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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

  • 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


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

  • 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

  • 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

  • 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

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?

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

  • Important clinical & public health problem.


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)

  • 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


HISTORY OF HEAVY DRINKING (5+ in day)AUSTRALIAN FEMALE TWINS


PART ONE

CRITICAL PERSPECTIVES ON THE BEHAVIORAL GENETIC LITERATURE ON ALCOHOLISM


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

  • 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

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.


Risk = Genes + Environment+ Genes x Environment


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

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


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

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

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 !!!

  • 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

Lifetime Risk to cotwin of anRecurrence RiskN Prevalencea (%)alcohol-dependent twin (%)Ratio

MZ pairs5908.131.63.9

DZ pairs44010.224.42.4

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

(Kendler et al, 1992, reanalyzed)


Estimating Parameters of aSimple Genetic Model


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

(Kendler et al, 1992, reanalyzed)


Estimates of Genetic andEnvironmental Variances:(Australian 1981 Cohort, N=5995 twins)(DSM-IIIR Alcohol Dependence)

95%

Variance (%) Confidence Interval

Additive Genetic6432 - 73

Shared Environment 10 - 27

Non-Shared Environment 3527 - 47

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


Estimates of Genetic andEnvironmental Variances: (Australian 1989 Cohort, N=6250 twins)

DSM-IV Alcohol Dependence

95%

Variance (%) Confidence Interval

Additive Genetic5332 - 61

Shared Environment 00 - 16

Non-Shared Environment 4739 - 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.


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)

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

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

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


Shared Environmental Variance Estimates

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

00-1500-14


BEWARE: SIMPLIFYING ASSUMPTIONS!!


Hidden Assumption: 1

  • Unless environmental moderators are assessed and included in our models

Confounding of genetic effects and genotype x shared environment interaction effects


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

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 Estimates

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

00-1500-14


Estimates of Genetic andEnvironmental Variances: (Meta-analysis of U.S. data)

“Alcoholism” variously defined

95%

Variance (%) Confidence Interval

Additive Genetic5843 - 67

Shared Environment 20 – 16

NOTE:No genotype x gender interaction

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


Estimating a Model Allowing for Genetic Non-Additivity


Hidden Assumption 2

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.


We can conduct a sensitivity 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


PART TWO

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


  • 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?

  • 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

(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

  • 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).


Part A: GxE Interaction & Psychiatric Comorbidity


True for depression?

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

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


  • Kendler et al, 1993:

    “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.”


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??

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

%95% Confidence Interval

Genetic Variance3731-42

Shared Environmental Variance00-5

Non-Shared Environmental Variance6358-67

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


Major Depression Risk in Australian Women

Australian Adult Twin Surveys

1981 Cohort1989 Cohort

%95% CI%95% CI

Genetic Variance4429-533513-44

Shared Environmental Variance00-1200-17

Non-Shared Environmental Variance5647-656556-74

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


Sensitivity Analysis: Australian Twin Study, 1989 Cohort(Female like-sex pairs, Major Depression)


A More Direct Approach

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


Early Trauma Exposure in MOAFTS and Depression Risk

NCS traumatic events experienced by age 13 (European Ancestry)

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


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

Χ21=23.19, p<.0001

Χ21=0.07, p>0.9


A More Direct Test:Genetic 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).


Genetic and Environmental Variance Estimates for Early Trauma Count:African Americans


Genetic Risk-Factors 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:

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)


Genotype x Environment Interaction in Depression: Australian 1981 Twin Cohort (Cohort 1)

A Replication!


Danish Adoption Study

  • 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

“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

  • 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

G-E Interplay and the Initiation of Substance Use


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


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)

Earlier of

(a)drinking to intoxication

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


Age-at-Onset Distribution in Australian Women Born 1964-1971


Age-at-Onset Distribution in Australian Men Born 1964-1971


Lifetime Prevalence of Alcohol Dependence in Women, by age-of-onset of at-risk drinking


Lifetime Prevalence of Alcohol Dependence in Men


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)


Years of At-Risk Drinking as a Function of Age-at-Onset


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


SOME SIMPLE QUESTIONS

(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 (!)

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


Hazard Ratios as a Function of Age: Onset of Alcohol Dependence


SOME SIMPLE QUESTIONS

(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?


  • IT DEPENDS:

    • 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

Women6.5 Years (N=535)

Men6.6 Years (N=851)


DOES FAMILIAL RISK OF ALCOHOLISM INFLUENCE TIMING OF ONSET OF ALCOHOL USE?


YES, BUT THROUGH FAMILY NON-INTACTNESS(Note: All analyses of US data-sets control for ethnicity)


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

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

(Waldron et al, unpublished)


MOAFTS: Hazard Ratios as a function of parental age and marital status: First alcohol use

(Waldron et al, unpublished)


MOAFTS: Age at First Intoxication

(Waldron et al, unpublished)


MOAFTS: Age at First Intoxication

(Waldron et al, unpublished)


MOAFTS: Age at First MJ Use

(Waldron et al, unpublished)


MOAFTS: Age at First Sex

(Waldron et al, unpublished)


Update

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?


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

(Waldron et al, unpublished)


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


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)

≤1213-14

Early sex3.5 (1.1-11.4)4.2 (3.2-5.5)

Divorce2.1 (1.2-3.6)1.4 (1.1-1.8)

Maternal alcoholism2.0 (1.1-3.6)1.4 (1.0-2.0)

Paternal alcoholism1.9 (1.1-3.1)0.9NS (0.7-1.2)

(Waldron et al, unpublished)


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


PART C

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


Percent alcohol dependent by gender, as a function of heaviness-of-drinking


Percent alcohol dependent by gender, as a function of heaviness-of-drinking


Percent alcohol dependent by number of alcoholic parents: WOMEN


Percent alcohol dependent by number of alcoholic parents: MEN


Percent alcohol dependent by history of major depression: WOMEN


Percent alcohol dependent by history of nicotine dependence: WOMEN


Percent alcohol dependent by history of Childhood Sexual Abuse: WOMEN


Percent alcohol dependent by history of conduct disorder: WOMEN


Percent alcohol dependent by history of parental divorce: WOMEN


Percent alcohol dependent by history of parental divorce: MEN


  • And, of course, we should not neglect influences of heaviness of drinking (including genetic factors)

  • And also factors that influence persistence of problems (including genetic?)


Major Depression

Predicts--increased vulnerability

--earlier onset

--NOT heaviness of drinking

--NOT persistence of problems


Childhood Sexual Abuse

Predicts--increased vulnerability

--earlier onset (in women)

--heaviness of drinking

--persistence of problems


Conduct Disorder

Predicts--earlier onset

--heaviness of drinking

--persistence of problems

--NOT increased vulnerability


Parental Divorce

Predicts--earlier onset

--?? reduced vulnerability (Men)


Tobacco Dependence

Predicts--everything


CONCLUSIONS

  • 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.


CONCLUSIONS

  • 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.


AcknowledgementsMary Waldron, PhD (NIAAA postdoc)

Washington University Faculty Collaborators:

Kathy Bucholz, PhDElliot Nelson, MDWendy Reich, PhD

Pam Madden, PhDAnne Glowinski, MD RichardTodd,PhD,MD

Rosalind Neuman, PhDJohn Rohrbaugh, PhD Michael Lynskey, PhD

Alexandre Todorov, PhDMichele Pergadia, PhDErik 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!


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