Influence of OPRM1 Asn40Asp variant
(A118G) on [11C]carfentanil binding
potential: preliminary findings in
Elise M. Weerts1, Mary E. McCaul1,2, Hiroto Kuwabara3, Xiaoju Yang2, Xiaoqiang Xu2,
Robert F. Dannals3, J. James Frost3, Dean F. Wong1,3,4,5and Gary S. Wand1,2
1Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
2Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
3Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
4Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
5Department of Environmental Health Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
The Asn40Asp variant (A118G) of the m opioid receptor (OPRM1) gene is thought to contribute to the
development and treatment of alcohol dependence. Employing positron emission tomography (PET), we
first examined whether the single nucleotide polymorphism (SNP) modifies binding potential (BPND) of
the m-selective ligand [11C]carfentanil in healthy control (Con) and 5-d abstinent alcohol-dependent (AD)
subjects (unblocked basal scan). Second, we examined whether the allelic variants were associated with
differences in OPRM1 occupancy by naltrexone (50 mg) in AD subjects. Con and AD carriers of the G allele
(AG) had lower global BPNDat the basal scan than subjects homozygous for the A allele (AA). In AD
subjects, naltrexone occupancy was slightly higher in AG subjects (98.9%) compared to AA subjects
(93.1%), but this was not significant. We are the first to demonstrate using PET in healthy normal and AD
subjects that the A118G SNP alters OPRM1 availability.
Received 22 October 2011; Reviewed 12 December 2011; Revised 3 February 2012; Accepted 8 February 2012;
First published online 8 March 2012
Key words: Alcohol use disorders, genetics, imaging, m opioid receptors, naltrexone.
Variations in the gene encoding the m opioid receptor
(OPRM1) may be relevant to the development
and treatment of alcohol abuse and dependence. The
most prevalent single nucleotide polymorphism (SNP)
is a nucleotide exchange in exon 1 at the 118 position
of the N-terminal domain of the OPRM1 (rs1799971).
In vitro studies have shown that the minor allele (G) of
this SNP is associated with a lower level of receptor
glycosylation and a reduced receptor half-life (Huang
et al. 2012), increased binding affinity for b-endorphins
(Bond et al. 1998) and reduced receptor expression
(Zhang et al. 2005).
The first demonstration of in vivo biological signifi-
cance for the A118G SNP showed allele-specific cor-
tisol responses to the opiate receptor antagonist
naloxone. This result has been replicated in Cauca-
sians (for review see Uhart & Wand, 2009). The minor
allele (G) also has been associated with decreased pu-
pil constriction to an opioid agonist, decreased anal-
gesic response to electrical stimuli-induced pain and
decreased opioid-induced respiratory depression in
studies in healthy subjects as well as decreased clinical
potency of opioid agonists (Lotsch & Geisslinger,
There is also evidence that the A118G SNP affects
studies in heavy drinkers who were administered al-
cohol, subjects who were carriers of the G allele report
a heightened response to alcohol (Ray et al. 2011a) and
greater urges to drink after exposure to alcohol-related
cues (van den Wildenberg et al. 2007). The A118G SNP
Address for Correspondence: G. S. Wand, M.D., 720 Rutland Avenue,
Ross 863, Baltimore, MD 21205, USA.
Tel.: 410 955 7225Fax: 410 955 0841
International Journal of Neuropsychopharmacology (2013), 16, 47–53. f CINP 2012
may also influence alcohol treatment efficacy of the
opioid antagonist naltrexone (Kranzler & Edenberg,
2010); alcohol-dependent (AD) subjects carrying the
G allele were more likely to respond to naltrexone
allele. Taken together, these data suggest that the A118
SNP influences responses to alcohol and naltrexone
efficacy via functional differences in m opioid receptor
Based on the literature, it seemed likely that vari-
ations in the gene encoding the OPRM1 may influence
receptor availability in the human brain. If the A118G
SNP genotype significantly contributes to individual
differences in m receptor availability, then such differ-
ences likely would not be limited to regional changes,
but could be expected to occur across brain regions
with high m opioid receptor density (i.e. genotype dif-
ferences in global OPRM1 availability). Previously, we
examined positron emission tomography (PET) imag-
ing of regional differences in OPRM1 binding poten-
tial (BPND) employing the radiotracer [11C]carfentanil
(CFN). We studied untreated, healthy control (Con)
subjects as well as recently abstinent AD subjects
before and during treatment with the FDA rec-
ommended therapeutic dose of naltrexone (50 mg/d;
Weerts et al. 2008, 2011). We have genotyped most of
these same AD and Con subjects for the A118G SNP
and now present a secondary analysis to: (1) examine
the effect of genotype on global OPRM1 availability
in recently abstinent AD subjects and Con subjects;
(2) determine whether there were allele-specific effects
(A vs. G) in AD subjects on the degree of OPRM1
blockade by naltrexone.
Subjects in the current analyses included 25 AD and 28
Con subjects aged 21–60 yr. Subjects provided in-
Institutional Review Board approved informed con-
sent document. A detailed description of the assess-
ment instruments used to determine study eligibility
as well as demographics information on all subjects is
provided in previous publications (Weerts et al. 2008,
2011). AD subjects were actively drinking prior to
Clinical Research Unit (CRU) admission (see below)
and met DSM-IV criteria for alcohol dependence
based on an in-person interview using the Semi-
Structured Assessment for the Genetics of Alcoholism
(SSAGA-II). For all subjects, smoking status (smokers
vs. non-smokers) and smoking intensity were also
determined via the SSAGA-II and alcohol drinking
intensity and patterns were characterized via the time
line follow back (Sobell & Sobell, 1992). Smokers were
defined as persons who reported smoking >100 ciga-
rettes in their lifetime and currently smoked regularly
(three or more cigarettes per day). Age-matched Con
subjects were enrolled if they consumed less than
eight drinks per week for women and <15 drinks per
week for men and did not meet lifetime DSM-IV cri-
teria for either alcohol abuse or dependence. Con and
AD subjects were excluded if they met current or life-
time DSM-IV diagnostic criteria for another Axis I
disorder, including other drug abuse/dependence
(except nicotine), had a positive urine drug toxicology
test at screening or hospital admission, had any on-
going health problems or reported maternal drinking
during pregnancy. In addition, AD subjects were
excluded if they reported alcohol-related seizures
or the need for medication during previous detoxi-
All subjects were admitted to the CRU after a
negative breath alcohol test and completed scans
under an in-patient protocol. During the in-patient
stay, subjects could not smoke; nicotine-dependent
subjects received a transdermal nicotine patch (21 mg)
each day. Con subjects were admitted the night before
the PET scan, were instructed not to drink for 48 h
before admission and had to provide an alcohol-free
breathalyser test at the time of admission. AD subjects
completed detoxification and remained on the CRU
for 19 d (Weerts et al. 2008). Pre-naltrexone PET ima-
ging (basal scan) was conducted after alcohol with-
drawal symptoms subsided (day 5) as determined by
Clinical Institutes Withdrawal Assessment (CIWA-
AR) scores. For AD subjects only, naltrexone (50 mg
p.o.) was administered beginning on day 15 at 09:00
and 21:00 hours and then at 21:00 hours each day for
the remainder of their CRU stay; nurses observed
subjects swallowing the pill to ensure medication
compliance. PET imaging during naltrexone mainten-
ance (naltrexone scan) was conducted after subjects
had received four doses of 50 mg (day 18). Previous
studies have demonstrated that plasma levels of nal-
trexone and its active metabolite 6-b-naltrexol reach
stability within 3 d of naltrexone dosing (Huang et al.
1997; McCaul et al. 2000).
DNA extraction and SNP analysis
Blood samples were genotyped for OPRM1 poly-
morphism A118G SNP (rs1799971) as described pre-
viously (Chong et al. 2006). Subjects were categorized
according to genotype (AA vs. AG).
48E. M. Weerts et al.
To ensure adequate assessment and control of any
relevant substructure that might be present in our
population, DNA samples for each subject were
genotyped using a panel of 23 microsatellite markers
with high efficiency at clustering individuals into
population subgroups (Smith et al. 2001; Yang et al.
2005). Short tandem repeat markers D1S252, D2S319,
D12S352, D17S799, D8S272, D1S196, D7S640, D8S1827,
D7S657, D22S274, D5S407, D2S162, D10S197, D11S935,
D9S175 and D5S410 were selected from Applied
Biosystems (USA) Linkage Mapping Set v. 2.5.
Markers D7S2469, D16S3017, D10S1786, D15S1002,
D6S1610 and D1S2628 were synthesized by Applied
Biosystems with fluorescent dye PET1to allow geno-
typing in the same lane with the other markers.
Population stratification was determined using
Structure software version 2.3.2 (http:/ /pritch.bsd.
uchicago.edu/structure.html) following the methods
of Pritchard et al. (2000). The model included the
possibility of admixture and correlated allele fre-
quencies between populations; all other parameters
were kept at default values.
PET imaging procedures
PET imaging procedures are described in detail pre-
viously (Weerts et al. 2011). Briefly, subjects under-
went magnetic resonance imaging prior to PET
imaging for anatomical identification of regions, atro-
phy correction and alignment of PET imaging planes
within and across subjects. PET scans were acquired in
3D mode on a GE Advance PET scanner (GE Medical
Systems, USA). A 10-min transmission scan was ob-
tained using rotating germanium-68 rods before in-
jection of [11C]CFN. After i.v. bolus administration of
the radiotracer [11C]CFN, a set of 25 images with vari-
able time periods (6r30, 5r60, 5r120, 9r480 s) was
acquired over the 90-min scan. [11C]CFN injected was
20.0¡0.6 mCi SA: 21,482.7¡3310.1 mCi/mmol for
the Con group and 19.3¡0.5 mCi SA: 18,444.1¡
2849.6 mCi/mmol for the AD group. The CFN dose
did not exceed 0.04 mg/kg. Correcting for attenuation
scatter and dead-time and physical decay (to the in-
jection time), images were reconstructed in a 128r
128r35 matrix with a pixel size of 2r2r4.25 mm
with filtered back projection methods using a ramp
filter (Kinahan & Rogers, 1989).
Derivation of PET outcome variables
BPND of [11C]CFN was determined using reference
tissue graphical analysis (RTGA) with occipital lobe as
the reference region and setting the brain-to-blood
clearance rate constant of the reference region (k2R) at
0.104/min (Endres et al. 2003). Estimates of BPND
using RTGA have been shown to be highly correlated
with those obtained from the arterial input-based
kinetic model (Endres et al. 2003). We selected brain
regions based on two criteria: regions with moderate
to high BPNDand/or regions thought to be involved in
regulation of alcohol reward, learning and memory as
well as dependence and withdrawal. The 14 volumes
of interest (VOIs) were frontal lobe, temporal lobe,
parietal lobe, fusiform gyrus, cingulate, hippocampus,
amygdala, cerebellum, insula, ventral striatum, pu-
tamen, caudate nucleus, globus pallidus and thala-
mus. These VOIs encompassed a substantial portion of
the brain containing m opioid receptors (497¡59.7 ml).
For the current analysis, global BPNDwas calculated
as a weighted average of all 14 VOIs. Naltrexone oc-
cupancy of OPRM1 was calculated as [1 x (naltrexone
scan global BPND/Basal scan global BPND)].
Three main multi-linear models were constructed with
basal global BPND as the dependent variable and
genotype as the independent variable for all subjects
(AD and Con, n=53), Con group only and AD group
only. Alcohol dependence diagnosis (for the all-
subject model only), gender and smoking status were
added as covariates to the model based on significant
effects for these variables on BPNDin our previous
study (Weerts et al. 2011). Naltrexone scan results
(global BPNDand naltrexone occupancy) were ana-
lysed using similar models with gender and smoking
Since the frequency of the 118G SNP varies across
populations, we conducted a sensitivity analysis by
adding ancestral population as a covariate to the
above models. Also, since the G allele is very rare in
African Americans, we completed a secondary analy-
sis in the subset of European ancestry subjects in
the study. We also completed a series of secondary
analyses to examine other potential confounding fac-
tors that may have an effect on BPND. Because the
number of drinking days per week was not balanced
between the two genotype groups in AD subjects
(p=0.05, see Results), we added it as covariate to the
main models. Since smoking intensity may influence
BPND, we also completed a secondary analysis where
we replaced the smoking status (smoker vs. non-
smoker) with cigarettes per day as a covariate in the
main models. All statistical analyses were carried out
using SAS version 9.2 (SAS Institute, USA).
Association of A118G with m opioid receptor availability49
Two distinct genetic ancestral populations were
identified and comparison of self-reported race and
ancestral population groups showed comparable
results: group 1 (N=23) was largely composed of in-
dividuals who self-reported their race as African–
American (96%) and group 2 (N=30) was largely
composed of individuals who self-reported their race
as Caucasian (96.7%). There were 39 AA subjects, 14
AG subjects and no GG subjects. Mean ages, mean
body weight, mean drinking days per week, mean
drinks per drinking day and distribution of AA and
AG subjects for gender, self-reported race, ancestral
population and smoking status in AD and Con groups
are shown in Table 1.
When genotype (AA vs. AG), gender (male vs. fe-
male), smoking status (smokers vs. non-smokers) and
alcohol dependence diagnosis (AD vs. Con) were in-
cluded in the model, there was a significant effect of
genotype (p=0.0011) on the basal scan global BPNDin
all subjects. Specifically, global BPND was lower in
carriers of the G allele (mean 0.479¡0.023 S.E.M.) of
the A118G SNP compared to subjects who were
homozygous for the A allele (mean 0.567¡0.016
S.E.M.). As shown in Supplementary Table S1 (available
online), the genotype differences in BPND were
observed across the 14 VOI used to determine global
BPND in AD and Con subjects. The significant geno-
type difference in global BPNDwas preserved when
we added ancestral population to the model (p=
0.0039) and also when we included only Caucasian
subjects of European ancestry in the overall model
Adding smoking intensity (cigarettes per day) in-
stead of smoking status in our overall model did not
alter the genotype effect. Specifically, when genotype,
gender, cigarettes per day and alcohol dependence
diagnosis were included in the model, there was a
significant effect of genotype (p=0.0084) on the basal
scan global BPNDin all subjects. Carriers of the G allele
had lower global BPND(mean 0.483¡0.023 S.E.M.) than
subjects who were homozygous for the A allele (mean
0.557¡0.015 S.E.M.). The genotype effect was also pre-
served when we included cigarettes per day and in-
cluded only Caucasian subjects of European ancestry
in the model (p=0.044).
Similar results were obtained when AD and Con
subjects were analysed separately (Fig. 1a,b). After
adjusting for gender and smoking status, global BPND
Table 1. Age and distribution of gender, race and smoking status for AA and AG genotypes in alcohol dependent (AD) and
healthy control (Con) subjects
AD (n=19) Con (n=20) AD (n=6)Con (n=8)
Mean age, yr (S.D.)a
Mean kg weight (S.D.)a
Self-reported race (n)
Ancestral markers (n)
Smoking status (n)
Mean cigarettes/d (S.D.)b
Mean drinks/d (S.D.)a
Mean drinking days/wk (S.D.)c
aNo significant difference between groups.
bSmokers only included in means; no significant difference between groups.
cp=0.052 for AD AA compared to AD AG only.
50E. M. Weerts et al.
was lower in Con subjects carrying the G allele
(p=0.010) and in AD subjects carrying the G allele
(p=0.030) compared to Con or AD subjects who
were homozygous for the A allele. When ancestral
population was added to the model, the global BPND
in Con carriers of the G allele remained significantly
lower than global BPNDin Con subjects homozygous
for the A allele (p=0.027). However, there was now a
trend for lower global BPNDin AD carriers of the G
allele compared to AD subjects homozygous for the
A allele (p=0.062). Likewise, when we limited our
analysis to Con Caucasian subjects of European an-
cestry in the model, genotype differences were pre-
served in Con subjects (n=9 AA, n=6 AG, p=0.025),
with G carriers showing lower BPND than subjects
homozygous for the A allele. Genotype differences
were not maintained in AD Caucasian subjects of
European ancestry (n=10 AA, n=5 AG, p=0.221).
Naltrexone treatment resulted in a robust reduction
in global BPNDin all AD subjects regardless of geno-
type (Fig. 1c). Although not reaching statistical sig-
nificance, global BPND was lower in G carriers
compared to subjects homozygous for the A allele
during naltrexone treatment. Examination of genotype
differences in naltrexone occupancy yielded similar
results. Naltrexone occupancy was higher in subjects
who were carriers of the G allele (98.9%¡4.0) than in
subjects homozygous for the A allele (93.1%¡2.2);
this effect was not statistically significant (p=0.178).
Since our sample size was small, we completed a
power calculation to determine the sample size
necessary to observe a significant difference between
allele groups in global BPNDduring naltrexone block-
ade, setting p<0.05 and power >0.8. Results showed
that with a sample size of 88 subjects a significant
difference in BPNDwould likely be detected if similar
In a secondary analysis, we also examined possible
genotype differences in measures related to alcohol
drinking, dependence and withdrawal. On the day of
the scan, withdrawal symptoms were low, as in-
dicated by pre-PET CIWA-AR mean scores (AA
0.7¡1.6 S.D. and AG 0.3¡0.8 S.D.) and did not differ
significantly between genotype groups. Dependence
severity as determined by the Alcohol Dependence
Scale scores also did not differ between genotype
groups (AD AG 20.5+7.1 SD vs. AD AA 16.8+4.9 S.D.).
There was a trend (p=0.052) for a genotype effect on
mean number of drinking days per week in the 90 d
that preceded hospital admission in AD subjects; AD
carriers of the G allele reported drinking more days
per week compared to AD subjects who were homo-
zygous for the A allele (Table 1). When number of
drinking days per week was included in the model
with genotype, alcohol diagnosis, gender and smoking
status, the significant genotype differences were pre-
served in all subjects (p=0.0018) and also in AD
(p=0.048) and Con subjects (p=0.019) when analysed
The current findings indicate that OPRM1 A118G SNP
influenced OPRM1 availability in Con and AD sub-
jects. Specifically, Con and AD subjects who were
carriers of the G allele had lower global [11C]CFN
BPNDat the basal scan than subjects who were homo-
zygous for the A allele. It is notable that, despite the
chronic history of heavy drinking and alcohol depen-
dence in our AD subjects, the effects of the SNP
Fig. 1. Mean global binding potential (BPND) of [11C]carfentanil, (a) for the basal scan in healthy controls, (b) for the basal scan in
alcohol dependent (AD) subjects and (c) the naltrexone scan in AD subjects. Data shown are genotype group means¡S.E.M.
adjusted for ancestral population, gender and smoking status. * p=0.027,+p=0.062.
Association of A118G with m opioid receptor availability 51
remained robust. Similar findings of lower BPND
across multiple brain regions in G allele subjects were
obtained in smokers under active and placebo nicotine
administration procedures (Ray et al. 2011b).
There are at least two potential mechanisms for the
observation of lower [11C]CFN BPND in G carriers.
First, G carriers may have fewer OPRM1 receptors
compared to carriers who are homozygous for the A
allele. Using in vitro binding studies, Zhang et al.
(2005) have shown a lower OPRM1 Bmax in cell lines
expressing the G allele compared to cell lines
expressing the A allele. This may result from the
alterations in glycosylation that reduce OPRM1 half-
life (Huang et al. 2012). Additionally, Bond et al.
(1998) have shown that the G allele receptor has in-
creased binding affinity for b-endorphins. This implies
that the PET ligand [11C]CFN will compete less ef-
ficiently with endogenous b-endorphins in G allele
Several recent alcohol treatment clinical trials have
reported greater naltrexone efficacy in G allele carriers
compared to patients who are homozygous for the A
allele (Anton et al. 2008; Oslin et al. 2006). In laboratory
studies, naltrexone decreased subjective responses to
alcohol more in social drinkers who were carriers of
the G allele (Ray et al. 2011a; Setiawan et al. 2011),
although no genotype differences were found in
treatment-seeking heavy drinkers (Tidey et al. 2008). It
is plausible that these pharmacogenetic effects may
result from allele specific differences in the degree of
naltrexone blockade between allele groups. This scen-
ario may be accounted for by the finding of decreased
OPRM1 Bmax in the G variant of the receptor com-
pared to the receptor encoded by the A allele. That is,
G-allele patients with lower OPRM1 density will
achieve more complete naltrexone blockade than
patients who have higher OPRM1 density at the
recommended therapeutic dose (50 mg). Although al-
lele group differences in naltrexone blockade did not
achieve statistical significance in this study, we believe
that it may be attributable to our relatively small
sample of AD subjects. Our power estimate indicated
that significant effects would be observed with the
typical sample size enrolled in pharmacotherapy
clinical trials. However, it is important to note that
both allele groups achieved greater than 90% blockade
of OPRM1. Therefore, even if effects were statistically
significant, these small allele specific differences in
naltrexone blockade seem unlikely to translate into a
significant therapeutic advantage for G carriers. Other
mechanisms must be at work.
This is the first demonstration in healthy social
drinkers and AD subjects of the effects of the A118G
SNP on OPRM1 availability. Importantly, naltrexone
blockade was >90% in both allele groups. Thus, pre-
vious observations of differences in therapeutic effi-
cacy of naltrexone as a function of the A118G SNP
cannot be readily explained by differences in the ex-
tent of naltrexone blockade at the OPRM1.
For supplementary material accompanying this paper,
Alcoholism (NIAAA) provided financial support for
research related to the subject matter of this manu-
script from the grants R01AA11872 (PI: J. J. Frost),
R01AA11855 (PI: M. E. McCaul) and R37AA12303 (PI:
G. S. Wand). The authors acknowledge the technical
support of Dr Hayden T. Ravert, Mr Robert Smoot
and Mr Daniel Holt for their radiochemistry ex-
pertise and Ms Karen Edmonds and Mr David
Clough for their PET acquisition and reconstruction
National Instituteof Alcohol Abuseand
Statement of Interest
Dr Wand is the recipient of a gift fund from the
Kenneth Lattman Foundation. He is an investigator
in a post marketing study for Eli Lilly & Company,
entitled The Global Hypopituitary Control and Com-
plications Study (HypoCCS). He is an investigator in a
post marketing study for Ipsen entitled Somatuline
Depot (lanreotide) Injection for Acromegaly (SODA).
Dr Wong is a consultant for Amgen. Between 2009 and
the present, Dr Wong has received funding from the
following companies: Acadia; Amgen; Avid; Biotie;
Bristol Myers Squibb; GE; Intracellular; J&J; Lilly;
Luhdeck; Merk; Orexigen; Otuska; Roche; Sanofi-
Aventi; Sepracor. Dr McCaul was principal investi-
gator on a contract (A Phase 2 Study of LY2196044
Compared with Naltrexone and Placebo in the Treat-
ment of Alcohol Dependence) funded by Lilly Re-
search Laboratories; Drs Weerts and Wand were
co-investigators on this project.
Anton RF, Oroszi G, O’Malley S, Couper D, et al. (2008).
An evaluation of mu-opioid receptor (OPRM1) as a
predictor of naltrexone response in the treatment of
alcohol dependence: results from the Combined
52E. M. Weerts et al.
Pharmacotherapies and Behavioral Interventions for Download full-text
Alcohol Dependence (COMBINE) study. Archives of
General Psychiatry 65, 135–144.
Bond C, LaForge KS, Tian M, Melia D, et al. (1998).
Single-nucleotide polymorphism in the human mu opioid
receptor gene alters beta-endorphin binding and
activity: possible implications for opiate addiction.
Proceedings of the National Academy of Sciences USA 95,
Chong RY, Oswald L, Yang X, Uhart M, et al. (2006). The
micro-opioid receptor polymorphism A118G predicts
cortisol responses to naloxone and stress.
Neuropsychopharmacology 31, 204–211.
Endres CJ, Bencherif B, Hilton J, Madar I, et al. (2003).
Quantification of brain mu-opioid receptors with
[11C]carfentanil: reference-tissue methods. Nuclear
Medicine and Biology 30, 177–186.
Huang P, Chen C, Mague SD, Blendy JA, et al. (2012). A
common single nucleotide polymorphism A118G of the
mu opioid receptor alters its N-glycosylation and protein
stability. Biochemical Journal 441, 379–386.
Huang W, Moody DE, Foltz RL, Walsh SL (1997).
Determination of naltrexone and 6-beta-naltrexol in
plasma by solid-phase extraction and gas
chromatography-negative ion chemical
ionization-mass spectrometry. Journal of Analytical
Toxicology 21, 252–257.
Kinahan PE, Rogers JG (1989). Analytic 3D image
reconstruction using all detected events. IEEE Transactions
on Nuclear Science 36, 964–968.
Kranzler HR, Edenberg HJ (2010). Pharmacogenetics of
alcohol and alcohol dependence treatment. Current
Pharmaceutical Design 16, 2141–2148.
Lotsch J, Geisslinger G (2011). Pharmacogenetics of
new analgesics. British Journal of Pharmacology 163,
McCaul ME, Wand GS, Rohde C, Lee SM (2000). Serum
6-beta-naltrexol levels are related to alcohol responses in
heavy drinkers. Alcohol Clinical and Experimental Research
Oslin DW, Berrettini WH, O’Brien CP (2006). Targeting
treatments for alcohol dependence: the pharmacogenetics
of naltrexone. Addiction Biology 11, 397–403.
Pritchard JK, Stephens M, Donnelly P (2000). Inference of
population structure using multilocus genotype data.
Genetics 155, 945–959.
Ray LA, Barr CS, Blendy JA, Oslin D, et al. (2011a). The role
of the Asn40Asp polymorphism of the mu opioid receptor
gene (OPRM1) on alcoholism etiology and treatment: a
critical review. Alcoholism Clinical and Experimental
Research. Published online: 6 September 2011. doi:10.1111/
Ray R, Ruparel K, Newberg A, Wileyto EP, et al. (2011b).
Human mu opioid receptor (OPRM1 A118G)
polymorphism is associated with brain mu-opioid receptor
binding potential in smokers. Proceedings of the National
Academy of Sciences USA 108, 9268–9273.
Setiawan E, Pihl RO, Cox SM, Gianoulakis C, et al. (2011).
The effect of naltrexone on alcohol’s stimulant properties
and self-administration behavior in social
drinkers: influence of gender and genotype. Alcoholism
Clinical Experimental Research 35, 1134–1141.
Smith MW, Lautenberger JA, Shin HD, Chretien JP, et al.
(2001). Markers for mapping by admixture linkage
disequilibrium in African American and Hispanic
populations. American Journal of Human Genetics 69,
Sobell LC, Sobell MB (1992). Timeline followback: a
technique for assessing self-reported alcohol consumption.
In: Litten RZ, Allen J, (Eds.), Measuring Alcohol
Consumption: Psychosocial and Biological Methods. Totowa,
NJ: Humana Press; pp. 41–72.
Tidey JW, Monti PM, Rohsenow DJ, Gwaltney CJ, et al.
(2008). Moderators of naltrexone’s effects on drinking,
urge, and alcohol effects in non-treatment-seeking heavy
drinkers in the natural environment. Alcoholism Clinical and
Experimental Research 32, 58–66.
Uhart M, Wand GS (2009). Stress, alcohol and drug
interaction: an update of human research. Addiction Biology
van den Wildenberg E, Wiers RW, Dessers J, Janssen RG,
et al. (2007). A functional polymorphism of the mu-opioid
receptor gene (OPRM1) influences cue-induced craving for
alcohol in male heavy drinkers. Alcoholism Clinical and
Experimental Research 31, 1–10.
Weerts EM, Kim YK, Wand GS, Dannals RF, et al. (2008).
Differences in delta- and mu-opioid receptor blockade
measured by positron emission tomography in
naltrexone-treated recently abstinent alcohol-dependent
subjects. Neuropsychopharmacology 33, 653–665.
Weerts EM, Wand GS, Kuwabara H, Munro CA, et al.
(2011). Positron emission tomography imaging of mu- and
delta-opioid receptor binding in alcohol-dependent and
healthy control subjects. Alcoholism Clinical and
Experimental Research 35, 2162–2163.
Yang BZ, Zhao H, Kranzler HR, Gelernter J (2005). Practical
population group assignment with selected informative
markers: characteristics and properties of Bayesian
clustering via STRUCTURE. Genetics and Epidemiology 28,
Zhang Y, Wang D, Johnson AD, Papp AC, et al. (2005).
Allelic expression imbalance of human mu opioid receptor
(OPRM1) caused by variant A118G. Journal of Biological
Chemistry 280, 32618–32624.
Association of A118G with m opioid receptor availability 53