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Alcohol Clin Exp Res. 2017 May;41(5):998-1003. doi: 10.1111/acer.13373. Epub 2017 Apr 10.

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

Author information

1
Yale School of Medicine, New Haven, Connecticut.
2
Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut.
3
Yale School of Public Health, New Haven, Connecticut.
4
Department of Psychiatry, University of Pennsylvania Perelman School of Medicine and VISN 4 MIRECC, Crescenz VAMC, Philadelphia, Pennsylvania.

Abstract

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.

KEYWORDS:

ADH1B ; AUDIT-C; African American; Alcohol Use Disorder; Arg369Cys; Electronic Health Record Data; Trajectory Analyses; rs2066702

PMID:
28295416
PMCID:
PMC5501250
[Available on 2018-05-01]
DOI:
10.1111/acer.13373
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