Emma C Johnson1, Sandra Sanchez-Roige2, Laura Acion3, Mark J Adams4, Kathleen K Bucholz1, Grace Chan5, Michael J Chao6, David B Chorlian7, Danielle M Dick8,9, Howard J Edenberg10,11, Tatiana Foroud11, Caroline Hayward12, Jon Heron13, Victor Hesselbrock5, Matthew Hickman13, Kenneth S Kendler14, Sivan Kinreich7, John Kramer3, Sally I-Chun Kuo8, Samuel Kuperman3, Dongbing Lai11, Andrew M McIntosh4, Jacquelyn L Meyers7, Martin H Plawecki15, Bernice Porjesz7, David Porteous16, Marc A Schuckit2, Jinni Su17, Yong Zang18, Abraham A Palmer2,19, Arpana Agrawal1, Toni-Kim Clarke4, Alexis C Edwards14. 1. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA. 2. Department of Psychiatry, University of California San Diego, San Diego, CA, USA. 3. Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, USA. 4. Division of Psychiatry, University of Edinburgh, Edinburgh, UK. 5. Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA. 6. Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, USA. 7. Department of Psychiatry, Suny Downstate Medical Center, Brooklyn, NY, USA. 8. Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA. 9. Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA. 10. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA. 11. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA. 12. MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Molecular Medicine, Edinburgh, UK. 13. University of Bristol, Bristol Medical School, Population Health Sciences, Bristol, UK. 14. Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. 15. Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA. 16. University of Edinburgh, Institute of Genetics & Molecular Medicine, Centre for Genomic and Experimental Medicine, Edinburgh, UK. 17. Department of Psychology, Arizona State University, Tempe, AZ, USA. 18. Department of Biostatistics, Indiana University School of Medicine, Bloomington, IN, USA. 19. University of California San Diego, Institute for Genomic Medicine, San Diego, CA, USA.
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.
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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