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Genetic Studies of Gambling
Disorder
Marc N. Potenza, M.D., Ph.D.
Professor of Psychiatry, Child Study,
and Neurobiology
Director, Yale Gambling Center
of Research Excellence (CORE)
Director, Women and Addictions Core,
Women’s Health Research at Yale
Senior Scientist, The National Center on
Addiction and Substance Abuse
Yale University School of Medicine
Disclosures
• Consultant to Lundbeck, Ironwood, Shire,
INSYS, Rivermend Health, Lakelight
Therapeutics/Opiant and Jazz Pharmaceuticals
• Research Grants from National Center for
Responsible Gaming
• Research Gift from Mohegan Sun
• Consultant to Gambling and Legal Entities on
Issues Related to Impulse Control Disorders
EASG, Sept 14, 2016
Overview
• Genetics of Gambling Disorder
• Heritability & ACE Estimates from Twin Studies
• Molecular Genetic Studies
• Allelic Variation and Behavioral and Brain
Responses (Endophenotypes and
Transdiagnostic Considerations)
• Conclusions
EASG, Sept 14, 2016
Heritability: Genetic and
Environmental Contributions
• Many Conditions Including Gambling Disorder
Aggregate within Families
• Greater Likelihoods of Conditions in Individuals with
Affected Family Members May Reflect Either
Environmental or Genetic Factors or Both
• Twin Studies Offer the Opportunity to Estimate the
Degree to Which Specific Conditions (and Their Co-
Occurrences) May Reflect Environmental or Genetic
Contributions (Shah et al., 2005)
EASG, Sept 14, 2016
Twin Studies
• Classic Studies Assume Rearing of Twins in Similar
Environment
• Equal Environment Assumption and Possible
Overestimation of Genetic Contributions (Shah et al.,
2005)
• Data May be Modeled to Estimate Genetic (A), Shared
Environmental (C), and Unique Environmental (E)
Contributions
• E Also Includes Error Estimation
EASG, Sept 14, 2016
Twin Datasets: VET-R and
Australian Twin Samples• Several Large Samples of Twins Have Assessed for
Pathological Gambling Using DSM Criteria
• Vietnam Era Twin Registry (VET-R; Eisen et al, 1998)
• Australian Study (Slutske et al, 2010)
• Each Dataset Has Strengths and Limitations
• VET-R Comprised of Over 7000 Male Twins Serving
During Time of Vietnam Era Conflict
• Australian Sample Smaller But Has Both Men and
Women and Molecular Genetic Measures
EASG, Sept 14, 2016
Association Between PG and MD
in VET Sample
Variable OR (95% CI) p-value
Alcohol Abuse/Dependence 2.7 (1.5, 4.7) 0.001
Drug Abuse/Dependence 1.9 (1.0, 3.3) 0.04
Antisocial Personality D/O 2.5 (1.1, 5.5) 0.02
Generalized Anxiety D/O 3.0 (1.3, 6.5) 0.007
Major Depression 2.0 (1.1, 3.4) 0.02
NS = Age, Income, HS Education, College Education,
Nicotine Dependence, PTSD, Panic D/O
Unadjusted OR for MD = 4.1 (2.6-6.5)
OR for MD Adjusting for Sociodemographics = 4.1 (2.6-6.5)
Bivariate Biometric Model for PG & MD
Potenza et al, 2005, Arch Gen Psychiatry
Bivariate Biometric Model for PG & GAD
Giddens et al, 2011, J Affect Dis
APG
CPG
EPG
EGAD
AGAD
CGAD
PG GAD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.53*
PG 0.65* 0.0 0.35* rC=0.0
GAD 0.38* 0.0 0.62* rE=0.0
EASG, Sept 14, 2016
Bivariate Biometric Model for PG & PD
Giddens et al, 2011, J Affect Dis
APG
CPG
EPG
EPD
APD
CPD
PG PD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.34*
PG 0.64* 0.0 0.36* rC=0.0
PD 0.43* 0.0 0.57* rE=0.31*
EASG, Sept 14, 2016
Bivariate Biometric Model for PG & AD
Slutske et al, 2000, Arch Gen Psychiatry
APG
CPG
EPG
EAD
AAD
CAD
PG AD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.35*
PG 0.64* 0.0 0.36* rC=0.0
AD 0.55* 0.0 0.45* rE=0.26*
EASG, Sept 14, 2016
Bivariate Biometric Model for PPG & AD
APPG
CPPG
EPPG
EAD
AAD
CAD
PPG AD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.45*
PPG 0.49* 0.0 0.51* rC=0.0
AD 0.55* 0.0 0.45* rE=0.16*
Slutske et al, 2000, Arch Gen PsychiatryEASG, Sept 14, 2016
Bivariate Biometric Model for PPG & ND
Xian et al, 2014, Addiction
APPG
CPPG
EPPG
END
AND
CND
PPG ND
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.22*
PPG 0.49* 0.0 0.51* rC=0.0
ND 0.61* 0.0 0.39* rE=0.24*
EASG, Sept 14, 2016
Bivariate Biometric Model for PPG & CAD
Xian et al, 2014, Addiction
APPG
CPPG
EPPG
ECAD
ACAD
CCAD
PPG CAD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.32*
PPG 0.48* 0.0 0.52* rC=0.0
CAD 0.28* 0.34 0.39* rE=0.36*
EASG, Sept 14, 2016
Bivariate Biometric Model for PPG & SAD
Xian et al, 2014, Addiction
APPG
CPPG
EPPG
ESAD
ASAD
CSAD
PPG SAD
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.32*
PPG 0.50* 0.0 0.50* rC=0.0
SAD 0.54* 0.0 0.46* rE=0.0
EASG, Sept 14, 2016
OC Latent Classes
• PG and PPG Typically Co-Occur at Elevated Odds with
Multiple Forms of Psychopathology (Consistent with
VET-R Findings)
• Population-Based Studies Do Not Support Increased
Odds Between PPG and OCD (Cunningham-Williams et
al, 1998)
• Transdiagnostic Measure of Compulsivity Linked to
PPG / Addictive Disorders (Fineberg et al, 2014)
• Latent Classes of OC Features Differing Qualitatively
and Quantitatively Identified in Follow-up Survey of
VET-R Participants and Linked to PPG (Scherrer et al,
2015)
EASG, Sept 14, 2016
OC Latent Classes
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
rituals
compulsions
repeang
everordering
hoarding
violentimages
concerngerms
luckynumbers
symmetrical
fearillness-contaminaon
endorsementprobab
ility
Class1(64.1%)
Class2(20.8%)
Class3(7.9%)
Class4(7.2%)
Class 1 – Low OC Class 2 – Symmetry / Order / Rituals
Class 3 – Germs / Contamination / Rituals Class 4 – High OC
Scherrer et al, 2015, JAMA PsychiatryEASG, Sept 14, 2016
Odds Ratios Reflecting
OC Class / GD Relationships
C2 v C1 C3 v C1 C4 v C1
No Criteria 1 1 1
1+ Criteria 2.2* 1.6* 3.4*
0-3 Criteria 1 1 1
4+ Criteria 2.0* 2.0# 3.1*
Scherrer et al, 2015, JAMA PsychiatryEASG, Sept 14, 2016
Bivariate Biometric Model for GD & OC
Scherrer et al, 2015, JAMA Psychiatry
AGD
CGD
EGD
EOC
AOC
COC
GD OC
rE
rC
rA
Parameter Estimates from Best Fitting Models
a2 c2 e2 rA=0.44*
GD 0.64* 0.0 0.36* rC=0.0
OC 0.37* 0.0 0.63* rE=0.0
EASG, Sept 14, 2016
Summary of VET-R Findings
• Greater Genetic Contributions to More Stringently
Thresholded Levels of Problem/Pathological Gambling
in Men (Eisen et al, 1998)
• Co-occurrences Between (P)PG and MD, GAD, SAD and
OC Classes Appear Linked to Predominantly Genetic
Factors
• Co-occurrences Between (P)PG and PD, AD, ND and
CUD Appear Linked to Both Environmental and Genetic
Factors
EASG, Sept 14, 2016
Australian Twin Findings
• Similar Genetic and Environmental Contributions to
PPG in Men and Women (Slutske et al, 2010)
• Genetic Contributions to Gambling Age of Onset
Greater in Men than in Women; Shared Environmental
Factors Greater in Women (Richmond-Rakert et al,2013)
• Genetic Factors Linked to Temperament and ODD May
Explain Link Between Gambling Age of Onset and
Gambling Problems (Slutske et al, 2014)
• Local Area Disadvantage May Increase Likelihood of
Genetic Expression of Propensity to Gamble and
Develop Gambling Problems (Slutske et al, 2015)
EASG, Sept 14, 2016
Molecular Genetic Studies• Most Studies Small or Not Well Characterized or Both
(Leeman and Potenza, 2013)
• 2 GWAS (for GD) Published to Date With No Findings
Surviving Genome-Wide Significance Thresholding for
Individual Sites (Lind et al, 2013; Lang et al, 2016)
• Suggestive Significance for Non-Exonic Regions Close
to MT1X, ATXN1, and VLDLR (Lind et al, 2013)
• Significant Findings for Polygenic Risk Score for
Alcohol Dependence and For Pathways Relating to
Huntington’s Disease, AMPK Signalling, and Apoptosis
(Lang et al., 2016)
EASG, Sept 14, 2016
Allelic Variants with Known
Functional Correlates: COMT
• COMT Val-158-Met Allelic Functional Variation with
Met Allele Associated with 40% Less Enzymatic
Activity, Higher Dopamine Levels in the PFC and in
Some Cases Better Cognitive Functioning (Grant et al,
2013)
• In Proof of Concept Study, Treatment Outcome to
Tolcapone (A COMT Inhibitor) was Found to Be
Associated with Val-158-Met Status and Linked
Preliminarily to Fronto-parietal Circuitry Function
(Grant et al, 2013)
EASG, Sept 14, 2016
Yale Department of Psychiatry Grand Rounds - March 21, 2003
Tolcapone Treatment
Outcome by COMT Genotype
Grant et al, 2013, Eur NeuropsychopharmEASG, Sept 14, 2016
Yale Department of Psychiatry Grand Rounds - March 21, 2003
Changes In Fronto-Parietal
Activation With Tolcapone Tx
Grant et al, 2013, Eur NeuropsychopharmEASG, Sept 14, 2016
Allelic Variants with Known
Functional Correlates: DBH• DBH, a Gene Whose Enzymatic Product is Involved in
Dopamine / Norepinephrine Conversion, Has a Functional
Allelic Variant rs1611115 Associated with 35%-52% of
Enzymatic Activity (Cubells et al, 2000)
• TT Individuals Have Lowest Enzymatic Activity and CC
Highest, With CT Individuals Showing Intermediate Levels
(Zabetian et al, 2001)
• T Carriers Have Been Found to Demonstrate Less Empathy,
Lower Conscientiousness, Higher Neuroticism, More
Novelty-Seeking and Greater Drug-Use Severity (See Yang
et al, in press)
EASG, Sept 14, 2016
DBH Allelic Variation and Emotional
and Motivational Responses• We Investigated in 43 Individuals (18 PG – 9 T Carrier, 9 CC;
25 HC – 14 T Carrier, 11 CC) Subjective and Brain
Responses to Films of Sad, Gambling and Cocaine Content
(Yang et al, in press)
• We Hypothesized and Observed A Main Effect of DBH
Genotype on Subjective Responses to the Sad Tapes
(Greater in CC: 6.88(1.7) Vs. T Carrier: 5.22(2.3); p=.035),
Suggestive of A Transdiagnostic/Endophenotypic Feature
(Yang et al, in press)
• We Hypothesized and Observed Main Effects of DBH and
Interactive DBH-by-Condition Effects on Cortico-limbic-
Striatal Brain Activations
EASG, Sept 14, 2016
Yale Department of Psychiatry Grand Rounds - March 21, 2003
A
Main Effect of DBH
z = -8z = -11 z = -4 z = -1
mOFC vmPFC
F-value
4.09
16.54
Amygdala
VentralStriatum
R
Main Effect of DBH
CC Individuals Show Greater Activation Than Do T Carriers
Yang et al, in press, J Behav AddictionEASG, Sept 14, 2016
Yale Department of Psychiatry Grand Rounds - March 21, 2003
ADBH x Condition
z = - 4 z = 5
Hippocampus
Putamen
R L
F-value
3.12
11.14
DBH-by-Condition Effect
CC Individuals Relative to T Carriers Show Greater
Recruitment of Thalamus, Putamen, Insula, Hippocampus,
dlPFC, ACC and PCC During Sad Tapes
(No Differences in Responses to Other Tapes)
Yang et al, in press, J Behav AddictionEASG, Sept 14, 2016
Conclusions & Future Directions• Significant Progress Has Been Made in
Understanding the Genetics of Pathological Gambling / Gambling Disorder
• Gene-by-Environment Studies Providing Insight
• More GWAS Studies Are Needed to Identify Genetic Regions Linked to Gambling Disorder
• Understanding the Molecular Genetic Contributions to Gambling Disorder and Clinical Features That May Represent Therapeutic Targets May Help Prevention and Treatment Strategies
• Similar Studies in Other Behavioral Addictions Are Needed
EASG, Sept 14, 2016
AcknowledgmentsDiv Substance Abuse
Bruce Rounsaville
Kathleen Carroll
Suchitra Krishnan-Sarin
Stephanie O’Malley
Et al
Gambling Center
Of Excellence
Iris Balodis
Corey Pilver
Sarah Yip
Justin Wareham
Scott Bullock
Shane Kraus
Monica Solorzano
Ardeshir Rahman
Yvonne Yau
And Many Others!
Women & Addictions
Carolyn Mazure
Rani Desai
Et al
NIH (NIDA, NIAAA, ORWH) VA CASAColumbia WHR DMHAS NCRG Moh Sun
Imaging
Todd Constable
Godfrey Pearlson
Rajita Sinha
Bruce Wexler
Robert Fulbright
Cheryl Lacadie
Patrick Worhunsky
Jiansong Xu
Judson Brewer
Hedy Kober
Elise DeVito
Michael Stevens
Bao-Zhu Yang et al
Translational
Jane Taylor
R. A. Chambers
Genetics
Joel Gelernter
Seth Eisen
Hong Xian
Jeff Scherrer
Justine Giddens
Wendy Slutske
Kamini Shah et al
RCTs
Jon Grant
SW Kim et al
CT Partnerships
Marvin Steinberg & CCPG
Loreen Rugle & PGS