Genetics of Alcohol Response

Addiction Medicine

State of the Art Conference

October, 2003

Ray White

Alcoholism

• Many definitions– Precision important for specific studies

– Working definition: • alcohol craving has become encompassing drive

• Individual is losing, or has lost, job, family, health

• Economic and personal costs astronomical– 10s of thousands of traffic deaths each year

– Affect not only individual, but family, bystanders…

• Cultural context

Genetics and Molecular Etiology

• Cell and molecular biology describe molecular pathology of disease state

• Often cannot distinguish cause from effect or consequence of disease– Important to understand chain of causality in order to

intervene– Genetics can define components that are “sufficient” to

cause disease• APC gene

• Genetics can identify etiologic components not seen by cell, molecular biology analyses– APC gene – Molecular mechanism– Therapeutic targets

Alcoholism Has Genetic ComponentTwin Pair Concordance RatesTwin Group #Pairs Prevalence Concordance

% S.E. % S.E.

MZF 932 5.3 0.6 29.5 6.1

DZF 534 6.4 0.7 16.7 6.1

MZM 396 21.2 1.7 56.1 4.6

DZM 231 24.2 2.0 33.3 5.7

DZUS 592 26.1 1.7 59.5 8.1

6.4 0.9 14.0 2.8

M

F

Heath et al, Psych Med 27:1381 (1997)

Genes for AlcoholismHow do we find them?

• Positional cloning with families– Global scan for disease susceptibility genes (+)

– All genes in region become candidates

*M1

*M1

*M1

*M1

*M1

*M1

*M1

*M1

*M1

*M1

Ascertain Ascertain family clusterfamily cluster

Expand into pedigreeExpand into pedigreeAnalyze by linkageAnalyze by linkage

* *

*

Positional Cloning with Families

• Global scan for disease susceptibility genes (+)• All genes in region become candidates

13131212111111111212

21212222232324242525

**

MCCMCC

DP1DP1

SRP14SRP19

APC

500 kb500 kb5 mb5 mb

30-40 mb30-40 mb

CHROMOSOME 5

100 kb100 kb

260 kb260 kb

L5.48L5.48

L5.63L5.63 L5.79,71,63L5.79,71,63

MM11

*M1

*M1 *

M1

*M1

*M1

*M1

*M1

*M1

*M1

*M1

Genes for Alcoholism Positional Cloning with Families

Reich et al. Amer. J. Med Gen 81:207 (1998)

Faroud et al, Alcohol: Clin and Exp Res 24:933 (2000)

Genes for AlcoholismCOGA Replica Set

Genes for AlcoholismLinkage Mapping in Families:

Conclusions and Caveats• Significant linkage findings strongly support genetic

component to alcoholism

• Broad peaks– Many genes implicated

– 20 to 40 genes in most regions

– More data does not solve problem

• Replication uncertain– Peaks lower, breadth maintained

• Only rarely has led to gene identification in complex diseases – – But often in rare, syndromic diseases

Genes for AlcoholismAssociations in Populations

• Experiment– Genotype SNPs in cases and controls

• Expected outcome– Some SNPs show increased frequency among

cases

– Association of SNP haplotypes identifies chromosome region carrying mutation (Dvariant) causing disease susceptibility

High frequency marker SNPs (alleles @ 10% - 50%)

SNP3DvariantSNP1 SNP2

Genes for AlcoholismFunctional Candidates:

Associations in Populations• 5 variants – Individuals with family history;

% Alcoholism Diagnosis as function of genotype– 5-HT Transporter,

• LL–57% SL–21% SS-12.5% ; p=0.04

– 5-HT2A T102C,

– 5-HT2A TYR,

– 5-HT2C CYS-SER,

– GABAA6

• Pro/Pro-23% Pro/Ser-71% ; p=0.02

Schuckit, M. A., C. Mazzanti, et al. (1999). Biol Psychiatry 45(5): 647-51

Genes for AlcoholismAssociation in Populations: Conclusions and Caveats

• Common SNP markers may miss variants that are low frequency (1% - 2%)

• Most interesting variants may be in the low frequency class

Individual Response to Alcohol Challenge

• Broad range of response among individuals

• Not explained by pharmacokinetics

– Response largely independent of blood alcohol levels

– Response ranges from extreme sensitivity to “hollow leg”

• Response measured by questionnaire (euphoric feeling…) and by body sway index

Level of Response Heritable

Schuckit, M.A., et al., A genome-wide search for genes that relate to a low level of response to alcohol. Alcohol Clin Exp Res, 2001. 25(3): p. 323-9.

Key Finding: Level of Response Predicts Risk of

Alcoholism• Entering college students with family history of alcoholism

– No strong personal alcohol history

– Tested with questionnaire and physiological measures

– Followed for extended period

• Low response seen in– 40% of children of alcoholics

– 10% of family history negative controls

• Individuals with a low response to alcohol – 4-fold more likely to become alcoholic

– Drink more, hang out with heavy drinking groups

• May explain much of inherited alcoholism risk

J Stud Alcohol. 1998 Sep;59(5):485-94. Biological, psychological and environmental predictors of the alcoholism risk: a longitudinal study.Schuckit MA

Level of Response Mapping

Schuckit, M.A., et al., A genome-wide search for genes that relate to a low level of response to alcohol. Alcohol Clin Exp Res, 2001. 25(3): p. 323-9.

Schuckit, M.A., et al., A genome-wide search for genes that relate to a low level of response to alcohol. Alcohol Clin Exp Res, 2001. 25(3): p. 323-9.

Multipoint Sib-Pair Linkage - Chromosomes 1, 21

Sibs from Lower Third of FIRST 5 (SRE)

Chromosome 1 Chromosome 21

Mapping Studies: Alcoholism

vs Response Level

COGA Alcoholism StudyChromosome 1

COGA Level of ResponseChromosome 1

Mapping Response Level Genes II

Wilhelmsen, K.C., et al., Alcohol Clin Exp Res, 2003. 27(7): p. 1041-7.

• Too many candidates in linkage

• Too much heterogeneity– Locus heterogeneity often lethal for linkage and

association approaches

– Allelic heterogeneity often kills association• Association needs common variants

• New approaches needed - Candidate Genes– Sequencing for rare variants

– Isoallelic cohorts for phenotypic characterization

Positional Cloning: Not Working in Complex Disease

Excellent Candidate Genes for Alcohol Response

• Positional Candidates – in chromosomal locations that are implicated in disease

• Functional Candidates – in pathways that are implicated in disease process; e.g. (cAMP/PKA)

• Model system Candidates – found in animal models to impact alcohol response

• Best candidates meet all three criteria

Genes for Alcoholism Functional Candidates

Genes for Alcoholism Functional Candidates

A2R

cAMP

AC Gs

cAMP inducible genesCREB*

PKA

CRE

envelopeNuclear

RII

Ion

Ch

an

nels

C

C

C

Slide: Anastasia Constantinescu

A Mouse Model: PKA RII Knockout

Measures of acute sensitivity to the sedative effects of ethanol, consumption of nonalcoholic tastants, and plasma ethanol levels (mean ± SEM). a, Time to regain the righting reflex (minutes) after injection of ethanol (4.0 gm/kg; i.p.). b, Consumption (milliliters per kilograms per day) of solutions containing either sucrose (Suc) or quinine (Qui). c, Plasma ethanol concentration (milligrams per deciliter) either 1 or 3 hr after ethanol injection (4.0 gm/kg; i.p). ANOVAs indicated that RII  /   mice recovered from ethanol-induced sedation significantly sooner than RII +/+ mice. On the other hand, RII  /   and RII +/+ mice did not differ significantly in consumption of nonalcoholic tastants or plasma ethanol levels. RII  /   versus RII +/+, *p < 0.05

Consumption of ethanol by mutant mice lacking the RII subunit of PKA (RII  /  ) and wild-type control mice (RII +/+) maintained on a 129 SvJ × C57BL/6 hybrid background. a, Consumption (grams per kilogram) of a 20% ethanol solution. b, Consumption (grams per kilogram per day) at each ethanol solution (8-d average). c, Ethanol preference ratios (volume of ethanol consumed/total fluid consumed) as a measure of relative ethanol preference. All values reported as mean ± SEM. ANOVAs indicated that the RII  /   mice drank significantly more ethanol than RII +/+ mice. RII  /   versus RII +/+, *p < 0.05.

Thiele, Todd E., Willis, Brandon, Stadler, Julia, Reynolds, James G., Bernstein, Ilene L., McKnight, G. StanleyJ. Neurosci. 2000 20: 75-

PKA RII

Chromosome 7 Mapping

Candidate Genes II:Ethanol Consumption by

PKC -/- Mice

Reduced responding for ethanol-reinforced lever presses in PKC /

mice compared with PKC +/+ mice. (A) Total number of ethanol-reinforced lever presses in a 16-h period, averaged across 8 weeks of testing. PKC / mice (open bars) demonstrated a significantly lower total number of lever presses than PKC +/+ mice (filled bars; t = 2.8, P< 0.05). (B) Total number of ethanol-reinforced lever presses following two different durations of ethanol deprivation. PKC / mice demonstrated a significant reduction in total number of lever presses following 104 h ethanol deprivation compared with PKC +/+ mice [F (1,23)genotype = 7.4, P< 0.05]. In addition, only PKC +/+ mice

demonstrated a significant reduction in total number of lever presses following 104 vs. 32 h ethanol deprivation [F (1,23)duration = 21.6, P<

0.01]. (C) Number of ethanol reinforcers per bout were reduced in PKC / mice following 104 h ethanol deprivation compared with wild-type controls [F (1,23)genotype = 5.0, P< 0.05], and decreased as a

function of duration of ethanol deprivation period [F (1,23)duration = 9.0,

P< 0.05] only in PKC +/+ mice. (Inset) Number of ethanol self-administration bouts did not differ among genotypes, but number of bouts decreased as a function of duration of ethanol deprivation period in both genotypes [F (1,23)duration = 27.3, P< 0.01]. (D) Rate of

lever pressing for ethanol reinforcement was significantly reduced in PKC / mice compared with wild-types [F (1,23)genotype = 21.9, P< 0.01].

Similarly, rate of lever pressing decreased as a function of duration of ethanol deprivation period [F (1,23)duration = 8.4, P< 0.05] only in PKC

+/+ mice. Data are expressed as mean ± SEM. *Significantly different from 104 h deprivation period. Significantly different from wild-type controls

Choi, D. S., D. Wang, et al. (2002). J Neurosci 22(22): 9905-11

Genetics of GenesCandidates

• Goal is discovery of phenotypes associated with variants in interesting genes

• Candidates emerge from – functional analysis – biochemical pathways

– Model organisms

• Problem: Since most interesting variants <1%-2% frequency, usually see only one example– Risky to draw conclusions from one example

– Need an isoallelic cohort -analogous to mouse knockouts

– 25 – 50 subjects who carry same rare allele

Genetics of Genes: Bottom-UpDriven by Gene Activity –

Phenotype is the Object of Discovery

• Functional candidates– Many pathways offer exciting

candidates

– Ultimately a “Genetics of the Genes”

Positional Cloning:Top-DownPhenotype-driven Pathway to

Novel Candidate Genes• Mapping reduces the complexity >100

fold– Primary mapping

– Secondary mapping by association with variants identified in affected sample sets

– Functional candidates within mapped region

• Functional candidates– Many pathways now offer exciting candidates

– Ultimately a “Genetics of Genes”

Next Generation Positional Cloning:Gene Mapping in Superfamilies

• Central Problem: Most allele carriers for complex disease susceptibility alleles do not display phenotype– Low, moderate penetrance alleles need bigger window to

ascertain families

– Cannot extend families by following footprints of disease

• Superfamilies provide bigger window, reveal footprints of low, moderate penetrance alleles– > 1,000 family members

• Superfamilies display multiple aspects of phenotype– Expect, and are seeing, extensive overlap among

cancers

SuperfamiliesUtah Population Databases

Advantages Reduces heterogeneity

Founder logic – one locus, one allele

Reveals dominant alleles with reduced penetrance

50,000 Families, each founded by a pioneer couple

Many kinships >1,000 Pedigrees recorded in computerized

database

Superfamilies Provide Identity-By-Descent Mapping

• One gene, one variant brought in by founding pioneer couple

• Power for high resolution mapping

• Pairwise testing shows IBD segment 10mb – 20mb

Co-Aggregation of Cancers in Superfamilies

Outcome Disease

Breast

Colon Lung

Melanoma

Ovary

Pancreas

Prostate

Breast 2.4 1.8 0.7 3.5 0.2 2.7

Colorectal

3.0 1.2 0.3 0.4 2.2 2.7

Lung 3.5 1.8 1.1 0.7 1.1 3.5

Melanoma

1.7 0.5 1.3 0.9 1.4 4.2

Ovarian 10.5 0.9 1.8 1.4 4.0 2.4

Pancreas 0.5 3.7 1.5 1.5 4.3 8.3

Prostate 2.2 1.8 1.3 1.4 0.6 0.8

Co-aggregating Disease

Odds ratios for SF at “high-risk” for Outcome Disease given that they are classified as “high-risk” for Co-aggregating Disease. If OR is significantly different from 1.0, number is in light mustard-biege.

• Will see association through genome scan with STR markers

Affected Individuals within Affected Individuals within Kinship Kinship Identical-by-Descent at Identical-by-Descent at

Susceptibility LocusSusceptibility Locus

A Portion of a Very Large Utah Family

D5S656APC

D5S2065D5S659

D5S1720

D5S494D5S639

ATA24E05

D5S592

D5S615

D5S1391

D5S1393

D5S1346

D5S2501

D5S2051

D5S421124.8-125.1 mb125.7 mb

126.7 mb127.2 mb127.5 mb

130.8 mb131.0 mb

131.8 mb

139.0 mb

141.3 mb

112.2 mb

118.3 mb

122.4 mb

123.4 mb

124.3 mb

132.7 mb

Chromosome 5

APC

Conserved Conserved Chromosomal Chromosomal Segment SizeSegment Size