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