Genetic correlation and gene-environment interaction between alcohol problems and educational level in young adulthood.
ABSTRACT A lower level of education often co-occurs with alcohol problems, but factors underlying this co-occurrence are not well understood. Specifically, whether these outcomes share part of their underlying genetic influences has not been widely studied. Educational level also reflects various environmental influences that may moderate the genetic etiology of alcohol problems, but gene-environment interactions between educational attainment and alcohol problems are unknown.
We studied the two non-mutually exclusive possibilities of common genetic influences and gene-environment interaction between alcohol problems and low education using a population-based sample (n = 4,858) of Finnish young adult twins (M(age) = 24.5 years, range: 22.8-28.6 years). Alcohol problems were assessed with the Rutgers Alcohol Problem Index and self-reported maximum number of drinks consumed in a 24-hour period. Years of education, based on completed and ongoing studies, represented educational level.
Educational level was inversely associated with alcohol problems in young adulthood, and this association was most parsimoniously explained by overlapping genetic influences. Independent of this co-occurrence, higher education was associated with increased relative importance of genetic influences on alcohol problems, whereas environmental factors had a greater effect among twins with lower education.
Our findings suggest a complex relationship between educational level and alcohol problems in young adulthood. Lower education is related to higher levels of alcohol problems, and this co-occurrence is influenced by genetic factors affecting both phenotypes. In addition, educational level moderates the importance of genetic and environmental influences on alcohol problems, possibly reflecting differences in social-control mechanisms related to educational level.
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ABSTRACT: Objective To determine the relationship between persistence or change in leisure-time physical activity habits and waist gain among young adults.Methods Population-based cohort study among 3383 Finnish twin individuals (1578 men) from five birth cohorts (1975-1979), who answered questionnaires at mean ages of 24.4 y (SD 0.9) and 33.9 y (SD 1.2), with reported self-measured waist circumference. Persistence or change in leisure-time physical activity habits was defined based on thirds of activity metabolic equivalent h/day during follow-up (mean 9.5 y; SD 0.7).ResultsDecreased activity was linked to greater waist gain compared to increased activity (3.6 cm, P < 0.001 for men; 3.1 cm, P < 0.001 for women). Among same-sex activity discordant twin pairs, twins who decreased activity gained an average 2.8 cm (95%CI 0.4 to 5.1, P = 0.009) more waist than their co-twins who increased activity (n = 85 pairs); among MZ twin pairs (n = 43), the difference was 4.2 cm (95%CI 1.2 to 7.2, P = 0.008).Conclusions Among young adults, an increase in leisure-time physical activity or staying active during a decade of follow-up was associated with less waist gain, but any decrease in activity level, regardless baseline activity, led to waist gain that was similar to that associated with being persistently inactive.Obesity 05/2014; · 3.92 Impact Factor
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ABSTRACT: BACKGROUND: Body mass index (BMI) is associated with subjective well-being. Higher BMI is believed to be related with lower well-being. However, the association may not be linear. Therefore, we investigated whether a nonlinear (U-shaped) trend would better describe this relationship, and whether eating disorders might account for the association in young adults. METHODS: FinnTwin16 study evaluated multiple measures of subjective well-being, including life satisfaction, General Health Questionnaire (GHQ-20), satisfaction with leisure time, work, and family relationships, and satisfaction with sex life in young adulthood in the 1975--79 birth cohorts of Finnish twins (n=5240). We studied the relationship between indicators of subjective well-being and BMI both in full birth cohorts and in subgroups stratified by lifetime DSM-IV eating disorders. RESULTS: We found an inverse U-shaped relationship between all indicators of subjective well-being and BMI in men. There was no overall association between BMI and subjective well-being in women. However, there was an inverse U-shaped relationship between BMI and indicators of subjective well-being in women with a lifetime eating disorder and their healthy female co-twins. Subjective well-being was optimal in the overweight category. CONCLUSIONS: Both underweight and obesity are associated with impaired subjective well-being in young men. The BMI reflecting optimal subjective well-being of young men may be higher than currently recognized. Categorization of body weight in terms of BMI may need to be reassessed in young men. BMI and subjective well-being are related in women with a lifetime eating disorder, but not in the general population of young women.BMC Public Health 03/2013; 13(1):231. · 2.08 Impact Factor
Conference Paper: Social intelligence indicators for addiction disorder patients.[Show abstract] [Hide abstract]
ABSTRACT: Abstract Alcohol and drug addiction is a bio-psycho-social illness that affects a person not only physically but also influences his psyche, thinking and behavior as well as his attitude towards himself and his closest friends and others. In this paper, particular attention is paid to the three components of addiction disorder patients’ social intelligence (SI): social information processing (SIP), social skills (SS) and social awareness (SA). Using the Social Intelligence Test, 241 respondents were questioned; all Riga Center of Psychiatry and Addiction Disorder department patients. The mean arithmetical indicators were statistically relevant and significantly higher for males than females (in SIP factor); drug addict indicators were higher than those of alcoholics in all three SI factors. Male drug addict indicators were statistically relevant and significantly higher in the SIP and SA factors when compared to those of male alcoholics. For female drug addicts and alcoholics the SI factors had no significant statistical difference. In this paper, the research results were analyzed. The results allude to the respondents’ difficulties in adequately and critically assessing their own aptitudes of social intelligence as well as their various ways of responding that are deemed socially acceptable. Key-words: social intelligence, social skills, social information processing, social awareness, alcoholics, drug addicts, substance use disorders, gender.7th Annual International Scientific Conference „New Dimensions in the development of Society”; 10/2011
210 JOURNAL OF STUDIES ON ALCOHOL AND DRUGS / MARCH 2011
Genetic Correlation and Gene–Environment Interaction
Between Alcohol Problems and Educational Level in
ANTTI LATVALA, M.A.,† DANIELLE M. DICK, PH.D.,† ANNAMARI TUULIO-HENRIKSSON, PH.D.,†
JAANA SUVISAARI, M.D., PH.D., RICHARD J. VIKEN, PH.D.,† RICHARD J. ROSE, PH.D.,† AND JAAKKO KAPRIO, M.D., PH.D.†
Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland
ABSTRACT. Objective: A lower level of education often co-occurs
with alcohol problems, but factors underlying this co-occurrence are not
well understood. Specifi cally, whether these outcomes share part of their
underlying genetic infl uences has not been widely studied. Educational
level also refl ects various environmental infl uences that may moderate
the genetic etiology of alcohol problems, but gene–environment interac-
tions between educational attainment and alcohol problems are unknown.
Method: We studied the two nonmutually exclusive possibilities of
common genetic infl uences and gene–environment interaction between
alcohol problems and low education using a population-based sample (n
= 4,858) of Finnish young adult twins (Mage = 24.5 years, range: 22.8-
28.6 years). Alcohol problems were assessed with the Rutgers Alcohol
Problem Index and self-reported maximum number of drinks consumed
in a 24-hour period. Years of education, based on completed and ongo-
ing studies, represented educational level. Results: Educational level
was inversely associated with alcohol problems in young adulthood,
and this association was most parsimoniously explained by overlapping
genetic infl uences. Independent of this co-occurrence, higher education
was associated with increased relative importance of genetic infl uences
on alcohol problems, whereas environmental factors had a greater effect
among twins with lower education. Conclusions: Our fi ndings suggest a
complex relationship between educational level and alcohol problems in
young adulthood. Lower education is related to higher levels of alcohol
problems, and this co-occurrence is infl uenced by genetic factors affect-
ing both phenotypes. In addition, educational level moderates the im-
portance of genetic and environmental infl uences on alcohol problems,
possibly refl ecting differences in social-control mechanisms related to
educational level. (J. Stud. Alcohol Drugs, 72, 210-220, 2011)
Alcoholism grants AA-12502, AA-00145, and AA-09203 awarded to Richard
J. Rose, and AA-15416 awarded to Danielle M. Dick; Academy of Finland
grants 100499 and 118555 awarded to Jaakko Kaprio. Jaakko Kaprio is also
supported by the Centre of Excellence in Complex Disease Genetics of the
Academy of Finland.
Helsinki, P.O. Box 41, FIN-00014 University of Helsinki, Helsinki, Finland.
Received: August 7, 2010. Revision: November 3, 2010.
*This study was supported by National Institute on Alcohol Abuse and
†Antti Latvala is also with the Department of Public Health, University of
Correspondence may be sent to him at that address or via email at: antti.
email@example.com . Danielle M. Dick is with the Virginia Institute for Psychiatric
and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA.
Annamari Tuulio-Henriksson is also with the Research Department, Social
Insurance Institution, Helsinki, Finland. Richard J. Viken and Richard J.
Rose are with the Department of Psychological and Brain Sciences, Indiana
University, Bloomington, IN. Jaakko Kaprio is also with the Department
of Public Health and the Institute for Molecular Medicine, University of
Helsinki, Helsinki, Finland.
of medical conditions, health behaviors, and mortality (Adler
et al., 1994). Substance use disorders, including alcohol
dependence, are more prevalent among those with more
truncated educational achievement (Jacobi et al., 2004; Kes-
sler et al., 2005; Suvisaari et al., 2009). Longitudinal studies
have highlighted interconnections between developmental
patterns of alcohol problems and educational outcomes,
suggesting both that poor school success predicts drinking
problems later in life and that heavy drinking in adolescence
predicts lower education (Crum et al., 2006; Kessler et al.,
1995; Pitkänen et al., 2008; Swendsen et al., 2009).
In addition to possible causal infl uences between alcohol
problems and low educational achievement, there may be
common underlying factors that infl uence both outcomes,
thus giving rise to their co-occurrence. One such factor
DUCATIONAL LEVEL IS A KEY COMPONENT of
socioeconomic status and strongly relates to a multitude
might be genetic background, as individual differences both
in alcohol-related and educational outcomes arise, in part,
because of differences in genetic makeup (Agrawal and Lyn-
skey, 2008; Baker et al., 1996; Dick et al., 2009a; Johnson
et al., 2009a; Silventoinen et al., 2004). Alcohol and other
substance use disorders are associated with poorer cognitive
abilities (Beatty et al., 2000; Latvala et al., 2009; Tapert and
Brown 2000), and we have found evidence of partial genetic
overlap between poorer verbal cognitive ability and alcohol
dependence symptoms in early adulthood (Latvala et al.,
in press). Because cognitive abilities are highly predictive
of the level of education to be attained (Deary et al., 2007)
and because genetic factors contribute to this association
(Bartels et al., 2002; Johnson et al., 2006), the question
arises whether the co-occurrence of alcohol problems and
low education is also, in part, the result of genetic infl uences
common to these behavioral outcomes. This perspective ex-
LATVALA ET AL. 211
tends the prevailing approaches to the relationship between
socioeconomic status and health outcomes, namely the social
causation, social selection, and interactionist perspectives
(Conger and Donnellan, 2007), which would argue, respec-
tively, that low education leads to alcohol problems, alcohol
problems lead to low education, or educational level and
alcohol problems reciprocally infl uence each other.
Genetic and environmental infl uences on variation in a
trait of interest, or covariation of two or more traits, can
be assessed in twin studies using the different degree of
genetic relatedness between identical (monozygotic [MZ])
and fraternal (dizygotic [DZ]) twins (Boomsma et al., 2002).
Besides demonstrating the presence of genetic infl uences,
however, twin studies are increasingly beginning to address
the phenomenon of differing genetic infl uences conditional
on environmental variation, or gene–environment interaction
(Heath et al., 2002; Sher et al., 2010; van der Zwaluw and
Engels, 2009). Pioneering studies found the heritability (i.e.,
proportion of phenotypic variance explained by genetic vari-
ance) of different indices of alcohol use to be dependent on
various environmental contexts, such as marital status (Heath
et al., 1989), religious upbringing (Koopmans et al., 1999),
and urban versus rural residency (Dick et al., 2001; Rose et
al., 2001). More recent studies have reported, for example,
enhanced genetic infl uences on adolescent substance use
in environments with lower parental monitoring and more
substance-using friends (Dick et al., 2007a, 2007b).
Educational level is related to many facets of an indi-
vidual’s environment throughout the life span, ranging from
chemical exposures to interpersonal relations (Evans and
Kantrowitz, 2002; Gallo et al., 2006). It is thus conceivable
that education might also have a moderating effect on the
genetic etiology of alcohol problems. For example, it might
be posited that education-related differences in homogeniz-
ing environmental infl uences, such as social norms, modify
the importance of genetically infl uenced characteristics of
the individual—an example of social context as a control
mechanism (Shanahan and Hofer, 2005). However, this and
other possible types of gene–environment interaction effects
between education and alcohol problems have not been ex-
Finland is a Nordic country whose educational system
offers public schooling of uniform quality (Organisation for
Economic Co-Operation and Development, 2007) without
tuition fees, rendering educational opportunities virtually
independent of fi nancial and other family backgrounds. This
feature, combined with another—that only a small propor-
tion of the population totally abstains from alcohol (Hela-
korpi et al., 2009)—makes Finland an informative setting
for a genetic study of educational level in relation to alcohol
problems. We used data from Finnish twins in early adult-
hood to examine the two nonmutually exclusive scenarios of
common genetic infl uences and gene–environment interac-
tion between alcohol problems and low education.
Sample and measures
survey of FinnTwin16, a population-based longitudinal study
of fi ve consecutive birth cohorts (1975-1979) of Finnish
twins (Kaprio et al., 2002; Rose et al., 1999). FinnTwin16
was initiated in 1991 when the 1975 cohort was sequentially
enrolled in 10 mailouts during 1-2 months following the
twins’ 16th birthdays. Baseline questionnaire data collec-
tion was completed in 1996 with pairwise response rates
exceeding 88%, yielding baseline data from 2,733 twin
pairs. Subsequent follow-up assessments were made at ages
17, 18.5, and approximately 25 years. The fi rst three waves
were tightly controlled for age, in appreciation of the rapid
development of alcohol use in adolescence. In young adult-
hood, the surveys were telescoped into a 30-month period,
with each birth year assessed in a 6-month window during
2000-2002 (Kaprio et al., 2002). The baseline and follow-
up assessments included surveys of health habits and at-
titudes, symptom checklists, personality scales, and social
relationships. Zygosity was determined on the basis of a
well-validated questionnaire, containing items on the twins’
similarity and confusability, completed by both co-twins and
their parents at the baseline (Kaprio et al., 2002). Wave IV
data of the outcomes of interest were available for a total of
4,974 individuals from 2,671 twin pairs (from 838 MZ, 879
same-sex DZ, and 954 opposite-sex DZ pairs). Of the sam-
ple, 54.8% were females, and the mean age was 24.5 years
(range: 22.8-28.6 years). The data collection procedures of
FinnTwin16 were approved by the Ethical Committee of the
Faculty of Medicine, University of Helsinki, and by the In-
stitutional Review Board of Indiana University. Participants
provided written informed consent.
Alcohol problems were assessed with the Rutgers Al-
cohol Problem Index (RAPI), a self-report measure of
alcohol-related problems experienced during the previous
12 months (White and Labouvie, 1989). The original RAPI
has 23 items. In the FinnTwin16 young adult data collec-
tion, the item on whether alcohol use interfered with school
work or examination preparation was omitted, creating a
22-item Finnish adaptation of RAPI with four response
options. The internal consistency of this adapted version in
the FinnTwin16 sample was as good (Cronbach’s α = .90)
as that of the original RAPI (Cronbach’s α = .92) (White
and Labouvie, 1989). The full RAPI scale without missing
items was available for 4,260 twins from complete twin pairs
(425 female MZ, 280 male MZ, 373 same-sex female DZ,
312 same-sex male DZ, and 740 opposite-sex DZ pairs) and
561 individual twins (68 MZ females, 60 MZ males, 68 DZ
females from same-sex pairs, 119 DZ males from same-sex
pairs, 173 DZ females from opposite-sex pairs, and 73 DZ
males from opposite-sex pairs).
The present study is based on the Wave IV questionnaire
212 JOURNAL OF STUDIES ON ALCOHOL AND DRUGS / MARCH 2011
reported estimate of maximum number of alcoholic drinks
consumed in a 24-hour period during the lifetime (maximum
drinks). This measure has been used in genetic studies as
a quantitative phenotype closely related to diagnosis of
alcohol dependence (Saccone et al., 2005). We had data on
maximum drinks for 4,048 twins from complete twin pairs
(407 female MZ, 264 male MZ, 348 same-sex female DZ,
303 same-sex male DZ, and 702 opposite-sex DZ pairs) and
519 individual twins (55 MZ females, 54 MZ males, 59 DZ
females from same-sex pairs, 110 DZ males from same-sex
pairs, 173 DZ females from opposite-sex pairs, and 68 DZ
males from opposite-sex pairs).
Information on the attained level of education was
available as categorical classifi cations of each participant’s
completed and ongoing studies. Using this information, a
variable representing the estimated total years of education
was created. This was done on the basis of the standard du-
ration of each type of education. In the Finnish educational
system, compulsory education continues through Grade 9
(age 16). Secondary education is divided into vocational
(nonacademic) and academic secondary education (high
school), which typically takes 2 and 3 years to complete,
respectively. Tertiary education is provided by polytechnic
schools and universities, lasting typically 3.5 and 5 years, re-
spectively. Polytechnic schools train professionals in various
fi elds in response to labor market needs, whereas universities
conduct scientifi c research and provide the highest levels of
education. To enter tertiary education, academic second-
ary education is generally required, although some excep-
tions exist. For the participants who still had their studies
underway when completing the young adult questionnaire,
ongoing studies were taken into account by using half of the
standard duration of the type of education in question as an
average estimate of years studied. For example, individuals
who reported having completed academic secondary educa-
tion and were currently studying in the university were thus
given the value 14.5 (9 + 3 + 2.5) for years of education.
Information on education was available for 4,516 twins
from complete twin pairs (448 female MZ, 298 male MZ,
393 same-sex female DZ, 336 same-sex male DZ, and 783
opposite-sex DZ pairs) and 453 individual twins (45 MZ
females, 47 MZ males, 50 DZ females from same-sex pairs,
100 DZ males from same-sex pairs, 159 DZ females from
opposite-sex pairs, and 52 DZ males from opposite-sex
The data included 116 individuals (2.3% of the sample)
who could be classifi ed as probable lifetime abstainers based
on their responses throughout the data collection. These indi-
viduals were excluded from all analyses to avoid the assump-
tion that a single unidimensional distribution encompasses
both initiation of alcohol use and problem drinking. Thus,
the fi nal sample contained 4,858 twin individuals (2,414
complete pairs and 30 individual twins).
As another indicator of alcohol problems, we used a self-
maximum drinks was studied with linear regression models,
adjusting for familial clustering of the data (Williams, 2000).
As a result of strong positive skewness, Box-Cox transfor-
mations of mean RAPI and maximum drink scores were
used in the analyses, but values of untransformed variables
are presented as descriptive information. For a fi rst estima-
tion of the presence of genetic infl uences on these traits and
their covariation, twin and cross-twin cross-trait correlations
were compared in different zygosity groups. In these com-
parisons, larger correlations within MZ than DZ pairs sug-
gest the presence of genetic infl uences. Based on within-pair
analyses, we proceeded into biometrical twin modeling.
In basic biometrical twin models, variance in a trait is
partitioned into additive genetic infl uences (A), common
environmental infl uences (C), and unique environmental
infl uences (E) (Neale and Cardon, 1992). Additive genetic
infl uences represent the sum of the individual effects of
each gene on the phenotype, and thus correlate 1.0 between
MZ twins, who share all their genes identical by descent,
and 0.5 between DZ twins, who share, on average, 50% of
their segregating genes. Common environmental infl uences
refers to all environmental infl uences that make twins in a
pair more similar to each other, and, by defi nition, correlate
1.0 between both MZ and DZ twins. Unique environmental
infl uences, in contrast, are environmental infl uences that af-
fect only one member of the twin pair. They are uncorrelated
between both MZ and DZ twins, making co-twins thus more
In all twin modeling, the signifi cance of each parameter
in the model is tested by dropping the parameter and evaluat-
ing the change in -2 log likelihood between the initial model
and the nested submodel. Model comparisons are made with
a likelihood ratio chi-square test, and a signifi cant change in
the chi-square value indicates that dropping the parameter
signifi cantly decreases model fi t, suggesting that the param-
eter should be retained in the model (Neale and Cardon,
Modeling was initiated with standard univariate analysis
for each of the outcomes using the full sample including
opposite-sex DZ pairs. These models yield estimates of
additive genetic, common environmental, and unique en-
vironmental infl uences on the outcomes, and also enable
testing the presence of quantitative and qualitative sex dif-
ferences in genetic infl uences. Following that initial phase,
trivariate Cholesky decomposition models for education
and the alcohol variables (RAPI and maximum drinks) were
estimated. In multivariate analysis, the association between
traits is modeled by decomposing the phenotypic covariance
of the variables into proportions accounted for by A, C, and
E effects. The degree of association of the genetic factors
infl uencing the traits is estimated as the genetic correlation
The association of educational level with RAPI and
LATVALA ET AL. 213
between the latent genetic factors for the two traits. Com-
mon and unique environmental correlations are estimated in
a similar fashion.
Gene–environment interaction effects between education-
al level and the alcohol problem variables were investigated
with univariate moderation models in which a standardized
variable of years of education served as a moderator for
RAPI and maximum drinks. These models are extensions of
the standard univariate model, modifi ed to include a mod-
eration component (Purcell, 2002). As shown in Figure 1, in
addition to the standard paths a, c, and e, which indicate the
additive genetic effect, the common environmental effect,
and the unique environmental effect, respectively, a mod-
eration coeffi cient β is included on each of these paths. In
the moderation model, the additive genetic value is a linear
function of the moderator variable M (educational level in
the present models), represented by the equation a + βXM,
where βX, an unknown parameter to be estimated, represents
the magnitude of the moderating effect. A βX that is signifi -
cantly different from zero is taken as evidence for a modera-
tion effect on additive genetic infl uences. Moderation effects
on common and unique environmental infl uences, βY and βZ,
are estimated similarly. The pathway µ + βMM models the
main effects of the moderator variable on the outcome. Im-
portantly, this pathway also includes any covariance between
the moderator and the outcome, including genetic correla-
tion. Moderation effects are thus not confounded by possible
common genetic infl uences on the moderator (education in
FIGURE 1. Moderation model (shown for one twin of the pair only). Depicted as circles, the latent variables A, C, and E indicate additive genetic, common
environmental, and unique environmental infl uences, respectively, on the trait (T) of interest. The triangle indicates the mean of T. The paths a, c, and e in-
dicate the magnitude of each latent variance component’s effect on the trait. Each path includes a β term, which indicates the moderation coeffi cient for the
moderator variable M.
214 JOURNAL OF STUDIES ON ALCOHOL AND DRUGS / MARCH 2011
the present study) and outcome variables (RAPI and maxi-
mum drinks). All genetic modeling in the present study was
performed in Mx (Neale et al., 2006), a structural equation
modeling program developed specifi cally for analyzing twin
and family data.
drinks in twin individuals, stratifi ed by zygosity and sex, are
shown in Table 1. Nearly half the sample (44.9%) reported
completed or ongoing polytechnic or university (tertiary)
education, whereas only 5.0% reported no studies beyond
compulsory education. Women were more highly educated
than men (p < .001), and in both sexes, MZ twins had a
slightly higher education than DZ twins (p < .05). Robust
sex differences were also observed in the alcohol variables,
with men having higher values than women (both variables:
Distributions of educational level, RAPI, and maximum
p < .001). The twin types were also found to differ in these
variables, with DZ twins scoring higher than MZ twins
(RAPI: p < .05; maximum drinks: p < .001).
Table 2 gives the phenotypic correlations between years
of education, RAPI, and maximum drinks in men and
women. The correlations between education and the alcohol
problem variables were of modest size but highly signifi cant
in both sexes. As an example of education-related differences
in alcohol problems, the mean of RAPI was 11.4 (95% CI
[9.3, 13.5]) among men with compulsory education only,
compared with 6.4 (95% CI [5.9, 6.8]) in those with tertiary
education, and the numbers of reported maximum drinks in
these educational categories were 25.0 (95% CI [22.7, 27.3])
and 20.4 (95% CI [19.9, 21.0]), respectively. Twin and cross-
twin cross-trait correlations between years of education and
alcohol problems are given in Table 3 and 4, respectively. In
most cases, they were larger within MZ than DZ twin pairs,
suggesting genetic infl uences on these traits.
environmental, and unique environmental infl uences on
educational level, RAPI, and maximum drinks are pre-
sented in Table 5. Constraining the additive genetic, com-
mon environmental, and unique environmental paths equal
in males and females did not signifi cantly decrease model
fi t for RAPI, χ2Δ(3) = 2.53, p = .47, whereas a signifi cant
Standardized estimates of additive genetic, common
TABLE 1. Distribution of educational level and alcohol problems in twin individuals across
zygosity and sex
(n = 920)
(n = 626) (n = 1,580) (n = 4,858)
(n = 1,732)
Education, M (SD)
RAPI, M (SD)
Max. drinks, M (SD)
21.1 (10.2) 21.8 (9.8)
Notes: Dizygotic female (DZF) and dizygotic male (DZM) individuals in the table are drawn
from both same-sex and opposite-sex DZ pairs. MZF = monozygotic females; MZM = monozy-
gotic males; RAPI = Rutgers Alcohol Problem Index; max. drinks = maximum number of drinks
consumed in a 24-hour period.
TABLE 2. Phenotypic correlations between educational level and alcohol
Education RAPI Max. drinks
Notes: Correlations for males are below and those for females above the
diagonal. RAPI = Rutgers Alcohol Problem Index; max. drinks = maximum
number of drinks consumed in a 24-hour period.
**p < .01; ***p < .001.
TABLE 3. Twin correlations for educational level and alcohol problems
MZF SS-DZF MZM SS-DZM OS-DZ
Notes: MZF = monozygotic females; SS-DZF = dizygotic females from
same-sex pairs; MZM = monozygotic males; SS-DZM = dizygotic males
from same-sex pairs; OS-DZ = opposite-sex pairs; RAPI = Rutgers Alcohol
Problem Index; max. drinks = maximum number of drinks consumed in a
TABLE 4. Cross-twin cross-trait correlations between educational level and
Education RAPI drinks Education RAPI drinks
Notes: Monozygotic correlations are below and same-sex dizygotic cor-
relations above the diagonal. The correlations were calculated using the
double-entry method. RAPI = Rutgers Alcohol Problem Index; max. drinks
= maximum number of drinks consumed in a 24-hour period
LATVALA ET AL. 215
decrease for education, χ2Δ(3) = 25.15, p < .001, and
maximum drinks, χ2Δ(3) = 90.73, p < .001, was observed.
Constraining the genetic correlation to .5 in opposite-sex
DZ twins was statistically possible for RAPI, χ2Δ(1) =
0.63, p = .426, and maximum drinks, χ2Δ(1) = 0.001, p
= .975, but not for education, χ2Δ(1) = 7.69, p < .01, sug-
gesting qualitative sex differences in the genetic infl uences
on educational level. The best fi tting models for RAPI and
maximum drinks did not include common environmental
infl uences, χ2Δ(1) < 0.01, p > .99, χ2Δ(2) = 0.59, p = .75,
whereas removing the C component signifi cantly decreased
model fi t for education, χ2Δ(2) = 22.17, p < .001.
TABLE 5. Standardized estimates [95% confi dence intervals] of additive genetic (A), common environmental (C), and unique
environmental (E) infl uences on educational level and two alcohol problem measures from univariate models
A C E A C E
Notes: RAPI = Rutgers Alcohol Problem Index; max. drinks = maximum number of drinks consumed in a 24-hour period.
FIGURE 2. Genetic and environmental contributions to the covariance between education and two alcohol problem variables. The fi gure shows the estimates
of additive genetic correlation (rA) and unique environmental correlation (rE) and their 95% confi dence intervals (in brackets) from the fi nal, best fi tting
trivariate Cholesky decomposition for years of education, the Rutgers Alcohol Problem Index (RAPI) scores, and maximum number of drinks consumed in a
24-hour period (max. drinks) separately for the sexes. Depicted as circles, the latent variables A, C, and E indicate additive genetic, common environmental,
and unique environmental variance components.
216 JOURNAL OF STUDIES ON ALCOHOL AND DRUGS / MARCH 2011
mental, and unique environmental correlation between
educational level and the two alcohol problem variables was
tested in trivariate models separately by sex. For both alcohol
problem variables and education, covariance resulting from
correlated genetic infl uences was signifi cant in both sexes
(females: p < .05 for education and RAPI, p < .01 for educa-
tion and maximum drinks; males: p < .01 for education and
RAPI, p < .05 for education and maximum drinks), whereas
covariance resulting from correlated environmental infl u-
ences could be removed from the models without statistically
signifi cant decrease in model fi t. In contrast, both additive
genetic and unique environmental sources of covariance
contributed signifi cantly to the association between RAPI
and maximum drinks in both sexes (females: p < .001 for
rA and rE; males: p < .001 for rA and p < .01 for rE). Figure
2 summarizes the genetic and environmental contributions
to the covariance between years of education, RAPI, and
In the univariate moderation models, signifi cant modera-
tion effects were found for both alcohol problem variables
The signifi cance of additive genetic, common environ-in both sexes. For RAPI, educational level moderated unique
environmental infl uences such that higher education was re-
lated to decreased unique environmental variance (females: p
< .05; males: p < .01), whereas moderation effects on A and
C infl uences were not statistically signifi cant. For maximum
drinks, signifi cant moderation effects on both common and
unique environmental paths were detected (females: p <
.001 for both effects; males: p < .01 for moderation on C,
p < .001 for moderation on E). Higher education was also
related to decreased unique environmental variance in maxi-
mum drinks, whereas the effect on common environmental
infl uences was more complex. An increase in C variance
was found related to both low and high levels of education,
whereas C variance was reduced close to zero at the mean of
the education distribution. This nonlinear change in variance
occurred because the moderating effect changed the direc-
tion of the C effect on maximum drinks from negative at low
educational level to positive at high educational level.
As a result of these moderating effects, additive genetic
infl uences explained a larger proportion of variance in both
alcohol variables in those with higher education, whereas
FIGURE 3. Additive genetic (A), common environmental (C), and unique environmental (E) variance components of the Rutgers Alcohol Problem Index
scores (top panel) and maximum number of drinks in a 24-hour period (bottom panel) in females (left) and males (right) as a function of educational level in
standard deviation units.
LATVALA ET AL. 217
common and unique environmental infl uences were more
important in twins with lower education. For example, the
heritability of RAPI in men increased from .29 at low educa-
tion (1.5 SD below the mean) to .56 at high education (1.5
SD above the mean). These moderating effects of educational
level on RAPI and maximum drinks are shown graphically
in Figure 3.
twins in early adulthood, we present evidence of genetic
correlation and gene–environment interaction between edu-
cational level and two indicators of alcohol problems. As
anticipated, twins with lower education reported signifi cantly
more problems related to alcohol use within the last 12
months and higher numbers of consumed alcoholic drinks
in a 24-hour period during the lifetime. Biometrical twin
modeling suggested that genetic factors infl uence this co-
occurrence, with a proportion of the genetic variation that
increases the risk for alcohol problems also predisposing
to attaining lower education. Consistent with earlier studies
(Agrawal and Lynskey, 2008; Baker et al., 1996; Dick et
al., 2009a; Johnson et al., 2009a; Silventoinen et al., 2004),
heritability estimates of educational level and alcohol prob-
lems were moderate, ranging from 32% to 48%. In addition,
independently of this genetically infl uenced co-occurrence,
educational level also moderated the environmental infl u-
ences specifi c to alcohol problems. As a result, the relative
importance of genetic infl uences on both indicators of alco-
hol problems was greater among those with a higher level of
Our results extend the currently scarce genetically in-
formed research on the relationship between alcohol use
behaviors and education. Two recent studies reported on ge-
netic correlation and gene–environment interaction between
these phenomena, respectively, but neither study assessed
both with the same sample. In their multivariate analysis of
young adult data from the Minnesota Twin Family Study,
Johnson et al. (2009b) reported overlapping genetic infl u-
ences on education and an alcohol use composite, including
symptoms of alcohol abuse/dependence (diagnosed ac-
cording to the Diagnostic and Statistical Manual of Mental
Disorders, Third Edition, Revised [DSM-III-R]; American
Psychiatric Association, 1987) and maximum number of
drinks. Their multivariate model also included IQ, assessed
in adolescence, and most of the shared genetic variance with
alcohol use in fact refl ected both IQ and education. A large
proportion of the covariance of IQ, education, and alcohol
use also seemed to be the result of overlapping common en-
vironmental infl uences, but the authors concluded that their
sample of 626 twin pairs lacked suffi cient statistical power
to distinguish between genetic and common environmen-
tal infl uences in the multivariate setting. Timberlake et al.
Using data from a population-based sample of Finnish
(2007), on the other hand, investigated the effects of college
attendance on drinking behaviors, and because their sample
included twins and siblings, they could model gene–envi-
ronment interaction. Results suggested that college students
exhibited greater genetic infl uence on quantity of alcohol
consumed per drinking episode—a fi nding parallel to the
present gene–environment interaction results. However, as
discussed by the authors, the experiences and drinking-pro-
moting infl uences related to college attendance in the United
States may be quite specifi c to that environmental context,
such as participation in fraternities/sororities and various
athletic programs. Thus, our fi ndings in Finnish young adults
are likely to refl ect, at least partly, different mechanisms of
moderation by education.
Studies on the relationship between education and other
substance use-related outcomes are also relevant to the
present fi ndings. Genetically informed studies on educa-
tion and substance use other than alcohol are equally few in
number, however. McCaffery et al. (2008) reported smoking
initiation to have a negative correlation with educational at-
tainment in male twins, and this correlation was explained
by an overlap in both genetic and environmental infl uences.
Educational attainment also signifi cantly moderated the vari-
ance in smoking initiation, with higher education also being
associated with reduced variance in that study, but whether
this interaction occurred with genetic or environmental
components could not be resolved. In the present sample,
smoking correlated with alcohol problems and also had an
inverse association with education, suggesting that the pres-
ent fi ndings might be at least partly replicated with smoking.
Our results suggest that, at least in Finland, where edu-
cational opportunities are relatively equal, genetic factors
contribute to the association between alcohol problems and
low education. Although the present modeling results with
cross-sectional data cannot rule out possible causal relations
between these outcomes, they do indicate that genetically
infl uenced individual differences should be considered as
one possible mechanism underlying the associations between
components of socioeconomic status and health behaviors
(Conger and Donnellan, 2007). General intelligence has
been suggested as one such factor underlying socioeconomic
inequalities in health (Der et al., 2009; Gottfredson, 2004).
Importantly, the present multivariate modeling results mir-
rored our previous results on the inverse association between
verbal intelligence and alcohol problems (Latvala et al., in
press), suggesting that the genetic correlation between edu-
cation and alcohol problems might also encompass general
intelligence, as was the case in the study by Johnson et al.
(2009b), discussed above. Interestingly, a recent study on
a large, population-based sample of Swedish male twins
reported a strong inverse association between smoking sta-
tus and IQ, and found no support for a causal relationship
between these traits (Wennerstad et al., 2010). However, de-
spite their strong correlation, intelligence and education also
218 JOURNAL OF STUDIES ON ALCOHOL AND DRUGS / MARCH 2011
seem to have independent associations with health outcomes
(Batty et al., 2009; Lager et al., 2009).
The present gene–environment interaction analyses in-
dicated that higher education was associated with reduced
unique environmental variance in alcohol problems, whereas
there was no direct moderation on additive genetic variance.
Educational level also moderated the common environmental
variance component in maximum drinks. These moderation
effects were similar in men and women, and they resulted
in increased relative importance of genetic infl uences on
alcohol problems in those with higher education. This
fi nding may seem contradictory, as higher education was
related to lower level of alcohol problems. It is important to
realize, however, that the moderation models were adjusted
for education, so that the genetic and environmental infl u-
ences estimated, as well as their moderation effects, concern
only variation in alcohol problems that is independent of
educational level. This was accomplished by including the
main effect of the moderator variable in the means model.
This feature of the model also makes sure that the modera-
tion effect is not an artifact produced by genetic correlation
(Purcell, 2002), which was found to explain the association
between educational level and alcohol problems in the pres-
What might be the environmental factors related to
higher education that lead to higher heritability of alcohol
problems? In Finland as elsewhere, education is related
to generally better prospects in life, including less un-
employment, better working conditions, higher salaries,
better neighborhood quality, and better health (Evans and
Kantrowitz, 2002; Havén, 1999). One especially important
environmental correlate of higher education in Finland is
urban residency (Havén, 1999). Previous studies in Finn-
ish twins have reported increased heritability of drinking
behaviors and behavior problems in adolescence in urban
environments, whereas common environmental factors seem
to be more important in rural environments (Dick et al.,
2001, 2009b; Rose et al., 2001). These fi ndings have been
interpreted as refl ecting higher levels of social control and
structural constraints placed on people in more rural environ-
ments, whereas urban environments are presumed to allow
individual, genetically infl uenced behavioral characteristics
to be more freely expressed (Shanahan and Hofer, 2005).
In the present sample of young adults, education was
indeed strongly related to urban residency: 86% of those
with at least academic secondary education reported urban
residency, whereas the proportion was 58% for those with
lower education. There were other notable “environmen-
tal” differences, as well. Those young adults with less than
academic secondary education were more often married or
cohabiting (68% vs. 50%), were more likely to have children
(24% vs. 6%), and were more likely to be working (and
not, e.g., studying; 60% vs. 35%) at the time of the current
assessment as young adults. All these differences in the
personal environment and life situation might contribute to
the increased importance of genetic infl uences and reduced
environmental infl uences in those with higher education. For
example, besides urban residency, also being married has
been associated with less genetic infl uence on alcohol con-
sumption (Heath et al., 1989). Importantly, all these features
also seem compatible with the scenario of less social control
related to higher level of education.
Limitations of the present study include the fact that
only a relatively crude estimate of years of education was
available. However, we conducted the analyses also using an
ordinal variable created from the original categorical clas-
sifi cations of completed and ongoing studies, and a similar
pattern of results as reported here was found in both multi-
variate and moderation analyses. Second, a large proportion
of the sample still had their studies underway when complet-
ing the young adult questionnaire, but this information was
taken into account in the variable for years of education.
Further, as a result of strong positive skewness, Box-Cox
transformed alcohol problem variables were used in the
analyses. This is potentially problematic, as it is known that
variable transformations can result in artifactual interaction
effects (Purcell, 2002). This does not seem to have been the
case in the present study, as similar moderation effects were
detected also using the raw untransformed alcohol variables.
For example, the heritability of the raw RAPI scores in-
creased from 0.20 at low education (−1.5 SD units) to 0.53
at high education (+1.5 SD units) in men, and similarly from
0.17 to 0.44 in women.
A further limitation is that gene–environment interaction
effects and genetic correlation were not modeled simulta-
neously using the moderated Cholesky approach (Purcell
2002). The more simple univariate moderation approach was
chosen because of limited statistical power to reliably detect
specifi c moderation effects on shared and nonshared genetic
and environmental infl uences on education and alcohol prob-
lems when the phenotypic associations between these traits
were weak. The moderated Cholesky model has also been
criticized for potentially producing spurious interaction
effects (Rathouz et al., 2008). Importantly, simulations by
Purcell (2002) suggested that the presence of genetic cor-
relation between the moderator and outcome variables does
not lead to artifi cial interaction effects when the main effect
of the moderator is included in the univariate moderation
model, as was done in the present analyses.
A fi nal limitation is that diagnoses of alcohol use disor-
ders were not available. A subsample (n = 602) of the present
data did in fact provide information on DSM-III-R alcohol
dependence (Latvala et al., in press). In that subsample, the
alcohol problem indicators used in the present study, RAPI
and maximum drinks, had moderate positive correlations
with the number of alcohol dependence criteria met (r = .55
and r = .50, respectively). RAPI scores in late adolescence
robustly predicted alcohol diagnoses in early adulthood, with
LATVALA ET AL. 219
the odds ratio of outcome alcohol diagnosis per unit increase
in adolescent RAPI exceeding 10 (Dick et al., in press). In
the present study, RAPI and maximum drinks were moder-
ately correlated, and shared genes explained approximately
80% of this correlation in men and 70% in women.
In conclusion, the present study of a population-based
sample of Finnish twins suggests a complex relationship
between educational level and alcohol problems in young
adulthood. Lower education is related to signifi cantly higher
levels of alcohol problems, and this co-occurrence is in-
fl uenced by genetic factors that both increase the risk for
alcohol problems and predispose to lower educational attain-
ment. Independent of this co-occurrence, higher educational
level is associated with increased relative importance of ge-
netic infl uences on alcohol problems, whereas common and
unique environmental infl uences play a more important role
in young adults with lower education, possibly refl ecting dif-
ferences in social control mechanisms related to educational
level. All in all, these results underline the importance of
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