lifetime prevalences of externalizing and substance use disorders among twins from same-sex pairs

0

0.1

0.2

0.3

0.4

0.5

Co

nduc

t Dis

orde

rA

ntis

oci

al P

ers

Alc

ohol

Dep

ende

nce

Alc

ohol

Ab

/Dep

Nic

otin

e A

b/D

epC

affe

ine

Hea

vy U

seA

ny D

rug

Ab/

De

pC

ann

abis

Ab

/Dep

Sed

ativ

e A

b/D

epS

timul

ant A

b/D

epC

oca

ine

Ab/

De

pO

piat

e A

b/D

epH

allu

cino

gen

Ab/

Dep

Bro

ad B

ulim

ia

pro

po

rtio

n a

ffect

ed

femalesmales

Lifetime Prevalences of Externalizing and Substance Use Disorders

Among Twins from Same-Sex Pairs

3-Stage Conditional Model

A I E IC I

TobaccoInitiation

A R E RC R

RegularTobacco Use

A D E DC D

Persistence /Nicotine

Dependence

Contingent Causal Common Pathway

Proportions of VarianceBest Fitting CCC Model

TIRUND

TIRU

FTND

TIRU

P

0% 20% 40% 60% 80% 100%

No significant sex differences in proportions of variance or causal paths, but sex differences allowed in thresholds, No significant shared environmental effects for TI, RU and ND

Ai Ar Ad Ei Er Ed

.89

.69

.87

.93-.29

.87

.70

Genetic Epidemiology of Alcoholism

• Family Studies

• Adoption Studies– Denmark– Sweden

• Twin Studies – Virginia, Sweden, Australia, WW-II and

Vietnam Era Veteran twin registries

Estimated Genetic Proportions of Variance in Risk for Substance Abuse/Dependence

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Any

Sub

stan

ce

Can

nabi

s

Sed

ativ

es

Stim

ulan

ts

Coc

aine

Opi

ates

Hal

luci

noge

ns

females

males

combined

0

Genetic Epidemiology of Substance Abuse

• How do genetic risk factors for drug abuse relate to risk for psychiatric disorders?

Genetic Factors

Major Depression

GAD PhobiaAlcohol

Dep

DrugAbuseor Dep

ASP ASP ASP ASP ASP

AC2 AC1

.54 .24 .13 .33 .06 .10 .58 .18 .65 .21

.00 .00 .22 .38 .46

AdultAntisocialBehavior

ConductDisorder

ASP

.00

ASP

.17

.53 .56 .11 .37

Genetic Epidemiology of Substance Abuse

• How well does personality capture the genetic risk factors for substance initiation?

Genetic IndividualEnviron

Novelty Seeking

Shared Environ

83%0%17%

Cannabis Use

Genetic IndividualEnviron

Shared Environ

25%42%5%7%3%

18%

Results from Bivariate Twin Model for Overlap of Novelty Seeking and Cannabis Use among Males

Adapted from Table 1, Agrawal et al (2004), Twin Research, 7, 72-81

Are the Genetic Risk Factors for Drug Abuse in Part Genes for Personality?

• Genetic correlation between Novelty seeking (NS) and

– Cannabis use – Males +0.96, Females +0.19

– Cocaine use – Males +0.62, Female +0.30

Are the Genetic Risk Factors for Drug Use in Part Genes for Personality?

• Genetic correlation between Extraversion and

– Cannabis use +0.42

– Cocaine use +0.36

• Genetic correlation between Neuroticism and

– Cannabis use +0.18

– Cocaine use +0.18

Genetic Epidemiology of Substance Abuse

• How do the genetic risk for different forms of substance abuse relate to each other?

Genetic Epidemiology of Substance Abuse

• Begin to consider mediational models

• Genes → Intermediate phenotype → Drug Use

• Or, how do genes contribute to well understood risk factors for drug use and abuse?

Study the Availability of Drugs

Life history data collection 8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs

Measures of drug availability

- Alcohol - Marijuana - Stimulants- Cigarettes - Cocaine

“When you were…how easy would it have been to get [substance] if you wanted to use (it / them)?”

0. Very easy

1. Somewhat easy

2. Somewhat difficult

3. Very difficult

Age 0 1 2 3

8-11 21% 17% 22% 41%

12-14 29% 27% 23% 21%

15-17 52% 31% 11% 6%

18-21 89% 9% 2% <1%

Item endorsement

Alcohol

Age 0 1 2 3

8-11 2% 4% 6% 88%

12-14 8% 11% 16% 65%

15-17 24% 21% 19% 36%

18-21 45% 25% 14% 17%

22-25 46% 26% 14% 13%

Item endorsement

Marijuana

Age 0 1 2 3

8-11 <1% 1% 2% 96%

12-14 2% 2% 9% 88%

15-17 6% 8% 18% 68%

18-21 17% 17% 21% 46%

22-25 21% 18% 23% 39%

Item endorsement

Cocaine

0

1

1

2

2

3

1 2 3 4

E

C

A

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1 2 3 4

A

C

E

Alcohol

8-11yrs 12-14yrs 15-17yrs 18-21yrs

8-11yrs 12-14yrs 15-17yrs 18-21yrs

0

1

2

3

4

5

6

7

1 2 3 4 5

E

C

A

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5

A

C

E

Marijuana

8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs

8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs

0

1

2

3

4

5

6

7

8

9

1 2 3 4 5

E

C

A

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1 2 3 4 5

A

C

E

Cocaine

8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs

8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs

N 0 1

2 3

1. 8-11 Cigarettes (b42) 179043.5 20.4

15.1 20.9

2. 12-14 Cigarettes (c42) 179556.8 24.3

12.0 6.8

3. 15-17 Cigarettes (d42) 179278.4 15.6

4.1 2.0

• Unstandardized and standardized proportions of variance in CIGARETTE availability. Variance components include latent genetic and environmental effects attributable to intercept and slope factors in the full biometrical DCS model.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1 2 3

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1 2 3

Other Key Intermediate Phenotype – Peer Group Deviance

• Genes can act to increase liability to drug use disorders through influencing selection into high risk environments.

– Example here – deviance of peer group

– Many studies show peer group deviance to be a powerful predictor of subsequent drug use.

Modeling Time and Development and “Outside the Skin” Pathways

• Measures of peer group deviance retrospectively reported by a life history method.

• ~750 male-male twin pairs from Virginia Twin Registry.

• Evaluate 4 ages.

• Use a latent biometrical growth curve model– Can look separately at “genetics” of mean levels

at different ages and

– “Genetics” of slope (or trajectory).

Peer Group Deviance

05

101520253035

8-11 12-14 15-18 19-22

Ages

A

C

E

Peer Group Deviance

0%

20%

40%

60%

80%

100%

8-11 12-14 15-18 19-22

Ages

E

C

A

Genetics of the Trajectory of Change in Peer Group Deviance

From Ages 8-22

• a2 = 0.43

• c2 = 0.22

• e2 = 0.35

• So, not only is the mean levels of peer group deviance influenced by genetic factors, but so is the rate of change over time.

0

0.2

0.4

0.6

0.8

1910-1924 1925-1939 1940-1958

Birth Cohort

Female Presence Female Heritability

Male Presence Male Heritability

Prevalence And Heritability OfRegular Tobacco Use

Three Birth Cohorts Of Men And Women In Sweden

PrevalenceOf

Heritability

Linkage And Association

• Linkage – in families. Sweeps entire genome. Good for genes of moderate to large effect.

• Association – in populations. Examines only small distances. Can detect genes of relatively small effect.

• If a base pair equals 1 cm, the human genome equals 33,000 km – around 80% of the way around the world. A linkage peak for a complex trait is ~ 200 km and association is detectable over distances from 50-200 meters.

Irish Affected Sib-Pair Study of Alcohol DependenceSamples & Measures

Probands ascertainedInterview & DNA

N=591(M=364, F=227)

Parents contactedBrief Interview & DNA

N=213(M=82, F=131)

Affected siblings referredInterview & DNA

N=610(M=413, F=197)

733 sib pairs (sibship size: 2-8)

Control GroupsScreened n = 72

Semi-screened ~ 600

Prescott et al., Alc Clin Exp Res, 2005

Sample & Measures

IASPSAD families with DNA and informative for linkage (N=511 sib pairs, 485 families)

4 cM genome scan - deCODE genetics (Iceland) 1081 markers x 1500 individuals (1,621,500)

Outcomes used for linkage analysis

AD: DSM-IV Alcohol dependence

SX: DSM-IV AD symptom count (range 3-7)

Genome-wide LOD Scores for DSM-IV Alcohol Dependence

-3

-2

-1

0

1

2

3

4

5

chromosome position

LOD

12345678910111213141516171819202122

Ch1

Ch13

Ch22

Genome-wide LOD Scores for DSM-IV

Alcohol Dependence Symptoms

-3

-2

-1

0

1

2

3

4

5

cM

LOD

12345678910111213141516171819202122

Ch4

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 50 100 150 200

chromosome 4 position (cM)

LOD

Symptom Count

Alcohol Dependence

Chromosome 4 Linkage Results

Peak LOD = 4.59(p<.000002)

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 50 100 150 200

chromosome 4 position (cM)

LOD

Symptom Count

Alcohol Dependence

Chromosome 4 linkage in other studies

Southwest Indians: AD – Long et al. 1998

U.S. Collaborative (COGA): # symptoms – Reich et al 1998; max drinks - Saccone et al., 2000; alc response - Schuckit et al., 2001; severity – Corbett et al, 2005

Mission Indians: severity - Ehlers et al., 2004

Chromosome 4 NPL LOD Scores for Symptom Dropping Analyses

-1

0

1

2

3

4

5

0 50 100 150 200

cM

Lo

d

medical consequences

lack of controlrestricted activities

withdrawalfailed to quit

bingingtolerance

ADSX

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 50 100 150 200

chromosome 4 position (cM)

LOD

Symptom Count

Alcohol DependenceADH cluster (1a,1b,1c,4,5,6,7)

ADH Follow-Up Association Studies

• 27 SNP markers identified in 7 genes

• Unrelated Case-Control designStage 1:

• 328 cases randomly selected from probands & affected sibs• 328 screened population controls

• Single-marker analyses

• Haplotype analyses – Haploview, WHAP

ADH Marker information

• 24 markers genotyped in ADH gene– ADH5 (including 3 SNPs: RS896992 RS1154405 RS1154400)

– ADH4 (including 4 SNPs: RS1042364 RS1984360 RS1126671 RS4699712)

– ADH6 (including 3 SNPs: RS3857224 RS2187483 RS4699733)

– ADH1A (including 2 SNPs: RS1229976 RS1229967)

– ADH1B (including 3 SNPs: RS1042026 RS1789882 RS1353621)

– ADH1C (including 3 SNPs: RS1614972 RS1662060 RS3133158)

– ADH7 (including 6 SNPs: RS894369 RS284786 RS1154454 RS1154458 RS1154460 RS971074)

Block structures of ADH gene from Haploview

Standard Color Scheme

D' < 1 D' = 1

LOD < 2 white blue

LOD >=2

shades of pink/red

bright red

(No missing data, n=383)(All data, filter out genotype missing over 25%, n=644)

Association results for single marker analyses

p<.05 p<.10

All Subjects (n=644)

Excluding cases with missing data (n=383)

Carol: Block 3 here is the Block 4 in above table. Notice that in the sample including only non-missing data, LD map from Haploview doesn’t have the block of marker 19 & 20

Haplotype association results for each block

The role of GABAThe role of GABAAA in alcohol dependence in alcohol dependence

Most of the genes encoding for GABAA receptor subunits are organized in clusters located on different chromosomes. Thus, GABRA2, GABRA4, GABRB1, and GABRG1, encoding α2, α4, β1 and γ1 are on chromosome 4p13-12 whereas GABRA5, GABRB3, and GABRG3 encoding for α5, β3 and γ3 are located on 15q11-13. The clustering may have functional significance as studies suggest that variations in GABAA receptor genes contribute to differences in risk for alcoholism.

Alcoholism and GABAAlcoholism and GABAAA receptor genes on 4p13-12 receptor genes on 4p13-12

Several studies have reported the potential association of GABAA receptors and alcohol dependence.

Song et al (2003) performed a family based association study using the large COGA (Collaborative Study on the Genetics of Alcoholism) sample. A modest association (P<0.03) was observed with GABRB1 and AD using microsatellite markers.

Variations in GABRA2 were shown to be highly associated with AD as well as the beta frequency of the electroencephalogram (Edenberg et al, 2004). A comparision of the high-risk and low-risk haplotype coding sequences showed no differences hence the effect was postulated to be mediated through gene regulation. Further work has revealed a complex pattern of alternative splicing and promoter use (Tian et al, 2005).

Other studies include Covault et al (2004) who reported an allelic and haplotypic association with GABRA2 and AD. Lappalainen et al (2005) showed that GABRA2 may play a role in risk for AD in a Russian population.

LD Pattern - D’ PlotLD Pattern - D’ Plot

Single marker results using WhapSingle marker results using Whap

SNP rs # HapMap location GABAA gene P-value

1497570 45962576 GABRG1 0.836

1948609 45978314 GABRG1 0.683

2221020 46019107 GABRG1 0.947

1391168 46030701 GABRG1 0.541

490434 46108821 GABRA2 0.0075

497068 46166219 GABRA2 0.0165

279871 46221275 GABRA2 0.0167

279858 46230135 GABRA2 0.0678

279845 46245265 GABRA2 0.0685

279826 46249751 GABRA2 0.226

279827 46250244 GABRA2 0.138

279828 46250352 GABRA2 0.255

279836 46254612 GABRA2 0.0304

2055943 46882821 GABRA4 0.767

1512135 46889430 GABRA4 0.724

2280072 46910712 GABRA4 0.193

2055940 46913455 GABRA4 0.413

989808 47082323 GABRB1 0.219

1372496 47123350 GABRB1 0.000004

6284 47237761 GABRB1 0.987

2070922 47321590 GABRB1 0.375

Results of haplotype analysis using WhapResults of haplotype analysis using Whap

GABRA2

GABRA2 & GABRB1

HAPLOTYPE FREQUENCY P-VALUE

122212221 0.485 0.0205

211121112 0.470 0.0556

222212221 0.019 0.142

121121112 0.015 0.411

122221111 0.011 0.184

HAPLOTYPE FREQUENCY P-VALUE

1222122211 0.389 0.817

2111211121 0.368 0.00352

2111211122 0.109 0.0322

1222122212 0.102 0.0000697

2222122211 0.017 0.25

1211211121 0.015 0.427

This study provides further evidence that GABRA2 receptor gene is associated with AD. Previous studies have shown that SNPs in the 3’ region of the α2 subunit are significantly associated with AD (Covault et al, 2004; Edenberg et al, 2004). This study replicates the earlier findings; SNP rs490434 which is localized to the 3’ region produced a P-value of 0.0075. However the most significantly associated SNP in the current study is localized to GABRB1. SNP rs1372496 gave a single marker significance (P-value = 0.000004) when analyzed for AD.

Association Studies of Smoking Initiation and Nicotine Dependence

• Unrelated subjects from two twin studies

• Subjects were classified into 3 groups based on the score of the Fagerstrom Tolerance Questionnaire– 244 NonSmokers– 215 Low-ND smokers (FTQ score 0-2)– 229 High-ND smokers (FTQ score 7-11)

April 20, 2023Sam CHEN

A Summary of Genes StudiedA Summary of Genes Studied

Gene chr function SI ND

Epac 12 cAMP signal transduction pathway ± +

PTEN 10 regulate AKT/PKB pathway ++ +

Rhoa 3 Ras gene family, signal pathway +++ ±

Ywhag 7signal transduction (mitosis and cellularproliferation) - -

MAP3K2 2 MAPK signaling pathway - -

MAP3K4 6 MAPK signaling pathway - -

MAP12 22 MAPK signaling pathway ± ±

ARHGAP15 2 a potential regulator of Rac1 - +

GABAB2 9 GABA B2 receptor - -

OPRM1 6 opioid mu receptor + +

April 20, 2023Sam CHEN

PTEN: Single Marker PTEN: Single Marker AssociationAssociation

Marker name

Genotype Allele

SI ND SI ND

P-value P-value P-value P-value

rs1234221 0.0898 0.2437 0.0311 0.7252

rs2299939 0.7165 0.8929 0.5279 0.7720

rs1234213 0.0007 0.0821 0.0002 0.0278

rs2735343 0.0036 0.3908 0.0028 0.2105

rs701848 0.4749 0.0856 0.1503 0.1161

April 20, 2023Sam CHEN

PTEN: Haplotype AssociationPTEN: Haplotype Association

MarkerGlobal p value Haplotype

Frequency(case:ctrl)

Oddsratio

Haplotype p value

Smoking Initiation

1-3 0.0053 1-2 0.36:0.27 1.33 0.0017

3-5 0.0078 2-1 0.35:0.26 1.38 0.0006

1-3-5 0.0431 1-2-1 0.32:0.24 1.34 0.0037

1-2-3-4 0.0308 1-1-2-2 0.34:0.27 1.25 0.0167

2-3-4-5 0.0647 1-2-2-1 0.34:0.26 1.28 0.0105

Nicotine Dependence

3-5 0.0504 2-1 0.40:0.31 1.3 0.0058

April 20, 2023Sam CHEN

Rhoa: Single Marker AssociationRhoa: Single Marker Association

  Allelic association Genotype association

Marker SI ND SI ND

rs6784820 0.04319 0.47528 0.10830 0.74069

rs2177268 0.10068 0.28590 0.23371 0.51899

rs2878298 0.00349 0.70204 0.00005 0.00070

rs974495 0.26305 0.75573 0.11984 0.92688

rs3448 0.10857 0.55643 0.27922 0.80291

April 20, 2023Sam CHEN

Rhoa: Haplotype Association (SI)Rhoa: Haplotype Association (SI)Marker Haplotype Case Freq Control Freq OR Chisq P value

1-3-4 1-1-2 180.5 0.292 68.8 0.189 1.8 8.16 0.0043

2-1-1 69.4 0.112 74.6 0.205 0.7 13.38 0.0003

  2-2-1 83.7 0.136 16.2 0.045 4 7.52 0.0061

  Global: LRS 28.89          

DF 6

    P 6.38E-05          

Marker Haplotype Case Freq Control Freq OR Chisq P value

1-3-4-5 1-1-2-2 100.0 0.171 29.7 0.086 2.0 7.39 0.00657

2-1-1-1 62.5 0.107 70.8 0.205 0.5 12.76 0.00035

  2-2-1-1 82.1 0.140 14.6 0.042 3.3 9.57 0.00198

  Global: LRS 33.08          

DF 8

    P 5.96E-05