest is suspected. We make some suggestions as to how to check for possible interactions and confirm the results from genetic analyses of BP, but warn that these should be reviewed when data on real pharmacogenetic interactions become available.