Original Investigation
May 2016

Genome-wide Association Study of Cannabis Dependence Severity, Novel Risk Variants, and Shared Genetic Risks

Author Affiliations
  • 1Section of Biomedical Genetics, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
  • 2Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut
  • 3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 4Mental Illness Research Education and Clinical Center, Veterans Affairs (VA) Stars and Stripes Healthcare Network, Philadelphia VA Medical Center, Philadelphia, Pennsylvania
  • 5Department of Genetics, Yale School of Medicine, West Haven, Connecticut
  • 6Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
  • 7VA Cooperative Studies Program Coordinating Center, West Haven, Connecticut
  • 8Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
  • 9Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
  • 10Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts
  • 11Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
  • 12Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
  • 13Department of Psychiatry, VA Connecticut Healthcare Center, Yale University School of Medicine, West Haven, Connecticut
  • 14Department of Neurobiology, Yale School of Medicine, New Haven, Connecticut

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Psychiatry. 2016;73(5):472-480. doi:10.1001/jamapsychiatry.2016.0036

Importance  Cannabis dependence (CAD) is a serious problem worldwide and is of growing importance in the United States because cannabis is increasingly available legally. Although genetic factors contribute substantially to CAD risk, at present no well-established specific genetic risk factors for CAD have been elucidated.

Objective  To report findings for DSM-IV CAD criteria from association analyses performed in large cohorts of African American and European American participants from 3 studies of substance use disorder genetics.

Design, Setting, and Participants  This genome-wide association study for DSM-IV CAD criterion count was performed in 3 independent substance dependence cohorts (the Yale-Penn Study, Study of Addiction: Genetics and Environment [SAGE], and International Consortium on the Genetics of Heroin Dependence [ICGHD]). A referral sample and volunteers recruited in the community and from substance abuse treatment centers included 6000 African American and 8754 European American participants, including some from small families. Participants from the Yale-Penn Study were recruited from 2000 to 2013. Data were collected for the SAGE trial from 1990 to 2007 and for the ICGHD from 2004 to 2009. Data were analyzed from January 2, 2013, to November 9, 2015.

Main Outcomes and Measures  Criterion count for DSM-IV CAD.

Results  Among the 14 754 participants, 7879 were male, 6875 were female, and the mean (SD) age was 39.2 (10.2) years. Three independent regions with genome-wide significant single-nucleotide polymorphism associations were identified, considering the largest possible sample. These included rs143244591 (β = 0.54, P = 4.32 × 10−10 for the meta-analysis) in novel antisense transcript RP11-206M11.7;rs146091982 (β = 0.54, P = 1.33 × 10−9 for the meta-analysis) in the solute carrier family 35 member G1 gene (SLC35G1); and rs77378271 (β = 0.29, P = 2.13 × 10−8 for the meta-analysis) in the CUB and Sushi multiple domains 1 gene (CSMD1). Also noted was evidence of genome-level pleiotropy between CAD and major depressive disorder and for an association with single-nucleotide polymorphisms in genes associated with schizophrenia risk. Several of the genes identified have functions related to neuronal calcium homeostasis or central nervous system development.

Conclusions and Relevance  These results are the first, to our knowledge, to identify specific CAD risk alleles and potential genetic factors contributing to the comorbidity of CAD with major depression and schizophrenia.