Gene-Environment Interplay in Alcoholism
Download
1 / 132

Presenter - PowerPoint PPT Presentation


  • 219 Views
  • Updated On :

Gene-Environment Interplay in Alcoholism (and other substance use disorders): Presentation to Yale CTNA April 24, 2006 Do Not Copy Slides Without Permission From Dr. Heath. Andrew C. Heath, D.Phil. Midwest Alcoholism Research Center Department of Psychiatry

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Presenter' - LeeJohn


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Slide1 l.jpg

Gene-Environment Interplay in Alcoholism

(and other substance use disorders):

Presentation to Yale CTNA April 24, 2006

Do Not Copy Slides Without Permission From Dr. Heath

Andrew C. Heath, D.Phil.

Midwest Alcoholism Research CenterDepartment of Psychiatry

Washington University School of Medicine


Presenter s disclosure of interest l.jpg
Presenter’s Disclosure of Interest

  • Sources of Research Support as PI

    • 1. HD049024 (R01)

    • 2. AA07728 (R37)

    • 3. AA13321 (R01)

    • 4. AA11998 (P50)

    • 5. AA07580 (T32)

    • 6. AA015210 (R01)

    • 7. AA09022 (R01)

      from the National Institute of Child Health and Human Development

      and the National Institute on Alcohol Abuse and Alcoholism.

Name & Presentation Date: Andrew C. Heath, D.Phil., April 24, 2006

  • Consulting Relationships

  • -- Palo Alto VA (Jacob)

  • Stock Equity (> $10,000)

  • -- NONE

  • Speaker’s Bureau(s)

  • -- NONE


Slide3 l.jpg

Genetic Epidemiology of Alcoholism

Understanding, in the general population, the complex interplay of genetic and environmental risk-factors in determining (a) differences in alcoholism risk, (b) the comorbidity of alcoholism with other disorders, and (c) differences in the course of alcohol problems and associated outcomes through time.


Australia oz 81 l.jpg
AUSTRALIA: OZ '81

  • AUSTRALIAN 1981 (‘Older’) TWIN COHORT(Heath, Martin, Bucholz, Madden et al.)

    • Recruited from volunteer twin panel

    • Mostly born 1940-1964 (N=5,995 interviews)

    • Spouses also interviewed (N=3,814 interviews); now interviewing the offspring generation.

    • Subsample underwent alcohol challenge testing (N=412)

    • Also used to identify informative sibships for gene-mapping efforts (Madden NAG; Heath/Todd/ Martin/Whitfield Alcohol IRPG).


Australia oz 89 l.jpg
AUSTRALIA – OZ '89

  • AUSTRALIAN 1989 TWIN COHORT(Heath, Martin, Bucholz, Madden, et al.)

    • Born 1964-1971 (N=6,250 interviews)

    • Recruited from volunteer twin panel, enrolled when children

    • Heavy drinking cohort, with high rates of alcohol problems in women as well as men!

    • Just starting to interview the offspring generation.

    • Also used to identify informative sibships for gene-mapping efforts.


U s a vets l.jpg
U.S.A.: VETS

  • VIETNAM-ERA TWIN PANEL (VETS) (Tsuang, Eisen, True, Jacob, Bucholz, et al.)

    • Identified through military service in the Vietnam Era

    • All male twin panel (N=6712 interviews in Harvard Drug Study)

    • Follow-up assessments of offspring of alcohol dependent and control pairs (Jacob, True) and of drug dependent and control pairs (Bucholz)


U s a moafts l.jpg
U.S.A.: MOAFTS

MISSOURI ADOLESCENT FEMALE TWIN STUDY (MOAFTS)(Heath, Madden, Bucholz, et al.)

  • Ascertainment from birth records

  • Parent intake interviews with cohorts of twins aged 11, 13, 15, 17, 19 (n=3666 parents, 2190 families)

  • Twin intake interviews at 13, 15, 17, 19 (N=3702 twins, 1799 complete pairs)

  • Continued follow up of twin birth cohort through age 25 (N=3780 – including new participants)

  • New NICHD-funded follow up focused on mating, reproducing & parenting.


Why study alcoholism l.jpg
WHY STUDY ALCOHOLISM?

  • “Ideal phenotype” for studying the complex interplay of genetic factors, environmental risk mechanisms.

  • Important clinical & public health problem.


Why emphasize alcohol problems in women l.jpg
WHY EMPHASIZE ALCOHOL PROBLEMSIN WOMEN?

  • Early studies relatively uninformative about genetic influences on risk of alcohol problems in women;

  • Increasing rates of female alcohol use, problems, drinking to intoxication in recent cohorts;

  • Women are usually custodial parents – importance of female alcohol abuse/dependence for GxE interaction? Implications for risk to offspring in next generation?


Why australia australian 1989 cohort 90 70 cohort l.jpg
WHY AUSTRALIA?: AUSTRALIAN 1989 COHORT (“90/70” COHORT)

  • 90% of men have consumed 9 or more standard drinks in a day

  • 70% of women have consumed 7 or more standard drinks in a day

  • <1% of men, even fewer women, lifetime abstainers


History of intoxication australian female twins l.jpg
HISTORY OF INTOXICATION:AUSTRALIAN FEMALE TWINS



Part one l.jpg
PART ONE

CRITICAL PERSPECTIVES ON THE BEHAVIORAL GENETIC LITERATURE ON ALCOHOLISM


A medical genetic model of alcoholism l.jpg
A “Medical Genetic” model of alcoholism

a) Threshold model

b) Multiple-threshold model

UNAFFECTED

UNAFFECTED

AFFECTED

MILD

SEVERE

CASES

CASES

Alcoholism Risk

Alcoholism Risk


Assumptions of strong medical genetic model l.jpg
Assumptions of Strong “Medical Genetic” Model

  • To understand the familial transmission of alcoholism, it is sufficient to understand the underlying GENETIC mechanisms.

  • We can ignore environmental influences as we investigate genes that contribute to risk.

    REALLY?


Genotype x environment interaction l.jpg
Genotype x Environment Interaction

The importance of genetic influences may be greatly increased under some environmental conditions, and decreased under others – beyond what would be predicted from the average effects of genetic, environmental risk-factors in the population.

= statistical interaction. Same caveats apply.


Slide17 l.jpg

Risk = Genes + Environment+ Genes x Environment


In the beginning pioneering danish adoption study l.jpg
In the beginning …Pioneering Danish Adoption Study

  • At least in males, risk of alcoholism appeared to be genetically transmitted within families

(Goodwin et al, 1973)


Swedish stockholm adoption study temperance board registrations l.jpg
Swedish (Stockholm) Adoption Study- Temperance Board Registrations

(Cloninger et al, 1981, 1988, and reanalyzed in Heath et al, 1997)


Stockholm adoption study unlike sex relative data l.jpg
Stockholm Adoption Study: Unlike-Sex Relative Data

Temperance Board Registrations

Tetrachoric correlation = 0.17 ± 0.11


Genetic and environmental variance estimates in the stockholm adoption study l.jpg
Genetic and Environmental Variance Estimates in the Stockholm Adoption Study

Additive Genetic 95% Confidence Variance Interval %

Pooled: 37 19-56

Significance of genotype x gender interaction: X2 = 1.51, d.f. = 3, p = 0.68.

NOTE: No significant association of adoptee and adoptive parent temperance board registration(s), suggesting family environment effects unimportant.


Adoption study findings for alcoholism l.jpg
Adoption Study Findings for Alcoholism Stockholm Adoption Study

Danish; Swedish (Bohman, Cloninger, Sigvardsson); Iowa (Cadoret)

  • Alcohol outcomes in adopted-away offspring (female as well as male) correlate with alcoholism/antisocial personality disorder in biological parents, NOT (usually) with psychopathology of adoptive parents.

  • Suggests intergenerational transmission of alcoholism may be determined by genetic transmission, not environmental transmission.

  • In general, evidence for genotype x environment interaction effects at best equivocal: almost no robust, replicated examples.

  • No significant evidence for genotype x gender interaction – genetic effects equally important in women and men, but sometimes too few women included, given lower base rate of alcohol problems in women, to allow significant effects to be found.


Warning l.jpg
Warning !!! Stockholm Adoption Study

  • In the Stockholm Adoption Study, 32% of biological fathers and 4.7% of biological mothers of adoptees had one or more temperance board registrations.

  • Approximately 14% of Swedish males from the general population had at least one temperance board registration.

  • Fewer than 4% of adoptive families had one or more parents with at least one temperance board registration.

    i.e., Adoptees tend to come from high-risk genetic backgrounds, are exposed to low risk rearing environments. Not ‘ideal’ for GxE analyses.

  • Most of the information from adoption studies is about biological FATHER alcoholism.


Example twin study virginia twin study dsm iiir alcohol dependence female like sex pairs l.jpg
Example Twin Study: Virginia Twin Study – DSM-IIIR Alcohol Dependence: Female Like-Sex Pairs

Lifetime Risk to cotwin of an Recurrence Risk N Prevalencea (%) alcohol-dependent twin (%) Ratio

MZ pairs 590 8.1 31.6 3.9

DZ pairs 440 10.2 24.4 2.4

a Proportion of individuals reporting a history of alcohol dependence.

(Kendler et al, 1992)


Female like sex twin correlations for dsm iiir alcohol dependence virginia twin study l.jpg
Female Like-Sex Twin Correlations for DSM IIIR Alcohol Dependence:Virginia Twin Study

(Kendler et al, 1992, reanalyzed)


Estimating parameters of a simple genetic model l.jpg
Estimating Parameters of a Dependence:Simple Genetic Model


Slide27 l.jpg
Genetic and Environmental Variance Components: Alcohol Dependence in Women (Virginia Twin Study)(WITH CONFIDENCE INTERVALS!)

(Kendler et al, 1992, reanalyzed)


Slide28 l.jpg
Estimates of Genetic and Dependence in Women (Virginia Twin Study)Environmental Variances:(Australian 1981 Cohort, N=5995 twins)(DSM-IIIR Alcohol Dependence)

95%

Variance (%) Confidence Interval

Additive Genetic 64 32 - 73

Shared Environment 1 0 - 27

Non-Shared Environment 35 27 - 47

NOTE: No genotype x gender interaction: X2 = 0.38, d.f. = 1, p = 0.54 (Heath et al., 1997).


Estimates of genetic and environmental variances australian 1989 cohort n 6250 twins l.jpg
Estimates of Genetic and Dependence in Women (Virginia Twin Study)Environmental Variances: (Australian 1989 Cohort, N=6250 twins)

DSM-IV Alcohol Dependence

95%

Variance (%) Confidence Interval

Additive Genetic 53 32 - 61

Shared Environment 0 0 - 16

Non-Shared Environment 47 39 - 55

NOTE: No genotype x gender interaction

(X2 = 1.61, d.f=3, p=0.66) (Knopik et al., 2004)

Controlling for sociodemographic variables and prior psychiatric history reduces heritability minimally.


Slide30 l.jpg

We can elaborate this simple model by jointly modeling (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).


Predictors of alcoholism risk 1989 cohort l.jpg
Predictors of Alcoholism Risk (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).(1989 Cohort)

Gender ↑ risk in males

Education ↑ risk in early school leavers

Religion ↓ risk with religious involvement

Depression ↑ risk

Conduct Disorder ↑ risk

Childhood sexual abuse ↑ risk (Women only)

(Knopik et al, 2004)


Unadjusted versus adjusted genetic variance estimates l.jpg
Unadjusted versus Adjusted Genetic Variance Estimates (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).

Unadjusted (95% CI) Adjusted (95% CI)

53% 33-61% 47% 28-55%


Shared environmental variance estimates l.jpg
Shared Environmental Variance Estimates (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).

Unadjusted (95% CI) Adjusted (95% CI)

0 0-15 0 0-14


Slide34 l.jpg

BEWARE: SIMPLIFYING ASSUMPTIONS!! (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).


Hidden assumption 1 l.jpg
Hidden Assumption: 1 (probit) regressions on hypothesized mediating variables of alcoholism risk, estimating residual genetic and environmental effects (and, in principle, G x E interaction effects).

  • Unless environmental moderators are assessed and included in our models

Confounding of genetic effects and genotype x shared environment interaction effects


Slide36 l.jpg
Contributions of Genetic, Shared Environment, Genotype x Shared Environment Interaction Effects to Twin/Sib Resemblance


Confounding of gxse interaction applies equally to sibling data with genetic marker information l.jpg
Confounding of GxSE Interaction Applies Equally to Sibling Data with Genetic Marker Information

i.e. GxSE can be very helpful – if we can identify the environmental conditions

under which genetic effects are especially strongly amplified.


Shared environmental variance estimates38 l.jpg
Shared Environmental Variance Estimates Data with Genetic Marker Information

Unadjusted (95% CI) Adjusted (95% CI)

0 0-15 0 0-14


Estimates of genetic and environmental variances meta analysis of u s data l.jpg
Estimates of Genetic and Data with Genetic Marker InformationEnvironmental Variances: (Meta-analysis of U.S. data)

“Alcoholism” variously defined

95%

Variance (%) Confidence Interval

Additive Genetic 58 43 - 67

Shared Environment 2 0 – 16

NOTE: No genotype x gender interaction

(X2 = 0.49, d.f=3, p=0.92)


Estimating a model allowing for genetic non additivity l.jpg
Estimating a Model Allowing for Genetic Non-Additivity Data with Genetic Marker Information


Hidden assumption 2 l.jpg
Hidden Assumption 2 Data with Genetic Marker Information

Strong genetic non-additivity could mask shared environmental influences in the classical twin design, producing zero estimates for shared environmental variance across multiple studies. ASSUMED ABSENT.


Slide42 l.jpg

We can conduct a Data with Genetic Marker Informationsensitivity analysis, fixing different assumed values of the shared environmental variance, and estimating the dominance ratio, i.e. the ratio of non-additive to additive genetic variance, as a free parameter, to determine whether significant shared environmental variance is plausible.


Sensitivity analysis alcohol dependence l.jpg
Sensitivity Analysis: Alcohol Dependence Data with Genetic Marker Information


Part two l.jpg
PART TWO Data with Genetic Marker Information

RECONSIDERING GENE-ENVIRONMENT INTERPLAY IN ALCOHOLISM – A WORK IN PROGRESS


Slide45 l.jpg

  • What’s wrong with the “medical genetic” model for alcoholism??

    • By oversimplifying our model for alcoholism inheritance, it may cause us to overlook complexity, or to infer complexities (e.g., Gene-environment interactions) where none exist.

    • It tempts us to neglect the importance of environmental influences in alcoholism;

    • It may cause us to miss opportunities for gene-discovery (by NOT stratifying on environmental exposure).


Environmental influences l.jpg
ENVIRONMENTAL INFLUENCES? alcoholism??

  • GENDER (why have rates of nicotine dependence in men versus women converged, but not rates of alcohol dependence?)

  • COHORT differences

  • PRE-NATAL (& PRE-CONCEPTION) influences

  • EARLY CHILDHOOD TRAUMA (e.g., childhood physical & sexual abuse)

  • OTHER CHILDHOOD ENVIRONMENTAL influences (e.g. parental divorce)

  • Etc etc etc


Some simple questions l.jpg
SOME SIMPLE QUESTIONS alcoholism??

(1) Can genetic factors EVER explain cohort differences in rates of alcoholism?

(2) In the general community, do male alcoholics or female alcoholics on average report faster progression from first intoxication to onset of alcohol dependence?

(3) … do alcoholics reporting early-onset of alcohol use or alcoholics reporting later onset of alcohol use (e.g. age 13 versus age 18) have faster progression to alcohol dependence?

(4) If we match on history of alcohol use (level of consumption), are women more likely to develop alcohol problems or men?

(5) Replace gender in (2)-(4) by other hypothesized environmental modifiers of alcoholism risk, e.g. CSA, or psychiatric risk-factors or comorbid conditions (e.g., nicotine dependence).


Characterizing gene environment interplay five routes to increased risk l.jpg
Characterizing Gene-Environment Interplay: Five Routes to Increased Risk

  • Timing of exposure – age at onset of alcohol use;

  • Amount of exposure – quantity consumed;

  • Dependence vulnerability – risk differences, controlling for exposure history;

  • Delayed desistance – persistence in excessive or problem drinking;

  • Psychiatric comorbidity, via (1) – (4).



True for depression l.jpg
True for depression? Increased Risk

“ Major depression is a familial disorder, and its familiality mostly or entirely results from genetic influences”.

(Sullivan et al., 2000, Am J Psychiatry)


Slide55 l.jpg

  • Kendler et al, 1993: Increased Risk

    “Comorbidity between MD (Major Depression) and alcoholism in women is substantial, and appears to result largely from genetic factors that influence the risk to both disorders.”


Slide56 l.jpg
There Are Good Reasons to Anticipate Important Environment Effects Associated with Parental Alcoholism

  • because parental alcoholism is a major predictor of high-risk environmental exposures, from conception to young adulthood.

  • high risk exposures are especially likely if biological mother is alcoholic.

    • fetal alcohol and tobacco exposure;

    • family socioeconomic disadvantage, divorce;

    • childhood physical, sexual abuse, other early trauma;

    • parent-parent, parent-child conflict;

    • impaired parental supervision, high-risk peers.


Intergenerational influences on psychiatric comorbidity l.jpg
Intergenerational Influences on Psychiatric Comorbidity?? Effects Associated with Parental Alcoholism

PARENTAL ALCOHOLISM

OFFSPRING GENETIC RISK OF DEPRESSION

OFFSPRING HIGH-RISK ENVIRONMENTAL EXPOSURES

OFFSPRING DEPRESSION

(Simplified from Heath & Nelson, 2002)


Genetic and environmental contributions to major depression risk meta analysis results l.jpg
Genetic and Environmental Contributions to Major Depression Risk:Meta-Analysis Results

% 95% Confidence Interval

Genetic Variance 37 31-42

Shared Environmental Variance 0 0-5

Non-Shared Environmental Variance 63 58-67

(Sullivan et al., 2000, Am J. Psychiat)


Major depression risk in australian women l.jpg
Major Depression Risk in Australian Women Risk:

Australian Adult Twin Surveys

1981 Cohort1989 Cohort

% 95% CI % 95% CI

Genetic Variance 44 29-53 35 13-44

Shared Environmental Variance 0 0-12 0 0-17

Non-Shared Environmental Variance 56 47-65 65 56-74

(Bierut, et al., 1999, Arch Gen Psychiatry; Heath, et al., unpublished)


Sensitivity analysis australian twin study 1989 cohort female like sex pairs major depression l.jpg
Sensitivity Analysis: Australian Twin Study, 1989 Cohort Risk:(Female like-sex pairs, Major Depression)


A more direct approach l.jpg
A More Direct Approach Risk:

  • Can we identify indices of family environmental risk that are strongly associated with outcomes of interest?


Early trauma exposure in moafts and depression risk l.jpg
Early Trauma Exposure in MOAFTS and Depression Risk Risk:

NCS traumatic events experienced by age 13 (European Ancestry)

Polyserial correlation with depression = 0.37 (95% CI 0.31-0.42)


Slide63 l.jpg
Association between Maternal Alcoholism (by Parent Report) and Offspring Early Trauma Exposure (by Offspring Report)

Χ21=35.27, p<.0001

Χ21=17.41, p<.0001


Association between paternal alcoholism by parent report and offspring early trauma exposure l.jpg
Association between Paternal Alcoholism (by Parent Report) and Offspring Early Trauma Exposure

Χ21=23.19, p<.0001

Χ21=0.07, p>0.9


Slide65 l.jpg
A More Direct Test: and Offspring Early Trauma ExposureGenetic and Environmental Variance Estimates for Early Trauma Count:European Ancestry

i.e., we appear to be indexing environmental risk (but have not yet completely excluded possibility of confounding with genetic risk in parents).



Slide67 l.jpg

Genetic Risk-Factors Trauma Count:for Alcohol Dependence(in parents)

Genetic Risk Factorsfor Depression(in parents)

Parental AlcoholDependence

Parental Depression

??

Genetic Risk-Factors for Depression(in children)

High-Risk EnvironmentalExposure of Children

Genetic Risk-Factors forAlcohol Dependence(in children)

Interactive Effect of GeneticVulnerability to DepressionHigh-Risk Environmental Exposure

Alcohol Dependence(in children)

Depression(in children)

??


Estimating the effects of genotype x environment interaction l.jpg
Estimating the Effects of Genotype x Environment Interaction:

Note: We control simultaneously for the overall regression of offspring risk on parental alcoholism (to control for GE correlation effects associated with parental alcoholism).


Genotype x environment interaction in depression 1989 twin cohort cohort 2 l.jpg
Genotype x Environment Interaction in Depression: Interaction:1989 Twin Cohort (Cohort 2)


Genotype x environment interaction in depression australian 1981 twin cohort cohort 1 l.jpg
Genotype x Environment Interaction in Depression: Interaction:Australian 1981 Twin Cohort (Cohort 1)

A Replication!


Danish adoption study l.jpg
Danish Adoption Study Interaction:

  • Goodwin, Schulsinger, Knop, Mednick & Guze (1977)

    “Neither group (adopted away daughters of alcoholics, controls) had higher (than general population) rates of psychopathology, e.g., depression. However, daughters of alcoholics raised by their biological parents had significantly more depression.”


Iowa adoption study l.jpg
Iowa Adoption Study Interaction:

“Major depression in females was predicted by an alcoholic diathesis only when combined with the disturbed adoptive parent variable.”

(Cadoret et al, 1996: Am J Psychiat 153:892-9)


Update l.jpg
UPDATE Interaction:

  • High-risk environmental exposures associated with parental alcoholism may be important (inter-generational) contributors to the observed comorbidity of alcohol use and other psychiatric disorders.


Part b l.jpg
PART B Interaction:

G-E Interplay and the Initiation of Substance Use


Slide75 l.jpg
Genetic and shared environmental variance estimates for early-onset substance use (by age 14) in MOAFTS


Slide76 l.jpg
Genetic and shared environmental variance estimates for early-onset substance use (by age 14) in MOAFTS


Onset of at risk drinking in the australian twin panel 1989 cohort l.jpg
Onset of At-Risk Drinking in the Australian Twin Panel (1989 Cohort)

Earlier of

(a) drinking to intoxication

(b) drinking monthly for six months or longer (“regular drinking”)





Lifetime prevalence of alcohol dependence in men l.jpg
Lifetime Prevalence of Alcohol Dependence in Men age-of-onset of at-risk drinking


Crude association of alcohol dependence rates with age at onset of at risk drinking l.jpg
Crude Association of Alcohol Dependence Rates with Age-at-Onset of At-Risk Drinking

# Women: OR = 5.2 (3.3-8.1); Men: OR = 2.6 (1.9-3.7)



Slide84 l.jpg
Cumulative incidence of alcohol dependence as function of years of at-risk drinking, stratified by age at onset


Some simple questions86 l.jpg
SOME SIMPLE QUESTIONS years of at-risk drinking, stratified by age at onset

(3) … do alcoholics reporting early-onset of alcohol use or alcoholics reporting later onset of alcohol use (e.g. age 13 versus age 18) have faster progression to alcohol dependence?


Heath s paradox l.jpg
Heath’s Paradox (!) years of at-risk drinking, stratified by age at onset

Alcoholics who start at-risk drinking earlier have delayed onset of alcohol dependence.



Some simple questions91 l.jpg
SOME SIMPLE QUESTIONS Dependence

(2) In the general community, do male alcoholics or female alcoholics on average report faster progression from first alcohol use to onset of alcohol dependence?


Slide92 l.jpg

  • IT DEPENDS: Dependence

    • On the distributions of age at initiation of drinking in women versus men

    • AND the shape of the hazard distributions for onset of alcohol problems in women versus men.


Delay from onset of alcohol use to onset of alcohol dependence 1989 cohort l.jpg
Delay from Onset of Alcohol Use to Onset of Alcohol Dependence: 1989 Cohort

Women 6.5 Years (N=535)

Men 6.6 Years (N=851)



Yes but through family non intactness note all analyses of us data sets control for ethnicity l.jpg
YES, BUT THROUGH FAMILY OF ALCOHOL USE?NON-INTACTNESS(Note: All analyses of US data-sets control for ethnicity)


Slide96 l.jpg

Percentage of EA families in MOAFTS where family dissolution has occurred, as a function of alcoholism assessed by maternal report vs twin report


Contrast groups l.jpg
Contrast Groups has occurred, as a function of alcoholism assessed by maternal report vs twin report

Parental Status

Divorced, parental alcoholism

Divorced, no parental alcoholism

Never married, parental alcoholism

Never married, no parental alcoholism

Still married, parental alcoholism

Still married, no parental alcoholism – comparison group


Moafts hazard ratios as a function of parental alcoholism and marital status first alcohol use l.jpg
MOAFTS: Hazard Ratios as a function of parental alcoholism and marital status: First alcohol use

(Waldron et al, unpublished)


Moafts hazard ratios as a function of parental age and marital status first alcohol use l.jpg
MOAFTS: Hazard Ratios as a function of parental age and marital status: First alcohol use

(Waldron et al, unpublished)


Moafts age at first intoxication l.jpg
MOAFTS: Age at First Intoxication marital status: First alcohol use

(Waldron et al, unpublished)


Moafts age at first intoxication103 l.jpg
MOAFTS: Age at First Intoxication marital status: First alcohol use

(Waldron et al, unpublished)


Moafts age at first mj use l.jpg
MOAFTS: Age at First MJ Use marital status: First alcohol use

(Waldron et al, unpublished)


Moafts age at first sex l.jpg
MOAFTS: Age at First Sex marital status: First alcohol use

(Waldron et al, unpublished)


Update106 l.jpg
Update marital status: First alcohol use

In the MOAFTS birth cohort (female twins):

  • Parental divorce or never marriage is strongly associated with very early alcohol & illicit drug use

  • This association is especially strong in families with a parental history of alcoholism

  • Parental alcoholism, in intact families, is not an important predictor of very early onset substance use

  • Early (voluntary) sexual behavior shows a similar pattern of association – potential mediator?


Slide107 l.jpg
Impact of divorce and/or parental alcoholism on very early-onset alcohol use: women, NESARC(Hazard Ratios)

(Waldron et al, unpublished)


Slide108 l.jpg
Impact of divorce and/or parental alcoholism on very early-onset alcohol use: men, NESARC(Hazard Ratios)


Slide109 l.jpg

What are potential confounders in relationship between parental marital status and early substance involvement?

  • Severity of parental alcoholism

  • Number of alcoholic parents (1 vs 2)

  • Comorbid anti-social traits (in parents, offspring)

  • History of major depression (in parents, offspring)


Final model very early onset alcohol use in moafts hazard ratios and 95 confidence interval l.jpg
Final Model: Very-early Onset Alcohol Use in MOAFTS: Hazard Ratios (and 95% confidence interval)

≤12 13-14

Early sex 3.5 (1.1-11.4) 4.2 (3.2-5.5)

Divorce 2.1 (1.2-3.6) 1.4 (1.1-1.8)

Maternal alcoholism 2.0 (1.1-3.6) 1.4 (1.0-2.0)

Paternal alcoholism 1.9 (1.1-3.1) 0.9NS (0.7-1.2)

(Waldron et al, unpublished)


Slide111 l.jpg
“Parenting” correlates of early onset alcohol use (controlling for parental alcoholism & family dissolution)


Part c l.jpg
PART C (controlling for parental alcoholism & family dissolution)

Should we pay greater attention to differences in dependence vulnerability, i.e., conditioning on drinking history?












Slide123 l.jpg


Major depression l.jpg
Major Depression heaviness of drinking (including genetic factors)

Predicts -- increased vulnerability

-- earlier onset

-- NOT heaviness of drinking

-- NOT persistence of problems


Childhood sexual abuse l.jpg
Childhood Sexual Abuse heaviness of drinking (including genetic factors)

Predicts -- increased vulnerability

-- earlier onset (in women)

-- heaviness of drinking

-- persistence of problems


Conduct disorder l.jpg
Conduct Disorder heaviness of drinking (including genetic factors)

Predicts -- earlier onset

-- heaviness of drinking

-- persistence of problems

-- NOT increased vulnerability


Parental divorce l.jpg
Parental Divorce heaviness of drinking (including genetic factors)

Predicts -- earlier onset

-- ?? reduced vulnerability (Men)


Tobacco dependence l.jpg
Tobacco Dependence heaviness of drinking (including genetic factors)

Predicts -- everything


Conclusions l.jpg
CONCLUSIONS heaviness of drinking (including genetic factors)

  • We can consider genes that influence alcohol dependence risk as risk-factors that act jointly with other risk-factors (psychiatric, environmental, etc.)

  • This perspective forces us to ask questions that traditionally have been neglected by psychiatric geneticists—about

    • timing of onset of use;

    • level of use;

    • vulnerability conditional on use;

    • Persistence.


Conclusions131 l.jpg
CONCLUSIONS heaviness of drinking (including genetic factors)

  • Consideration of these different aspects of risk should provide a firmer foundation for dissecting the joint contributions of genetic and environmental factors to alcoholism risk.

  • It may also lead us to adopt new approaches for gene discovery efforts, taking advantage of information about ENVIRONMENTAL risk-factors.


Acknowledgements mary waldron phd niaaa postdoc l.jpg
Acknowledgements heaviness of drinking (including genetic factors)Mary Waldron, PhD (NIAAA postdoc)

Washington University Faculty Collaborators:

Kathy Bucholz, PhD Elliot Nelson, MD Wendy Reich, PhD

Pam Madden, PhD Anne Glowinski, MD RichardTodd,PhD,MD

Rosalind Neuman, PhD John Rohrbaugh, PhD Michael Lynskey, PhD

Alexandre Todorov, PhD Michele Pergadia, PhD Erik Sirevaag, PhD

External Faculty Collaborators:

Bill True, PhD, and Qiang Fu, PhD St. Louis University

Ted Jacob, PhD, and Randy Haber, PhD, Palo Alto Veterans Administration

Ken Sher, PhD, and Wendy Slutske, PhD, University of Missouri–Columbia

Nicholas Martin, PhD, QIMR, Brisbane, Australia

Valerie Knopik, PhD, Brown University

AND MANY OTHER COLLEAGUES AND TRAINEES!


ad