Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Alcohol Clin Exp Res (Hoboken). 2023 Dec 12;48(2):283–294. doi: 10.1111/acer.15239

Associations between alcohol use disorder polygenic score and remission in participants from high-risk families and the Indiana Biobank

Dongbing Lai 1, Sally I-Chun Kuo 2, Leah Wetherill 1, Fazil Aliev 2, Michael Zhang 1, Marco Abreu 1, Tae-Hwi Schwantes-An 1, Danielle Dick 2, Meredith W Francis 3, Emma C Johnson 4, Chella Kamarajan 5, Sivan Kinreich 5, Samuel Kuperman 6, Jacquelyn Meyers 5, John I Nurnberger 1,7, Yunlong Liu 1, Howard J Edenberg 1,8, Bernice Porjesz 5, Arpana Agrawal 4, Tatiana Foroud 1, Marc Schuckit 9, Martin H Plawecki 7, Kathleen K Bucholz 4, Vivia V McCutcheon 4
PMCID: PMC10922306  NIHMSID: NIHMS1950638  PMID: 38054532

Abstract

Background:

In the U.S., ~50% of those who meet criteria for alcohol use disorder (AUD) during their lifetimes do not remit. We previously reported that a polygenic score for AUD (PGSAUD) was positively associated with AUD severity as measured by DSM-5 lifetime criteria count, and AUD severity was negatively associated with remission; therefore, we hypothesized that PGSAUD would be negatively associated with remission.

Methods:

Individuals of European (EA) and African ancestry (AA) from the Collaborative Study on the Genetics of Alcoholism (COGA) who met lifetime criteria for AUD, and two EA cohorts ascertained for studying liver diseases and substance use disorders, respectively, from Indiana Biobank, were included. In COGA, 12-month remission was defined as any period of ≥12 consecutive months without AUD criteria except craving and was further categorized as abstinent and non-abstinent. In Indiana Biobank, remission was defined based on ICD codes and could not be further distinguished as abstinent or non-abstinent. Sex and age were included as covariates. COGA analyses included additional adjustment for AUD severity, family history of remission, and AUD treatment history.

Results:

In COGA EA, PGSAUD was negatively associated with 12-month and non-abstinent remission (P≤0.013, betas between −0.15 and −0.10) after adjusting for all covariates. In contrast to COGA findings, PGSAUD was positively associated with remission (P=0.004, beta=0.28) in Indiana Biobank liver diseases cohort but not in Indiana Biobank substance use disorder cohort (P=0.17, beta=0.15).

Conclusions:

PGSAUD was negatively associated with 12-month and non-abstinent remission in COGA EA, independent of behavioral measures of AUD severity and family history of remission. The discrepant results in COGA and Indiana Biobank might reflect different ascertainment strategies: Indiana Biobank participants were older and had higher rates of liver diseases, suggesting remission due to alcohol-related health conditions that manifested in later life.

Keywords: polygenic score, remission, alcohol use disorder severity, alcohol use disorder treatment history, family history of remission

INTRODUCTION

Alcohol use disorder (AUD) is a chronic disease with devastating effects on individuals, families, and society (Sacks et al., 2015, Navarro et al., 2011, Nayak et al., 2019, Greenfield et al., 2015, Karriker-Jaffe et al., 2018). In the U.S., the 12-month and lifetime prevalence of DSM-5 AUD (American Psychiatric Association, 2022) are 10.6% and 29.1%, respectively (Grant et al., 2015). Although remission is common, only 11% of individuals with past-year alcohol dependence remitted within three years (Dawson et al., 2012) and approximately half of individuals with lifetime AUD remitted within 20 years of the condition’s onset (Fleury et al., 2016). The probability of remission and the type of remission (i.e., abstinent and non-abstinent) are both linked to AUD severity. Individuals who do not remit usually have greater severity in terms of alcohol consumption and symptoms, and among individuals who do remit, those who abstain from alcohol have greater severity than those who reduce alcohol consumption without abstaining (Dawson et al., 2005, Fan et al., 2019a, Lee et al., 2018). Identifying factors that influence the likelihood of remission can contribute to timely and effective treatments tailored to AUD severity and abstinence goals.

Of the many factors contributing to the development of AUD, genetics confers substantial vulnerability, with an estimated heritability of ~50% (Heath et al., 1997, Prescott and Kendler, 1999, Verhulst et al., 2015). To date, however, the contribution of genetics to remission from AUD is understudied. Genetic influences on the development of AUD had a negative association with remission in females but not males in a population-based sample of young adult twins (McCutcheon et al., 2012). A machine-learning model detected a negative association between a polygenic score (PGS) for problematic alcohol consumption and remission from AUD in African Ancestry (AA) males but not in AA females or in European Ancestry (EA) males or females (Kinreich et al., 2021). These scant, mixed results indicate the need for additional studies that examine genetic influences on remission to clarify whether and for whom genetic factors might influence the probability of remission.

Family history of AUD, which captures both genetic and environmental sources of risk, is a strong and consistent predictor of risk for AUD (Dawson et al., 1992, Karriker-Jaffe et al., 2021, Lai et al., 2022a). Many studies have investigated the relationship between family history of AUD and remission but without significant results (Dawson, 1996, Dawson et al., 2005, Bottlender and Soyka, 2005, Knop et al., 2007, Penick et al., 2010, Gilder et al., 2008, Dawson et al., 2007, Lopez-Quintero et al., 2011). However, we have reported that family history of remission, explicitly defined as not having active AUD at time of interview, was associated with remission in young adult twins (McCutcheon et al., 2012) and in the Collaborative Study on the Genetics of Alcoholism (COGA) sample (McCutcheon et al., 2017). COGA participants with lifetime AUD were more than three times as likely to be abstinent remitted if they had a first-degree relative who was abstinent five years after AUD treatment when compared to participants whose relative had active AUD (McCutcheon et al., 2017). Since family history of remission indexes both genetic and shared environmental factors, it is not clear to what extent these findings are due to genetics, environment, or interactions among them.

Recent genome-wide association studies (GWAS) for AUD and related traits have identified trait-associated genes and facilitated the creation of PGS that capture individual’s genetic liability for a trait, calculated by summing associated risk variants across the genome and weighting by their effect sizes (Khera et al., 2018, Lai et al., 2022a, Lai et al., 2022b). We have developed an AUD PGS (PGSAUD) by using only concordant variants among different studies that can evaluate AUD risk at a level comparable to that associated with first-degree family history of AUD in an EA sample (Lai et al., 2022a). We also developed a PGSAUD for use in AA populations by utilizing population-concordant variants, with dramatically increased performance compared to current AA PGSAUD (Lai et al., 2022b). In our previous study, we found that PGSAUD was positively associated with AUD severity as measured by DSM-5 AUD criterion counts (Lai et al., 2022a). Since AUD severity is negatively associated with remission (Fan et al., 2019b, Dawson et al., 2005, Lee et al., 2018), we hypothesized that PGSAUD would be negatively associated with the probability of remission from AUD. We tested our hypothesis by using COGA samples consisting of participants with diagnostic interview defined DSM-5 AUD from families enriched with AUDs and comparison families not enriched for but not excluding AUDs. Importantly, we adjusted for covariates that index AUD severity to partition out behavioral indicators of severity from the genetic liability indexed by PGSAUD. We also included treatment history and history of remission in family members as sources of information on the course of AUD that are complementary to PGSAUD. Additionally, we tested our hypothesis using two cohorts from Indiana Biobank that were selected for the study of liver diseases and substance use disorders, respectively, and not ascertained for studying AUD, in order to determine whether our hypothesis is supported in samples with different ascertainment strategies. This study is pre-registered at the Open Science Framework (osf.io/x8ct6).

MATERIALS AND METHODS

Study sample 1: Collaborative Study on the Genetics of Alcoholism (COGA)

COGA is a multi-site study that began in 1989 with the goal of identifying vulnerability and protective genes for AUD (Dick et al., 2023). High-risk and comparison families were ascertained and enrolled in the study via probands who were in treatment at 6 sites for what was then termed “alcohol dependence” (Johnson et al., 2023, Nurnberger et al., 2004, Dick et al., 2023). All data collection sites used consistent data collection procedures. This study was approved by the Institutional Review Boards from all centers and every participant provided informed consent. All participants were interviewed using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) (Bucholz et al., 1994, Hesselbrock et al., 1999), which includes a thorough history of AUD symptoms including ages of symptom onset and recency. In the present analyses, AUD cases consisted of participants who reported two or more DSM-5 AUD criteria occurring within the same 12-month period (American Psychiatric Association, 2022). Among individuals who met lifetime AUD criteria, those who reported no current criteria other than craving for at least 12 consecutive months were coded as 12-month remission. We further characterized remission as abstinent (no alcohol consumption) and non-abstinent (still consuming alcohol in any amount). Participants with lifetime AUD who never met remission criteria were defined as having current AUD (i.e., not remitted). For participants with more than one interview, AUD and remission were coded if criteria were met at any interview. Individuals who did not meet lifetime criteria for DSM-5 AUD were not included in these analyses. In total, 3,249 EA and 1,163 AA participants were included. The average numbers of interviews were 3.06 (SE 1.96) for EA and 2.75 (SE 1.95) for AA, respectively.

Covariates available in COGA (but not in Indiana Biobank) included AUD duration (years from first AUD onset to remission or the last interview), lifetime maximum alcohol consumption during a 24-hour period (maxdrink24), and maximum lifetime AUD criterion count. History of remission in the family was based on the self-reports of participants’ first-degree relatives who were themselves interviewed about their own history of AUD. First-degree family members who met lifetime AUD criteria were coded as remitted if they reported no current criteria other than craving for at least 12 consecutive months. Individuals with one or more first-degree family members who had a history of remission were coded as having a family history of remission. AUD treatment history was based on participant report of having ever been treated for a drinking problem, and if so, the type of treatment. Professional treatment was coded for participants who accessed an inpatient or outpatient alcohol program, and mutual help for participants who reported ever having attended Alcoholics Anonymous or another mutual help group. Variables representing mutual help only, professional treatment only, and both professional treatment and mutual help (both treatments) were included to account for treatment history.

Study sample 2: Indiana Biobank

Indiana Biobank is a state-wide collaborative effort that combines state-of-the-art centralized biobanking and linked electronic health records (EHR) (Lai et al., 2022b). Each participant’s de-identified EHR data are available via the Indiana Network for Patient Care (INPC) supported by the Regenstrief Institute at Indiana University School of Medicine. Two cohorts from Indiana Biobank were utilized in this study: (1) ascertained to study liver diseases and matched controls (N=575), and (2) ascertained to study substance use disorders (SUD, including alcohol, cannabis, cocaine, opioid, other substances, N=460). Only participants with AUD from both cohorts, determined based on ICD codes (ICD9: 303 (Alcohol dependence syndrome) and 305.0 (Nondependent alcohol abuse); ICD10: F10 (Alcohol related disorders)) (Lai et al., 2022b), were included in the current analyses. Remission was determined based on clinical entry of specific ICD9 (303.03 (Acute alcoholic intoxication in alcoholism, in remission), 303.93 (Other and unspecified alcohol dependence, in remission), and 305.03 (Alcohol abuse, in remission)) or ICD10 codes (F10.11 (Alcohol abuse, in remission) and F10.21 (Alcohol dependence, in remission)), but these did not specify remission type as abstinent or not. These ICD codes were used in a large-scale GWAS of AUD (Kranzler et al., 2019). Participants with AUD for whom the remission codes were not clinically entered were defined as current AUD.

Genotype data processing and imputation

COGA EA samples were genotyped on three different arrays: Illumina Human1M and OmniExpress 12v1 arrays (Illumina, San Diego, CA), and the SmokeScreen array (Biorealm LLC, Walnut, CA). COGA AA samples were genotyped using Illumina Human2.5M array (Illumina San Diego, CA). Genotyping, data processing and quality control information of COGA samples were reported previously (Lai et al., 2019a, Lai et al., 2019b). Briefly, a set of 47,000 independent variants (defined as linkage disequilibrium (LD) r2 <0.5) that were genotyped in all arrays with high genotyping quality (missing rate <2%, minor allele frequency (MAF) >10%, Hardy-Weinberg Equilibrium (HWE) P-value >0.001) was used to confirm and update the reported family structures. This set of variants was also used to calculate the principal components (PC) of population stratification using Eigenstrat (Price et al., 2006). Based on the first two PCs, samples that clustered with the European and African samples from the 1000 Genomes Project (Phase 3, version 5, NCBI GRCh37) were considered as EA and AA samples, respectively. Before imputation, variants with palindromic alleles, missing rate >5%, MAF <3%, and HWE P-value < 0.0001 were excluded. We used SHAPIT2 (Delaneau et al., 2013) and Minimac3 (Das et al., 2016) for phasing the genotypes and for imputation, respectively. Imputation to 1000 Genomes Project was performed separately by array due to different array contents. Variants with imputation quality score R2 ≥0.3 and MAF ≥0.01 were retained for analysis.

Indiana Biobank liver diseases cohort samples were genotyped using Illumina Infinium Global Screening Array (GSA, Illumina, San Diego, CA) by Regeneron (Tarrytown, NY). Indiana Biobank SUD cohort samples were genotyped using Illumina Infinium Global Diversity Array (GDA, Illumina, San Diego, CA) by Sampled (Piscataway, NJ). The same data processing procedure as in COGA was used and both cohorts were imputed to 1000 Genomes Project using the Michigan Imputation Server (Das et al., 2016). The number of AA participants in these Indiana Biobank cohorts is small (N<100), thus we limited our analysis to EA participants only. All Indiana Biobank samples used in the analyses were from unrelated individuals.

PGSAUD calculation

For EA participants, the discovery dataset used to calculate PGSAUD was from the meta-analysis of two large EA GWAS of AUD-related phenotypes: AUD determined using ICD codes from the Million Veteran Program (MVP-AUD; N=202,004, 7.3% are female, Mean age=63.3. GWAS results are available through dbGaP: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v11.p1) (Kranzler et al., 2019) and scores derived from the problem subscale (questions 4–10) of the Alcohol Use Disorder Identification Test (AUDIT) from the UK Biobank (UKBB-AUDIT-P; N=121,604, 56.2% are female, Mean age=56.1. GWAS results are available from the senior authors upon request) (Sanchez-Roige et al., 2019). As described previously, since both GWAS used different instruments and had different participants, we only retained variants with the same direction of effects in both MVP-AUD and UKBB-AUDIT-P to exclude study-specific findings and false positives due to random variations, i.e., retained variants were likely AUD-associated in general populations (Lai et al., 2022a). PRS-CS (Ge et al., 2019) was used to estimate the posterior effect sizes of each variant through a Bayesian regression framework using continuous shrinkage priors. European samples from 1000 Genomes Project were used as the LD reference panel. For AA participants, the discovery GWAS was from the meta-analysis of GWAS of problematic alcohol use in EA cohorts (EA-PAU) (Zhou et al., 2020) and the AA GWAS of AUD from the Million Veteran Program (AA-AUD) (Kranzler et al., 2019). Both GWAS results are available through dbGaP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v11.p1). As described above, only variants with the same direction of effects in both EA-PAU and AA-AUD were retained (Lai et al., 2022b). PRS-CSx (Ruan et al., 2022) was used to estimate the posterior effect sizes of each variant with African and European samples from 1000 Genomes Project as the LD reference panel for AA and EA participants respectively. PLINK (Chang et al., 2015, Purcell et al., 2007) was used to calculate PGSAUD using imputation dosages. PGSAUD was standardized (Mean =0, SD =1) for ease of interpretation. The lists of variants and their weights used to calculate PGSAUD are available at the PGS catalog (EA: https://www.pgscatalog.org/publication/PGP000345/; AA: https://www.pgscatalog.org/publication/PGP000346/).

Statistical models

A generalized estimating equation (GEE) model was used to test the association of PGSAUD with COGA 12-month, abstinent, and non-abstinent remission versus current AUD among participants with lifetime AUD; a random effect was included to adjust for family relationships. A logistic regression model was used to test for remission versus current AUD in Indiana Biobank. For both COGA and Indiana Biobank samples, sex, age, and the first 10 ancestral principal components (PCs) were included as covariates. For remitted participants, age was defined as the age at first remission; for current AUD, age was the age at the last interview (COGA) or age at the last medical encounter (Indiana Biobank). For COGA EA participants, an additional array indicator was included to account for effects due to the use of different genotyping arrays. Additional covariates included in COGA analyses only were AUD duration, AUD criterion count, maximum drinks in 24 hours, family history of remission, and alcohol treatment history. Covariates were included even if they were not significant, for the purpose of accurately estimating the effects of PGSAUD.

RESULTS

COGA sample

Table 1 summarizes characteristics of participants with lifetime AUD from COGA, separately reporting EA and AA individuals. In COGA EA, a total of 41.09% participants met criteria for 12-month remission, with similar proportions of participants having abstinent and non-abstinent remission. PGSAUD did not differ between individuals with current AUD and participants with 12-month remission, but compared to the current AUD group, the 12-month remission group was more likely to be female and younger, to have a longer AUD duration, higher AUD criterion count and maxdrinks24, a higher prevalence of family history of remission, and to have accessed any treatment, particularly mutual help and both mutual help and professional treatment. Results for the abstinent subgroup were similar except abstinent individuals were comparable in age and sex composition to individuals with current AUD. Interestingly, compared to those with current AUD, non-abstinent individuals had significantly lower PGSAUD, met fewer AUD criteria and had lower maxdrinks24, and were less likely to have accessed any treatment (mostly due to lower endorsement of both professional treatment and mutual help participation).

Table 1:

Descriptive statistics for the samples used in analyses.

COGA EA COGA AA Indiana Biobank liver diseases cohort Indiana Biobank SUD cohort
Remitted Current AUD Remitted Current AUD Remission Current AUD Remission Current AUD
12-month Abstinent Non-abstinent 12-month Abstinent Non-abstinent
N (%) 1,335 (41.09) 681 (20.96) 654 (20.13) 1,914 (58.91) 343 (29.49) 175 (15.05) 168 (14.45) 820 (70.51) 180 (31.30) 395 (68.70) 129 (28.04) 331 (71.96)
Mean PGSAUD (SD) −0.03 (1.03) 0.07 (1.05) −0.13 (1.00)** 0.02 (0.98) 0.06 (0.99) 0.04 (0.98) 0.07 (1.00) 0.13 (0.96) 0.15 (1.03)* −0.07 (0.98) 0.10 (0.06) −0.04 (0.99)
Female (%) 618 (46.29)*** 262 (38.47) 356 (54.43)*** 736 (38.45) 156 (45.88) 82 (46.86) 75 (44.64) 327 (39.88) 59 (32.78)* 171 (43.29) 47 (36.43) 150 (45.32)
Mean age (SD) 29.93 (10.09)*** 32.50 (10.28) 27.26 (9.14)*** 32.85 (12.69) 31.55 (9.36)*** 32.84 (8.51) 30.20 (0.77)*** 34.82 (11.41) 55.40 (10.07)*** 59.26 (13.06) 50.50 (11.16)** 54.46 (13.47)
AUD duration (SD) 15.35 (11.26)*** 17.75 (11.10)*** 12.86 (10.90)** 11.57 (10.74) 13.85 (10.24)*** 15.65 (9.39)*** 11.97 (10.76) 10.94 (10.16) - - - -
AUD criterion count (SD) 6.60 (3.12)*** 8.28 (2.74)*** 4.84 (2.46)*** 5.70 (3.07) 6.52 (3.12)*** 7.80 (2.88)*** 5.19 (2.79) 5.61 (3.00) - - - -
maxdrinks24 (SD) 29.54 (34.91)* 36.18 (43.57)*** 22.63 (20.48)*** 27.14 (18.32) 29.06 (24.59) 34.21 (27.95)** 23.69 (19.17) 27.46 (25.96) - - - -
Family history of remission (%) 968 (72.51)*** 498 (73.13)*** 470 (71.87)*** 1,165 (60.87) 187 (54.52)*** 102 (58.29)*** 85 (50.60)* 313 (38.17) - - - -
Any treatment 682 (51.09)*** 520 (76.36)*** 162 (24.77)*** 714 (37.30) 189 (55.10)** 131 (74.86)*** 58 (34.52)** 377 (45.98) - - - -
Mutual help only (%) 154 (11.54)*** 100 (14.68)*** 54 (8.26) 136 (7.10) 28 (8.16) 14 (8.00)* 14 (8.33) 51 (6.22) - - - -
Professional treatment only (%) 25 (1.87) 12 (1.76) 13 (1.99) 50 (2.61) 8 (2.33) 8 (4.57) 0 34 (4.15) - - - -
Both treatments (%) 503 (37.68)*** 408 (59.91)*** 95 (14.53)*** 528 (27.59) 153 (44.61)** 109 (62.28)*** 44 (26.19)** 292 (35.61) - - - -
Open in a new tab

Note: PGSAUD were standardized. Indiana biobank are EA only. “-“ indicates not available. “*”, “**”, and “***” indicate P-values <0.05, <0.01, and <0.001, respectively when compared to Current AUD. Maxdrinks24: lifetime maximum alcohol consumption during a 24-hour period. For remitted participants, age was the age at the first remission; for current AUD, age was the age at the last interview (COGA) or age at the last medical encounter (Indiana Biobank).

In COGA AA, 29.49% of participants met criteria for 12-month remission, with similar proportions of abstinent and non-abstinent remission. No differences in PGSAUD between individuals with current AUD and those in 12-month, abstinent, and non-abstinent remission were observed, but the 12-month remission group compared to the current AUD group was younger, had a longer AUD duration, a higher AUD criterion count, a higher prevalence of family history of remission, and were more likely to have accessed both professional treatment and mutual help participation. AA abstinent participants compared to those with current AUD had a longer AUD duration, a higher criterion count and maxdrinks24, a higher prevalence of family history of remission, and were more likely to have accessed any treatment, mutual help only, and both professional and mutual help participation. The non-abstinent group compared to the current AUD group was younger, had a higher prevalence of family history of remission, and was equally likely to have accessed mutual help but less likely to have accessed both professional treatment and mutual help participation.

Table 2 summarizes the results of the primary analyses of 12-month remission (COGA) and remission (Indiana Biobank) as the outcomes using the GEE and logistic regression models. In COGA, increasing PGSAUD was negatively associated with remission in EA but was not associated in AA. In both EA and AA, female sex, AUD duration, and both treatments were associated with increased probability of 12-month remission, and older age with decreased probability of remission. In EA only, family history of remission, mutual help only, and utilizing both mutual help and professional treatments were associated with increased probability of 12-month remission. COGA results for abstinent and non-abstinent remission outcomes are presented in Table 3. PGSAUD were associated with decreased probability of non-abstinent remission in EA but had no association with EA abstinent remission or AA non-abstinent or abstinent remission. Family history of remission was associated with increased likelihood of remission in EA and AA abstinent and non-abstinent subgroups. Except for professional treatment only in EA and the non-abstinent subgroup in AA, all other treatments were associated with remission.

Table 2:

Parameter estimates of 12-month remission (COGA) and remission (Indiana Biobank) as the outcomes.

Variable COGA EA COGA AA Indiana Biobank liver diseases cohort Indiana Biobank SUD cohort
beta SE P beta SE P beta SE P beta SE P
PGSAUD −0.10 0.04 0.013 −0.06 0.07 0.36 0.28 0.10 0.004 0.15 0.11 0.17
Sex (female) 0.52 0.08 <.0001 0.44 0.14 0.002 −0.31 0.10 0.002 −0.22 0.11 0.046
Age −0.10 0.01 <.0001 −0.11 0.01 <.0001 −0.03 0.01 <.0001 −0.03 0.01 0.003
AUD duration 0.10 0.01 <.0001 0.09 0.01 <.0001 - - - - - -
AUD criterion count −0.002 0.02 0.92 0.03 0.03 0.39 - - - - - -
maxdrinks24 0.001 0.002 0.73 −0.003 0.003 0.40 - - - - - -
Family history of remission 0.41 0.10 <0.001 0.59 0.17 0.0003 - - - - - -
Mutual help only 0.64 0.15 <.0001 0.54 0.30 0.07 - - - - - -
Professional treatment only −0.09 0.27 0.73 −0.05 0.49 0.92 - - - - - -
Both treatments 0.54 0.15 0.0002 0.45 0.21 0.03 - - - - - -
Open in a new tab

Note: Significant P-values are in bold. All analyses were adjusted for 10 genetic principal components. For COGA EA, array indicators were also included. For sex, male is the reference group. “-“ indicates not available. Maxdrinks24: lifetime maximum alcohol consumption during a 24-hour period.

Table 3.

COGA Parameter estimates for abstinent and non-abstinent vs. current AUD in EA and AA samples.

Variable COGA EA COGA AA
Abstinent vs. Current AUD Non-abstinent vs. Current AUD Abstinent vs. Current AUD Non-abstinent vs. Current AUD
beta SE P beta SE P beta SE P beta SE P
PGSAUD −0.06 0.05 0.22 −0.15 0.05 0.0044 −0.07 0.10 0.50 −0.04 0.09 0.67
Sex (female) 0.40 0.11 0.0002 0.57 0.11 <.0001 0.62 0.20 0.002 0.25 0.18 0.17
Age −0.10 0.01 <.0001 −0.15 0.02 <.0001 −0.15 0.02 <.0001 −0.09 0.02 <.0001
AUD duration 0.09 0.01 <.0001 0.16 0.02 <.0001 0.11 0.02 <.0001 0.09 0.02 <.0001
AUD criterion count 0.13 0.03 <.0001 −0.15 0.03 <.0001 0.12 0.04 0.008 −0.07 0.04 0.07
maxdrinks24 0.003 0.002 0.09 −0.01 0.01 0.25 0.001 0.004 0.83 −0.01 0.005 0.14
Family history of remission 0.31 0.13 0.01 0.49 0.12 <.0001 0.75 0.21 0.0004 0.55 0.20 0.007
Mutual help only 1.27 0.18 <.0001 0.15 0.20 0.47 1.01 0.44 0.02 0.23 0.35 0.52
Professional treatment only 0.31 0.36 0.39 −0.23 0.31 0.46 1.25 0.60 0.04 - - -
Both treatments 1.07 0.18 <.0001 −0.21 0.19 0.25 1.26 0.36 0.0004 −0.27 0.27 0.31
Open in a new tab

Note: Significant P-values are in bold. For COGA AA, no non-abstainers utilized professional treatment only, which caused the GEE model to have no solution; therefore, it was excluded from analysis. Maxdrinks24: lifetime maximum alcohol consumption during a 24-hour period.

Indiana Biobank sample

Descriptive statistics for both Indiana Biobank cohorts are presented in Table 1. 31.30% of liver diseases cohort and 28.04% of SUD cohort participants were determined to have ICD codes denoting remission. In the liver diseases cohort, participants in the remitted group had higher PGSAUD and a lower proportion of females than participants with current AUD. In the SUD cohort, the same trends were observed for PGSAUD and percentage of females, but these differences did not reach significance. In both cohorts, remitted participants were younger than individuals with current AUD. Table 2 displays regression results. PGSAUD were positively associated with remission in the liver diseases cohort but had no association in the SUD cohort. Female sex and older age significantly decreased the probability of remission in both Indiana cohorts.

To explore a potential explanation for the opposite effects of PGSAUD in COGA EA and the Indiana Biobank cohorts, we conducted exploratory analyses. Since liver diseases are likely attributable in large part to chronic alcohol use and are a significant cause of morbidity and mortality, we calculated the prevalence of liver disease in all study samples. As shown in Table 4, 46.22%−78.33% of Indiana Biobank participants reported the presence of liver diseases in contrast to 3.78%−8.01% of COGA participants reported having liver diseases. The prevalence of liver diseases was markedly higher among remitted participants than those with current AUD in both Indiana Biobank cohorts, with a smaller difference between remitted participants and those with current AUD in COGA EA.

Table 4:

Percentages of participants with liver diseases in each study cohort.

COGA EA COGA AA Indiana Biobank liver diseases cohort Indiana Biobank SUD cohort
12-month remission Current AUD 12-monthremission Current AUD remission Current AUD remission Current AUD
N 1,335 1,914 343 820 180 395 129 331
liver disease (%) 107 (8.01)** 106 (5.54) 21 (6.12) 31 (3.78) 141 (78.33)*** 226 (57.22) 78 (60.47)** 153 (46.22)
Open in a new tab

Note: “*”, “**”, and “***” indicate P-values <0.05, <0.01, and <0.001, respectively, when compared to current AUD.

DISCUSSION

In this study, we investigated associations of PGSAUD with the probability of remission among participants with lifetime AUD from four distinct samples: COGA, comprising interviewed participants from (1) EA and (2) AA families with high familial risk for AUD and comparison families, and two cohorts of participants from the Indiana Biobank ascertained for (3) liver diseases or (4) substance use disorders. We hypothesized that higher PGSAUD, which indexes greater genetic liability to AUD and AUD severity, would be associated with decreased likelihood of remission. We found that higher PGSAUD were associated with decreased remission probability for COGA EA 12-month and non-abstinent remission, as expected, but that PGSAUD had the opposite direction of effect in Indiana Biobank liver diseases cohort, where higher PGSAUD were associated with increased probability of remission. No association of PGSAUD with COGA EA abstinent remission, COGA AA remission (12-month, abstinent, or non-abstinent), or Indiana Biobank SUD cohort remission was found.

The negative association of PGSAUD with 12-month and non-abstinent remission in COGA EA is consistent with the previous finding that genetic influences on AUD inhibit the likelihood of remission in young adult female twins (McCutcheon et al., 2012). It seems that the significant association with 12-month remission was driven primarily by non-abstinent remission. Importantly, PGSAUD was associated with reduced probability of non-abstinent remission in EA after adjusting for AUD duration and AUD criterion count, which suggests that PGSAUD and behavioral measures index different aspects of severity. PGSAUD are based on genetic signatures that remain constant over time, but measures of AUD severity require disease progression before they manifest. Therefore, PGSAUD might eventually be useful in combination with early indicators of alcohol misuse, such as binge drinking, to help identify individuals at greater risk for developing alcohol problems for referral to preventive interventions or treatment early in the progression of AUD (Nurnberger et al., 2022, Barr et al., 2020, Miller et al., 2023).

That family history of remission was associated with increased probability of abstinent and non-abstinent remission in COGA EA and AA independent of PGSAUD, which partitioned out genetic influences on AUD, suggests that family history could potentially index genetic and social influences that affect remission separately from AUD. Our previous work found that a family history of abstinent (but not non-abstinent) remission in a first-degree relative was associated with increased probability of remission independent of AUD severity, professional treatment and mutual help participation (McCutcheon et al., 2017). In that study, the first-degree relatives were all probands who had been recruited from AUD treatment, most of whom were abstinent if they were remitted. The current study extends that work with the inclusion of PGSAUD, showing that family history of remission is significant independent of measured genetic influences on AUD, and that it extends to individuals in non-abstinent remission, who have lower severity and PGSAUD on average than individuals in abstinent remission, as well as to abstinent remission. These findings indicate that, just as PGSAUD and family history of AUD are complementary to each other in evaluating the risk for AUD (Lai et al., 2022a), so PGSAUD and family history of remission capture distinct sources of variability in evaluating the probability of remission. Furthermore, PGSAUD might be useful for individuals who are unaware of their family history due to separation from family (e.g., adopted) or family denial of alcohol problems (Schuckit et al., 2020).

PGSAUD had no association with abstinent remission despite the fact that PGSAUD was higher among abstinent individuals than among non-abstinent individuals and those with current AUD. It is possible that there are genetic influences on remission that are not captured in PGSAUD. However, we found that AUD treatment history was associated with abstinent remission in both EA and AA but had no association with non-abstinent remission, a finding opposite that of PGSAUD, which was associated with non-abstinent but not with abstinent remission. This may reflect the fact that 76.36% of abstinent individuals reported a history of treatment but 75.23% of non-abstinent individuals reported no treatment. This is consistent with evidence that remitted individuals who abstain from alcohol use tend to have more severe AUD histories and are more likely to receive treatment (Dawson, 1996, Dawson et al., 2006, Fan et al., 2019b), and with evidence that individuals who are treated for AUD differ substantially from those with AUD who are not treated (Fein and Landman, 2005, Dawson, 1996, Dawson et al., 2006). The different results in abstinent and non-abstinent demonstrate that individuals with less severe AUD, who have lower PGSAUD and/or who have not yet progressed to severe AUD, were less likely to seek treatment and PGSAUD was a significant indicator for future remission, whereas those with severe AUD were more likely to seek treatment and treatment history became the significant indicator for future remission. The treatment rates of greater than 50% in COGA EA and AA samples in this study were higher than the 19.8% rate found among individuals with lifetime AUD in the U.S population (Venegas et al., 2021). This is likely due to the fact that AUD risk family ascertainment was based on recruitment of probands who were in treatment for alcohol dependence but may also reflect the fact that individuals with more severe AUD are more likely to perceive a need for treatment (Pinedo and Villatoro, 2020). Nonetheless, the strong association of treatment with remission provides evidence that treatment is effective even among individuals at highest genetic risk.

At first glance, the findings from the COGA EA and Indiana Biobank liver disease cohort appear to contradict each other. However, post-hoc exploratory analyses showed that most Indiana Biobank participants in both cohorts had liver diseases (Table 4). Of the remitted individuals who had liver diseases, 88.65% and 71.79% of remission in Indiana Biobank liver diseases cohort and SUD occurred subsequent to their liver disease diagnoses (data not shown), suggesting that they were “sick quitters” (Kerr and Ye, 2010). In contrast, ≤8% of COGA participants reported the presence of liver diseases. Additionally, Indiana Biobank participants were older than COGA participants (mean ages at remission or last encounter >54 versus <35 years). Increased age might provide more opportunity to reduce one’s problematic drinking (Leggat et al., 2022, Britton et al., 2015, Knott et al., 2018, Molander et al., 2010), but in a clinical sample, increased age might also reflect a longer history of problematic consumption and an elevated risk of associated medical problems. This may also explain the non-significant findings in Indiana Biobank SUD cohort - they were about 5 years younger than those in Indiana Biobank liver diseases cohort. In Indiana Biobank samples, being female was associated with reduced probability of remission. Possible explanation for this is that more males had liver diseases than females (65.12% and 58.01% affected males in Indiana Biobank liver diseases cohort and SUD cohort). Taken together, it is likely that participants in Indiana Biobank cohorts resemble those having higher PGSAUD who may have experienced difficulties with remission when they were young and relatively healthy, who then reduced or stopped their drinking as serious health conditions emerged in later life. This again highlights the potential use of PGSAUD to identify high-risk individuals before they develop severe AUD and intervene to reduce the incidence of alcohol-related health problems in later life. We note that, unlike COGA, Biobanks lack some variables of interest that limit our ability to evaluate findings in as much depth as is possible in COGA, which includes AUD severity and family history of remission. Biobanks are collections of electronic health records designed for clinical convenience and are commonly used to quickly increase sample sizes to study the genetic and other underpinnings of many diseases, and more effort should be devoted to collect study-related covariates. However, longitudinal and deeply phenotyped research cohorts such as COGA are still critical to study the risk and resilience factors associated with AUD and related conditions. These research cohorts cannot be replaced by Biobanks and their sample sizes, especially those from under-studied populations, should be dramatically increased.

We acknowledge that remission is not a static construct and includes much individual variation in type and length of remission and periods of symptomatic drinking. A limitation of this study is that we did not account for this variation but instead elected to use a simple definition of at least one 12-month period of remission to increase power to detect a genetic association and increase the potential for replication in other samples that may have less detail about alcohol use and AUD. Despite the strength of examining both EA and AA samples, the COGA AA sample size was small, especially for non-abstinent remission. In addition, the sample size of the AA discovery dataset used to calculate AA PGSAUD was much smaller than EA discovery dataset, resulting in sub-optimal performance of AA PGSAUD. Therefore, the non-significant findings of AA PGSAUD could be at least partially due to small sample sizes of COGA AA and the AA discovery dataset. Another limitation is that participants in Indiana Biobank were not ascertained to study AUD and related traits. All data were obtained from EHR, removing the ability to determine remission types, AUD severity, treatment history, and family history of remission, all of which limits our ability to perform detailed analyses. Lastly, to test our hypothesis, we used PGS derived using AUD GWAS as the discovery datasets, which may be different from the remission datasets used in this study.

The variants selected in calculating PGSAUD were based on P values, effect sizes, and linkage disequilibrium from two meta-analyses, after filtering for the same direction of effect. No functional annotations were used due to the limited knowledge of genetic mechanisms contributing to risk for AUD. Furthermore, the performance of PGSAUD depends on the statistical power of the discovery datasets, which are still small and therefore the PGSAUD is an imprecise estimate of genetic liability for AUD. Additionally, it is worth noting that within-cohort variability in the AUD GWAS in MVP-AUD and UKBB-AUDIT-P that may not have accounted for all possible confounders may have influenced effect size estimation in those cohorts. In Both GWAS, sensitivity analyses were conducted to provide assurances that this was not the case. However, it is plausible that any residual effect estimation bias in the individual GWAS may have impacted our PGSAUD analyses in COGA and Indiana Biobank, if, for instance, our regression missed a specifically influential confounder. Environmental influences and psychiatric comorbidities, as well as their interactions with genetic factors are also significant contributors to AUD. Assessing risk for AUDs is therefore complex, with many potential gene-environment interactions that can mitigate the influence of high genetic risk or amplify the influence of low genetic risk. Therefore, PGSAUD alone, absent a broader assessment of risk, could easily be mis-interpreted to determine the need for treatment while ignoring other important factors in the development of AUD (de Hemptinne and Posthuma, 2023). However, while there is the potential to misuse genetic information, we believe that the potential benefits are much greater as it is a common clinical practice to escalate care for those identified to be at greater risk. It is our hope that eventually PGSAUD can be used in combination with behavioral or other assessments and family history to provide timely and effective treatment for those in the early stages of AUD before they progress to severe AUD and related health conditions.

In summary, we showed that PGSAUD was negatively associated with the probability of 12-month and non-abstinent remission even after adjusting for AUD severity, family history of remission, and treatment history in COGA EA. More work is needed to understand the influence of PGSAUD on remission probability, particularly its importance in relation to and interplay with family and treatment history. Although we tested a high-risk familial sample and two cohorts from Indiana Biobank in the current study, more studies, especially studies of under-represented populations such as AA and Hispanics are needed to confirm our findings.

ACKNOWLEDGMENTS

The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, T. Foroud; Scientific Director, A. Agrawal; Translational Director, D. Dick, includes ten different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, T. Foroud, Y. Liu, M.H. Plawecki); University of Iowa Carver College of Medicine (S. Kuperman, J. Kramer); SUNY Downstate Health Sciences University (B. Porjesz, J. Meyers, C. Kamarajan, A. Pandey); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield, D. Dick, R. Hart, J. Salvatore); The Children’s Hospital of Philadelphia, University of Pennsylvania (L. Almasy); Icahn School of Medicine at Mount Sinai (A. Goate, P. Slesinger); and Howard University (D. Scott). Other COGA collaborators include: L. Bauer (University of Connecticut); J. Nurnberger Jr., L. Wetherill, X., Xuei, D. Lai, S. O’Connor, (Indiana University); G. Chan (University of Iowa; University of Connecticut); D.B. Chorlian, J. Zhang, P. Barr, S. Kinreich, G. Pandey (SUNY Downstate); N. Mullins (Icahn School of Medicine at Mount Sinai); A. Anokhin, S. Hartz, E. Johnson, V. McCutcheon, S. Saccone (Washington University); J. Moore, F. Aliev, Z. Pang, S. Kuo (Rutgers University); A. Merikangas (The Children’s Hospital of Philadelphia and University of Pennsylvania); H. Chin and A. Parsian are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting- Kai Li, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA).

This study was made possible, in part, with support from the Indiana Clinical and Translational Sciences Institute funded, in part by Award Number UL1TR002529 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award, and the National Center for Research Resources, Construction grant number RR020128 and the Lilly Endowment. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The authors acknowledge the Indiana University Pervasive Technology Institute for providing [HPC (Big Red II, Karst, Carbonate), visualization, database, storage, or consulting] resources that have contributed to the research results reported within this paper.

Sources of support:

U10AA008401, UL1TR002529, RR020128, Lilly Endowment, R01AA030563.

Footnotes

Conflict of interests

The authors declare no conflict of interests.

References

  1. AMERICAN PSYCHIATRIC ASSOCIATION; 2022. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR™). [Google Scholar]
  2. BARR PB, KSINAN A, SU J, JOHNSON EC, MEYERS JL, WETHERILL L, LATVALA A, ALIEV F, CHAN G, KUPERMAN S, NURNBERGER J, KAMARAJAN C, ANOKHIN A, AGRAWAL A, ROSE RJ, EDENBERG HJ, SCHUCKIT M, KAPRIO J & DICK DM 2020. Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples. Translational Psychiatry, 10, 196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. BOTTLENDER M & SOYKA M 2005. Outpatient alcoholism treatment: predictors of outcome after 3 years. Drug Alcohol Depend, 80, 83–9. [DOI] [PubMed] [Google Scholar]
  4. BRITTON A, BEN-SHLOMO Y, BENZEVAL M, KUH D & BELL S 2015. Life course trajectories of alcohol consumption in the United Kingdom using longitudinal data from nine cohort studies. BMC Med, 13, 47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. BUCHOLZ KK, CADORET R, CLONINGER CR, DINWIDDIE SH, HESSELBROCK VM, NURNBERGER JI JR., REICH T, SCHMIDT I & SCHUCKIT MA 1994. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol, 55, 149–58. [DOI] [PubMed] [Google Scholar]
  6. CHANG CC, CHOW CC, TELLIER LC, VATTIKUTI S, PURCELL SM & LEE JJ 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience, 4, 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. DAS S, FORER L, SCHONHERR S, SIDORE C, LOCKE AE, KWONG A, VRIEZE SI, CHEW EY, LEVY S, MCGUE M, SCHLESSINGER D, STAMBOLIAN D, LOH PR, IACONO WG, SWAROOP A, SCOTT LJ, CUCCA F, KRONENBERG F, BOEHNKE M, ABECASIS GR & FUCHSBERGER C 2016. Next-generation genotype imputation service and methods. Nat Genet, 48, 1284–1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. DAWSON DA 1996. Correlates of past-year status among treated and untreated persons with former alcohol dependence: United States, 1992. Alcohol Clin Exp Res, 20, 771–9. [DOI] [PubMed] [Google Scholar]
  9. DAWSON DA, GOLDSTEIN RB & GRANT BF 2007. Rates and correlates of relapse among individuals in remission from DSM-IV alcohol dependence: a 3-year follow-up. Alcohol Clin Exp Res, 31, 2036–45. [DOI] [PubMed] [Google Scholar]
  10. DAWSON DA, GOLDSTEIN RB, RUAN WJ & GRANT BF 2012. Correlates of Recovery from Alcohol Dependence: A Prospective Study Over a 3-Year Follow-Up Interval. Alcohol: Clinical and Experimental Research, 36, 1268–1277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. DAWSON DA, GRANT BF, STINSON FS & CHOU PS 2006. Estimating the effect of help-seeking on achieving recovery from alcohol dependence. Addiction, 101, 824–34. [DOI] [PubMed] [Google Scholar]
  12. DAWSON DA, GRANT BF, STINSON FS, CHOU PS, HUANG B & RUAN WJ 2005. Recovery from DSM-IV alcohol dependence: United States, 2001–2002. Addiction, 100, 281–92. [DOI] [PubMed] [Google Scholar]
  13. DAWSON DA, HARFORD TC & GRANT BF 1992. Family history as a predictor of alcohol dependence. Alcohol Clin Exp Res, 16, 572–5. [DOI] [PubMed] [Google Scholar]
  14. DE HEMPTINNE MC & POSTHUMA D 2023. Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits. Nature Neuroscience, 26, 932–941. [DOI] [PubMed] [Google Scholar]
  15. DELANEAU O, ZAGURY JF & MARCHINI J 2013. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods, 10, 5–6. [DOI] [PubMed] [Google Scholar]
  16. DICK DM, BALCKE E, MCCUTCHEON V, FRANCIS M, KUO S, SALVATORE J, MEYERS J, BIERUT LJ, SCHUCKIT M, HESSELBROCK V, EDENBERG HJ, PORJESZ B, KUPERMAN S, KRAMER J & BUCHOLZ K 2023. The collaborative study on the genetics of alcoholism: Sample and clinical data. Genes Brain Behav, 22, e12860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. FAN AZ, CHOU SP, ZHANG H, JUNG J & GRANT BF 2019a. Prevalence and Correlates of Past-Year Recovery From DSM-5 Alcohol Use Disorder: Results From National Epidemiologic Survey on Alcohol and Related Conditions-III. Alcohol Clin Exp Res, 43, 2406–2420. [DOI] [PubMed] [Google Scholar]
  18. FAN AZ, CHOU SP, ZHANG H, JUNG J & GRANT BF 2019b. Prevalence and Correlates of Past-Year Recovery From DSM-5 Alcohol Use Disorder: Results From National Epidemiologic Survey on Alcohol and Related Conditions-III. Alcohol Clin Exp Res. [DOI] [PubMed] [Google Scholar]
  19. FEIN G & LANDMAN B 2005. Treated and treatment-naive alcoholics come from different populations. Alcohol, 36, 19–26. [PMC free article] [PubMed] [Google Scholar]
  20. FLEURY MJ, DJOUINI A, HUỲNH C, TREMBLAY J, FERLAND F, MÉNARD JM & BELLEVILLE G 2016. Remission from substance use disorders: A systematic review and meta-analysis. Drug and Alcohol Dependence, 168, 293–306. [DOI] [PubMed] [Google Scholar]
  21. GE T, CHEN CY, NI Y, FENG YA & SMOLLER JW 2019. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun, 10, 1776. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. GILDER DA, LAU P, COREY L & EHLERS CL 2008. Factors associated with remission from alcohol dependence in an American Indian community group. Am J Psychiatry, 165, 1172–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. GRANT BF, GOLDSTEIN RB, SAHA TD, CHOU SP, JUNG J, ZHANG H, PICKERING RP, RUAN WJ, SMITH SM, HUANG B & HASIN DS 2015. Epidemiology of DSM-5 Alcohol Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry, 72, 757–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. GREENFIELD TK, KARRIKER-JAFFE KJ, KAPLAN LM, KERR WC & WILSNACK SC 2015. Trends in Alcohol’s Harms to Others (AHTO) and Co-occurrence of Family-Related AHTO: The Four US National Alcohol Surveys, 2000–2015. Subst Abuse, 9, 23–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. HEATH AC, BUCHOLZ KK, MADDEN PA, DINWIDDIE SH, SLUTSKE WS, BIERUT LJ, STATHAM DJ, DUNNE MP, WHITFIELD JB & MARTIN NG 1997. Genetic and environmental contributions to alcohol dependence risk in a national twin sample: consistency of findings in women and men. Psychol Med, 27, 1381–96. [DOI] [PubMed] [Google Scholar]
  26. HESSELBROCK M, EASTON C, BUCHOLZ KK, SCHUCKIT M & HESSELBROCK V 1999. A validity study of the SSAGA--a comparison with the SCAN. Addiction, 94, 1361–70. [DOI] [PubMed] [Google Scholar]
  27. JOHNSON EC, SALVATORE JE, LAI D, MERIKANGAS AK, NURNBERGER JI, TISCHFIELD JA, XUEI X, KAMARAJAN C, WETHERILL L, COLLABORATORS C, RICE JP, KRAMER JR, KUPERMAN S, FOROUD T, SLESINGER PA, GOATE AM, PORJESZ B, DICK DM, EDENBERG HJ & AGRAWAL A 2023. The collaborative study on the genetics of alcoholism: Genetics. Genes, Brain and Behavior, n/a, e12856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. KARRIKER-JAFFE KJ, CHARTIER KG, BARES CB, KENDLER KS & GREENFIELD TK 2021. Intersection of familial risk and environmental social control on high-risk drinking and alcohol dependence in a US national sample of adults. Addict Behav, 113, 106668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. KARRIKER-JAFFE KJ, LI L & GREENFIELD TK 2018. Estimating mental health impacts of alcohol’s harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States. Addiction, 113, 1826–1839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. KERR WC & YE Y 2010. Relationship of Life-Course Drinking Patterns to Diabetes, Heart Problems, and Hypertension Among Those 40 and Older in the 2005 US National Alcohol Survey. Journal of Studies on Alcohol and Drugs, 71, 515–525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. KHERA AV, CHAFFIN M, ARAGAM KG, HAAS ME, ROSELLI C, CHOI SH, NATARAJAN P, LANDER ES, LUBITZ SA, ELLINOR PT & KATHIRESAN S 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet, 50, 1219–1224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. KINREICH S, MCCUTCHEON VV, ALIEV F, MEYERS JL, KAMARAJAN C, PANDEY AK, CHORLIAN DB, ZHANG J, KUANG W, PANDEY G, VITERI SS-SD, FRANCIS MW, CHAN G, BOURDON JL, DICK DM, ANOKHIN AP, BAUER L, HESSELBROCK V, SCHUCKIT MA, NURNBERGER JI, FOROUD TM, SALVATORE JE, BUCHOLZ KK & PORJESZ B 2021. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Translational Psychiatry, 11, 166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. KNOP J, PENICK EC, NICKEL EJ, MEDNICK SA, JENSEN P, MANZARDO AM & GABRIELLI WF 2007. Paternal alcoholism predicts the occurrence but not the remission of alcoholic drinking: a 40-year follow-up. Acta Psychiatr Scand, 116, 386–93. [DOI] [PubMed] [Google Scholar]
  34. KNOTT CS, BELL S & BRITTON A 2018. The stability of baseline-defined categories of alcohol consumption during the adult life-course: a 28-year prospective cohort study. Addiction, 113, 34–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. KRANZLER HR, ZHOU H, KEMBER RL, VICKERS SMITH R, JUSTICE AC, DAMRAUER S, TSAO PS, KLARIN D, BARAS A, REID J, OVERTON J, RADER DJ, CHENG Z, TATE JP, BECKER WC, CONCATO J, XU K, POLIMANTI R, ZHAO H & GELERNTER J 2019. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications, 10, 1499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. LAI D, JOHNSON EC, COLBERT S, PANDEY G, CHAN G, BAUER L, FRANCIS MW, HESSELBROCK V, KAMARAJAN C, KRAMER J, KUANG W, KUO S, KUPERMAN S, LIU Y, MCCUTCHEON V, PANG Z, PLAWECKI MH, SCHUCKIT M, TISCHFIELD J, WETHERILL L, ZANG Y, EDENBERG HJ, PORJESZ B, AGRAWAL A & FOROUD T 2022a. Evaluating risk for alcohol use disorder: Polygenic risk scores and family history. Alcohol Clin Exp Res, 46, 374–383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. LAI D, SCHWANTES-AN TH, ABREU M, CHAN G, HESSELBROCK V, KAMARAJAN C, LIU Y, MEYERS JL, NURNBERGER JI JR., PLAWECKI MH, WETHERILL L, SCHUCKIT M, ZHANG P, EDENBERG HJ, PORJESZ B, AGRAWAL A & FOROUD T 2022b. Gene-based polygenic risk scores analysis of alcohol use disorder in African Americans. Transl Psychiatry, 12, 266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. LAI D, WETHERILL L, BERTELSEN S, CAREY CE, KAMARAJAN C, KAPOOR M, MEYERS JL, ANOKHIN AP, BENNETT DA, BUCHOLZ KK, CHANG KK, DE JAGER PL, DICK DM, HESSELBROCK V, KRAMER J, KUPERMAN S, NURNBERGER JI JR., RAJ T, SCHUCKIT M, SCOTT DM, TAYLOR RE, TISCHFIELD J, HARIRI AR, EDENBERG HJ, AGRAWAL A, BOGDAN R, PORJESZ B, GOATE AM & FOROUD T 2019a. Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria. Genes Brain Behav, 18, e12579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. LAI D, WETHERILL L, KAPOOR M, JOHNSON EC, SCHWANDT M, RAMCHANDANI VA, GOLDMAN D, JOSLYN G, RAO X, LIU Y, FARRIS S, MAYFIELD RD, DICK D, HESSELBROCK V, KRAMER J, MCCUTCHEON VV, NURNBERGER J, TISCHFIELD J, GOATE A, EDENBERG HJ, PORJESZ B, AGRAWAL A, FOROUD T & SCHUCKIT M 2019b. Genome-wide association studies of the self-rating of effects of ethanol (SRE). Addict Biol, e12800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. LEE MR, BONESS CL, MCDOWELL YE, VERGÉS A, STEINLEY DL & SHER KJ 2018. Desistance and Severity of Alcohol Use Disorder: A Lifespan-Developmental Investigation. Clin Psychol Sci, 6, 90–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. LEGGAT G, LIVINGSTON M, KUNTSCHE S & CALLINAN S 2022. Alcohol consumption trajectories over the Australian life course. Addiction, 117, 1931–1939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. LOPEZ-QUINTERO C, HASIN DS, DE LOS COBOS JP, PINES A, WANG S, GRANT BF & BLANCO C 2011. Probability and predictors of remission from life-time nicotine, alcohol, cannabis or cocaine dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Addiction, 106, 657–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. MCCUTCHEON VV, GRANT JD, HEATH AC, BUCHOLZ KK, SARTOR CE, NELSON EC, MADDEN PA & MARTIN NG 2012. Environmental influences predominate in remission from alcohol use disorder in young adult twins. Psychol Med, 42, 2421–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. MCCUTCHEON VV, SCHUCKIT MA, KRAMER JR, CHAN G, EDENBERG HJ, SMITH TL, BENDER AK, HESSELBROCK V, HESSELBROCK M & BUCHOLZ KK 2017. Familial association of abstinent remission from alcohol use disorder in first-degree relatives of alcohol-dependent treatment-seeking probands. Addiction, 112, 1909–1917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. MILLER AP, KUO SI-C, JOHNSON EC, TILLMAN R, BRISLIN SJ, DICK DM, KAMARAJAN C, KINREICH S, KRAMER J, MCCUTCHEON VV, PLAWECKI MH, PORJESZ B, SCHUCKIT MA, SALVATORE JE, EDENBERG HJ, BUCHOLZ KK, MEYERS JL & AGRAWAL A 2023. Specific diagnostic criteria identify those at high risk for progression from ‘preaddiction’ to severe alcohol use disorder. medRxiv, 2023.06.12.23291164. [Google Scholar]
  46. MOLANDER RC, YONKER JA & KRAHN DD 2010. Age-related changes in drinking patterns from mid- to older age: results from the Wisconsin longitudinal study. Alcohol Clin Exp Res, 34, 1182–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. NAVARRO HJ, DORAN CM & SHAKESHAFT AP 2011. Measuring costs of alcohol harm to others: a review of the literature. Drug Alcohol Depend, 114, 87–99. [DOI] [PubMed] [Google Scholar]
  48. NAYAK MB, PATTERSON D, WILSNACK SC, KARRIKER-JAFFE KJ & GREENFIELD TK 2019. Alcohol’s Secondhand Harms in the United States: New Data on Prevalence and Risk Factors. J Stud Alcohol Drugs, 80, 273–281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. NURNBERGER JI JR., WANG Y, ZANG Y, LAI D, WETHERILL L, EDENBERG HJ, ALIEV F, PLAWECKI MH, CHORLIAN D, CHAN G, BUCHOLZ K, BAUER L, KAMARAJAN C, SALVATORE JE, KAPOOR M, HESSELBROCK V, DICK D, BIERUT L, MCCUTCHEON V, MEYERS JL, PORJESZ B, KRAMER J, KUPERMAN S, KINREICH S, ANOKHIN AP & COLLABORATIVE STUDY ON THE GENETICS OF, A. 2022. High Polygenic Risk Scores Are Associated With Early Age of Onset of Alcohol Use Disorder in Adolescents and Young Adults at Risk. Biol Psychiatry Glob Open Sci, 2, 379–388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. NURNBERGER JI JR., WIEGAND R, BUCHOLZ K, O’CONNOR S, MEYER ET, REICH T, RICE J, SCHUCKIT M, KING L, PETTI T, BIERUT L, HINRICHS AL, KUPERMAN S, HESSELBROCK V & PORJESZ B 2004. A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands. Arch Gen Psychiatry, 61, 1246–56. [DOI] [PubMed] [Google Scholar]
  51. PENICK EC, KNOP J, NICKEL EJ, JENSEN P, MANZARDO AM, LYKKE-MORTENSEN E & GABRIELLI WF 2010. Do premorbid predictors of alcohol dependence also predict the failure to recover from alcoholism? J Stud Alcohol Drugs, 71, 685–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. PINEDO M & VILLATORO AP 2020. The role of perceived treatment need in explaining racial/ethnic disparities in the use of substance abuse treatment services. Journal of Substance Abuse Treatment, 118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. PRESCOTT CA & KENDLER KS 1999. Genetic and environmental contributions to alcohol abuse and dependence in a population-based sample of male twins. Am J Psychiatry, 156, 34–40. [DOI] [PubMed] [Google Scholar]
  54. PRICE AL, PATTERSON NJ, PLENGE RM, WEINBLATT ME, SHADICK NA & REICH D 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 38, 904–9. [DOI] [PubMed] [Google Scholar]
  55. PURCELL S, NEALE B, TODD-BROWN K, THOMAS L, FERREIRA MA, BENDER D, MALLER J, SKLAR P, DE BAKKER PI, DALY MJ & SHAM PC 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 81, 559–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. RUAN Y, LIN Y-F, FENG Y-CA, CHEN C-Y, LAM M, GUO Z, AHN YM, AKIYAMA K, ARAI M, BAEK JH, CHEN WJ, CHUNG Y-C, FENG G, FUJII K, GLATT SJ, HA K, HATTORI K, HIGUCHI T, HISHIMOTO A, HONG KS, HORIUCHI Y, HWU H-G, IKEDA M, ISHIWATA S, ITOKAWA M, IWATA N, JOO E-J, KAHN RS, KIM S-W, KIM SJ, KIM SH, KINOSHITA M, KUNUGI H, KUSUMAWARDHANI A, LEE J, LEE BD, LEE H-J, LIU J, LIU R, MA X, MYUNG W, NUMATA S, OHMORI T, OTSUKA I, OZEKI Y, SCHWAB SG, SHI W, SHIMODA K, SIM K, SORA I, TANG J, TOYOTA T, TSUANG M, WILDENAUER DB, WON H-H, YOSHIKAWA T, ZHENG A, ZHU F, HE L, SAWA A, MARTIN AR, QIN S, HUANG H, GE T & STANLEY GLOBAL ASIA I 2022. Improving polygenic prediction in ancestrally diverse populations. Nature Genetics, 54, 573–580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. SACKS JJ, GONZALES KR, BOUCHERY EE, TOMEDI LE & BREWER RD 2015. 2010 National and State Costs of Excessive Alcohol Consumption. Am J Prev Med, 49, e73–e79. [DOI] [PubMed] [Google Scholar]
  58. SANCHEZ-ROIGE S, PALMER AA, FONTANILLAS P, ELSON SL, ADAMS MJ, HOWARD DM, EDENBERG HJ, DAVIES G, CRIST RC, DEARY IJ, MCINTOSH AM, CLARKE TK, AGEE M, ALIPANAHI B, AUTON A, BELL RK, BRYC K, FURLOTTE NA, HINDS DA, HUBER KE, KLEINMAN A, LITTERMAN NK, MCCREIGHT JC, MCINTYRE MH, MOUNTAIN JL, NOBLIN ES, NORTHOVER CAM, PITTS SJ, SATHIRAPONGSASUTI JF, SAZONOVA OV, SHELTON JF, SHRINGARPURE S, TIAN C, TUNG JY, VACIC V, WILSON CH & TEAM AR 2019. Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts. American Journal of Psychiatry, 176, 107–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. SCHUCKIT MA, CLARKE DF, SMITH TL & MENDOZA LA 2020. Characteristics associated with denial of problem drinking among two generations of individuals with alcohol use disorders. Drug and Alcohol Dependence, 217, 108274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. VENEGAS A, DONATO S, MEREDITH LR & RAY LA 2021. Understanding low treatment seeking rates for alcohol use disorder: A narrative review of the literature and opportunities for improvement. The American Journal of Drug and Alcohol Abuse, 47, 664–679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. VERHULST B, NEALE MC & KENDLER KS 2015. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychological Medicine, 45, 1061–1072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. ZHOU H, SEALOCK JM, SANCHEZ-ROIGE S, CLARKE T-K, LEVEY DF, CHENG Z, LI B, POLIMANTI R, KEMBER RL, SMITH RV, THYGESEN JH, MORGAN MY, ATKINSON SR, THURSZ MR, NYEGAARD M, MATTHEISEN M, BØRGLUM AD, JOHNSON EC, JUSTICE AC, PALMER AA, MCQUILLIN A, DAVIS LK, EDENBERG HJ, AGRAWAL A, KRANZLER HR & GELERNTER J 2020. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nature Neuroscience. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES