Specific genes associated with marijuana addiction

“A genetic factor in cannabis dependence severity is important for the public to know. Look into the paragraph of Design, Setting, and Participants to appreciate the quality of this research. A 90% association of this genetic factor with another psychiatric condition or addiction is stunning.

The first paragraph is an announcement in the popular media. I looked up the original article and publish that article’s abstract below the horizontal line.”  Bill Chesnut, MD

To go back to New Health News: http://billchesnutmd.com/new-health-news

Researchers identify specific genes associated with marijuana addiction

TIME (3/30, Szalavitz) reports that “specific genes associated with marijuana addiction have been identified – and some of them are also linked to increased risk for depression and schizophrenia.” Researchers arrived at this conclusion after studying “the genes of nearly 15,000 people from three different groups.” The study’s findings may “help explain why 90% of people with marijuana addictions also suffer from another psychiatric condition or addiction.” The study was published online in JAMA Psychiatry.


Below is the abstract of that original research publication March 30, 2016.

Online First >

Original Investigation | March 30, 2016

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

Richard Sherva, PhD1; Qian Wang, MS2; Henry Kranzler, MD3,4; Hongyu Zhao, PhD2,5,6,7; Ryan Koesterer, MS1; Aryeh Herman, PsyD8; Lindsay A. Farrer, PhD1,9,10,11,12; Joel Gelernter, MD7,8,13,14

JAMA Psychiatry. Published online March 30, 2016. 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); andrs77378271 (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.