genetic epidemiology of smoking behavior
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Genetic Epidemiology of Smoking Behavior
Kenneth S. Kendler
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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 ModelContingent Causal Common Pathway
A I E IC I
TobaccoInitiation
A R E RC R
RegularTobacco Use
A D E DC D
Persistence /Nicotine
Dependence
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
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stan
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Can
nabi
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ativ
es
Stim
ulan
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Coc
aine
Opi
ates
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luci
noge
ns
females
males
combined
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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?
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
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Alcohol
8-11yrs 12-14yrs 15-17yrs 18-21yrs
8-11yrs 12-14yrs 15-17yrs 18-21yrs
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Marijuana
8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs
8-11yrs 12-14yrs 15-17yrs 18-21yrs 22-25yrs
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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)
1790 43.5 20.4 15.1 20.9
2. 12-14 Cigarettes (c42)
1795 56.8 24.3 12.0 6.8
3. 15-17 Cigarettes (d42)
1792 78.4 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.
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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
ACE
Peer Group Deviance
0%
20%
40%
60%
80%
100%
8-11 12-14 15-18 19-22
Ages
ECA
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.
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1910-1924 1925-1939 1940-1958
Birth Cohort
Female Presence Female HeritabilityMale 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 analysisAD: DSM-IV Alcohol dependenceSX: DSM-IV AD symptom count (range 3-7)
Genome-wide LOD Scores for DSM-IV Alcohol Dependence
-3
-2
-1
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chromosome position
LOD
12345678910111213141516171819202122
Ch1
Ch13
Ch22
Genome-wide LOD Scores for DSM-IV
Alcohol Dependence Symptoms
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-2
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cM
LOD
12345678910111213141516171819202122
Ch4
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chromosome 4 position (cM)
LOD
Symptom Count
Alcohol Dependence
Chromosome 4 Linkage Results
Peak LOD = 4.59(p<.000002)
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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
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cM
Lo
d
medical consequences
lack of controlrestricted activities
withdrawalfailed to quit
bingingtolerance
ADSX
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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 GABAA 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 GABAA 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’ Plot
Single marker results using WhapSNP 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 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)
A 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 + +
PTEN: Single Marker Association
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
PTEN: 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
Rhoa: 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
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
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