Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,932)
Open accessPreprint — published as 10.1176/appi.ajp.2018.18040369 · Paper page
Abstract
Alcohol use disorders ( AUD ) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test ( AUDIT ) from two population-based cohorts of European ancestry: UK Biobank ( UKB ; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption ( AUDIT-C ), and for items 4-10, which focus on the problematic consequences of drinking ( AUDIT-P ). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we 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 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (r g =0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (r g =0.22, p=3.0×10 −10 ), major depressive disorder (r g =0.26, p=5.6×10 −3 ), and attention-deficit/hyperactivity disorder (ADHD; r g =0.23, p=1.1×10 −5 ), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (r g =−0.24, p=3.7×10 −3 ) and ADHD (rg=−0.10, p=1.8×10 −2 ). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of ≤4 as controls and ≥12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (r g =0.82, p=3.2×10 −6 ) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Subsequent analyses suggest these genetic correlation estimates are sensitive to sample characteristics, with somewhat higher genetic correlations reported in analysis of alcohol consumption in the full UK Biobank (rg = 0.75) and of AUDIT in combined data from 23andMe participants and UK Biobank (rg = 0.39).
“Subsequent analyses suggest these estimates are sensitive to sample characteristics, with somewhat higher genetic correlations reported in analysis of alcohol consumption in the full UK Biobank29 (rg = 0.75) and of AUDIT in combined data from 23andMe participants and UK Biobank30 (rg = 0.39).”
From Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders (2018) · cites this paper as [30]
This distinction appears to be the case in studies that used specific cut-offs to harmonize AUDIT scores with AD data.
“This appears to be the case in studies that used specific cut-offs to harmonize AUDIT scores with AD data30,34.”
From Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders (2018) · cites this paper as [30]
That study also found that a score reflecting items related to problem drinking (AUDIT-P) resulted in a stronger genetic correlation (rg = 0.64) than a score related to alcohol consumption alone (rg = 0.33).
“Interestingly, that study also found that a score reflecting items related to problem drinking (AUDIT-P) resulted in a stronger genetic correlation (rg = 0.64) than a score related to alcohol consumption alone (rg = 0.33).”
From Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders (2018) · cites this paper as [30]
The larger of these studies reports that the genetic correlation between AD and AUDIT scores is maximized at an AUDIT cutoff ≥ 20 (with controls defined as those scoring ≤ 4; rg = 0.90).
“The larger of these studies30 reports that the genetic correlation between AD and AUDIT scores is maximized at an AUDIT cutoff ≥ 20 (with controls defined as those scoring ≤ 4; rg = 0.90).”
From Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders (2018) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Self-citationMETAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083 · see citing works
Genome-wide association study reveals genetic risk underlying Parkinson's disease
Simón‐Sánchez, J., Schulte, C., Brás, J., Sharma, M., Gibbs, J. R., Berg, D., Paisán‐Ruiz, C., Lichtner, P., Scholz, S. W., Hernandez, D. G., et al. (2009) · Nature Genetics · doi:10.1038/ng.487 · Cited by 1,916 · see citing works
Satake, W., Nakabayashi, Y., Mizuta, I., Hirota, Y., Ito, C., Kubo, M., Kawaguchi, T., Tsunoda, T., Watanabe, M., Takeda, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.485 · Cited by 1,308 · see citing works
Detection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies
Verhulst, B., Neale, M. C., & Kendler, K. S. (2015) · Psychological Medicine · doi:10.1017/s0033291714002165 · Cited by 652 · see citing works
23andMe Research Team, Walters, R. K., Polimanti, R., Johnson, E. C., McClintick, J. N., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0275-1 · Cited by 632 · see citing works
Self-citationAssociations Between Socioeconomic Factors and Alcohol Outcomes.
Collins, S. E. (2016) · Europe PMC (PubMed Central) · Cited by 573 · see citing works
Pfefferbaum, A., Lim, K. O., Zipursky, R. B., Mathalon, D. H., Rosenbloom, M. J., Lane, B., Ha, C. N., & Sullivan, E. V. (1992) · Alcoholism Clinical and Experimental Research · doi:10.1111/j.1530-0277.1992.tb00702.x · Cited by 566 · see citing works
UK Biobank: Current status and what it means for epidemiology
Allen, N., Sudlow, C., Downey, P., Peakman, T., Danesh, J., Elliott, P., Gallacher, J., Green, J., Matthews, P., Pell, J., et al. (2012) · Health Policy and Technology · doi:10.1016/j.hlpt.2012.07.003 · Cited by 521 · see citing works
Bayesian refinement of association signals for 14 loci in 3 common diseases
Maller, J., McVean, G., Byrnes, J., Vukcevic, D., Palin, K., Su, Z., Howson, J. M. M., Auton, A., Myers, S. R., Morris, A. P., et al. (2012) · Nature Genetics · doi:10.1038/ng.2435 · Cited by 481 · see citing works
Clarke, T.-K., Adams, M. J., Davies, G., Howard, D. M., Hall, L. S., Padmanabhan, S., Murray, A. D., Smith, B. H., Campbell, A., Hayward, C., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.153 · Cited by 440 · see citing works
Self-citationPfefferbaum, A., Sullivan, E. V., Mathalon, D. H., Shear, P. K., Rosenbloom, M. J., & Lim, K. O. (1995) · Alcoholism Clinical and Experimental Research · doi:10.1111/j.1530-0277.1995.tb01598.x · Cited by 420 · see citing works
Gelernter, J. E., Kranzler, H. R., Sherva, R., Almasy, L. A., Koesterer, R., Smith, A. H., Anton, R. F., Preuss, U. W., Ridinger, M., Rujescu, D., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.145 · Cited by 370 · see citing works
FGF21 Regulates Sweet and Alcohol Preference
Talukdar, S., Owen, B. M., Song, P., Hernandez, G., Zhang, Y., Zhou, Y., Scott, W. T., Paratala, B., Turner, T., Smith, A., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2015.12.008 · Cited by 342 · see citing works
Probst, C., Roerecke, M., Behrendt, S., & Rehm, J. T. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu043 · Cited by 271 · see citing works
Genetic differences in alcohol sensitivity and the inheritance of alcoholism risk
Heath, A. C., Madden, P. A. F., Bucholz, K. K., Dinwiddie, S. H., Slutske, W. S., Bierut, L. J., Rohrbaugh, J., Statham, D. J., Dunne, M. P., Whitfield, J. B., et al. (1999) · Psychological Medicine · doi:10.1017/s0033291799008909 · Cited by 269 · see citing works
Corticotropin releasing factor: A key role in the neurobiology of addiction
Zorrilla, E. P., Logrip, M. L., & Koob, G. F. (2014) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2014.01.001 · Cited by 255 · see citing works
Schumann, G., Liu, C., O’Reilly, P., Gao, H., Song, P., Xu, B., Ruggeri, B., Amin, N., Jia, T., Preis, S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611243113 · Cited by 241 · see citing works
Self-citationBierut, L. J., Goate, A. M., Breslau, N., Johnson, E. O., Bertelsen, S., Fox, L., Agrawal, A., Bucholz, K. K., Grucza, R., Hesselbrock, V, et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.124 · Cited by 218 · see citing works
Self-citationMacGregor, S., Lind, P. A., Bucholz, K. K., Hansell, N. K., Madden, P. A. F., Richter, M. M., Montgomery, G. W., Martin, N. G., Heath, A. C., & Whitfield, J. B. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn372 · Cited by 194 · see citing works
Frank, J., Cichon, S., Treutlein, J., Ridinger, M., Mattheisen, M., Hoffmann, P., Herms, S., Wodarz, N., Soyka, M., Zill, P., et al. (2012) · Addiction Biology · doi:10.1111/j.1369-1600.2011.00395.x · Cited by 176 · see citing works
Comorbidity of alcohol and substance dependence with attention-deficit/hyperactivity disorder (ADHD)
Ohlmeier, M. D., Peters, K., Wildt, B. T. T., Zedler, M., Ziegenbein, M., Wiese, B., Emrich, H. M., & Schneider, U. (2008) · Alcohol and Alcoholism · doi:10.1093/alcalc/agn014 · Cited by 174 · see citing works
Molecular genetic contributions to socioeconomic status and intelligence
Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell, A. I., Michelle Luciano, A., Smith, B. H., Padmanabhan, S., Hocking, L. J., et al. (2014) · Intelligence · doi:10.1016/j.intell.2014.02.006 · Cited by 153 · see citing works
Self-citationErdmann, J., Willenborg, C., Nahrstaedt, J., Preuss, M. H., König, I. R., Baumert, J. J., Linsel‐Nitschke, P., Gieger, C., Tennstedt, S., Belcredi, P., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq405 · Cited by 143 · see citing works
Fone, D. L., Farewell, D. M., White, J., Lyons, R. A., & Dunstan, F. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-002337 · Cited by 128 · see citing works
Comorbidity of Alcoholism and Psychiatric Disorders: An Overview
Petrakis, I. L., Gonzalez, G. M., Rosenheck, R. A., & Krystal, J. H. (2002) · Europe PMC (PubMed Central) · Cited by 127 · see citing works
Quillen, E. E., Chen, X., Almasy, L. A., Yang, F., He, H., Li, X., Wang, X., Liu, T., Hao, W., Deng, H., et al. (2013) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32213 · Cited by 112 · see citing works
Untangling the tau gene association with neurodegenerative disorders
Pittman, A. M., Fung, H., & de Silva, R. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl190 · Cited by 110 · see citing works
Cardenas, V. A., Studholme, C., Meyerhoff, D. J., Song, E., & Weiner, M. W. (2005) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2005.01.002 · Cited by 105 · see citing works
Sanchez‐Roige, S., Fontanillas, P., Elson, S. L., The 23andMe Research Team, Gray, J. C., de Wit, H., Davis, L. K., MacKillop, J., & Palmer, A. A. (2019) · Addiction Biology · doi:10.1111/adb.12574 · Cited by 101 · see citing works
Self-citationSUBSTANCE ABUSE AND BIPOLAR COMORBIDITY
Sonne, S. C., & Brady, K. T. (1999) · Psychiatric Clinics of North America · doi:10.1016/s0193-953x(05)70098-8 · Cited by 95 · see citing works
Polygenic risk scores for smoking: predictors for alcohol and cannabis use?
Vink, J. M., Hottenga, J., de Geus, E. J. C., Willemsen, G. A. H. M., Neale, M. C., Furberg, H., & Boomsma, D. I. (2014) · Addiction · doi:10.1111/add.12491 · Cited by 67 · see citing works
Mbarek, H., Milaneschi, Y., Fedko, I. O., Hottenga, J., de Moor, M. H. M., Jansen, R., Gelernter, J., Sherva, R., Willemsen, G., Boomsma, D. I., et al. (2015) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32379 · Cited by 66 · see citing works
Functional Limitations, Socioeconomic Status, and All‐Cause Mortality in Moderate Alcohol Drinkers
Lee, S. J., Sudore, R. L., Williams, B. A., Lindquist, K. J., Chen, H. L., & Covinsky, K. E. (2009) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2009.02184.x · Cited by 61 · see citing works
Bustamante, M., Standl, M., Bassat, Q., Vilor‐Tejedor, N., Medina‐Gómez, C., Bonilla, C., Ahluwalia, T. S., Bačelis, J., Bradfield, J. P., Tiesler, C. M. T., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw264 · Cited by 40 · see citing works
Connecting the dots, genome-wide association studies in substance use
Nivard, M. G., Verweij, K. J. H., Minică, C. C., Treur, J. L., Derks, E. M., Stringer, S., Verweij, K. J. H., Gillespie, N. A., Vink, J. M., Minică, C. C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.14 · Cited by 28 · see citing works
Kilcoyne, B., Shmulewitz, D., Meyers, J. L., Aharonovich, E., Greenstein, E., Frisch, A., Weizman, A., Spivak, B., Edenberg, H. J., Gelernter, J. E., et al. (2014) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2014.75.635 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- Jorgenson E, Thai KK, Hoffmann TJ, et al.: Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study. Mol Psychiatry 2017; 22:1359-1367 certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
- Saunders JB, Babor TF: AUDIT questionnaire. Addiction 1993; 88:1-2
- Barbeira A, Shah KP, Torres JM, et al.: MetaXcan: Summary Statistics Based Gene-Level Association Method Infers Accurate PrediXcan Results. bioRxiv 2016; 045260
- Hurley TD, Edenberg HJ: Genes encoding enzymes involved in ethanol metabolism. Alcohol Res Curr Rev 2012; 34:339-44
- certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Cited by
Sorted by citations
23andMe Research Team, Walters, R. K., Polimanti, R., Johnson, E. C., McClintick, J. N., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0275-1 · Cited by 632 · see citing works
Self-citationPolimanti, R., Peterson, R. E., Ong, J.-S., MacGregor, S., Edwards, A. C., Clarke, T.-K., Frank, J., Gerring, Z., Gillespie, N. A., Lind, P. A., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291719000667 · Cited by 108 · see citing works
Self-citationPolimanti, R., Peterson, R. E., Ong, J.-S., MacGregor, S., Edwards, A. C., Clarke, T.-K., Frank, J., Gerring, Z., Gillespie, N. A., Lind, P. A., et al. (2018) · doi:10.1101/412098 · Cited by 108 · see citing works
Self-citation
- Authors
- Sanchez-Roige, S., Palmer, A. A., Fontanillas, P., Elson, S. L., The 23andMe Research Team, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Adams, M. J., Howard, D. M., Edenberg, H. J., Davies, G., Crist, R. C., Deary, I. J., McIntosh, A. M., & Clarke, T.-K.
- Affiliations
- University of California San Diego (US)
- 23andMe (United States) (US)
- University of Edinburgh (GB)
- Indiana University School of Medicine (US)
- University of Pennsylvania (US)
- Corresponding author
- Clarke, Toni-Kim
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2018-03-06
- OpenAlex
- W2794263701
- Cited by
- 14
- References
- 43
- Citations by year
- 2022: 1 · 2019: 8 · 2018: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- Wellcome Trust (104036/Z/14/Z, 104036)
- Centre for Cognitive Ageing and Cognitive Epidemiology (MR/K026992/1)
- Directorate for Biological Sciences
- Medical Research Council (MR/K026992/1, 104036/Z/14/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Pathology and Forensic Medicine › Alcohol Consumption and Health Effects
- Keywords
- alcoholism, Alcohol Use Disorders Identification Test, AUDIT-C, alcohol consumption, genetic correlation, schizophrenia, major depressive disorder, attention deficit hyperactivity disorder, ADH1B, ADH1C, KLB, GCKR, genetic susceptibility loci, population-based cohort
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
