Validating Harmful Alcohol Use as a Phenotype for Genetic Discovery Using Phosphatidylethanol and a Polymorphism in <i><scp>ADH</scp>1B</i>

Justice, Amy C.; McGinnis, Kathleen A.; Tate, Janet P.; Becker, William C.; Zhao, Hongyu; Kranzler, Henry R.

doi:10.1111/acer.13373

Cited by 15 publications

(33 citation statements)

References 21 publications

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“…The AUDIT-C yielded many GWS findings that did not overlap with those for AUD, which reflects genetic independence of the traits. This broadens our previous observations using SNPs in ADH1B , in which we validated the AUDIT-C score as an alcohol-related phenotype 33 . In that study, after accounting for the effects of AUDIT-C score, AUD diagnoses accounted for unique variance in the frequency of the minor alleles.…”

Section: Discussionsupporting

confidence: 75%

“…Our analyses used AUDIT-C data collected from October 1, 2007–February 23, 2017. We validated the phenotype in a sample of 1,851 participants from the Veterans Aging Cohort Study 33 , in which we found a highly significant association of AUDIT-C scores with the plasma concentration of phosphatidylethanol, a direct, quantitative biomarker that is correlated with the level of alcohol consumption. In the AA part of this sample (n=1,503), the AUDIT-C score was highly significantly associated with rs2066702, a missense (Arg369Cys) polymorphism of ADH1B , the minor allele of which is common in that population and has been associated with alcohol dependence 25 .…”

Section: Methodsmentioning

confidence: 99%

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Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424)

Kranzler

Zhou

Kember

et al. 2019

Preprint

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SummaryAlthough alcohol consumption level and alcohol use disorder (AUD) diagnosis are both moderately heritable, their genetic risks and overlap are not well understood. We conducted genome-wide association studies of these traits using longitudinal Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) scores (reflecting alcohol consumption) and AUD diagnoses from electronic health records (EHRs) in a single, large multi-ancestry Million Veteran Program sample. Meta-analysis across population groups (N = 274,424) identified 18 genome-wide significant loci, 5 of which were associated with both traits and 13 with either AUDIT-C (N = 8) or AUD (N = 5). A significant genetic correlation between the traits reflects this overlap. However, downstream analyses revealed biologically meaningful points of divergence. Cell-type group partitioning heritability enrichment analyses indicated that central nervous system was the most significant cell type for AUDIT-C and the only significant cell type for AUD. Polygenic risk scores (PRS) for both traits were associated with alcohol-related disorders in two independent samples. Genetic correlations for 188 non-alcohol-related traits were significantly different for the two traits, as were the phenotypes associated with the traits’ polygenic risk scores. We conclude that EHR-derived, longitudinal, repeated measures of alcohol consumption level and AUD diagnosis can facilitate genetic discovery and help to elucidate the relationship between drinking level and AUD risk. Finally, although heavy drinking is a key risk factor for AUD, it is not a sufficient cause of the disorder.

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Section: Discussionsupporting

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424)

Kranzler

Zhou

Kember

et al. 2019

Preprint

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“…As we found in prior analyses of repeated longitudinal self‐reported measures of alcohol consumption (Justice et al . ; Justice et al . ), summary measures of repeated self‐reported smoking also demonstrate stronger associations than single, cross‐sectional reports that are often employed as phenotypes in large‐scale genetic studies (Sanchez‐Roige et al .…”

Section: Discussionmentioning

confidence: 99%

Using DNA methylation to validate an electronic medical record phenotype for smoking

et al. 2018

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A validated, scalable approach to characterizing (phenotyping) smoking status is needed to facilitate genetic discovery. Using established DNA methylation sites from blood samples as a criterion standard for smoking behavior, we compare three candidate electronic medical record (EMR) smoking metrics based on longitudinal EMR text notes. With data from the Veterans Aging Cohort Study (VACS), we employed a validated algorithm to translate each smoking-related text note into current, past or never categories. We compared three alternative summary characterizations of smoking: most recent, modal and trajectories using descriptive statistics and Spearman's correlation coefficients. Logistic regression and area under the curve analyses were used to compare the associations of these phenotypes with the DNA methylation sites, cg05575921 and cg03636183, which are known to have strong associations with current smoking. DNA methylation data were available from the VACS Biomarker Cohort (VACS-BC), a sub-study of VACS. We also considered whether the associations differed by the certainty of trajectory group assignment (<0.80/≥0.80). Among 140 152 VACS participants, EMR summary smoking phenotypes varied in frequency by the metric chosen: current from 33 to 53 percent; past from 16 to 24 percent and never from 24 to 33 percent. The association between the EMR smoking pairs was highest for modal and trajectories (rho = 0.89). Among 728 individuals in the VACS-BC, both DNA methylation sites were associated with all three EMR summary metrics (p < 0.001), but the strongest association with both methylation sites was observed for trajectories (p < 0.001). Longitudinal EMR smoking data support using a summary phenotype, the validity of which is enhanced when data are integrated into statistical trajectories.

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“…In this study, PEth was only measured in Cohort 1 using dried blood spot samples derived from frozen peripheral blood mononuclear cells stored at -80°C 5 . PEth can be detected at concentrations as low as 2 ng/ml.…”

Section: Phosphatidylethanol (Peth) Measurementmentioning

confidence: 99%

“…Phosphatidylethanol (PEth) is a lipid metabolite of ethanol formed from phosphatidylcholine in erythrocytes and has been proposed as a biomarker for alcohol consumption. Compared with self-reported data, PEth reliably detects ethanol levels up to 21 days after the last drink 4 , and the PEth level is highly correlated with alcohol consumption 5 . However, the clinical applicability of PEth is limited because its half-life the HAD predicting procedure using the preselected CpGs from the EWAS on AUDIT-C score was also performed.…”

Section: Introductionmentioning

confidence: 97%

DNA Methylation Signature on Phosphatidylethanol, not Self-Reported Alcohol Consumption, Predicts Hazardous Alcohol Consumption in Two Distinct Populations

Liang

Justice

So‐Armah

et al. 2019

Preprint

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The process of diagnosing hazardous alcohol drinking (HAD) is based on self-reported data and is thereby vulnerable to bias. There has been an interest in developing epigenetic biomarkers for HAD that might complement clinical assessment. Because alcohol consumption has been previously linked to DNA methylation (DNAm), here, we aimed to select DNAm signatures in blood to predict HAD from two demographically and clinically distinct populations (N total =1,549). We first separately conducted an epigenome-wide association study (EWAS) for phosphatidylethanol (PEth), an objective measure of alcohol consumption, and for self-reported alcohol consumption in Cohort 1.We identified 102 PEth-associated CpGs, including 32 CpGs previously associated with alcohol consumption or alcohol use disorders. In contrast, no CpG reached epigenomewide significance on self-reported alcohol consumption. Using a machine learning approach, two subsets of CpGs from EWAS on PEth and on self-reported alcohol consumption from Cohort 1 were separately tested for the prediction of HAD in Cohort 2. We found that a subset of 130 CpGs selected from the EWAS on PEth showed an excellent prediction of HAD with area under the ROC curve (AUC) of 91.31% in training set and 70.65% in validation set of Cohort 2. However, CpGs preselected from the EWAS on self-reported alcohol consumption showed a poor prediction of HAD with AUC 75.18% in the training set and 57.60% in the validation set. Our results demonstrate that an objective measure for alcohol consumption is a more informative phenotype than self-reported data for revealing epigenetic mechanism. The PEthassociated DNAm signature in blood is a robust biomarker for alcohol consumption.is approximately 4-7 days 6 . Thus, other more stable biomarkers for alcohol consumption are needed to inform clinical practice.Epigenetic signatures have emerged as attractive biomarkers for complex diseases such as cancers and neurodegenerative diseases 7 . Epigenetic markers may reflect environmental exposures, including alcohol consumption. Among these epigenetic markers, DNA methylation (DNAm) biomarkers are particularly attractive because they are relatively stable and capture an early stage of pathophysiological changes 8,9 . A recent longitudinal study on DNAm showed that most DNA methylome changes occurred 80-90 days before clinically detectable glucose elevation 10 , suggesting that DNAm is involved in an early stage of diabetes. Finally, epigenetic modifications can be reliably detected in noninvasive fluids and biospecimens 11 . Thus, the utility of epigenetic alterations has motivated the biomarker research field to develop epigenetic signatures derived from easily accessible cells for clinical use [12][13][14] .DNAm markers are emerging as diagnostic biomarkers in many areas of medicine and are applied to predict complex diseases 15 . For example, DNAm markers on the promoters of several genes, including BMP3, NDRG4, and SPEPT9, in blood or stool samples have been approved by the Food and Drug Administration...

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

Cited by 15 publications

References 21 publications

Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424)

Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424)

Using DNA methylation to validate an electronic medical record phenotype for smoking

DNA Methylation Signature on Phosphatidylethanol, not Self-Reported Alcohol Consumption, Predicts Hazardous Alcohol Consumption in Two Distinct Populations

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