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Genome-Wide Meta-Analysis of Longitudinal Alcohol Consumption Across Youth and Early Adulthood

  • Daniel E. Adkins (a1), Shaunna L. Clark (a1), William E. Copeland (a2), Martin Kennedy (a3), Kevin Conway (a4), Adrian Angold (a2), Hermine Maes (a5), Youfang Liu (a6), Gaurav Kumar (a1), Alaattin Erkanli (a2), Ashwin A. Patkar (a2), Judy Silberg (a5), Tyson H. Brown (a7), David M. Fergusson (a8), L. John Horwood (a8), Lindon Eaves (a5), Edwin J. C. G. van den Oord (a1), Patrick F. Sullivan (a9) and E. J. Costello (a2)...


The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N = 2,126, obs = 12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR < 0.1) and six others met our ‘suggestive’ criterion (FDR <0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.

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Corresponding author

address for correspondence: Dr Daniel E. Adkins, Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, McGuire Hall, Room 216B, PO Box 980533, Richmond, VA 23298-0581, USA. E-mail:


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