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RESEARCH REPORT
Using Mendelian randomization to explore the gateway
hypothesis: possible causal effects of smoking initiation and
alcohol consumption on substance use outcomes
Zoe E. Reed
1,2
| Robyn E. Wootton
2,3
| Marcus R. Munafò
1,2,4
1
School of Psychological Science, University of
Bristol, Bristol, UK
2
MRC Integrative Epidemiology Unit,
University of Bristol, Bristol, UK
3
Nic Waals Institute, Lovisenberg Diaconal
Hospital, Oslo, Norway
4
National Institute for Health Research Bristol
Biomedical Research Centre, University
Hospitals Bristol NHS Foundation Trust and
University of Bristol, Bristol, UK
Correspondence
Zoe E. Reed, Reed, School of Psychological
Science, University of Bristol, Bristol, UK.
Email: zoe.reed@bristol.ac.uk
Funding information
Medical Research Council, Grant/Award
Number: MC_UU_00011/7; National Institute
for Health Research (NIHR) Biomedical
Research Centre; South-Eastern Regional
Health Authority, Grant/Award Number:
2020024
Abstract
Background and Aims: Initial use of drugs such as tobacco and alcohol may lead to sub-
sequent more problematic drug use—the ‘gateway’hypothesis. However, observed
associations may be due to a shared underlying risk factor, such as trait impulsivity. We
used bidirectional Mendelian randomization (MR) to test the gateway hypothesis.
Design: Our main method was inverse-variance weighted (IVW) MR, with other methods
included as sensitivity analyses (where consistent results across methods would raise
confidence in our primary results). MR is a genetic instrumental variable approach used
to support stronger causal inference in observational studies.
Setting and participants: Genome-wide association summary data among European
ancestry individuals for smoking initiation, alcoholic drinks per week, cannabis use and
dependence, cocaine and opioid dependence (n= 1749–1 232 091).
Measurements: Genetic variants for exposure.
Findings: We found evidence of causal effects from smoking initiation to increased drinks
per week [(IVW): β= 0.06; 95% confidence interval (CI) = 0.03–0.09; P=9.44×10
−06
], can-
nabis use [IVW: odds ratio (OR) = 1.34; 95% CI = 1.24–1.44; P=1.95×10
−14
] and cannabis
dependence (IVW: OR = 1.68; 95% CI = 1.12–2.51; P= 0.01). We also found evidence of
an effect of cannabis use on the increased likelihood of smoking initiation (IVW: OR = 1.39;
95% CI = 1.08–1.80; P= 0.01). We did not find evidence of an effect of drinks per week on
other substance use outcomes, except weak evidence of an effect on cannabis use (IVW:
OR = 0.55; 95% CI = 0.16–1.93; P-value = 0.35). We found weak evidence of an effect of
opioid dependence on increased drinks per week (IVW: β= 0.002; 95% CI = 0.0005–0.003;
P=8.61×10
−03
).
Conclusions: Bidirectional Mendelian randomization testing of the gateway hypothesis
reveals that smoking initiation may lead to increased alcohol consumption, cannabis use
and cannabis dependence. Cannabis use may also lead to smoking initiation and opioid
dependence to alcohol consumption. However, given that tobacco and alcohol use
typically begin before other drug use, these results may reflect a shared risk factor or a
bidirectional effect for cannabis use and opioid dependence.
Received: 12 January 2021 Revised: 11 May 2021 Accepted: 11 August 2021
DOI: 10.1111/add.15673
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
Addiction. 2021;1–10. wileyonlinelibrary.com/journal/add 1
KEYWORDS
Alcohol consumption, cannabis, gateway hypothesis, Mendelian randomization, smoking initiation,
substance use
INTRODUCTION
Illicit substance use and substance use disorders result in a substantial
global burden on a range of health conditions [1,2]. Identifying causal
risk factors in the development of problematic substance use is impor-
tant for designing successful interventions and preventing subsequent
health problems.
The gateway hypothesis, in its simplest form, is the theory that
initial use of legal ‘gateway’drugs, including tobacco and alcohol, may
lead to illicit drug use such as cannabis, cocaine and opioids [3–5].
Previous studies have found associations between smoking initiation
and use of alcohol [6], cannabis [7,8], cocaine [9] and opioids [10].
Studies also suggest alcohol as a possible gateway drug [11–14].
Given that tobacco and alcohol consumption are likely to both occur
initially during adolescence, and typically before other drug-taking, it
is important to investigate both as potential gateway drugs. Prospec-
tive studies also support the gateway hypothesis for these outcomes
[7,8,15–17], suggesting possible causal relationships. Substance use
behaviours are moderately heritable (21–72% in twin studies)
[18–22]. Genetic correlations have also been found between different
substance use phenotypes (r
G
= 0.35–0.66) [23–26].
While these studies may support the gateway hypothesis it is
equally plausible that there are underlying shared risk factors; for
example, risk-taking or impulsive behaviours. Previous studies have
reported an association of attention deficit hyperactive disorder
(ADHD) with substance use outcomes [27,28] and ADHD genetic risk
with smoking initiation [29,30], supporting impulsivity as a potential
shared risk factor, although others—such as risk-taking, or adverse
childhood experiences —could also lead to these outcomes. In terms
of establishing whether the relationships between smoking and alco-
hol and other substance use are causal, there is some evidence
(e.g. from randomized controlled trials) that smoking cessation may
result in reduced substance use or abstinence [31], supporting a
possible causal effect of smoking on substance use outcomes.
Mendelian randomization (MR) is a well-established method for
causal inference based on instrumental variable (IV) analysis, which
attempts to overcome issues of residual confounding and reverse
causation [32–35]. MR uses genetic variants, assigned randomly at
conception, as IVs for an exposure to estimate the causal relationship
with an outcome. In two-sample MR [36] the single nucleotide poly-
morphism (SNP)-exposure and SNP-outcome estimates are obtained
from independent-sample genome-wide association studies (GWAS)
to estimate possible causal effects. Previous MR studies examining
this relationship examined cannabis use only, and used smaller GWAS
sample sizes than in the current study. One study found weak evi-
dence of a causal effect of smoking initiation on cannabis use [37],
while the other found no evidence [38]. Incorporating larger GWAS
and a range of substance use outcomes may improve power to detect
causal effects and provide clearer evidence as to whether or not these
relationships are due to a gateway effect.
We applied this two-sample MR approach to investigate the possi-
ble causal effect between both smoking initiation and alcohol con-
sumption (defined as drinks per week) and substance use outcomes of
cannabis use and dependence, cocaine dependence and opioid depen-
dence. We refer to these outcomes as ‘illicit substance use’,although
we acknowledge that cannabis is not illegal in all jurisdictions. We also
examined the association between smoking initiation and alcohol con-
sumption. We used a bidirectional approach (Fig. 1) to assess whether
there is evidence supporting the gateway hypothesis (i.e. that smoking
initiation/alcohol consumption can lead to use of other substances and
dependence) or whether there is evidence of a shared risk factor. Some
pathways (e.g. from opioid use to smoking initiation) are unlikely, so
analyses in this direction acted more as a sensitivity analysis, which
could help to identify a shared risk factor rather than a causal effect.
FIGURE 1 Bidirectional two-sample Mendelian randomization between smoking initiation/alcohol consumption and illicit substance use
outcomes. A directed acyclic graph (DAG) for the causal effect between smoking initiation/alcohol consumption and illicit substance use
outcomes. Evidence of a causal effect in the other direction may indicate a bidirectional effect or a common underlying risk factor
2REED ET AL.
METHODS
Data sources
We used GWAS summary statistics obtained from several consortia
and other samples, the details of which are shown in Table 1, together
with the variance explained by genome-wide significant SNPs and
SNP heritabilities where these were reported. GWAS were conducted
in samples of European ancestry. Sample overlap should be avoided
or reduced, so as not to bias the estimates towards a more
conservative effect estimate [39]. Therefore, we used GWAS with
certain samples excluded from the consortia (see Table 1).
Smoking initiation
The smoking initiation GWAS [23] identified 378 conditionally inde-
pendent genome-wide significant SNPs associated with ever being a
smoker, i.e. where participants reported ever being a regular smoker
in their life. See Supporting information for further details. The total
sample size was 1 232 091 for the GWAS and Sequencing Consor-
tium of Alcohol and Nicotine use (GSCAN) consortium; however, the
sample size for the GWAS in each of our analyses varied to try to
avoid sample overlap (see Table 1). Full genome-wide summary statis-
tics were only publicly available without 23andMe. We requested
23andMe summary statistics separately and meta-analysed them with
the publicly available data to recreate the original full GWAS summary
statistics. The meta-analysis was conducted using the genome-wide
association meta-analysis (GWAMA) software [44].
Drinks per week
The drinks per week GWAS [23] identified 99 independent
genome-wide significant SNPs associated with the average number of
alcoholic drinks consumed per week. See Supporting information for
further details.
Cannabis use
The cannabis use GWAS [40] identified eight independent genome-
wide significant SNPs associated with ever using cannabis. See
Supporting information for further details.
Cannabis dependence
The cannabis dependence GWAS [41] did not identify any genome-
wide significant SNPs associated with cannabis dependence. Cases
were established based on meeting three or more criteria for Diagnos-
tic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV)
cannabis dependence.
Cocaine dependence
The cocaine dependence GWAS [42] identified one genome-wide sig-
nificant SNP associated with cocaine dependence. All participants
were interviewed using the semi-structured assessment for drug
dependence and alcoholism (SSADA) and cocaine-dependent cases
were established based on responses according to the DSM-IV criteria
and reflect life-time cocaine dependence.
Opioid dependence
The opioid dependence GWAS [43] did not identify any genome-wide
significant SNPs associated with opioid dependence. All participants
were interviewed using the SSADA and opioid-dependent cases were
established based on responses according to the DSM-IV criteria and
reflect life-time opioid dependence.
Units for all binary measures were in log odds ratios (ORs) and for
the continuous drinks per week measure were per standard deviation
(SD) increase in the number of drinks per week.
Statistical analyses
MR was used to assess whether relationships may be causal by using
genetic variants as IV proxies for the exposures. Further details can be
found in the Supporting information. Two-sample MR was conducted
in R (version 4.0.0) [45] using the TwoSampleMR package (version
0.5.3) [46,47]. Genome-wide significant SNPs were selected as instru-
ments for the smoking initiation, alcohol and cannabis use exposures.
However, where cocaine, opioid and cannabis dependence were the
exposures, there were either too few or no genome-wide significant
SNPs, so we used a less stringent threshold of 1 × 10
−05
.
Multiple MR methods were used to assess the causal effects of:
(i) the exposure of smoking initiation/alcohol consumption on illicit
substance use outcomes and (ii) illicit substance use exposures on
smoking initiation/alcohol consumption. These were inverse-variance
weighted (IVW) [48], MR-Egger [49], weighted median [50], simple
mode and weighted mode [51] MR methods. We were interested in
the question of whether there is evidence of causal effects. We were
concerned with the strength of evidence for an effect, as opposed to
the effect estimate, and considered whether the direction of effect
was as predicted and the strength of statistical evidence against the
null. To do this we interpreted the P-value as a continuous measure of
statistical evidence [52] and considered whether our results were con-
sistent across different MR approaches. The IVW approach was our
main method, with the others being sensitivity analyses which make
different assumptions. We describe our findings in terms of lack of
evidence, weak evidence, evidence or strong evidence of an effect,
accounting for all these factors. The sensitivity methods have less sta-
tistical power than the IVW approach; therefore, we considered all
results and the consistency of the direction of effect observed among
MR AND THE GATEWAY HYPOTHESIS 3
TABLE 1 GWAS used for two-sample Mendelian randomization.
Phenotype Reference Consortium or sample Excluded samples Final sample size
r
2
and SNP
heritability
a
Smoking initiation (ever/
never)
Liu et al., 2019
[23]
GSCAN (23andMe, ALSPAC, ARIC, BEAGESS,
BLTS, CADD, COGEND, COPDGene,
deCODE, EGCUT, FHS, FinnTwin, GERA,
GfG, Harvard, HRS, HUNT, MCTFR,
MESA, METSIM, NESCOG, NAG-FIN,
NTR, QIMR, SardiNIA, UK Biobank, WHI)
Dependent on analysis, published GWAS
summary statistics were used other than
for analyses with outcomes for (i) cannabis
use where only the 23andMe sample was
included and (ii) drinks per week where
23andMe was excluded
All samples = 1 232 091
i) cannabis use = 599 289
ii) drinks per
week = 632 802
r
2
= 2.32%
h
2
= 7.8%
Drinks per week Liu et al., 2019
[23]
GSCAN Dependent on analysis, published GWAS
summary statistics were used other than
for analyses with outcomes for (i) cannabis
use where only the 23andMe sample was
included
All samples = 941 280
(i) cannabis use = 403 931
r
2
= 0.19%
h
2
= 4.2%
Cannabis use (ever/never) Pasman et al.,
2018 [40]
ICC (ALSPAC, BLTS, CADD, EGCUT1,
EGCUT2, FinnTwin, HUVH, MCTFR, NTR,
QIMR, TRAILS, Utrecht, Yale Penn EA), UK
Biobank and 23andMe
23andMe 162 082 r
2
= 0.15%
h
2
= 11%
Cannabis dependence Agrawal et al.,
2017 [41]
CATS, COGA-cc, COGA-f, OZALC, SAGE NA 2080 cases and 6435
exposed controls
Not reported
Cocaine dependence (DSM-
IV criteria)
Gelernter et al.,
2014 [42]
Yale (APT Foundation), University of CT,
MUSC, McLean Hospital, University of
Pennsylvania
NA 1809 cases and 292 exposed
controls
Not reported
Opioid dependence (DSM-
IV criteria)
Gelernter et al,
2014 [43]
Yale (APT Foundation), University of CT,
MUSC, McLean Hospital, University of
Pennsylvania
NA 1383 cases and 366 exposed
controls
Not reported
a
The r
2
reported was for genome-wide significant SNPs only and the SNP heritability (h
2
) was for all SNPs. GSCAN = GWAS and Sequencing Consortium of Alcohol and Nicotine use; ALSPAC = Avon
Longitudinal Study of Parents and Children; ARIC = Atherosclerosis Risk in Communities; BEAGESS = the Barrett’s and Esophageal Adenocarcinoma Genetic Susceptibility Study, BLTS = Brisbane Longitudinal
Twin Study; CADD = Center on Antisocial Drug Dependence; COGEND = Collaborative Genetic Study of Nicotine Dependence; COPDGene = Genetics of Chronic Obstructive Pulmonary Disease;
EGCUT = Estonian Genome Center; FHS = Framingham Heart Study; FinnTwin and NAG-FIN = Finnish Twin Cohort; GERA = Genetic Epidemiology Research in Adult Health and Aging; GfG = Genes for
Good; Harvard HRS = Health and Retirement Study; HUNT = the Nord-Trøndelag Health Study; MCTFR = Minnesota Center for Twin and Family Research; MESA = Multi-Ethnic Study of Atherosclerosis;
METSIM = Metabolic Syndrome in Men; NESCOG = Netherlands Study on Cognition, Environment and Genese; NTR = Netherlands Twin Register; WHI = Women’s Health Initiative; COGA = Collaborative
Study on the Genetics of Alcoholism; SAGE = Study of Addictions: Genes and Environment; OZALC = Australian Alcohol, Nicotine Addiction Genetics and Childhood Trauma; CATS = Comorbidity and Trauma
Study; SNP = single nucleotide polymorphism; GWAS = genome-wide association study.
4REED ET AL.
analyses. Inconsistent results for these sensitivity analyses may indi-
cate that some MR assumptions are violated (e.g. pleiotropic path-
ways are operating). Specifically, the IVW method constrains the
intercept to be zero and assumes that all SNPs are valid instruments
with no horizontal pleiotropy. Horizontal pleiotropy can be problem-
atic, as MR assumptions may be violated if the SNPs affect the out-
come via a different pathway. Therefore, we included additional tests
which can detect whether horizontal pleiotropy may be present. For
example, we included results for the Cochran’s test of heterogeneity,
which assesses whether there is heterogeneity in the SNP
exposure
–
SNP
outcome
associations for each SNP included in the instrument. If
there is evidence of heterogeneity this may indicate possible horizon-
tal pleiotropy.
The MR-Egger method tests whether there is overall directional
pleiotropy by not constraining the intercept, where a non-zero inter-
cept indicates directional horizontal pleiotropy. We also used the
Rucker’s Q-test to assess heterogeneity in the MR-Egger estimates
for individual SNPs, similar to the Cochran’s test. The weighted
median method provides an estimate under the assumption that at
least 50% of the SNPs are valid instruments (i.e. satisfy the IV assump-
tions). Finally, the mode-based approaches provide an estimate for
the largest cluster of similar SNPs, where the SNPs not in that cluster
could be invalid, with the weighted method taking into account the
largest weights of SNPs. Additionally, we estimated effects for single
SNP and leave-one-out analyses and plotted these results where
there was evidence for a causal effect.
We also estimated the mean F-statistic, unweighted and weighted
I-squared values for each of the analyses [53]. The F-statistic repre-
sents instrument strength, where a value under 10 may indicate a
weak instrument [53]. The I-squared value falls between 0 and 1 and
indicates the amount of bias in the ‘no measurement error’(NOME)
assumption in the MR-Egger estimate. If bias was apparent, we ran
simulation extrapolation (SIMEX) corrections and present these in
place of the MR-Egger results; if the bias was too large, neither were
presented (see Supporting information for further details).
Finally, we conducted multivariable MR (MVMR) to investigate
whether the causal effect of smoking initiation was independent of
that for the drinks per week exposure for any illicit substance use out-
comes where both exposures were associated with the outcome.
MVMR is an extension of MR that estimates the causal effect of mul-
tiple exposures on an outcome and assesses whether each exposure
is independent of the others [54]. Please note that our analyses were
not pre-registered, and therefore our results should be considered
exploratory.
RESULT
Evidence of causal effects of smoking initiation on
illicit substance use outcomes
Our two-sample MR results (Supporting information, Table S2 and
Fig. 2) indicated that there was evidence for a causal effect of smoking
initiation on increased drinks per week (IVW: β= 0.06; 95% CI = 0.03–
0.09; P-value = 9.44 × 10
−06
). The I-squared values (Supporting infor-
mation, Table S1) suggest that the MR-Egger method was unsuitable;
therefore, results are not presented for this. Results were in a consis-
tent direction with evidence of a causal effect among the different MR
analyses (see also Supporting information, Fig. S1). We observed evi-
dence of heterogeneity in results for the IVW method (see also
Supporting information, Fig. S2), but this was not necessarily indicative
of horizontal pleiotropy (see also Supporting information, Fig. S3).
Leave-one-out analyses did not reveal that any single SNP was driving
the association (Supporting information, Fig. S4).
We also found evidence of a causal effect of smoking initiation
on cannabis use (IVW: OR = 1.34; 95% CI = 1.24–1.44; P-
value = 1.95 × 10
−14
). Results were in a consistent direction among
MR analyses (see also Supporting information, Fig. S5), although evi-
dence for this was only found additionally for the weighted median
method. There was evidence of heterogeneity with both the IVW and
MR-Egger methods (see also Supporting information, Fig. S6) but not
horizontal pleiotropy (see also Supporting information, Fig. S7). Leave-
one-out analyses did not reveal that any single SNP was driving the
association (Supporting information, Fig. S8).
FIGURE 2 Forest plot for two-sample Mendelian randomization with smoking initiation as the exposure. Causal effects from the inverse-
variance weighted Mendelian randomization method where smoking initiation is the exposure. Effect estimates are presented as beta or odds
ratios (OR) depending on whether the outcome was continuous or binary, with 95% confidence intervals (CI). SNP = single nucleotide
polymorphism
MR AND THE GATEWAY HYPOTHESIS 5
We found evidence of a causal effect of smoking initiation on
cannabis dependence (IVW: OR = 1.68; 95% CI = 1.12–2.51;
P-value = 0.01). Results were in a consistent direction for the
SIMEX-adjusted MR-Egger and weighted median methods
(Supporting information, Table S1), although evidence for these was
weak (see also Supporting information, Fig. S9). There was no evi-
dence of heterogeneity or horizontal pleiotropy (also see Supporting
information, Figs S10 and S11). Leave-one-out analyses did not reveal
that any single SNP was driving the association (Supporting
information, Fig. S12).
Finally, we did not find evidence of a causal effect of smoking
initiation on cocaine dependence (IVW: OR = 1.21; 95% CI = 0.58–
2.53; P-value = 0.60) or opioid dependence (IVW: OR = 1.41; 95%
CI = 0.62–3.20; P-value = 0.41) with any of the MR analyses, except
for weak evidence for the SIMEX-adjusted (Supporting information,
Table S1) MR-Egger for cocaine dependence. There was no evidence
of heterogeneity or horizontal pleiotropy for cocaine or opioid
dependence.
Causal effects of illicit substance use exposures on
smoking initiation
For the direction of illicit substance use to smoking initiation
(Supporting information, Table S3 and Fig. 3) we found evidence of a
causal effect of cannabis use on smoking initiation (IVW: OR = 1.39;
95% CI = 1.08–1.80; P-value = 0.01) for all MR analyses except MR-
Egger. Results were in a consistent direction across MR analyses (see
also Supporting information, Fig. S13). We observed evidence of het-
erogeneity in these results for the IVW and MR-Egger methods (see
also Supporting information, Fig. S14), but not horizontal pleiotropy
(see also Supporting information, Fig. S15). Leave-one-out analyses
did not reveal that any single SNP was driving the association
(Supporting information, Fig. S16).
We did not find any evidence of a causal effect of drinks per week,
(IVW: OR = 1.26; 95% CI = 0.92–1.72; P-value = 0.15), cannabis depen-
dence (IVW: OR = 1.00; 95% CI = 0.99–1.01; P-value = 0.60), cocaine
dependence (IVW: OR = 1.00; 95% CI = 1.00–1.00; P-value = 0.42) or
opioid dependence (IVW: OR = 1.00; 95% CI = 0.99–1.01;
P-value = 0.80) on smoking initiation for any of the MR analyses.
Causal effects of drinks per week on illicit substance
use outcomes
When examining whether or not there was evidence for causal effects
of alcohol consumption (drinks per week) on the illicit substance use
phenotypes (Supporting information, Table S4 and Fig. 4) we did not find
any evidence for the IVW approach for cannabis use (IVW: OR = 0.55;
95% CI = 0.16–1.93; P-value = 0.35), although there was some evidence
of a causal effect with the other MR analyses. We did not find evidence
of a causal effect on cannabis dependence (IVW: OR = 2.73; 95%
CI = 0.62–11.95; P-value = 0.18), cocaine dependence (IVW: OR = 0.50;
95% CI = 0.09–2.79; P-value = 0.43) or opioid dependence (IVW:
OR = 0.38; 95% CI = 0.06–2.41; P-value = 0.30).
Causal effects of illicit substance use exposures on
drinks per week
For the reverse direction (Supporting information, Table S5 and Fig. 5)
we did not find evidence of a causal effect of cannabis use (IVW:
β=0.03; 95% CI=–0.009 to 0.07; P-value = 0.14), cannabis depen-
dence (IVW: β=−0.0003; 95% CI = –0.003 to 0.002; P-value = 0.80) or
cocaine dependence (IVW: β=0.0007; 95% CI=–0.00007 to 0.001;
P-value = 0.08) on drinks per week.
There was weak evidence to suggest a causal effect of opioid
dependence on drinks per week (IVW: β= 0.002; 95% CI = 0.0005–
0.003; P-value=8.61×10
−03
), although the effect size was very small
and this was not found for any other MR analyses (see also Supporting
information, Fig. S17). There was no evidence of heterogeneity (see also
Supporting information, Fig. S18) or horizontal pleiotropy (Supporting
information, Fig. S19). Leave-one-out analyses did not reveal that any
single SNP was driving the association (Supporting information, Fig. S20).
Multivariable MR analysis for cannabis use
We conducted MVMR analysis for cannabis use only due to evidence
of a causal effect of smoking initiation on cannabis use and weak evi-
dence of a causal effect of drinks per week on cannabis use. We
found evidence of a direct effect of smoking initiation, independent of
FIGURE 3 Forest plot for two-sample Mendelian randomization with smoking initiation as the outcome. Causal effects from the inverse-
variance weighted Mendelian randomization method where smoking initiation is the outcome. Effect estimates are presented as odds ratios
(OR) with 95% confidence intervals (CI). SNP = single nucleotide polymorphism
6REED ET AL.
drinks per week on cannabis use (OR = 1.35; 95% CI = 1.25–1.46;
P-value = 3.67 × 10
−12
). This result was similar to that from the two-
sample MR model. However, there was no evidence of a direct effect
of drinks per week on cannabis use (OR = 0.71; 95% CI = 0.29–1.76;
P-value = 0.47).
DISCUSSION
We examined whether there was evidence for causal effects of
smoking initiation and alcohol consumption on cannabis use and
dependence on cannabis, cocaine and opioids, which may support the
‘gateway’hypothesis. We also examined the reverse direction, where
evidence of an association, particularly in both directions, may be
indicative of an underlying common risk factor.
Our main findings were those for cannabis use and dependence,
which suggest that ever smoking may act as a gateway to subsequent
cannabis use and perhaps even dependence, although evidence was
weaker for the latter. This supports previous observational studies
demonstrating an association between these phenotypes [7,8,55], and
is in line with previous findings suggesting that tobacco is a gateway
drug to other more problematic substance use [5,6,8,10]. Our MR
analyses support stronger causal inference, although further triangula-
tion with other study designs would strengthen this. Previous litera-
ture also suggests that alcohol consumption may be causally
associated with cannabis use; however, our MVMR results suggest no
evidence for independent effects of alcohol consumption, only evi-
dence for a causal effect of smoking initiation on cannabis use.
We also found evidence for a potential causal pathway from can-
nabis use to smoking initiation. It has been previously suggested that
cannabis use may act as gateway to tobacco use, possibly due to the
form in which cannabis is used, i.e. if smoked with tobacco [56].
However, our finding of potential causal pathways between cannabis
use and smoking initiation in both directions may suggest that this
association is due to an underlying common risk factor, as opposed to
either being a gateway drug. We found that all the SNPs used in the
cannabis use instrument, except one, are in linkage disequilibrium
(LD) with genome-wide significant SNPs in the smoking initiation
GWAS (r
2
> 0.27, 250 kb window for three SNPs). As these genetic
instruments may overlap, this does not help us to disentangle the
reason behind this relationship.
There are several potential reasons for our results: (1) a causal
effect of smoking initiation on cannabis use, (2) a causal effect of can-
nabis use on smoking initiation, (3) a bidirectional effect, (4) an under-
lying shared risk factor, (5) horizontal pleiotropy (although our
sensitivity analyses suggested this was not biasing results) and (6) con-
founding due to LD. Without further understanding of the biological
function of these genetic variants it is difficult to conclude which of
these explanations (which are not mutually exclusive) could be true,
and this has been discussed previously in relation to mental health
behavioural risk factors [57,58]. Previous studies have suggested that
impulsive or risk-taking behaviours may be associated with smoking
initiation and substance use [59–61]. Additionally, cannabis use may
capture underlying risk-taking behaviours more than the dependence
measures, and this may be why we see a more consistent association
with this measure. Further research is needed to establish whether
there could be an underlying common cause, and if this might be
related to risk-taking behaviours. Other potential shared risk factors
should also be considered, and these may be genetic or environmental
in origin and may vary between different illicit substance use pheno-
types. In addition, it may be the case that smoking initiation, for exam-
ple, only acts as a gateway to other substances in the presence of
FIGURE 4 Forest plot for two-sample Mendelian randomization with drinks per week as the exposure. Causal effects from the inverse-
variance weighted Mendelian randomization method where drinks per week is the exposure. Effect estimates are presented as odds ratios
(OR) with 95% confidence intervals (CI). SNP = single nucleotide polymorphism
FIGURE 5 Forest plot for two-sample Mendelian randomization with drinks per week as the outcome. Causal effects from the inverse-
variance weighted Mendelian randomization method where drinks per week is the outcome. Effect estimates are presented as beta with 95%
confidence intervals (CI). SNP = single nucleotide polymorphism
MR AND THE GATEWAY HYPOTHESIS 7
mediators such as stressful life events or adverse circumstances.
Therefore, the mechanisms behind these associations need to be
examined further, and the possibility of a bidirectional relationship
should also be considered.
We found a potential causal effect of smoking initiation on
increased drinks per week, but did not find an association in the reverse
direction. It is plausible that an underlying risk-taking behaviour may
affect alcohol consumption via smoking. However, a biological mecha-
nism behind this association should also be considered and studied fur-
ther. Finally, we saw weak evidence of a causal effect of opioid
dependence on increased drinks per week; however, due to the low
power for the opioid dependence GWAS and the small effect size we
would interpret this with caution. Opioid dependence (compared with
ever use) is less probably explained by underlying risk-taking behaviour.
Therefore, research into alternative shared risk factors is warranted. It
may be the case that opioid dependence has a causal effect on
increased alcohol use, and this also warrants further investigation.
Limitations
Our study is the first, to our knowledge, to examine whether
causal pathways may exist between smoking initiation/alcohol con-
sumption and various illicit substance use phenotypes using an MR
approach. However, there are several limitations to note; for exam-
ple, some of our analyses may be limited in their power to detect a
causal effect. This is particularly the case where the dependence
measures were the exposures, as the GWAS discovery samples were
much smaller than those for drinks per week, cannabis use and
smoking initiation. Additionally, we used a less stringent P-value
threshold of 1 × 10
−05
due to a low number of genome-wide signifi-
cant SNPs. Therefore, these instruments may be less robustly
associated with the exposure, and pleiotropy could be introduced.
For our results with the dependence variables as exposures the CIs
were very narrow, which could be a result of the relaxed P-value
thresholds used. However, the absence of evidence here does not
mean we can exclude the possibility of an effect for this relationship.
Furthermore, the lower number of SNPs used for the dependence
exposures may mean that the instruments are weak, which may be
particularly problematic for MR-Egger. Additional caution should be
taken when interpreting this result for our finding of an effect of
opioid dependence on drinks per week, as the opioid dependence
exposure is a dichotomized variable for an underlying latent risk
factor. Thus, the estimate here is less interpretable than for our other
results and instead focus should be upon the direction and evidence
of an effect as opposed to the effect size. Therefore, our dependence
results should be interpreted with caution revisited once larger
GWAS become available.
We also found some evidence of heterogeneity and horizontal
pleiotropy for different analyses, meaning that these results should
be interpreted in light of this, as some of the SNPs used may be
associated with the outcome other than via the exposure. However,
the additional MR analyses, which account for this, were generally
in the same direction as our main results, although we were unable
to formally test for directional pleiotropy in some cases where the I-
squared estimate was low. In cases where the IVW shows evidence
for a causal effect but results are inconsistent across the sensitivity
analyses, this may be indicative of pleiotropy. However, inconsistent
effects across sensitivity analyses and no evidence from the IVW is
more likely to reflect a lack of evidence for an effect.
Another consideration is that the MR instruments used may not
be valid for smoking, as they may be picking up risk-taking behaviours
more than smoking itself [62]. Therefore, it would be useful to exam-
ine this further with other smoking-related phenotypes, such as
smoking heaviness. Additionally, while we tried to avoid sample
overlap, there was still some for the cannabis use GWAS (17% of the
sample was also present in the GWAS for smoking initiation and
drinks per week). Sample overlap could bias estimates towards a more
conservative effect estimate [39], which should be considered when
interpreting our results.
The MR analysis itself is subject to several limitations [33]. For
example, the GWAS used for MR may suffer from ‘winner’s curse’,
where the SNP-exposure estimates may be overestimated due to
selecting SNPs with the smallest P-values and biasing the MR esti-
mate towards the null. Thus, interpreting the direction of effect as
opposed to the effect size itself is more valid here. The effect estimate
may also be biased by trait heterogeneity; for example, different
aspects of substance use behaviours may be associated with the same
genetic variants and therefore it is difficult to gain a precise estimate
for a single aspect of any substance use behaviour.
Finally, our results should be considered in the context of the
multiple potential causal pathways that we have investigated.
CONCLUSION
While, to some extent, our findings support the gateway hypothesis
they also point to a potential underlying common risk factor, and with
better-powered GWAS or those with more precise instruments and
additional research we may be able to interrogate this further.
Triangulating our results with other approaches would help to answer
this question [63,64]. By so doing we may be able to identify risk fac-
tors to substance use which could ultimately help with intervention
design.
DECLARATION OF INTERESTS
None.
ACKNOWLEDGEMENTS
This work was supported in part by Public Health England, the UK
Medical Research Council Integrative Epidemiology Unit at the
University of Bristol (Grant ref.: MC_UU_00011/7) and the National
Institute for Health Research (NIHR) Biomedical Research Centre at
the University Hospitals Bristol National Health Service Foundation
8REED ET AL.
Trust and the University of Bristol. The views expressed in this
publication are those of the authors and not necessarily those of the
National Health Service, the National Institute for Health Research or
the Department of Health. R.W. was supported by a postdoctoral fel-
lowship from the South-Eastern Regional Health Authority (2020024).
We thank all the contributors to the consortia we have used GWAS
results from in our analyses. We would also like to thank the research
participants and employees of 23andMe for making this work
possible.
AUTHOR CONTRIBUTIONS
Zoe Reed: Data curation, formal analysis, investigation, methodology,
resources, software, visualization. Robyn Wootton: Methodology,
resources, visualization. Marcus Munafo: Conceptualization, funding
acquisition, methodology, project administration; supervision.
ORCID
Zoe E. Reed https://orcid.org/0000-0002-2990-6979
Robyn E. Wootton https://orcid.org/0000-0003-3961-3202
Marcus R. Munafò https://orcid.org/0000-0002-4049-993X
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SUPPORTING INFORMATION
Additional supporting information may be found in the online version
of the article at the publisher’s website.
How to cite this article: Reed ZE, Wootton RE, Munafò MR.
Using Mendelian randomization to explore the gateway
hypothesis: possible causal effects of smoking initiation and
alcohol consumption on substance use outcomes. Addiction.
2021;1–10. https://doi.org/10.1111/add.15673
10 REED ET AL.
