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Kendler KS, Lönn SL, Sundquist J, Sundquist K. The joint effects of genetic liability and the death of close relatives on risk for major depression and alcohol use disorder in a Swedish national sample. Psychol Med 2024; 54:1709-1716. [PMID: 38173119 DOI: 10.1017/s0033291723003641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND To determine whether genetic risk factors for major depression (MD) and alcohol use disorder (AUD) interact with a potent stressor - death of spouse, parent, and sibling - in predicting episodes of, respectively, MD and AUD. METHODS MD and AUD registrations were assessed from national Swedish registries. In individuals born in Sweden 1960-1970, we identified 7586, 388 459, and 34 370 with the loss of, respectively, a spouse, parent, and sibling. We started following subjects at age 18 or the year 2002 with end of follow-up in 2018. We examined time to event - a registration for MD within 6 months or AUD within a year - on an additive scale, using the Nelson-Aalen estimator. Genetic risk was assessed by the Family Genetic Risk Score (FGRS). RESULTS In separate models controlling for the main effects of death of spouse, parent, and sibling, FGRS, and sex, significant interactions were seen in all analyses between genetic risk for MD and death of relative in prediction of subsequent MD registration. A similar pattern of results, albeit with weaker interaction effects, was seen for genetic risk for AUD and risk for AUD registration. Genetic risk for bipolar disorder (BD) and anxiety disorders (AD) also interacted with event exposure in predicting MD. CONCLUSIONS Genetic risk for both MD and AUD act in part by increasing the sensitivity of individuals to the pathogenic effects of environmental stressors. For prediction of MD, similar effects are also seen for genetic risk for AD and BD.
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Affiliation(s)
- Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Sara L Lönn
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Family Medicine and Community Health, Department of Population Health, Lund University, Malmö, Sweden
| | - Kristina Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Family Medicine and Community Health, Department of Population Health, Lund University, Malmö, Sweden
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2
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Bountress KE, Cusack SE, Hawn SE, Grotzinger A, Bustamante D, Kirkpatrick RM, Edenberg HJ, Amstadter AB. Genetic associations between alcohol phenotypes and life satisfaction: a genomic structural equation modelling approach. Sci Rep 2023; 13:13443. [PMID: 37596344 PMCID: PMC10439217 DOI: 10.1038/s41598-023-40199-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 08/07/2023] [Indexed: 08/20/2023] Open
Abstract
Alcohol use (i.e., quantity, frequency) and alcohol use disorder (AUD) are common, associated with adverse outcomes, and genetically-influenced. Genome-wide association studies (GWAS) identified genetic loci associated with both. AUD is positively genetically associated with psychopathology, while alcohol use (e.g., drinks per week) is negatively associated or NS related to psychopathology. We wanted to test if these genetic associations extended to life satisfaction, as there is an interest in understanding the associations between psychopathology-related traits and constructs that are not just the absence of psychopathology, but positive outcomes (e.g., well-being variables). Thus, we used Genomic Structural Equation Modeling (gSEM) to analyze summary-level genomic data (i.e., effects of genetic variants on constructs of interest) from large-scale GWAS of European ancestry individuals. Results suggest that the best-fitting model is a Bifactor Model, in which unique alcohol use, unique AUD, and common alcohol factors are extracted. The genetic correlation (rg) between life satisfaction-AUD specific factor was near zero, the rg with the alcohol use specific factor was positive and significant, and the rg with the common alcohol factor was negative and significant. Findings indicate that life satisfaction shares genetic etiology with typical alcohol use and life dissatisfaction shares genetic etiology with heavy alcohol use.
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Affiliation(s)
- Kaitlin E Bountress
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St. Biotech One Suite 101, Richmond, VA, 23219, USA.
| | - Shannon E Cusack
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St. Biotech One Suite 101, Richmond, VA, 23219, USA
| | - Sage E Hawn
- Department of Psychology, Old Dominion University, Norfolk, USA
| | - Andrew Grotzinger
- Institute for Behavior Genetics, Behavioral, Psychiatric, and Statistical Genetics, University of Colorado Boulder, Boulder, USA
| | - Daniel Bustamante
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St. Biotech One Suite 101, Richmond, VA, 23219, USA
| | - Robert M Kirkpatrick
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St. Biotech One Suite 101, Richmond, VA, 23219, USA
| | | | - Ananda B Amstadter
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 800 E. Leigh St. Biotech One Suite 101, Richmond, VA, 23219, USA
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3
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Cusack SE, Aliev F, Bustamante D, Dick DM, Amstadter AB. A statistical genetic investigation of psychiatric resilience. Eur J Psychotraumatol 2023; 14:2178762. [PMID: 37052082 PMCID: PMC9987782 DOI: 10.1080/20008066.2023.2178762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/28/2023] [Indexed: 03/06/2023] Open
Abstract
Background: Although trauma exposure (TE) is a transdiagnostic risk factor for many psychiatric disorders, not everyone who experiences TE develops a psychiatric disorder. Resilience may explain this heterogeneity; thus, it is critical to understand the etiologic underpinnings of resilience.Objective: The present study sought to examine the genetic underpinnings of psychiatric resilience using genome-wide association studies (GWAS), genome-wide complex trait analysis (GCTA), and polygenic risk score (PRS) analyses.Method: Participants were 6,634 trauma exposed college students attending a diverse, public university in the Mid Atlantic. GWAS and GCTA analyses were conducted, and using GWAS summary statistics from large genetic consortia, PRS analyses examined the shared genetic risk between resilience and various phenotypes.Results: Results demonstrate that nine single-nucleotide polymorphisms (SNPs) met the suggestive of significance threshold, heritability estimates for resilience were non-significant, and that there is genetic overlap between resilience and AD, as well as resilience and PTSD.Conclusion: Mixed findings from the present study suggest additional research to elucidate the etiological underpinnings of resilience, ideally with larger samples less biased by variables such as heterogeneity (i.e. clinical vs. population based) and population stratification. Genetic investigations of resilience have the potential to elucidate the molecular bases of stress-related psychopathology, suggesting new avenues for prevention and intervention efforts.
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Affiliation(s)
- Shannon E. Cusack
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Fazil Aliev
- Department of African American Studies, Virginia Commonwealth University, Richmond, VA, USA
| | - Daniel Bustamante
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | | | - Danielle M. Dick
- Brain Health Institute, Rutgers Biomedical and Health Sciences, Rutgers University, Piscataway, NJ, USA
| | - Ananda B. Amstadter
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
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4
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Kandaswamy R, Allegrini A, Nancarrow AF, Cave SN, Plomin R, von Stumm S. Predicting Alcohol Use From Genome-Wide Polygenic Scores, Environmental Factors, and Their Interactions in Young Adulthood. Psychosom Med 2022; 84:244-250. [PMID: 34469941 DOI: 10.1097/psy.0000000000001005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Alcohol use during emerging adulthood is associated with adverse life outcomes, but its risk factors are not well known. Here, we predicted alcohol use in 3153 young adults aged 22 years from a) genome-wide polygenic scores (GPS) based on genome-wide association studies for the target phenotypes number of drinks per week and Alcohol Use Disorders Identification Test scores, b) 30 environmental factors, and c) their interactions (i.e., G × E effects). METHODS Data were collected from 1994 to 2018 as a part of the UK Twins Early Development Study. RESULTS GPS accounted for up to 1.9% of the variance in alcohol use (i.e., Alcohol Use Disorders Identification Test score), whereas the 30 measures of environmental factors together accounted for 21.1%. The 30 GPS by environment interactions did not explain any additional variance, and none of the interaction terms exceeded the significance threshold after correcting for multiple testing. CONCLUSIONS GPS and some environmental factors significantly predicted alcohol use in young adulthood, but we observed no GPS by environment interactions in our study.
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Affiliation(s)
- Radhika Kandaswamy
- From the Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience (Kandaswamy, Allegrini, Plomin), King's College London, London; Department of Education (Nancarrow, von Stumm), University of York, Heslington, York, United Kingdom; and School of Psychology (Cave), University of Nottingham, Nottingham, United Kingdom
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5
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Ma Y, Gu J, Lv R. Job Satisfaction and Alcohol Consumption: Empirical Evidence from China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:933. [PMID: 35055752 PMCID: PMC8775457 DOI: 10.3390/ijerph19020933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/31/2021] [Accepted: 01/13/2022] [Indexed: 12/10/2022]
Abstract
Despite growing attention to job satisfaction as a social determinant of alcohol-related behaviors, few studies focus on its diverse impacts on alcohol consumption. Using data from the China Family Panel Study in 2018, this study uses logistic regression analysis to examine how job satisfaction affects alcohol consumption in China, finding that people who were satisfied with their jobs were more likely to be regularly drinking. Employed people who were satisfied with their working environment and working hours were more likely to regularly drink, but those who were satisfied with their wages and working security were less likely to be regularly drinking. Findings suggest that the link between job satisfaction and alcohol consumption is dynamic. Employment policies, working wellbeing improvement programs, and alcohol policy improvement should, therefore, be designed on the basis of a comprehensive account of entire job-related attitudes.
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Affiliation(s)
- Yuna Ma
- Department of Social Work, School of Social Work, China Youth University for Political Sciences, Beijing 100091, China;
| | - Jiafeng Gu
- Institute of Social Science Survey, Peking University, Beijing 100871, China
| | - Ruixi Lv
- Department of Applied Mathematics, School of Science, Nanjing Forestry University, Nanjing 210042, China;
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6
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Elam KK, Ha T, Neale Z, Aliev F, Dick D, Lemery-Chalfant K. Age varying polygenic effects on alcohol use in African Americans and European Americans from adolescence to adulthood. Sci Rep 2021; 11:22425. [PMID: 34789846 PMCID: PMC8599703 DOI: 10.1038/s41598-021-01923-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 11/08/2021] [Indexed: 01/06/2023] Open
Abstract
Genetic effects on alcohol use can vary over time but are often examined using longitudinal models that predict a distal outcome at a single time point. The vast majority of these studies predominately examine effects using White, European American (EA) samples or examine the etiology of genetic variants identified from EA samples in other racial/ethnic populations, leading to inconclusive findings about genetic effects on alcohol use. The current study examined how genetic influences on alcohol use varied by age across a 15 year period within a diverse ethnic/racial sample of adolescents. Using a multi-ethnic approach, polygenic risk scores were created for African American (AA, n = 192) and EA samples (n = 271) based on racially/ethnically aligned genome wide association studies. Age-varying associations between polygenic scores and alcohol use were examined from age 16 to 30 using time-varying effect models separately for AA and EA samples. Polygenic risk for alcohol use was found to be associated with alcohol use from age 22-27 in the AA sample and from age 24.50 to 29 in the EA sample. Results are discussed relative to the intersection of alcohol use and developmental genetic effects in diverse populations.
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Affiliation(s)
- Kit K Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7th St., Suite 116, Bloomington, IN, 47405, USA.
| | - Thao Ha
- Department of Psychology, Arizona State University, Tempe, USA
| | - Zoe Neale
- Department of Psychology, Virgina Commonwealth University, Richmond, USA
| | - Fazil Aliev
- Department of Psychology, Virgina Commonwealth University, Richmond, USA
| | - Danielle Dick
- Department of Psychology, Virgina Commonwealth University, Richmond, USA
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7
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Johnson EC, Sanchez-Roige S, Acion L, Adams MJ, Bucholz KK, Chan G, Chao MJ, Chorlian DB, Dick DM, Edenberg HJ, Foroud T, Hayward C, Heron J, Hesselbrock V, Hickman M, Kendler KS, Kinreich S, Kramer J, Kuo SIC, Kuperman S, Lai D, McIntosh AM, Meyers JL, Plawecki MH, Porjesz B, Porteous D, Schuckit MA, Su J, Zang Y, Palmer AA, Agrawal A, Clarke TK, Edwards AC. Polygenic contributions to alcohol use and alcohol use disorders across population-based and clinically ascertained samples. Psychol Med 2021; 51:1147-1156. [PMID: 31955720 PMCID: PMC7405725 DOI: 10.1017/s0033291719004045] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds. METHODS We examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes. RESULTS In COGA, AUDIT-P PRS was associated with alcohol dependence, AUD symptom count, maximum drinks (R2 = 0.47-0.68%, p = 2.0 × 10-8-1.0 × 10-10), and increased likelihood of onset of alcohol dependence (hazard ratio = 1.15, p = 4.7 × 10-8); AUDIT-C PRS was not an independent predictor of any phenotype. In ALSPAC, the AUDIT-C PRS was associated with alcohol dependence (R2 = 0.96%, p = 4.8 × 10-6). In GS, AUDIT-C PRS was a better predictor of weekly alcohol use (R2 = 0.27%, p = 5.5 × 10-11), while AUDIT-P PRS was more associated with problem drinking (R2 = 0.40%, p = 9.0 × 10-7). Lastly, AUDIT-P PRS was associated with ICD-based alcohol-related disorders in the UKB subset (R2 = 0.18%, p < 2.0 × 10-16). CONCLUSIONS AUDIT-P PRS was associated with a range of alcohol-related phenotypes across population-based and ascertained cohorts, while AUDIT-C PRS showed less utility in the ascertained cohort. We show that AUDIT-P is genetically correlated with both use and misuse and demonstrate the influence of ascertainment schemes on PRS analyses.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Laura Acion
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Michael J Chao
- Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, USA
| | - David B Chorlian
- Department of Psychiatry, Suny Downstate Medical Center, Brooklyn, NY, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Jon Heron
- University of Bristol, Bristol Medical School, Population Health Sciences, Bristol, UK
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Matthew Hickman
- University of Bristol, Bristol Medical School, Population Health Sciences, Bristol, UK
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - Sivan Kinreich
- Department of Psychiatry, Suny Downstate Medical Center, Brooklyn, NY, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
| | - Sally I-Chun Kuo
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Jacquelyn L Meyers
- Department of Psychiatry, Suny Downstate Medical Center, Brooklyn, NY, USA
| | - Martin H Plawecki
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry, Suny Downstate Medical Center, Brooklyn, NY, USA
| | - David Porteous
- University of Edinburgh, Institute of Genetics & Molecular Medicine, Centre for Genomic and Experimental Medicine, Edinburgh, UK
| | - Marc A Schuckit
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Jinni Su
- Department of Psychology, Arizona State University, Tempe, AZ, USA
| | - Yong Zang
- Department of Biostatistics, Indiana University School of Medicine, Bloomington, IN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- University of California San Diego, Institute for Genomic Medicine, San Diego, CA, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Alexis C Edwards
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
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Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data. Behav Genet 2021; 51:358-373. [PMID: 33899139 DOI: 10.1007/s10519-021-10058-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 04/09/2021] [Indexed: 12/30/2022]
Abstract
Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.
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9
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Kinreich S, Meyers JL, Maron-Katz A, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Pandey G, Subbie-Saenz de Viteri S, Pitti D, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Edenberg HJ, Porjesz B. Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry 2021; 26:1133-1141. [PMID: 31595034 PMCID: PMC7138692 DOI: 10.1038/s41380-019-0534-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/11/2019] [Accepted: 09/20/2019] [Indexed: 11/09/2022]
Abstract
Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.
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Affiliation(s)
- Sivan Kinreich
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA.
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Chella Kamarajan
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Ashwini K Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - David B Chorlian
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Jian Zhang
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | | | - Dan Pitti
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
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Kinreich S, McCutcheon VV, Aliev F, Meyers JL, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Kuang W, Pandey G, Viteri SSSD, 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. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Transl Psychiatry 2021; 11:166. [PMID: 33723218 PMCID: PMC7960734 DOI: 10.1038/s41398-021-01281-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 12/02/2022] Open
Abstract
Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
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Affiliation(s)
- Sivan Kinreich
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA.
| | - Vivia V McCutcheon
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Fazil Aliev
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
- Faculty of Business, Karabuk University, Karabük, Turkey
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Chella Kamarajan
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Ashwini K Pandey
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - David B Chorlian
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Jian Zhang
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Weipeng Kuang
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | | | - Meredith W Francis
- Brown School of Social Work / Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Jessica L Bourdon
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Danielle M Dick
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - John I Nurnberger
- Departments of Psychiatry and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics at Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jessica E Salvatore
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
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11
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Kandaswamy R, Allegrini A, Plomin R, Stumm SV. Predictive validity of genome-wide polygenic scores for alcohol use from adolescence to young adulthood. Drug Alcohol Depend 2021; 219:108480. [PMID: 33388637 DOI: 10.1016/j.drugalcdep.2020.108480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/12/2020] [Accepted: 12/03/2020] [Indexed: 01/20/2023]
Abstract
BACKGROUND Adolescence is a critical period for experimenting with alcohol, and these early experiences have long-term influences on alcohol-related behaviours throughout adulthood. This study examined the utility of genome-wide polygenic scores (GPS) for predicting alcohol use during adolescence and young adulthood. METHODS We used GPS based on the Genome-wide association study and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) study on drinks per week to predict alcohol use in a longitudinal, UK-representative sample of unrelated adolescents aged 16 through to 22 years (Nmax = 3390). RESULTS At age 16, the GSCAN GPS predicted variance in alcohol consumption on a typical day (0.58 %), intake frequency (0.89 %), and hazardous drinking (i.e. ≥6 units at one occasion) (1.07 %). At age 22, the predictive power of the GPS had increased, explaining variance in alcohol consumption (0.61 %), intake frequency (1.69 %), and hazardous drinking (1.19 %). CONCLUSIONS The predictive validity of GPS for phenotypic alcohol use was evident in adolescence and increased in young adulthood. The findings suggest that GPS, which are available from birth, may be potentially useful for identifying individuals at risk for harmful and hazardous alcohol use. However, because the overall effect sizes were small, the utility of the GPS that are currently available is limited for the prediction of individual-level alcohol use.
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Affiliation(s)
- Radhika Kandaswamy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Andrea Allegrini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sophie von Stumm
- Department of Education, University of York, Heslington, York, UK
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12
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Im PK, Millwood IY, Chen Y, Guo Y, Du H, Kartsonaki C, Bian Z, Tan Y, Su J, Li Y, Yu C, Lv J, Li L, Yang L, Chen Z. Problem drinking, wellbeing and mortality risk in Chinese men: findings from the China Kadoorie Biobank. Addiction 2020; 115:850-862. [PMID: 31692116 PMCID: PMC7156287 DOI: 10.1111/add.14873] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 09/10/2019] [Accepted: 10/23/2019] [Indexed: 11/30/2022]
Abstract
AIMS To assess the associations of problem drinking with wellbeing and mortality in Chinese men. DESIGN Population-based prospective cohort study. SETTING Ten diverse areas across China. PARTICIPANTS A total of 210 259 men aged 30-79 years enrolled into China Kadoorie Biobank between 2004 and 2008. MEASUREMENTS Self-reported alcohol intake and indicators of problem drinking (i.e. drinking in the morning, unable to stop drinking, unable to work due to drinking, negative emotions after drinking, having shakes after stopping drinking) were assessed by questionnaire at baseline, along with stressful life events (e.g. divorce, income loss, violence) and wellbeing-related measures (e.g. life satisfaction, sleep problems, depression, anxiety). Problem drinking was defined as reporting at least one of the drinking problem indicators. Follow-up for mortality and hospitalized events was through linkage to death registries and national health insurance systems. Multivariate logistic regression models assessed cross-sectional relationships between problem drinking and stressful life events/wellbeing. Cox proportional hazards regression models estimated prospective associations of problem drinking with mortality/hospitalized events. FINDINGS A third of men were current regular drinkers (i.e. drank alcohol at least weekly), 24% of whom reported problem drinking: 8% of all men. Experience of stressful life events in the past 2 years, especially income loss [odds ratio (OR) = 1.86, 95% confidence interval (CI) = 1.45-2.39], was associated with increased problem drinking. Compared with low-risk drinkers (i.e. intake < 200 g/week, no reported problem drinking or habitual heavy drinking episodes), men with problem drinking had poorer self-reported health, poorer life satisfaction and sleep problems, and were more likely to have symptoms of depression and anxiety. Men with two or more problem drinking indicators had an approximately twofold higher risk for all-cause mortality as well as mortality and morbidity from external causes (i.e. injuries), respectively, and 15% higher risk for any hospitalization, compared with low-risk drinkers (all P < 0.01). CONCLUSION Eight per cent of men in China are problem drinkers, and this is associated with significantly increased risk of physical and mental health problems and premature death.
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Affiliation(s)
- Pek Kei Im
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Iona Y. Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Yu Guo
- Chinese Academy of Medical SciencesBeijingChina
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Zheng Bian
- Chinese Academy of Medical SciencesBeijingChina
| | - Yunlong Tan
- Chinese Academy of Medical SciencesBeijingChina
| | | | | | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthUniversity of OxfordOxfordUK
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13
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Chang LH, Whitfield JB, Liu M, Medland SE, Hickie IB, Martin NG, Verhulst B, Heath AC, Madden PA, Statham DJ, Gillespie NA. Associations between polygenic risk for tobacco and alcohol use and liability to tobacco and alcohol use, and psychiatric disorders in an independent sample of 13,999 Australian adults. Drug Alcohol Depend 2019; 205:107704. [PMID: 31731259 DOI: 10.1016/j.drugalcdep.2019.107704] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/18/2019] [Accepted: 10/21/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Substance use, substance use disorders (SUDs), and psychiatric disorders commonly co-occur. Genetic risk common to these complex traits is an important explanation; however, little is known about how polygenic risk for tobacco or alcohol use overlaps the genetic risk for the comorbid SUDs and psychiatric disorders. METHODS We constructed polygenic risk scores (PRSs) using GWAS meta-analysis summary statistics from a large discovery sample, GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN), for smoking initiation (SI; N = 631,564), age of initiating regular smoking (AI; N = 258,251), cigarettes per day (CPD; N = 258,999), smoking cessation (SC; N = 312,273), and drinks per week (DPW; N = 527,402). We then estimated the fixed effect of these PRSs on the liability to 15 phenotypes related to tobacco and alcohol use, substance use disorders, and psychiatric disorders in an independent target sample of Australian adults. RESULTS After adjusting for multiple testing, 10 of 75 combinations of discovery and target phenotypes remained significant. PRS-SI (R2 range: 1.98%-5.09 %) was positively associated with SI, DPW, and with DSM-IV and FTND nicotine dependence, and conduct disorder. PRS-AI (R2: 3.91 %) negatively associated with DPW. PRS-CPD (R2: 1.56 %-1.77 %) positively associated with DSM-IV nicotine dependence and conduct disorder. PRS-DPW (R2: 3.39 %-6.26 %) positively associated with only DPW. The variation of DPW was significantly influenced by sex*PRS-SI, sex*PRS-AI and sex*PRS-DPW. Such interaction effect was not detected in the other 14 phenotypes. CONCLUSIONS Polygenic risks associated with tobacco use are also associated with liability to alcohol consumption, nicotine dependence, and conduct disorder.
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Affiliation(s)
- Lun-Hsien Chang
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston QLD 4006, Australia; Faculty of Medicine, the University of Queensland, 20 Weightman St, Herston QLD 4006, Australia.
| | - John B Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston QLD 4006, Australia.
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota Twin Cities, 75 E River Rd, Minneapolis, MN 55455, USA.
| | - Sarah E Medland
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston QLD 4006, Australia.
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, 94 Mallett St, Camperdown NSW 2050, USA.
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston QLD 4006, Australia.
| | - Brad Verhulst
- Department of psychology, Michigan State University, 316 Physics Road #262, East Lansing, MI 48824, USA.
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA.
| | - Pamela A Madden
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA.
| | - Dixie J Statham
- School of Health and Life Sciences, Federation University, Federation University Australia, PO Box 663, Ballarat, VIC 3353, Australia.
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioural Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.
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14
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Zhong VW, Kuang A, Danning RD, Kraft P, van Dam RM, Chasman DI, Cornelis MC. A genome-wide association study of bitter and sweet beverage consumption. Hum Mol Genet 2019; 28:2449-2457. [PMID: 31046077 PMCID: PMC6606847 DOI: 10.1093/hmg/ddz061] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 07/14/2018] [Accepted: 08/09/2018] [Indexed: 01/07/2023] Open
Abstract
Except for drinking water, most beverages taste bitter or sweet. Taste perception and preferences are heritable and determinants of beverage choice and consumption. Consumption of several bitter- and sweet-tasting beverages has been implicated in development of major chronic diseases. We performed a genome-wide association study (GWAS) of self-reported bitter and sweet beverage consumption among ~370 000 participants of European ancestry, using a two-staged analysis design. Bitter beverages included coffee, tea, grapefruit juice, red wine, liquor and beer. Sweet beverages included artificially and sugar sweetened beverages (SSBs) and non-grapefruit juices. Five loci associated with total bitter beverage consumption were replicated (in/near GCKR, ABCG2, AHR, POR and CYP1A1/2). No locus was replicated for total sweet beverage consumption. Sub-phenotype analyses targeting the alcohol, caffeine and sweetener components of beverages yielded additional loci: (i) four loci for bitter alcoholic beverages (GCKR, KLB, ADH1B and AGBL2); (ii) five loci for bitter non-alcoholic beverages (ANXA9, AHR, POR, CYP1A1/2 and CSDC2); (iii) 10 loci for coffee; six novel loci (SEC16B, TMEM18, OR8U8, AKAP6, MC4R and SPECC1L-ADORA2A); (iv) FTO for SSBs. Of these 17 replicated loci, 12 have been associated with total alcohol consumption, coffee consumption, plasma caffeine metabolites or BMI in previous GWAS; none was involved in known sweet and bitter taste transduction pathways. Our study suggests that genetic variants related to alcohol consumption, coffee consumption and obesity were primary genetic determinants of bitter and sweet beverage consumption. Whether genetic variants related to taste perception are associated with beverage consumption remains to be determined.
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Affiliation(s)
- Victor W Zhong
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alan Kuang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rebecca D Danning
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health and Department of Biostatistics, Boston, MA, USA
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marilyn C Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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15
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Pasman JA, Verweij KJH, Vink JM. Systematic Review of Polygenic Gene-Environment Interaction in Tobacco, Alcohol, and Cannabis Use. Behav Genet 2019; 49:349-365. [PMID: 31111357 PMCID: PMC6554261 DOI: 10.1007/s10519-019-09958-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 05/05/2019] [Indexed: 01/03/2023]
Abstract
Studies testing the effect of single genetic variants on substance use have had modest success. This paper reviewed 39 studies using polygenic measures to test interaction with any type of environmental exposure (G×E) in alcohol, tobacco, and cannabis use. Studies using haplotype combinations, sum scores of candidate-gene risk alleles, and polygenic scores (PS) were included. Overall study quality was moderate, with lower ratings for the polygenic methods in the haplotype and candidate-gene score studies. Heterogeneity in investigated environmental exposures, genetic factors, and outcomes was substantial. Most studies (N = 30) reported at least one significant G×E interaction, but overall evidence was weak. The majority (N = 26) found results in line with differential susceptibility and diathesis-stress frameworks. Future studies should pay more attention to methodological and statistical rigor, and focus on replication efforts. Additional work is needed before firm conclusions can be drawn about the importance of G×E in the etiology of substance use.
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Affiliation(s)
- Joëlle A Pasman
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.
| | - Karin J H Verweij
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
- Amsterdam UMC, Amsterdam, The Netherlands
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
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16
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Johnson EC, St Pierre CL, Meyers JL, Aliev F, McCutcheon VV, Lai D, Dick DM, Goate AM, Kramer J, Kuperman S, Nurnberger JI, Schuckit MA, Porjesz B, Edenberg HJ, Bucholz KK, Agrawal A. The Genetic Relationship Between Alcohol Consumption and Aspects of Problem Drinking in an Ascertained Sample. Alcohol Clin Exp Res 2019; 43:1113-1125. [PMID: 30994927 PMCID: PMC6560626 DOI: 10.1111/acer.14064] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 04/04/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Genomewide association studies (GWAS) have begun to identify loci related to alcohol consumption, but little is known about whether this genetic propensity overlaps with specific indices of problem drinking in ascertained samples. METHODS In 6,731 European Americans who had been exposed to alcohol, we examined whether polygenic risk scores (PRS) from a GWAS of weekly alcohol consumption in the UK Biobank predicted variance in 6 alcohol-related phenotypes: alcohol use, maximum drinks within 24 hours (MAXD), total score on the Self-Rating of the Effects of Ethanol Questionnaire (SRE-T), DSM-IV alcohol dependence (DSM4AD), DSM-5 alcohol use disorder symptom counts (DSM5AUDSX), and reduction/cessation of problematic drinking. We also examined the extent to which an single nucleotide polymorphism (rs1229984) in ADH1B, which is strongly associated with both alcohol consumption and dependence, contributed to the polygenic association with these phenotypes and whether PRS interacted with sex, age, or family history of alcoholism to predict alcohol-related outcomes. We performed mixed-effect regression analyses, with family membership and recruitment site included as random effects, as well as survival modeling of age of onset of DSM4AD. RESULTS PRS for alcohol consumption significantly predicted variance in 5 of the 6 outcomes: alcohol use (Δmarginal R2 = 1.39%, Δ area under the curve [AUC] = 0.011), DSM4AD (Δmarginal R2 = 0.56%; ΔAUC = 0.003), DSM5AUDSX (Δmarginal R2 = 0.49%), MAXD (Δmarginal R2 = 0.31%), and SRE-T (Δmarginal R2 = 0.22%). PRS were also associated with onset of DSM4AD (hazard ratio = 1.11, p = 2.08e-5). The inclusion of rs1229984 attenuated the effects of the alcohol consumption PRS, particularly for DSM4AD and DSM5AUDSX, but the PRS continued to exert an independent effect for all 5 alcohol measures (Δmarginal R2 after controlling for ADH1B = 0.14 to 1.22%). Interactions between PRS and sex, age, or family history were nonsignificant. CONCLUSIONS Genetic propensity for typical alcohol consumption was associated with alcohol use and was also associated with 4 of the additional 5 outcomes, though the variance explained in this sample was modest. Future GWAS that focus on the multifaceted nature of AUD, which goes beyond consumption, might reveal additional information regarding the polygenic underpinnings of problem drinking.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
| | - Celine L St Pierre
- Division of Biological and Biomedical Sciences, Washington University School of Medicine, Saint Louis, Missouri
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Fazil Aliev
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- Department of Actuarial and Risk Management, Faculty of Business, Karabuk University, Karabük, Turkey
| | - Vivia V McCutcheon
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Danielle M Dick
- Department of Psychology and Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John Kramer
- Department of Psychiatry, Roy J and Lucille A Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Samuel Kuperman
- Department of Psychiatry, Roy J and Lucille A Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - John I Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana
| | - Marc A Schuckit
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
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17
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Chang LH, Couvy-Duchesne B, Liu M, Medland SE, Verhulst B, Benotsch EG, Hickie IB, Martin NG, Gillespie NA. Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug Alcohol Depend 2019; 197:271-279. [PMID: 30875648 PMCID: PMC11100300 DOI: 10.1016/j.drugalcdep.2019.01.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 12/25/2018] [Accepted: 01/19/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND Co-morbid substance use is very common. Despite a historical focus using genetic epidemiology to investigate comorbid substance use and misuse, few studies have examined substance-substance associations using polygenic risk score (PRS) methods. METHODS Using summary statistics from the largest substance use GWAS to date (258,797- 632,802 subjects), GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), we constructed PRSs for smoking initiation (PRS-SI), age of initiation of regular smoking (PRS-AI), cigarettes per day (PRS-CPD), smoking cessation (PRS-SC), and drinks per week (PRS-DPW). We then estimated the fixed effect of individual PRSs on 22 lifetime substance use and substance use disorder phenotypes collected in an independent sample of 2463 young Australian adults using genetic restricted maximal likelihood (GREML) in Genome-wide Complex Trait Analysis (GCTA), separately in females, males and both sexes together. RESULTS After accounting for multiple testing, PRS-SI significantly explained variation in the risk of cocaine (0.67%), amphetamine (1.54%), hallucinogens (0.72%), ecstasy (1.66%) and cannabis initiation (0.97%), as well as DSM-5 alcohol use disorder (0.72%). PRS-DPW explained 0.75%, 0.59% and 0.90% of the variation of cocaine, amphetamine and ecstasy initiation respectively. None of the 22 phenotypes including emergent classes of substance use were significantly predicted by PRS-AI, PRS-CPD, and PRS-SC. CONCLUSIONS To our knowledge, this is the first study to report significant genetic overlap between the polygenic risks for smoking initiation and alcohol consumption and the risk of initiating major classes of illicit substances. PRSs constructed from large discovery GWASs allows the detection of novel genetic associations.
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Affiliation(s)
- Lun-Hsien Chang
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, the University of Queensland, Brisbane, Australia.
| | - Baptiste Couvy-Duchesne
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Institute for Molecular Bioscience, the University of Queensland, Brisbane, Australia
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Sarah E Medland
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Brad Verhulst
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Eric G Benotsch
- Psychology Department, Virginia Commonwealth University, VA, USA
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Nathan A Gillespie
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Department of Psychology, Michigan State University, East Lansing, MI, USA
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