Validating Harmful Alcohol Use as a Phenotype for Genetic Discovery Using Phosphatidylethanol and a Polymorphism in ADH1B.

BACKGROUND: Although alcohol risk is heritable, few genetic risk variants have been identified. Longitudinal electronic health record (EHR) data offer a largely untapped source of phenotypic information for genetic studies, but EHR-derived phenotypes for harmful alcohol exposure have yet to be validated. Using a variant of known effect, we used EHR data to develop and validate a phenotype for harmful alcohol exposure that can be used to identify unknown genetic variants in large samples. Herein, we consider the validity of 3 approaches using the 3-item Alcohol Use Disorders Identification Test consumption measure (AUDIT-C) as a phenotype for harmful alcohol exposure. METHODS: First, using longitudinal AUDIT-C data from the Veterans Aging Cohort Biomarker Study Cohort (VACS-BC), we compared 3 metrics of AUDIT-C using correlation coefficients: (i) AUDIT-C closest to blood sampling (closest AUDIT-C), (ii) the highest value (highest AUDIT-C), (iii) and longitudinal trajectories generated using joint trajectory modeling (AUDIT-C trajectory). Second, we compared the associations of the 3 AUDIT-C metrics with phosphatidylethanol (PEth), a direct, quantitative biomarker for alcohol in the overall sample using chi-square tests for trend. Last, in the subsample of African Americans (AAs; n = 1,503), we compared the associations of the 3 AUDIT-C metrics with rs2066702 a common missense (Arg369Cys) polymorphism of the ADH1B gene, which encodes an alcohol dehydrogenase isozyme. RESULTS: The sample (n = 1,851, 94.5% male, 65% HIV+, mean age 52 years) had a median of 7 AUDIT-C scores over a median of 6.1 years. Highest AUDIT-C and AUDIT-C trajectory were correlated r = 0.86. The closest AUDIT-C was obtained a median of 2.26 years after the VACS-BC blood draw. Overall and among AAs, all 3 AUDIT-C metrics were associated with PEth (all p < 0.05), but the gradient was steepest with AUDIT-C trajectory. Among AAs (36% with the protective ADH1B allele), the association of rs2066702 with AUDIT-C trajectory and highest AUDIT-C was statistically significant (p < 0.05), and the gradient was steeper for the AUDIT-C trajectory than for the highest AUDIT-C. The closest AUDIT-C was not statistically significantly associated with rs2066702. CONCLUSIONS: EHR data can be used to identify complex phenotypes such as harmful alcohol use. The validity of the phenotype may be enhanced through the use of longitudinal trajectories.

AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations.

BACKGROUND AND AIMS: Longitudinal electronic health record (EHR) data offer a large-scale, untapped source of phenotypical information on harmful alcohol use. Using established, alcohol-associated variants in the gene that encodes the enzyme alcohol dehydrogenase 1B (ADH1B) as criterion standards, we compared the individual and combined validity of three longitudinal EHR-based phenotypes of harmful alcohol use: Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) trajectories; mean age-adjusted AUDIT-C; and diagnoses of alcohol use disorder (AUD). DESIGN: With longitudinal EHR data from the Million Veteran Program (MVP) linked to genetic data, we used two population-specific polymorphisms in ADH1B that are associated strongly with AUD in African Americans (AAs) and European Americans (EAs): rs2066702 (Arg369Cys, AAs) and rs1229984 (Arg48His, EAs) as criterion measures. SETTING: United States Department of Veterans Affairs Healthcare System. PARTICIPANTS: A total of 167 721 veterans (57 677 AAs and 110 044 EAs; 92% male, mean age = 63 years) took part in this study. Data were collected from 1  October 2007 to 1 May 2017. MEASUREMENTS: Using all AUDIT-C scores and AUD diagnostic codes recorded in the EHR, we calculated age-adjusted mean AUDIT-C values, longitudinal statistical trajectories of AUDIT-C scores and ICD-9/10 diagnostic groupings for AUD. FINDINGS: A total of 19 793 AAs (34.3%) had one or two minor alleles at rs2066702 [minor allele frequency (MAF) = 0.190] and 6933 EAs (6.3%) had one or two minor alleles at rs1229984 (MAF = 0.032). In both populations, trajectories and age-adjusted mean AUDIT-C were correlated (r = 0.90) but, when considered separately, highest score (8+ versus 0) of age-adjusted mean AUDIT-C demonstrated a stronger association with the ADH1B variants [adjusted odds ratio (aOR) 0.54 in AAs and 0.37 in AAs] than did the highest trajectory (aOR 0.71 in AAs and 0.53 in EAs); combining AUDIT-C metrics did not improve discrimination. When age-adjusted mean AUDIT-C score and AUD diagnoses were considered together, age-adjusted mean AUDIT-C (8+ versus 0) was associated with lower odds of having the ADH1B minor allele than were AUD diagnostic codes: aOR = 0.59 versus 0.86 in AAs and 0.48 versus 0.68 in EAs. These independent associations combine to yield an even lower aOR of 0.51 for AAs and 0.33 for EAs. CONCLUSIONS: The age-adjusted mean AUDIT-C score is associated more strongly with genetic polymorphisms of known risk for alcohol use disorder than are longitudinal trajectories of AUDIT-C or AUD diagnostic codes. AUD diagnostic codes modestly enhance this association.