Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts

Sandra Sanchez-Roige, Abraham A. Palmer, Pierre Fontanillas, Sarah L. Elson, Mark J. Adams, David M. Howard, Howard Edenberg, Gail Davies, Richard C. Crist, Ian J. Deary, Andrew M. McIntosh, Toni Kim Clarke, L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Jennifer C. McCreightMatthew H. McIntyre, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A.M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Catherine H. Wilson

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Objective: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (r g =0.76-0.92) and DSM-IV alcohol dependence (r g =0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (r g =0.22), major depressive disorder (r g =0.26), and attention deficit hyperactivity disorder (r g =0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (r g =20.24) and ADHD (r g =20.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores #4 as control subjects and those with scores $12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (r g =0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Original languageEnglish (US)
Pages (from-to)107-118
Number of pages12
JournalAmerican Journal of Psychiatry
Volume176
Issue number2
DOIs
StatePublished - Feb 1 2019
Externally publishedYes

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Genome-Wide Association Study
Meta-Analysis
Alcohols
Population
Alcohol Drinking
Diagnostic and Statistical Manual of Mental Disorders
Alcoholism
Major Depressive Disorder
Psychiatry
Proxy
Attention Deficit Disorder with Hyperactivity
Genes

ASJC Scopus subject areas

  • Psychiatry and Mental health

Cite this

Sanchez-Roige, S., Palmer, A. A., Fontanillas, P., Elson, S. L., Adams, M. J., Howard, D. M., ... Wilson, C. H. (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(2), 107-118. https://doi.org/10.1176/appi.ajp.2018.18040369

Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. / Sanchez-Roige, Sandra; Palmer, Abraham A.; Fontanillas, Pierre; Elson, Sarah L.; Adams, Mark J.; Howard, David M.; Edenberg, Howard; Davies, Gail; Crist, Richard C.; Deary, Ian J.; McIntosh, Andrew M.; Clarke, Toni Kim; Elson, L.; Fontanillas, Pierre; Furlotte, Nicholas A.; Hinds, David A.; Huber, Karen E.; Kleinman, Aaron; Litterman, Nadia K.; McCreight, Jennifer C.; McIntyre, Matthew H.; Mountain, Joanna L.; Noblin, Elizabeth S.; Northover, Carrie A.M.; Pitts, Steven J.; Sathirapongsasuti, J. Fah; Sazonova, Olga V.; Shelton, Janie F.; Shringarpure, Suyash; Tian, Chao; Tung, Joyce Y.; Vacic, Vladimir; Wilson, Catherine H.

In: American Journal of Psychiatry, Vol. 176, No. 2, 01.02.2019, p. 107-118.

Research output: Contribution to journalArticle

Sanchez-Roige, S, Palmer, AA, Fontanillas, P, Elson, SL, Adams, MJ, Howard, DM, Edenberg, H, Davies, G, Crist, RC, Deary, IJ, McIntosh, AM, Clarke, TK, Elson, L, Fontanillas, P, 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 2019, 'Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts', American Journal of Psychiatry, vol. 176, no. 2, pp. 107-118. https://doi.org/10.1176/appi.ajp.2018.18040369
Sanchez-Roige, Sandra ; Palmer, Abraham A. ; Fontanillas, Pierre ; Elson, Sarah L. ; Adams, Mark J. ; Howard, David M. ; Edenberg, Howard ; Davies, Gail ; Crist, Richard C. ; Deary, Ian J. ; McIntosh, Andrew M. ; Clarke, Toni Kim ; Elson, L. ; Fontanillas, Pierre ; Furlotte, Nicholas A. ; Hinds, David A. ; Huber, Karen E. ; Kleinman, Aaron ; Litterman, Nadia K. ; McCreight, Jennifer C. ; McIntyre, Matthew H. ; Mountain, Joanna L. ; Noblin, Elizabeth S. ; Northover, Carrie A.M. ; Pitts, Steven J. ; Sathirapongsasuti, J. Fah ; Sazonova, Olga V. ; Shelton, Janie F. ; Shringarpure, Suyash ; Tian, Chao ; Tung, Joyce Y. ; Vacic, Vladimir ; Wilson, Catherine H. / Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. In: American Journal of Psychiatry. 2019 ; Vol. 176, No. 2. pp. 107-118.
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abstract = "Objective: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (r g =0.76-0.92) and DSM-IV alcohol dependence (r g =0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (r g =0.22), major depressive disorder (r g =0.26), and attention deficit hyperactivity disorder (r g =0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (r g =20.24) and ADHD (r g =20.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores #4 as control subjects and those with scores $12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (r g =0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.",
author = "Sandra Sanchez-Roige and Palmer, {Abraham A.} and Pierre Fontanillas and Elson, {Sarah L.} and Adams, {Mark J.} and Howard, {David M.} and Howard Edenberg and Gail Davies and Crist, {Richard C.} and Deary, {Ian J.} and McIntosh, {Andrew M.} and Clarke, {Toni Kim} and L. Elson and Pierre Fontanillas and Furlotte, {Nicholas A.} and Hinds, {David A.} and Huber, {Karen E.} and Aaron Kleinman and Litterman, {Nadia K.} and McCreight, {Jennifer C.} and McIntyre, {Matthew H.} and Mountain, {Joanna L.} and Noblin, {Elizabeth S.} and Northover, {Carrie A.M.} and Pitts, {Steven J.} and Sathirapongsasuti, {J. Fah} and Sazonova, {Olga V.} and Shelton, {Janie F.} and Suyash Shringarpure and Chao Tian and Tung, {Joyce Y.} and Vladimir Vacic and Wilson, {Catherine H.}",
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TY - JOUR

T1 - Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts

AU - Sanchez-Roige, Sandra

AU - Palmer, Abraham A.

AU - Fontanillas, Pierre

AU - Elson, Sarah L.

AU - Adams, Mark J.

AU - Howard, David M.

AU - Edenberg, Howard

AU - Davies, Gail

AU - Crist, Richard C.

AU - Deary, Ian J.

AU - McIntosh, Andrew M.

AU - Clarke, Toni Kim

AU - Elson, L.

AU - Fontanillas, Pierre

AU - Furlotte, Nicholas A.

AU - Hinds, David A.

AU - Huber, Karen E.

AU - Kleinman, Aaron

AU - Litterman, Nadia K.

AU - McCreight, Jennifer C.

AU - McIntyre, Matthew H.

AU - Mountain, Joanna L.

AU - Noblin, Elizabeth S.

AU - Northover, Carrie A.M.

AU - Pitts, Steven J.

AU - Sathirapongsasuti, J. Fah

AU - Sazonova, Olga V.

AU - Shelton, Janie F.

AU - Shringarpure, Suyash

AU - Tian, Chao

AU - Tung, Joyce Y.

AU - Vacic, Vladimir

AU - Wilson, Catherine H.

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Objective: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (r g =0.76-0.92) and DSM-IV alcohol dependence (r g =0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (r g =0.22), major depressive disorder (r g =0.26), and attention deficit hyperactivity disorder (r g =0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (r g =20.24) and ADHD (r g =20.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores #4 as control subjects and those with scores $12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (r g =0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

AB - Objective: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (r g =0.76-0.92) and DSM-IV alcohol dependence (r g =0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (r g =0.22), major depressive disorder (r g =0.26), and attention deficit hyperactivity disorder (r g =0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (r g =20.24) and ADHD (r g =20.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores #4 as control subjects and those with scores $12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (r g =0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

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