Transcript (Title of Presentation) - Virginia Commonwealth University

Statistical Genetics of
Substance Use (SU) and
Substance Use Disorders
Kenneth S. Kendler, MD
Virginia Institute of Psychiatric and Behavioral
Genetics
Virginia Commonwealth University
Advanced Genetic Epidemiology Statistical
Workshop
Oct 22, 2012
Paradigm 1- Basic Genetic
Epidemiology - What Have We
Learned?
• Genetic factors play a substantial role in the
etiology of Substance Use Disorders (AD).
• Heritability – the proportion of individual
differences in a particular disorder or trait in a
particular population that results from genetic
differences between individuals.
• Heritability estimates typically in the range of
50-60%
• How does this compare to other psychiatric
and biomedical disorders?
Heritability Of Psychiatric Disorders
Heritability
Psychiatric Disorders Other Important Familial Traits
~zero
Language
Religion
20-40%
Anxiety disorders,
Depression, Bulimia,
Personality Disorders
Myocardial Infarction,
Normative Personality, Breast
Cancer, Hip Fracture
40-60%
Alcohol Dependence
Drug Dependence
Blood Pressure, Asthma
Plasma cholesterol, Prostate
Cancer, Adult-onset diabetes
60-80%
Schizophrenia
Bipolar Illness
Weight,
Bone Mineral Density
80-100%
Autism
Height, Total Brain Volume
How Consistent are the Estimates of
Heritability of AD Across Space and
Time?
• Heritability is not a characteristic of a disorder
– rather it is a feature of a disorder in a
specific population at a specific time.
• We will look quickly at twin studies of AD and
other SUDs.
Genetic & environmental proportions of variance in alcoholism
estimated from studies of male twins
Clinical sample
Figure 2a, Prescott, cotwin followup
Maes & Kendler, 2005
Population registry Volunteer registry
archival diagnosis personal interview
Summary Slide
• Based on published meta-analyses or ones we
did ourselves (with Joe Bienvenu) – pretty
large CIs.
• Main results of non-alcohol SUDs from two
studies – VATSPSUD and Vietnam Era twin
study. Some reports from the Australian and
Norwegian registiries.
How Consistent are the Estimates of
Heritability of AD Across Time?
• Swedish Temperance Board Registration
Data – 8,935 pairs of male-male twins born
1902-1949.
How Consistent are the Estimates of
Heritability of AD Across Space and
Time?
• Swedish Temperance Board Registration
Data – 8,935 pairs of male-male twins born
1902-1949.
• Complete birth cohort.
• Sweden underwent several dramatic
changes.
• Income increased 6-fold
• Government experimented with changes in
governmental control of access to alcohol.
How Consistent are the Estimates of
Heritability of AD Across Space and
Time?
• In 1917, Sweden adopted a nationwide
alcohol rationing system that strictly limited
the amount of alcohol that an individual was
permitted to purchase. An individual's official
limit varied according to sex, age, and
financial situations, and was, for men older
than 25 years, usually between 1 and 3 L of
hard liquor per month.
Interactions between gender, culture
and genes – the role of social
factors to constrain behavior.
• Tobacco consumption and year of birth in
Swedish twins.
• Study done with Nancy Pedersen on the
SATSA sample.
• Study males and females separately
Prevalence And Heritability Of
Regular Tobacco Use
Three Birth Cohorts Of Men And Women In Sweden
0.8
0.6
Heritability
0.4
0.2
0
1910-1924
1925-1939
1940-1958
Birth Cohort
Female Presence
Male Presence
Female Heritability
Male Heritability
How Consistent are the Estimates of
Heritability of SUDs Across Space
and Time?
• So, to the best of our knowledge, the heritability of
AD is relatively robust – across multiple European
populations living in Australia, North American and
Europe and across a half century of Swedish history
that saw dramatic changes in that country.
• But a quite different picture is seen for regular
smoking behavior with a large gene x cohort
interaction in women.
• Know much less about results for other psychoactive
substance abuse and dependence.
Paradigm 2- Advance Genetic
Epidemiology
• Many questions relevant to SUDs
• Begin with question of multivariate models –
• What is the relationship between the genetic
and environmental risk factors for SUDs and
for psychiatric disorders?
Paradigm 2- Advance Genetic
Epidemiology – Multivariate Models
• Examine this question in 2,111 personally
interviewed young adult members of the
Norwegian Institute of Public Health Twin
Panel. Statistical analyses were performed
with the Mx and Mplus programs.
Somatoform
.44
.65
Disorder
Panic Disorder
Major Depression
Agoraphobia
.88
.80
.81
.72
Generalized
Anxiety Disorder
Eating Disorders
Schizoid PD
.81
.71
Schizotypal PD
.67
Factor 1
Specific Phobia
Dysthymia
Axis I
.63
Axis II
.49
Internalizing
.56
Internalizing
.28
.23
.16
Factor 4
Axis I
Externalizing
.38
Axis II
.37
.48
Externalizing
.95
Drug Abuse /
Dependence
.87
.88
Conduct Disorder
Alcohol Abuse /
Dependence
.73
.84
.66
Dependent PD
.36
Factor 3
Antisocial PD
Social Phobia
.56
.36
.56
.61
.45
Avoidant PD
Factor 2
.51
.44
.35
Paranoid PD
Histrionic PD
Narcissistic PD
Obsessive –
Compulsive PD
Borderline PD
Paradigm 2- Advanced Genetic
Epidemiology – Multivariate Models
• Replicating earlier results from our Virginia
twin analyses and from the Minnesota group,
SUDs are genetically part of the externalizing
group of disorders.
Paradigm 2- Advanced Genetic
Epidemiology – Multivariate Models
• Let’s drill down deeper into the relationship
between AD and SUD to directly address the
question of the specificity or non-specificity of
genetic risk factors for AD.
.82
Illicit Substance
Genetic Factor
Licit Substance
Genetic Factor
.82
.77
.68
.15
.52
Cannabis
Dependence
Cocaine
Dependence
Alcohol
Dependence
Caffeine
Dependence
Nicotine
Dependence
.20
A
.31
A
.35
A
.56
A
.68
A
E1
.27
.48
.37
.14
.18
Cannabis
Dependence
Cocaine
Dependence
Alcohol
Dependence
Caffeine
Dependence
Nicotine
Dependence
.47
.28
.53
.80
.48
E
E
E
E
E
Paradigm 2- Advance Genetic
Epidemiology – Multivariate Models
• Similar to prior analyses from this sample,
these results suggest that ~ 70% of heritabiity
for AD is shared (this time with other drugs of
abuse) and 30% unique to AD.
• For cocaine dependence, for example, 85%
of total heritability is shared with other drugs
and 15% is unique.
• In general, pretty clear that non-specific
genetic effects outweigh specific effects.
Paradigm 2- Advance Genetic
Epidemiology – Development
• Genes and environment act through time.
• Focus on alcohol intake in 1796 members of
male-male pairs from the Virginia Adult Twin
Study of Psychiatric and Substance Use
Disorders.
• Assessed retrospectively using a life-history
calendar.
NICOTINE
Paradigm 2- Advanced Genetic
Epidemiology – Development
• One more developmental question –
• Do we see differential developmental
changes in the impact of specific genetic risk
factors for AD versus non-specific risk factors
for externalizing disorders.
• Again ~ 1700 males from VATSPSUD
Regression Coefficients Predicting
Alcohol Intake
0.25
0.2
Genetic Risk for
Alcohol
Dependence
0.15
0.1
Genetic Risk for
Externalizing
Disorders
0.05
0
12-14 15-17 18-21 22-25 26-29 30-33
Age
Paradigm 2- Advanced Genetic
Epidemiology
• Twin-family designs – ask a new set of
questions.
Paradigm 2- Advanced Genetic
Epidemiology
• How to capture the conditionality of genetic
influences on SUDs.
• No initiation, no chance to express genetic
risk.
• How to model?
• CCC model – causal, contingent, common
pathway.
Paradigm 2- Advanced Genetic
Epidemiology – Gene x Environment
Interaction
• Definition – the impact of genetic risk factors
on disease risk is dependent on the history of
environmental exposures. OR
• – the impact of environment risk factors on
disease risk is dependent on genotype.
• Probably no area of psychiatric genetics
research that is more controversial and
artifact prone.
• A range of conceptual and statistical issues Buyer beware!
Gene x Environment Interaction
Just show one classical example – in type I
(adult non-ASPD) alcoholism from
Cloninger’s Swedish adoption studies.
Risk only high in subjects at high genetic risk
and exposed to high risk environment.
Paradigm 2- Advanced Genetic
Epidemiology – Gene x Environment
Interaction
• Again ~ 1700 males from VATSPSUD
• Asked – would the heritability of alcohol
consumption in adolescence be modified by
key environmental risk factors
– Alcohol Availability
– Peer Deviance
– Prosocial Behaviors
Drinks/mo (z)
Alcohol Availability 12-14
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
Alc Avl=+1 sd
Alc Avl=mean
Alc Avl=-1 sd
Genetic Risk AD
Drinks/mo (z)
Peer Group Deviance 12-14
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
PGD=+1 sd
PGD=mean
PGD=-1 sd
Genetic Risk ExtD
Drinks/mo (z)
Lack of Prosocial Activities 12-14
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
LPSA=+1 sd
LPSA=mean
LPSA=-1 sd
Genetic Risk ExtD
Paradigm 2- Advance Genetic
Epidemiology – Gene x Environment
Interaction
• Many other interesting G x E findings for
alcohol use.
• A few other examples many from the work of
my colleague Danielle Dick.
• One general theme – Genetic effects on
alcohol use are more pronounced when
social constraints are minimized and/or when
the environment permits easy access to
alcohol and/or encourage its use.
Gene-Environment Interaction
Alcohol Use
• Marital Status (Heath et al., 1989)
• Religiosity (Koopmans et al., 1999)
• Urban/rural residency (Rose et al., 2001)
• Neighborhood characteristics (Dick et al.,
2001)
• Parenting/Peers (Dick et al., 2006, 2007)
Adoption Studies
• Let’s take a short detour in to adoption
studies.
• Obvious point about scientific inference
• More secure about any finding when you can
reach it by multiple routes, esp when they
have different potential biases.
Sample
• Follow-up in 9 public data bases (1961-2009) in
Sweden of adoptees and their biological and
adoptive relatives.
• Identified 18,115 adoptees born 1950-1993;
78,079 biological parents and siblings; 51,208
adoptive parents and siblings.
• DA recorded in medical, legal or pharmacy
registry records.
Sources of Data
• 1. The Swedish Hospital Discharge Register included all
hospitalizations (including for DA) for all people in
Sweden from 1964-2009. Every record has the main
discharge diagnosis and eight secondary diagnoses.
• 2. The Swedish Prescribed Drug Register included all
prescriptions in Sweden picked up by patients from May
1st 2005 through 2009. It is complete, as all prescriptions
are registered at the National Board of Health and
Welfare.
• 3. The Swedish mortality register contained all causes of
death and time of death from 1961-2009.
Sources of Data
• 4. The National Censuses provided information on
education and marital status in 1960, 1970, 1980, and
1990.
• 5. The Total Population Registry included annual data on
education and marital status from 1990-2009.
• 6. The Multi-Generation Register provided information on
family relationships from 1932 to 2009 including all
adoptions and adoptive and biological parents and
siblings. Biological siblings reared with the adoptee were
excluded.
• 7. The Outpatient Care Register included information
from all outpatient clinics in Sweden from 2001-2009.
Sources of Data
• 8. The Primary Health Care Register included outpatient
care data on diagnoses and time for diagnoses 20012007 for 1 million patients from Stockholm and middle
Sweden.
• 9 The Swedish Crime Register included national
complete data on all convictions, including those for DA,
from 1973-2007.
Definition of Drug Abuse
• We identified DA in the Swedish hospital discharge,
mortality, primary care, and outpatient care registers by
the following ICD codes: ICD8: Drug dependence (304);
ICD9: Drug psychoses (292), and Drug dependence
(304); ICD10: Mental and behavioral disorders due to
psychoactive substance use (F10-F19), except those
due to alcohol (F10) or tobacco (F17).
Definition of Drug Abuse
• DA was also identified in the Crime Register by codes
5011, 5012 which reflect crimes related to DA. Crimes
related only to alcohol abuse or to trafficking in or
possession of drugs of abuse were excluded.
• DA was identified in individuals in the Prescribed Drug
Register who had retrieved (on average) more than 4
daily doses a day for 12 months from either of Hypnotics
and Sedatives (Anatomical Therapeutic Chemical (ATC)
Classification System N05C and N05BA) or Opioids
(ATC: N02A). Cancer patients were excluded.
• The 820 unique cases of DA in our cohort
came from the following registries:
Discharge– 527, Crime – 313, Outpatient
– 264, Prescribed Drug – 118, and
Primary Health Care – 8. No unique cases
of DA were identified through the mortality
register.
Odds Ratios and 95% Confidence Intervals for the
Registration of Drug Abuse Between the Five
Registers Used in this Study
Crime
Hospital Discharge
Outpatient
Primary Health Care
Hospital
Discharge
Outpatient
Primary Health
Care
32.9 (32.233.4)
65.2 (63.9-66.5)
47.4 (41.8-53.7) 5.6 (5.3-5.9)
118.0 (115.7120.4)
69.8 (61.8-78.7) 20.9 (20.2-21.7)
94.4 (83.5106.8)
Drug Prescription
29.6 (28.530.8)
37.9 (30.9-46.4)
• In this study, we could perform both
adoption designs
– Affected parent → adopted away offspring
– Affected adoptee → biological and adoptive
relatives
• Design # 1 –
– Risk for DA was significantly elevated in
adopted away offspring of biological parents
with DA (OR=2.09, 95% CIs 1.66-2.62).
• Design # 2
– Risk for DA was significantly elevated in
biological full and half-siblings of adoptees
with DA (OR=1.84, 1.28-2.64 and OR=1.41,
1.19-1.67, respectively).
– Risk for DA was significantly elevated in
adoptive siblings of adoptees with DA
(OR=1.95, 1.43-2.65).
• Next, sought to create empirical indices of
genetic and environmental risk for DA in
the adoptees.
• Used a multiple regression approach from
which we derived an index.
• Genetic risk for DA in the adoptee was
indexed by a range of features including
– biological parental low educational attainment
and divorce
– a parental or sibling history of
• DA,
• criminal activity
• treatment for psychiatric or alcohol problems.
• Environmental risk for DA was predicted
by a diverse set of characteristics
including
– adoptive parental history of
•
•
•
•
divorce,
premature death,
criminal activity,
hospitalization for medical or alcohol problems,
– adoptive sibling history of
• DA
• hospitalization for psychiatric, alcohol or medical
problems.
• Examined individually in logistic regression, where the
genetic and environmental risk indices were divided into
ten deciles, both risk scores were strongly predictive of
DA.
• OR per decile = 1.13 for genetic risk (1.1310 =3.39)
• OR per decile = 1.10 for environmental risk (1.1010
=2.59,)
• The correlation between risk scores (a measure of
assortative placement) was small (+0.11) but significant
(p<0.001).
• Examined individually, DA in the adoptee was also
significantly predicted by male sex, a younger AFCAP
and a later birth year.
• Our key dependent variable, DA, is dichotomous. We
initially used logistic regression and modelled DA as a
function of the genetic risk score, the environmental risk
score, sex of the adoptee and AFCAP. However a key a
priori goal of these analyses was to determine if genetic
and environmental risk factors interacted in the etiology
of DA. We have previously argued that the scale of raw
probabilities, rather than the logistic scale, is more
appropriate for such analyses 16. Therefore, for our
analyses of gene x environment interaction, we used
PROC GENMOD in SAS 17 with the identity link and
specified the variance to be binomial. We specified the
effects of the explanatory variables (and the interaction
term) to be additive on the scale of probabilities. All p
values are reported two-tailed
• DA is an etiologically complex syndrome
strongly influenced by a diverse set of
genetic risk factors reflecting a specific
liability to DA and a vulnerability to other
externalizing disorders and by a range of
environmental factors reflecting marital
instability, and psychopathology and criminal
behavior in the adoptive home. Adverse
environmental effects on DA are more
pathogenic in individuals with high levels of
genetic risk.
Gene- Environment Correlation
• Better termed genetic control of sensitivity to
the environment.
• Time is too limited to give details.
• My sense is that this process is of substantial
importance in mediating the impact of genetic risk
factors on SUDs.
• That is, in part, genes impact on risk for SUDs by
increasing the chances that individuals seek out
high risk environments which expose them to
substances of abuse and encourage them in their
use and misuse.
Paradigm 2- Advance Genetic
Epidemiology
• Integrated etiologic models.
• To just get a start looking at causal pathways.
.37
.18
.08
Genetic Risk
Genetic Risk
Alcoholism
Ext Disorders
.17
.27
.18
.08
.12
.17
.27
Alcohol
Parental
Childhood Phys
Attendance
Household Use
Alcohol Attitude
Sexual Abuse
.13
.24
.07
.06
.22
-.10
.07
ADHD
.30
.34
Low Church
.06
.21
Birth Year
.19
-.06
.23
Neuroticism
.06
.14
.10
.27
.08
.09
.08
.06
.09
.13
.07
.08
.32
.13
.14
Sensation
Early Onset
Seeking
Anxiety Disorder
.37
.23
.23
.08
.07
.06
.08
-.08
.06
.06
.30
.14
.15
.08
.08
.13
-.06
Conduct
Low Parental
Peer Group
Alcohol
Disorder 15-17
Monitoring 15-17
Deviance 15-17
Availability 15-17
.12
-.06
-.06
.05
.12
.09
.09
.15
.30
Alcohol Use 15-17
.10
.16
.26
.08
.06
.07
.24
.12
Symptoms of Alcohol
Use Disorders
.37
Genetic Risk
Genetic Risk
Alcoholism
Ext Disorders
Low Church
Alcohol
Parental
Childhood Phys
Attendance
Household Use
Alcohol Attitude
Sexual Abuse
Birth Year
.21
.23
ADHD
Neuroticism
Sensation
Early Onset
Seeking
Anxiety Disorder
.37
.30
.22
.10
.07
.08
Conduct
Low Parental
Peer Group
Alcohol
Disorder 15-17
Monitoring 15-17
Deviance 15-17
Availability 15-17
.12
.05
-.06
.10
.30
Alcohol Use 15-17
.26
.07
.06
.24
Symptoms of Alcohol
Use Disorders
Genetic Risk
Genetic Risk
Alcoholism
Ext Disorders
Birth Year
.27
.34
.12
.17
Low Church
Alcohol
Parental
Childhood Phys
Attendance
Household Use
Alcohol Attitude
Sexual Abuse
ADHD
Neuroticism
Sensation
Early Onset
Seeking
Anxiety Disorder
.14
.08
.08
.13
.23
.06
.08
.06
.30
.14
.15
Conduct
Low Parental
Peer Group
Alcohol
Disorder 15-17
Monitoring 15-17
Deviance 15-17
Availability 15-17
.09
.12
.09
.08
.30
Alcohol Use 15-17
.16
.26
Symptoms of Alcohol
Use Disorders
.13
Paradigm 2- Advance Genetic
Epidemiology – Integrative
Developmental Model
• Evidence for two etiologic pathways
characterized by genetic and temperamental
factors and by psychosocial adversity.
Paradigm 2- Advance Genetic
Epidemiology – Multivariate Model
for DSM-IV Criteria for AD
• Attempted to distinguish two hypotheses.
• 1. Each of the seven AD criteria index the
same set of risk genes so that the diagnosis
of AD is genetically homogeneous.
• 2. The DSM-IV syndrome of AD is genetically
heterogeneous, arising from multiple sets of
risk genes that are each reflected by a distinct
set of diagnostic criteria.
– Rodent studies suggest relatively distinct
set of risk genes for different alcoholrelated traits.
Paradigm 2- Advance Genetic
Epidemiology – Multivariate Model
for DSM-IV Criteria for AD
• Long arduous task of complex model fitting.
• 7,548 personally interviewed male and female
twins from the Virginia Adult Twin Study of
Psychiatric and Substance Use Disorders
• Had to take account of the fact that lots of
people did not meet our screening criteria and
skipped out of the alcohol section.
• This is the best fit model --
A1
.67
.22
.29
Excessive
Quantity /
Frequency
.17
.36
.51
ES1
.42
.51
.26
Perception
of Alcohol
Problem
.22
.67
.01
ES2
.54
A2
.30
.24
Tolerance
.58
.19
.43
.33
.30
.54
Withdrawal
.45
ES3
.33
.45
ES4
E1
.30
.43
A3
.33
Loss of
Control
.63
.28
.19
.50
.27
ES5
.58
.28
.51
.28
Preoccu-
Desire to
Quit
.39
.50
pation
.47
ES6
E2
.41
.34
.36
ES7
.33
.48
.44
Activities
Given Up
.30
.45
.42
ES8
.29
.38
.47
Continued
Use Despite
Problems
.34
.29
.59
ES9
Paradigm 2- Advance Genetic
Epidemiology – Multivariate Model
for DSM-IV Criteria for AD
• This is the best fit model –
• Robustly supported second hypothesis –
evidence for three genetic factors, which we
tentatively called:
– heavy use and tolerance
– loss of control with alcohol associated
social dysfunction
– withdrawal and continued use despite
known problems.
8 Major Conclusions
• 1. SUDs are substantially heritable and
heritability estimates for AD appear to be
relatively stable across time and space. For
smoking, we have evidence of potentially
strong gene x cohort effects.
• 2. Roughly 2/3rds of genetic risk factors for
AD and other SUDs are not-disorder specific
but are shared with other forms of substance
abuse and with other externalizing disorders
generally.
• 3. In early adolescence, siblings resemblance for
alcohol, nicotine and cannabis consumption is
entirely due to environmental factors. With increasing
age, we see an increasing degree of genetic
influence.
• 4. For at least AD, we do not have strong evidence
from GE models for parent-offspring environmental
transmission.
• 5. Genes for SUDs appear to be rather substantially
moderated by environmental exposures, especially
those which either relax social constraints and/or
permit easy access but also those that reflect
adverse family environments.
Conclusions
• 6. G-E correlation is probably an important
etiologic factor in SUDs. Genes can impact
on SUDs via outside the skin pathways.
• 7. I presented one very rough integrated
etiologic model for AD – showing how
genetic/termpermental and environmental
adversity pathways might inter-relate in the
etiology.
• 8. DSM-IV criteria for AD appear, from a
genetic perspective, to be etiologically
complex reflecting multiple dimensions of
genetic risk. Would we see the same for other
SUDs?
Key Collaborators
• Mike Neale PhD
• Danielle Dick PhD
• Carol Prescott PhD
• Hermine Maes PhD
• Lindon Eaves PhD
• Charles Gardner
PhD
• Steve Aggen PhD
• John Myers MA
• Ted ReichbornKjennerud MD
• Nathan Gillispie
• Jan Sundquist
• Kristina Sundquist
Support
• NIAAA including our Alcohol Research Center
at VCU
• NIDA
• NIMH
• Virginia Commonwealth University’s
generous support for the Virginia Institute for
Psychiatric and Behavioral Genetics
• No conflicts of interest