Genetic susceptibility to substance abuse Nick Martin Queensland Institute of Medical Research, Brisbane Translational Medicine Canberra November 4, 2010.

Genetic susceptibility to substance abuse

Nick MartinQueensland Institute of Medical Research, Brisbane

Translational MedicineCanberra

November 4, 2010

Alcohol dependence in the US

Many substances Alcohol Nicotine Caffeine Cannabis Opioids Gambling

Genetic Epidemiology:4 Stages of Genetic Mapping

Are there genes influencing this trait? Genetic epidemiological studies

Where are those genes? Linkage analysis

What are those genes? Association analysis

What can we do with them ? Translational medicine

Phenotype

E C A D

UniqueEnvironment

AdditiveGeneticEffects

SharedEnvironment

DominanceGeneticEffects

e

ac

d

P = eE + aA + cC + dD

Variance components

Designs to disentangle G + E Family studies – G + C confounded

MZ twins alone – G + C confounded

MZ twins reared apart – rare, atypical, selective placement ?

Adoptions – increasingly rare, atypical, selective placement ?

MZ and DZ twins reared together

Extended twin design

MZ twins reared apart - note the same way of supporting their cans of beer

Designs to disentangle G + E Family studies – G + C confounded

MZ twins alone – G + C confounded

MZ twins reared apart – rare, atypical, selective placement ?

Adoptions – increasingly rare, atypical, selective placement ?

MZ and DZ twins reared together

Extended twin design

MZ and DZ twins: determining zygosity using ABI Profiler™ genotyping

(9 STR markers + sex)MZ DZ DZ

Identity at marker loci - except for rare mutation

Twin correlations for alcohol dependence factor score

Three scenarios

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

No G No C G and C

MZ

DZ

Twin Correlation

Causes of Variation

ACE Model for twin data

PT1

ACE

PT2

A C E

1

MZ=1.0 / DZ=0.5

e ac eca

Sources of variation in alcohol dependence

1%

64%

38%

Additivegenetic

Sharedenvironment

Non-sharedenvironment

Extended Twin Design

Truett, et al (1994) Behavior Genetics, 24: 35-49

Smoking: extended twin kinship data from Virginia & Australia

ML Correlations for pairs of relatives, and confidence intervals

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

MZ DZ SI PC GP Avmz Avdz Avsi Comz Codz SP Spmz Spdz Spsi Sppa SSmz SSdz Savmz Savdz

MZ & DZ: twins; SI: siblings; PC: parent-child; GP: grandparents; Av: avuncular pairs; Co: cousins; SP: spouses

US

OZ

4 Stages of Genetic Mapping

Are there genes influencing this trait? Genetic epidemiological studies

Where are those genes? Linkage analysis

What are those genes? Association analysis

What can we do with them ? Translational medicine

Linkage Markers…

Linkage for MaxCigs24 in Australia and Finland

AJHG, in press

But overall, the results of linkage studies have been disappointing,

so we have moved to -

Association Looks for correlation between specific

alleles and phenotype (trait value, disease risk)

Variation: Single Nucleotide Polymorphisms

High density SNP arrays – up to 1 million SNPs

500 000 - 1. 000 000 SNPs Human Genome - 3,1x109 Base Pairs

Genome-Wide Association Studies

5 x 10-8

CACNA1CAnkryin-G (ANK3)

Bipolar GWAS of 10,648 samplesBipolar GWAS of 10,648 samples

Sample Cases Controls P-valueSTEP 7.4% 5.8% 0.0013WTCCC 7.6% 5.9% 0.0008EXT 7.3% 4.7% 0.0002Total 7.5% 5.6% 9.1×10-9

Sample Case Controls P-valueSTEP 35.7% 32.4% 0.0015WTCCC 35.7% 31.5% 0.0003EXT 35.3% 33.7% 0.0108Total 35.6% 32.4% 7×10-8

X

>1.7 million genotyped and (high confidence) imputed SNPs

Ferreira et al (Nature Genetics, 2008)

Published Genome-Wide Associations through 6/2010, 904 published GWA at p<5x10-8 for 165 traits

NHGRI GWA Catalogwww.genome.gov/GWAStudies

Thorgeirsson et al., 2010

Furberg et al., 2010

Liu et al., 2010

GWAS for Smoking

Genome Wide Association with Cigarettes per DayA Proxy for Nicotine Dependence

Furberg et al., 2010

Chromosome 15 contains the strongest genetic contribution to the risk of developing nicotine dependence.

mRNA expression of CHRNA5 in brain

p=1.11x10-9

Minor allele of rs588765 is associated with increased mRNA expression of CHRNA5 in human frontal cortex.

This SNP explains 42% of the variance in mRNA expression. Wang et al., 2009aWang et al., 2009bFrom the lab of Alison GoateAdditional work see Smith et al and Saddee, 2010

D398N (rs16969968) Variant Lowers Receptor Response to Nicotine Agonists

Decreased receptor response yields increased nicotine dependence risk

Bierut et al., 2008From the lab of Jerry Stitzel

• Genetic risk of nicotine dependence at rs1051730 in CHRNA5 changes across birth cohort in the US, corresponding to changes in public policy.

• Increasing social restrictions may strengthen the genetic component contributing to smoking.

What do modest genetic effects mean?

• Many genes are involved in disease, which is consistent with genetic risk in the 1.1 range.

• If there are rare variants associated with disease, they must be very strong for us to detect them.

• No one gene will predict disease.• So we need to be clever – pathway analysis

Vink et al, 2009 American Journal of Human Genetics 84: 367-79, 2009

Pathway (Ingenuity) analysis of GWAS for smoking

What is the best phenotype to study?

• The best phenotype is one that is most associated with genetic variants.

• P value = sample size and genetic risk.

• To improve the p value you can –– Increase the sample size– Increase the genetic effect

Meta-Analysis of Genomewide Association Studies

Manolio T. N Engl J Med 2010;363:166-176

International research – the telcon

W.Coast USA 7am

E.Coast USA 10am

UK 3pm

Europe4pm

Brisbanemidnight

Progress

Alcohol Dependence Meta-Analysis

Samples

N (cases) N (controls)German 428 1,319US (SAGE) 1,235 1,433Australia (OZ-ALC) 1,320 1,762TOTAL 2,983 4,514

Treutlein et al., 2009Bierut et al., 2009Heath et al., in review

Meta-analysis Results

Top findings show similar results in the US and Australian samples.

Association findings with SNPs in the ADH region

Birley et al. 2009

GWAS for esophageal ca

ADH1B

ALDH2

Opioid Dependence: Candidate Opioid Dependence: Candidate Genes and G x E EffectsGenes and G x E Effects

(Comorbidity and Trauma Study)(Comorbidity and Trauma Study)

Elliot C. Nelson, M.D.Elliot C. Nelson, M.D.

Washington UniversityWashington University

Sample AscertainmentSample AscertainmentCases (N = 1500)Cases (N = 1500)

Clients currently receiving maintenance Clients currently receiving maintenance

treatment for heroin dependence in NSWtreatment for heroin dependence in NSW

Neighborhood controls (N = 1500)Neighborhood controls (N = 1500)

Recruited via posters placed in Recruited via posters placed in

employment centres, GP offices, and employment centres, GP offices, and

handed out at locations near the clinics or handed out at locations near the clinics or

via ads in local newspapers; little or no via ads in local newspapers; little or no

recreational opioid use recreational opioid use

Lifetime diagnosisLifetime diagnosis Male prev (%)Male prev (%) Female prev (%)Female prev (%)

Risk by case Risk by case status (controls status (controls

for gender)for gender)

CaseCase Control Control CaseCase ControlControl

PTSDPTSD 30.830.8 15.315.3 49.949.9 23.423.4 2.92 (2.28-3.74)2.92 (2.28-3.74)

DepressionDepression 53.853.8 49.649.6 68.268.2 52.552.5 1.52 (1.24-1.87)1.52 (1.24-1.87)

Conduct disorderConduct disorder 68.168.1 40.240.2 54.354.3 24.124.1 3.44 (2.77-4.27)3.44 (2.77-4.27)

ASPDASPD 67.867.8 34.934.9 53.253.2 22.022.0 3.96 (3.18-4.95)3.96 (3.18-4.95)

Nicotine dependenceNicotine dependence 65.465.4 46.746.7 66.366.3 36.736.7 2.71 (2.20-3.34)2.71 (2.20-3.34)

Alcohol dependenceAlcohol dependence 43.443.4 33.633.6 34.934.9 22.422.4 1.67 (1.33-2.08)1.67 (1.33-2.08)

Psychiatric Disorders Including Psychiatric Disorders Including Licit Drug DependenceLicit Drug Dependence

Drug Drug ClassClass

Male prev (%)Male prev (%) Female prev (%)Female prev (%) Risk by case status Risk by case status (controls for gender)(controls for gender)CaseCase ControlControl CaseCase ControlControl

CannabisCannabis 57.957.9 35.835.8 52.152.1 23.123.1 2.97 (2.39-3.70)2.97 (2.39-3.70)

StimulantStimulant 52.652.6 18.318.3 46.246.2 16.116.1 4.70 (3.65-6.05)4.70 (3.65-6.05)

SedativesSedatives 35.735.7 1.81.8 38.238.2 1.11.1 42.66 (20.07-90.70)42.66 (20.07-90.70)

CocaineCocaine 33.233.2 3.53.5 31.231.2 2.82.8 14.73 (8.84-24.54)14.73 (8.84-24.54)

Non-opioid Illicit Drug DependenceNon-opioid Illicit Drug Dependence

Genotypic data analysesGenotypic data analyses

Two-stages planned:Two-stages planned:

1) 136 Opioid receptor gene SNPs:1) 136 Opioid receptor gene SNPs:

OPRD1OPRD1 21; 21; OPRM1OPRM1 93; 93; OPRK1OPRK1

22;22;

2) SNPs from other genes 2) SNPs from other genes

Cases vs ATR controls – Cases vs ATR controls – OPRD1OPRD1 SNPs SNPs

Levran et al. Levran et al. 2008 (Kreek 2008 (Kreek lab) most lab) most significant hits significant hits (3 of 9) with (3 of 9) with Goldman Goldman Addiction chipAddiction chip

SNPSNP LD (rLD (r2) 2) P valueP value OROR

rs2236857rs2236857 1.01.0 0.0002950.000295 1.251.25

rs2236855rs2236855 1.01.0 0.0002950.000295 1.251.25

rs2298897rs2298897 0.910.91 0.0004690.000469 1.241.24

rs3766951rs3766951 0.600.60 0.0004110.000411 1.221.22

rs529520rs529520 0.300.30 0.001930.00193 1.181.18

rs760589rs760589 0.660.66 0.001090.00109 1.201.20

rs419335rs419335 0.420.42 0.001120.00112 1.201.20

rs204055rs204055 0.320.32 0.009980.00998 1.151.15

rs680090rs680090 0.270.27 0.0160.016 0.880.88

rs581111rs581111 0.010.01 0.0450.045 1.131.13

rs2236861rs2236861 0.250.25 0.0300.030 1.151.15

rs678849rs678849 0.290.29 0.0530.053 1.111.11

rs1042114rs1042114 0.040.04 0.520.52 0.950.95

Future DirectionsFuture Directions

GWAS genotyping pending including GWAS genotyping pending including

further cases from Perth (N~1000) and US further cases from Perth (N~1000) and US

(N~1500) - N~4000 total cases(N~1500) - N~4000 total cases

4 Stages of Genetic Mapping

Are there genes influencing this trait? Genetic epidemiological studies

Where are those genes? Linkage analysis

What are those genes? Association analysis

What can we do with them ? Translational medicine

DRD2 genotype and heroin addictionA1 allele frequency

0

5

10

15

20

25

30

35

40

45

success poor

f(A1)

• 95 Brisbane heroin addicts on methadone treatment program

• 54 successful treatment outcome

• 22 dropped out• 19 poor treatment

outcome

• Lawford et al (2000) Am J Med Genet 96:592-8

0

0.01

0.02

0.03

P < 0.1

P < 0.2

P < 0.3

P < 0.4

P < 0.5

R2 P=210-28

0.008

510-11

710-9

110-12

0.710.05

0.30 0.65 0.23 0.06

Schizophrenia Bipolar disorder Non-psychiatric (WTCCC)

CAD CD HT RA T1D T2DMGSEuro.

MGSAf-Am

O’Donovan STEP-BD WTCCC

A greater load of “nominal” schizophrenia alleles (from ISC)?

ISC X Test

Can predict bipolar from SzSNPs, but not other diseases

Predictive information on Risk from up to 50% of SNPs in a GWAS !

Translational medicine !

Translation into public health

Alcohol Nicotine Caffeine Cannabis Heroin Gambling

Birth Cohort Trends in Cannabis Use

0

10

20

30

40

50

60

8 9 10 11 12 13 14 15 16 17 18 19 20 21

1940-1944

1945-1949

1950-1954

1955-1959

1960-1964

1965-1969

1970-1974

1975-1979

1980-1984

Degenhardt et al, 2000

Using discordant twins to control genes and highlight environment

Australian twins born 1964-71311 same-sex twin pairs discordant for cannabis use before age 17 - 74 MZF, 62 MZM, 83 DZF and 92 DZM pairsNo significant interactions between zygosity and the effects of early cannabis use on later drug use and drug related harm

Drug Abuse/ Dependence in Twin Pairs Discordant for Cannabis Use Before Age 17

Lifetime Prevalence (%)

Early use Co-twins Cond OR 95% CI

Cannabis

51.7

42.3

1.57

.98-2.52

Sedatives 2.6 0.4 6.00 0.72-49.83

Cocaine/ Stimulants

15.4 5.6 3.79 1.60-9.01

Opioids 4.3 0.9 5.00 1.10-22.82

Any Drug 54.3 42.7 1.76 1.08-2.84

Alcohol 46.6 33.8 1.85 1.21-2.83

Lynskey et al, 2003

Conclusions Early cannabis use was associated with significantly increased risks for other illicit drug use and drug abuse/ dependence:

2.4 to 3.9 fold increase in odds of other drug use1.6 - 6.0 x increase in drug abuse/ dependence

Similar effects on depression and other psychopathologyThese results & other studies suggest early cannabis use may causally influence later drug use & drug-related problems