We found that EHR data can be used to phenotype a complex condition such as harmful alcohol use. The validity of the phenotype was enhanced when longitudinal data were integrated using trajectories or highest value rather than with a single cross-sectional measure. All three AUDIT-C metrics were associated with PEth concentrations, a highly sensitive and specific measure of heavy drinking (Wurst et al., 2015). Closest AUDIT-C scores were not significantly associated with the most frequently identified molecular genetic risk factor for alcohol dependence, a missense polymorphism in ADH1B, which encodes an alcohol metabolic enzyme. Highest AUDIT-C score was statistically significantly associated with the ADH1B polymorphism, rs2066702. However, AUDIT-C trajectory showed a steeper gradient and a robust and significant association with the variant. This is likely because the trajectory score provides greater information on the pattern of harmful drinking over time than either of the other two AUDIT-C metrics, and is thereby more reflective of typical alcohol exposure than either a single point in time or peak exposure.
