Edited by:
Reviewed by:
*Correspondence:
This article was submitted to Behavioral and Psychiatric Genetics, a section of the journal Frontiers in Genetics
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Despite evidence of substantial comorbidity between psychiatric disorders and substance involvement, the extent to which common genetic factors contribute to their co-occurrence remains understudied. In the current study, we tested for associations between polygenic risk for psychiatric disorders and substance involvement (i.e., ranging from ever-use to severe dependence) among 2573 non-Hispanic European–American participants from the Study of Addiction: Genetics and Environment. Polygenic risk scores (PRS) for cross-disorder psychopathology (CROSS) were generated based on the Psychiatric Genomics Consortium’s Cross-Disorder meta-analysis and then tested for associations with a factor representing general liability to alcohol, cannabis, cocaine, nicotine, and opioid involvement (GENSUB). Follow-up analyses evaluated specific associations between each of the five psychiatric disorders which comprised CROSS—attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BIP), major depressive disorder (MDD), and schizophrenia (SCZ)—and involvement with each component substance included in GENSUB. CROSS PRS explained 1.10% of variance in GENSUB in our sample (
Psychiatric disorders are genetically influenced complex traits, with heritability estimates ranging from 28% (generalized anxiety disorder) to 85% (bipolar disorder;
Though not included in PGC cross-disorder analyses, evidence suggests that substance use disorders are heritable (
Though prior genetic studies of comorbidity have been necessarily limited in scope (e.g., to common disorders among related individuals), the development of polygenic risk scores (PRS;
Non-Hispanic European–American adults who completed the Study of Addiction: Genetics and Environment (SAGE;
Sample demographics.
Demographics | |
---|---|
Female | 56.2% |
Age | 38.67 (9.76) |
Study of Origin ( | |
COGA | 927 |
FSCD | 557 |
COGEND | 1089 |
Participants completed a version of the Semi-Structured Assessment for the Genetics of Alcoholism (
Substance involvement distributions.
Involvement Group | Alcohol | Nicotine | Cannabis | Cocaine | Opioids |
---|---|---|---|---|---|
No/Non-Regular Usea | 258 | 595 | 659 | 1591 | 2043 |
Use, 0 Symptoms | 517 | 159 | 1153 | 416 | 305 |
Use, 1–2 Symptoms | 591 | 499 | 312 | 83 | 45 |
Use, 3–5 Symptomsb | 648 | 1120 | 278 | 131 | 52 |
Use, 6–7 Symptoms | 559 | 180 | 168 | 348 | 124 |
DNA was extracted from blood samples, and cell lines were developed as an additional DNA source. Samples were genotyped using lllumina Human1Mv1_CBeadChip at the Johns Hopkins Center for Inherited Disease Research (CIDR). Extensive and rigorous data cleaning was employed (
Polygenic risk scores were derived from the results of the PGC cross-disorder meta-analysis (CROSS;
Associations between each thresholded CROSS PRS and GENSUB were tested using ordinary least squares regression. Multinomial logistic regression was then used to test associations across each level of involvement (i.e., no/non-regular use, non-problem use, mild problems, moderate dependence, and severe dependence) for specific substances and individual disorder PRS. Due to the large number of non-independent tests performed, an empirical significance threshold for α = 0.05 was determined using 10,000 label-swapping permutations (see Supplementary Materials and Methods for details). The lowest level of involvement was used as the reference group; thus, resulting odds-ratios (ORs) reflect increases or decreases in association for each level of substance involvement relative to the lowest level (i.e., no/non-regular use). Wald chi-square tests (for 1° of freedom) were used to examine whether the magnitude of these resulting ORs could be equated to each other and thus establish whether differences in PRS existed across involvement levels (e.g., comparison of the OR between no use and use with no problems vs. the OR between no use and use with 1–2 symptoms). To determine whether specific disorder-substance associations were driven by GENSUB, significant analyses were repeated with GENSUB as a covariate. Covariates across all analyses included sex, age quartiles, three ancestrally informative principal components, and study of origin.
The confirmatory one-factor model fit the data reasonably well in our sample (comparative fit index = 0.992; root mean square error of approximation = 0.106), supporting our proposed unidimensional conceptualization of alcohol, cannabis, cocaine, nicotine, and opioid involvement. Factor loadings were generally comparable across substances, though the loading for nicotine was somewhat lower (Supplementary Figure
CROSS PRS were associated with increasing GENSUB factor scores (significant at 9 of 10
Analyses of individual disorder PRS and specific substance involvement revealed several noteworthy associations (see
ADHD PRS were negatively associated with non-problem cannabis use, and were positively associated with all levels of nicotine use (Supplementary Table
AUT PRS were not consistently associated with involvement with any of the substances tested (Supplementary Table
BIP PRS were associated with increasing problematic alcohol involvement, severe cocaine dependence, and specific levels of cannabis and opioid involvement (Supplementary Table
MDD PRS were associated with increased alcohol, cocaine, and nicotine involvement, as well as with multiple levels of cannabis involvement (Supplementary Table
SCZ PRS were associated with elevated alcohol, cannabis, and cocaine involvement, along with nicotine use and non-problem opioid use (Supplementary Table
Controlling for GENSUB revealed that the majority of nominally significant substance-disorder relationships were driven by associations between PRS and general substance involvement liability, though a few substance-disorder pairings appear to be specific:
The substantial comorbidity between psychiatric and substance use disorders is unequivocal (e.g.,
Associations between PRS and individual substances were only partially attributable to GENSUB, indicating specificity of certain relationships (e.g., ADHD PRS and nicotine involvement). This is significant, considering that twin studies implicate GENSUB as the primary source of genetic variance in individual substance use disorders (
ADHD PRS were associated with nicotine and cannabis involvement, even after controlling for GENSUB. These findings are markedly consistent with an expansive epidemiological and clinical literature documenting higher rates of cigarette smoking in individuals with ADHD, even after accounting for comorbid conduct problems (e.g.,
The lack of association between AUT and substance involvement was unsurprising given a mixed literature linking autism spectrum disorders to relatively reduced (e.g.,
Our findings of positive associations between BIP PRS and multiple levels of alcohol, cocaine, cannabis, and opioid involvement are consistent with observations of markedly elevated rates of substance use and use disorders in individuals with BIP (
Elevated polygenic liability to MDD in our sample was associated with increasing problematic use of alcohol, cocaine, nicotine, and cannabis, in-line with prior twin studies suggesting MDD shares genetic liability with alcohol (
SCZ PRS were associated with involvement across all substances tested, but only associations with non-problem cannabis use and severe cocaine dependence persisted upon inclusion of GENSUB. Notably, both substances have been previously implicated in the etiology of psychotic illness. Cocaine use is common among individuals with SCZ (
Some limitations of our study are noteworthy. First, comorbidities in the cross-disorder meta-analysis from which the PRS were derived, as well as in the target SAGE sample, may be subject to certain unmeasured confounds. For example, the PGC did not examine the extent of cocaine (or other substance) use in their sample population (
Second, SAGE was ascertained for liability to substance dependence, specifically to alcohol, nicotine, and cocaine; the generalizability of these findings to the general population is thus unclear. Additionally, the factor structure of GENSUB might be somewhat sample-specific, and residual associations with involvement with specific substances (i.e., non-problem cannabis use) may have been artifacts of sample ascertainment. However, this ascertainment strategy allowed us to study the full range of substance involvement—from never-use to severe dependence—across both licit (i.e., alcohol, nicotine) and illicit (i.e., cannabis, cocaine, opioids) drugs, which would not have been possible in a population-based sample of comparable size. Nonetheless, it is important to replicate these findings in other samples.
Third, though multiple nominally significant relationships between genetic risk for individual psychiatric disorders and involvement with specific substances emerged, few survived correction for the large number of statistical tests performed. These results thus may represent spurious associations and should be interpreted with caution. However, given the consistency of certain associations (e.g., ADHD and nicotine use) with prior genetic (e.g.,
Fourth, while our PRS approach yielded evidence that shared common genetic architecture contributes to comorbidity between psychopathology and substance involvement, it does not provide insight into specific biological (e.g., reward-related neural responsiveness, epigenetically medicated changes in gene expression), psychological (e.g., anhedonia, impulsivity), and/or experiential (e.g., early life stress, peer group) mechanisms through which this risk is manifest (e.g.,
Our study provides some of the first evidence that common polygenic liability to major psychiatric disorders is related to use and misuse of licit and illicit substances, providing new insights into the etiology of this well documented comorbidity. Future efforts might attempt to determine which specific biological pathways and networks underlie this shared genetic variance, or prospectively evaluate the predictive power of such PRS: for instance, whether polygenic risk for SCZ predicts onset, severity, and prognosis of illness in youth who experiment with cannabis and other drugs. Additionally, the inclusion of a substance use disorders workgroup in the second iteration of the PGC
CC, AA, and RB were responsible for the conception and design of the study. CC performed all analyses. CC, AA, and RB drafted the manuscript. LB collected the SAGE dataset and, along with SH, EN, KB, and ML, provided expertise on analyses. All authors critically reviewed content and provided feedback.
LB is listed as an inventor on Issued U.S. Patent 8,080,371, “Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. All the other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RB and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.
CC received support from the National Science Foundation (DGE-1143954) and the Mr. and Mrs. Spencer T. Olin Fellowship Program. AA (DA23668, DA32573), LB (DA036583), and SH (DA032680) received support from the National Institute on Drug Abuse. RB was supported by the Klingenstein Third Generation Foundation and the National Institute on Aging (AG045231).
Support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative [GEI; U01 HG004422; dbGaP study accession phs000092.v1.p1]. SAGE is one of the genome-wide association studies funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center [U01 HG004446]. Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism [COGA; U10 AA008401], the Collaborative Genetic Study of Nicotine Dependence [COGEND; P01 CA089392], and the Family Study of Cocaine Dependence [FSCD; R01 DA013423, R01 DA019963]. Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI [U01HG004438], the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” [HHSN268200782096C].
The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut, includes 10 different centers: University of Connecticut (V. Hesselbrock); Indiana University (H. J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, A. Goate, J. Rice, K. Bucholz); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield); Texas Biomedical Research Institute (L. Almasy), Howard University (R. Taylor) and Virginia Commonwealth University (D. Dick). Other COGA collaborators include: L. Bauer (University of Connecticut); D. Koller, S. O’Connor, L. Wetherill, X. Xuei (Indiana University); Grace Chan (University of Connecticut); S. Kang, N. Manz, (SUNY Downstate); J.-C Wang (Washington University in St. Louis); A. Brooks (Rutgers University); and F. Aliev (Virginia Commonwealth University). A. Parsian and M. Reilly are the NIAAA Staff Collaborators.
We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, currently a consultant with COGA, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant# UL1RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Funding support for GWAS genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438), and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). The authors thank Kim Doheny and Elizabeth Pugh from CIDR and Justin Paschall from the NCBI dbGaP staff for valuable assistance with genotyping and quality control in developing the dataset available at dbGaP. This publication is solely the responsibility of the authors and does not necessarily represent the official view of the funders.
The Supplementary Material for this article can be found online at: