A genome-wide association study of cocaine use disorder accounting for phenotypic heterogeneity and gene–environment interaction

A genome-wide association study of cocaine use disorder accounting for phenotypic heterogeneity and gene–environment interaction

J Psychiatry Neurosci 2020;45(1):34-44 | PDF | Appendix

Jiangwen Sun, PhD, BM; Henry R. Kranzler, MD; Joel Gelernter, MD; Jinbo Bi, PhD

Background: Phenotypic heterogeneity and complicated gene–environment interplay in etiology are among the primary factors that hinder the identification of genetic variants associated with cocaine use disorder.

Methods: To detect novel genetic variants associated with cocaine use disorder, we derived disease traits with reduced phenotypic heterogeneity using cluster analysis of a study sample (n = 9965). We then used these traits in genome-wide association tests, performed separately for 2070 African Americans and 1570 European Americans, using a new mixed model that accounted for the moderating effects of 5 childhood environmental factors. We used an independent sample (918 African Americans, 1382 European Americans) for replication.

Results: The cluster analysis yielded 5 cocaine use disorder subtypes, of which subtypes 4 (n = 3258) and 5 (n = 1916) comprised heavy cocaine users, had high heritability estimates (h2 = 0.66 and 0.64, respectively) and were used in association tests. Seven of the 13 identified genetic loci in the discovery phase were available in the replication sample. In African Americans, rs114492924 (discovery p = 1.23 × E−8), a single nucleotide polymorphism in LINC01411, was replicated in the replication sample (p = 3.63 × E−3). In a meta-analysis that combined the discovery and replication results, 3 loci in African Americans were significant genome-wide: rs10188036 in TRAK2 (p = 2.95 × E−8), del-1:15511771 in TMEM51 (p = 9.11 × E−10) and rs149843442 near LPHN2 (p = 3.50 × E−8).

Limitations: Lack of data prevented us from replicating 6 of the 13 identified loci.

Conclusion: Our results demonstrate the importance of considering phenotypic heterogeneity and gene–environment interplay in detecting genetic variations that contribute to cocaine use disorder, because new genetic loci have been identified using our novel analytic method.


Submitted Jun. 15, 2018; Revised Nov. 29, 2018; Revised Feb. 13, 2019; Accepted Apr. 6, 2019; Published online Sept. 6, 2019

Affiliations: From the Department of Computer Science, College of Science, Old Dominion University, Norfolk, VA (Sun); the Department of Computer Science and Engineering, University of Connecticut, School of Engineering, Storrs, CT (Sun [at the time of writing], Bi); the University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Center for Studies of Addiction and Corporal Michael Crescenz VAMC, Philadelphia, PA (Kranzler); and the Yale University School of Medicine, Department of Psychiatry, Division of Human Genetics and Departments of Genetics and Neurobiology; and VA CT Healthcare Center, New Haven, CT (Gelernter).

Funding: This work was supported by NIH grant R01DA037349 and NSF grants DBI-1356655 and CCF-1514357. J. Bi was also supported by NIH grant K02DA043063.

Competing interests: H. Kranzler is a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which was supported in the last 3 years by AbbVie, Alkermes, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, Pfizer, Arbor, and Amygdala Neurosciences. H. Kranzler and J. Gelernter are named as inventors on PCT patent application #15/878,640 entitled, “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. J. Sun and J. Bi declare no competing interests.

Contributors: J. Sun and J. Bi designed the study. H. Kranzler and J. Gelernter acquired the data, which J. Sun and J. Bi analyzed. J. Sun and J. Bi wrote the article, which all authors reviewed. All authors approved the final version to be published and can certify that no other individuals not listed as authors have made substantial contributions to the paper.

DOI: 10.1503/cjs.180098

Correspondence to: J. Bi, University of Connecticut, Computer Science and Engineering, 371 Fairfield Way, Unit 4155, Storrs, CT, 06269-9000, United States; jinbo.bi@uconn.edug>