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Disentangling the relationships between maternal smoking during
pregnancy and cooccurring risk factors
J. M. Ellingson, M. E. Rickert, P. Lichtenstein, N. Långström and B. M. D'Onofrio
Psychological Medicine / Volume 42 / Issue 07 / July 2012, pp 1547 1557
DOI: 10.1017/S0033291711002534, Published online: 25 November 2011
Link to this article: http://journals.cambridge.org/abstract_S0033291711002534
How to cite this article:
J. M. Ellingson, M. E. Rickert, P. Lichtenstein, N. Långström and B. M. D'Onofrio (2012). Disentangling the relationships
between maternal smoking during pregnancy and cooccurring risk factors. Psychological Medicine, 42, pp 15471557
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Disentangling the relationships between maternal
smoking during pregnancy and co-occurring
J. M. Ellingson1,2, M. E. Rickert1, P. Lichtenstein3, N. La ˚ngstro ¨m3and B. M. D’Onofrio1*
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
2Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
3Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden
Background. Maternal smoking during pregnancy (SDP) has been studied extensively as a risk factor for adverse
offspring outcomes and is known to co-occur with other familial risk factors. Accounting for general familial risk
factors has attenuated associations between SDP and adverse offspring outcomes, and identifying these confounds
will be crucial to elucidating the relationship between SDP and its psychological correlates.
Method. The current study aimed to disentangle the relationship between maternal SDP and co-occurring risk
factors (maternal criminal activity, drug problems, teen pregnancy, educational attainment, and cohabitation at
childbirth) using a population-based sample of full- (n=206313) and half-sister pairs (n=19363) from Sweden.
Logistic regression models estimated the strength of association between SDP and co-occurring risk factors. Bivariate
behavioral genetic models estimated the degree to which associations between SDP and co-occurring risk factors are
attributable to genetic and environmental factors.
Results. Maternal SDP was associated with an increase in all co-occurring risk factors. Of the variance associated
with SDP, 45% was attributed to genetic factors and 53% was attributed to unshared environmental factors. In
bivariate models, genetic factors accounted for 21% (non-drug-, non-violence-related crimes) to 35% (drug-related
crimes) of the covariance between SDP and co-occurring risk factors. Unshared environmental factors accounted for
the remaining covariance.
Conclusions. The genetic factors that influence a woman’s criminal behavior, substance abuse and her offspring’s
rearing environment all influence SDP. Therefore, the intergenerational transmission of genes conferring risk
for antisocial behavior and substance misuse may influence the associations between maternal SDP and adverse
Received 12 July 2011; Revised 12 October 2011; Accepted 14 October 2011; First published online 25 November 2011
Key words: Antisocial, behavior genetics, criminal, drug, pregnancy, smoking.
Nicotine is the most commonly abused substance by
mothers during pregnancy (22.9%; Office of Applied
Studies, 2007) and maternal smoking during preg-
nancy (SDP) is robustly associated with numerous
adverse outcomes in offspring, making it a significant
public health concern. These outcomes include peri-
natal health problems, such as lower birthweight (Rice
et al. 2009; Thapar et al. 2009), spontaneous abortion,
fetal mortality and sudden infant death syndrome
(see Ernst et al. 2001 for review). Maternal SDP is
also associated with psychological problems, such as
cognitive delays (Batty et al. 2006; Lambe et al. 2006;
Lundberg et al. 2010), attention-deficit hyperactivity
disorder (ADHD; Thapar et al. 2009; Lindblad &
Hjern, 2010), conduct disorder (CD; Silberg et al. 2003;
Brion et al. 2010), antisocial behavior (ASB; Rice et al.
2009; D’Onofrio et al. 2010a; Paradis et al. 2010) and
substance use disorders (Brennan et al. 2002). Research
has consistently supported a causal relationship for
SDP with many perinatal health problems, but evi-
dence has been inconsistent for its relationship with
psychological problems (Rice et al. 2009; Thapar et al.
Several studies evaluating the associations between
SDP and psychological problems have investigated
potential confounds. These studies have shown that
* Address for correspondence: Dr B. M. D’Onofrio, Department
of Psychological and Brain Sciences, Indiana University, 1101 East
10th St, Bloomington, IN 47405, USA.
Preliminary analyses of this work were presented at the Behavior
Genetics Association Conference (June 2011, Newport, Rhode Island).
Psychological Medicine (2012), 42, 1547–1557.
f Cambridge University Press 2011
SDP is not an isolated risk factor, but rather mothers
engaging in SDP also have lower levels of educational
attainment (Gilman et al. 2008a), less annual income
(Maughan et al. 2004; Monuteaux et al. 2006), more
substance use problems (Batty et al. 2006), engagement
in ASB (Maughan et al. 2004) and a greater probability
of having children with men engaging in ASB
(Maughan et al. 2004). Thus, to fully test whether these
relationships are causal, more rigorous studies ac-
counting for confounds between SDP and psycho-
logical outcomes have been necessary (Rutter et al.
Among the research accounting for such confounds,
there seems to be a pattern in which studies account-
ing for specific, measured confounds [e.g. parental
education and socio-economic status (SES)] show
attenuated but still significant associations between
SDP and psychological outcomes (Kandel et al. 1994;
Weissman et al. 1999; Wakschlag et al. 2006; Langley
et al. 2007; Neuman et al. 2007; Wiebe et al. 2009;
Ekblad et al. 2010; Espy et al. 2010; Wakschlag et al.
2010). However, studies accounting for general, un-
measured familial confounds (i.e. capturing all genetic
and environmental factors) show these associations to
be fully attenuated (Silberg et al. 2003; Gilman et al.
2008b; D’Onofrio et al. 2010a,b; Kuja-Halkola et al.
2010; Lindblad & Hjern, 2010; Lundberg et al. 2010).
For example, Silberg et al. (2003) found a model of
intergenerational transmission of CD liability (i.e. off-
spring liability due to the presence of maternal CD) to
better fit data than a model of direct effects from SDP
on offspring CD liability. Consistent with these find-
ings, in vitro fertilization studies, in which mothers
were not biologically related to the offspring but pro-
vided the prenatal and postnatal environments, have
found no relationship between SDP and ADHD or
ASB (Rice et al. 2009; Thapar et al. 2009). Thus, what
was once considered a causal relationship seems better
explained by familial confounds.
The quasi-experimental research suggests that the
specific, measured confounds explicitly included in
many epidemiological studies are only part of the
picture. Identifying the familial confounds is crucial
to elucidating the association between SDP and its
psychological correlates, allowing research to move
beyond the uncertainty of the nature of these re-
lationships (Rutter et al. 2001). The aim of the current
study was to facilitate the identification of such
familial confounds by determining the degree to
which genetic and environmental factors account for
the relationship between SDP and behavioral corre-
lates of maternal SDP–maternal ASB, substance use
problems, and other maternal risk factors for offspring
(teen pregnancy, cohabitation status and low level of
The current study disentangled these relationships
using a large, population-based sample, which is
particularly beneficial for investigating low base rate
behavior (e.g. 0.9% of women are convicted of violent
crimes; Frisell et al. 2011). In addition, family members
with varying degrees of genetic relatedness were
identified to test multivariate behavioral genetic
models. To our knowledge, only one other published
study has included SDP in a multivariate behavioral
genetic model (Agrawal et al. 2008). In that study,
genetic factors accounted for 34% of the variance in
SDP and 42% of the covariance between SDP and
nicotine dependence. Given that the phenotypes in the
current study (e.g. externalizing outcomes) are less
related to SDP than nicotine dependence, we hypo-
thesized that genetic factors would account for a
smaller, but significant, proportion of the covariance
in all multivariate behavioral genetic models. This
hypothesis is consistent with multivariate research
showing that externalizing disorders have a common
underlying factor that is primarily composed of gen-
etic influences (Krueger et al. 2002). This hypothesis is
also consistent with a passive gene–environment cor-
relation, wherein mothers are providing the prenatal
environment, in addition to the postnatal environment
and genetic transmission of other risk factors (Plomin
et al. 1977).
We analyzed a population-based sample, based on
data from multiple nationwide registers maintained
by Swedish government agencies and research in-
stitutes. The information in these registers was linked
using a unique identification (ID) number assigned to
each individual. In addition, ID numbers of family
members (e.g. biological parents, offspring) were
available, allowing familial relationships (e.g. sibling)
and genetic relatedness (e.g. sharing one or both
parents) to be determined.
The Multi-Generation Register contains identifying
information (e.g. ID number) of the biological and
adoptive parents of each child born in Sweden since
1932 (Statistics Sweden, 2006). The Swedish Medical
Birth Register contains data collected throughout
the pregnancy and at childbirth for over 99% of all
births in Sweden since 1973 (Centre for Epidemiology,
2003). Data were merged from the Swedish Medical
Birth Register and the Multi-Generation Register to
match each mother with her children and pregnancy/
childbirth data (e.g. maternal SDP, maternal age at
1548 J. M. Ellingson et al.
birth, cohabitation status) and to match each mother
with her own parents and sisters.
Data for co-occurring risk factors
The Swedish National Crime Register, held by the
National Council for Crime Prevention, contains in-
formation about the nature of every conviction in
Sweden since 1973, including data on the number of
offenses, date of the crime, and sentencing. The
Hospital Discharge Register contains information
about the nature of hospitalizations in Sweden since
1973, including psychiatric diagnoses from the ICD-10
(WHO, 1992; Centre for Epidemiology, 2005). The
Register of Education contains information about
the highest level of educational attainment for each
individual since 1990 (Statistics Sweden). Data were
merged from the National Crime Register, Hospital
Discharge Register and Register of Education to
obtain data for maternal criminal, psychiatric and
educational phenotypes respectively.
Data for exclusion criteria
The Cause of Death Register, kept by the National
Board of Health and Welfare, contains information
about all registered deaths since 1952. The Migration
Register, held by Statistics Sweden, contains infor-
mation from registered migrations, including dates of
immigrating to, or emigrating from, Sweden. Data
were merged from the Cause of Death Register and the
Migration Register to determine which individuals
were deceased or had emigrated and should be
excluded from data analyses.
Several inclusion criteria were applied to the sample.
First, given this study’s focus on maternal SDP, par-
ticipants were restricted to females with at least one
biological child born after SDP data became available
in 1982. Birth-related data (e.g. SDP, maternal age)
were retained from the first childbirth of each mother.
There were 1600609 mothers for whom such data
were available. Second, mothers born after 1995 were
excluded from analyses, as they had not yet entered
the high-risk period for some co-occurring risk factors
(e.g. substance use problems) as of the last wave
of data collection. Third, individuals belonging to a
multiple birth set (e.g. twins, triplets), or who either
were deceased or had emigrated out of Sweden as of
the last wave of data collection, were excluded from
analyses. Therefore, the current sample comprised
mothers who were born before 1995, had given birth in
Sweden after 1982, and were still living in Sweden as
of 2009. There were 1193080 mothers meeting the
The ID numbers of each mother’s parents (i.e.
maternal and paternal ID numbers) were then used
to construct families. First, mothers with common
maternal ID numbers were grouped into maternal
families (i.e. sisters with the same mother were
grouped together). There were 924946 maternal
families available in the data set. Second, the two old-
est sisters of each maternal family were identified and
retained for subsequent steps; that is, each family
consisted of the two oldest sisters who had at least one
biological child. Third, paternal ID numbers were
used to determine the genetic relatedness of each sister
pair. Full sisters were identified as having the same
paternal ID number (i.e. sharing 50% of segregating
genes) and half sisters were identified as having dif-
ferent paternal ID numbers (i.e. sharing 25% of segre-
gating genes). Sisters without paternal ID numbers
(i.e. for whom genetic relatedness was unknown) or
who were adopted into different families (i.e. sister
pairs that may not have been raised together) were
excluded from analyses.
In total, there were 225676 maternal families with a
sister pair meeting all criteria, of which there were
206313 (91.42%) full- and 19363 (8.58%) half-sister
pairs. Mothers’ average age at the end of follow-up
(2009) was 44.02 (S.D.=8.60) years.
Maternal SDP was assessed by self-report at the first
antenatal visit and measured on a three-point ordinal
scale as a non-smoker (0 cigarettes/day), moderate
smoker (1–9 cigarettes/day) or heavy smoker (o10
cigarettes/day). Self-reports of SDP during antenatal
visits have been shown to be valid compared to retro-
spective self-reports (i.e. after pregnancy; Jacobson
et al. 2002) and bioassays (e.g. serum cotinine levels;
Pickett et al. 2009). For example, a large majority (94%)
of maternal self-reports of non-smoking are in agree-
ment with serum cotinine levels (Lindqvist et al. 2002).
In the current sample, 14.17% of mothers engaged
in moderate SDP and 7.58% engaged in heavy SDP
(21.76% of the total sample). Notably, mothers from
half-sister pairs reported considerably higher rates
of any SDP (33.91%) than those from full-sister
pairs (20.62%), which reflects an increased prevalence
of environmental risk factors (e.g. lower SES) and
adverse offspring outcomes (e.g. poorer educational
outcomes) in blended families (Ginther & Pollak,
Criminal histories were based on the Swedish
Penal Code. Convictions were categorized as violent,
Disentangling SDP and co-occurring risk factors1549
drug-related, substance-related driving or other of-
fenses. In addition, the date of conviction was used to
determine the individual’s age when the crime was
committed. To simplify analyses, only data for the
first conviction of each type of criminal offense were
retained for each individual.
Violent crime was defined as attempted/completed
murder, manslaughter and filicide, aggravated as-
sault, gross violation of a person’s integrity, kidnap-
ping and illegal constraint, illegal coercion and threat,
harassment, aggravated robbery, aggravated arson,
and/or threats or violence against an officer. Drug
crime was defined as offenses related to the manu-
facturing and/or distribution of illicit drugs. Driving
crime was defined as offenses related to operating a
motor vehicle under the influence of a controlled sub-
stance. Other crimes consisted of any non-violent and
non-drug-related conviction. In the current sample of
mothers, there was at least one lifetime conviction
related to violent crime in 1.11%, drug crime in 0.74%,
driving crime in 1.03%, and other crime in 8.65%. In
total, 11.07% had at least one conviction of any type.
Diagnoses during psychiatric hospitalizations were
based on the ICD-10. Only hospitalizations related to
alcohol or drug use were analyzed, as internalizing
disorders have not been associated with SDP (Brion
et al. 2010). Again, the date of discharge from the
hospital was used to determine the individual’s age
when hospitalized, and only the first psychiatric dis-
charge for alcohol- or drug-related hospitalizations
was retained. In the current sample, there was at least
one lifetime hospitalization in 1.33% related to alcohol
use, 0.95% related to drug use, and 1.92% related to
any substance use.
Other maternal risk factors
Maternal teen pregnancy status was determined by
the mother’s age at the birth of her first child. The
average age of first childbirth was 27.04 (S.D.=4.95)
years, and 4.56% of mothers had teen pregnancies.
Maternal cohabitation status was based on whether
the mother reported living with her spouse or partner
at the time of her first childbirth. Of the mothers in the
current study, 6.86% reported not living with a spouse
Education was based on the highest level of edu-
cational attainment for each mother. The Register of
Education categorizes each person into one of seven
levels. Low level of educational attainment was as-
sessed by combining the first two categories (no edu-
cation beyond primary and lower secondary school:
9.50% in the current study).
Logistic regression models were used to identify the
strength of association between SDP and co-occurring
risk factors. Logistic regression models were fitted
using SAS version 9.2 (SAS Institute Inc., USA).
PROC SURVEYLOGISTIC was used to account for familial
clustering. Dummy-coded variables were created to
compare moderate and heavy SDP to no smoking.
In addition, polychoric correlations were used to
determine the within- and cross-sister associations
involving SDP and co-occurring risk factors, with full-
and half-sister dyads being analyzed separately to
help explore the degree to which genetic and en-
vironmental factors may influence each trait and the
associations with SDP (Neale & Cardon, 1992).
Univariate behavioral genetic analyses
Structural equation models (SEMs) were fitted to
estimate the degree to which variance in each pheno-
type is associated with additive genetic (A), common
environmental (C), and unshared environmental (E)
factors. This is done by using genetically informed
data and imposing variance and covariance con-
straints, from which latent variables are assumed to
represent the biometrical (ACE) factors (e.g. con-
straining sibling correlations of the A factors to 0.5
for full siblings and 0.25 for half siblings). Thus,
behavioral genetic models estimated the covariances
between full- (calculated as 0.5*A+C) and half-
sibling pairs (calculated as 0.25*A+C) and the per-
centage of phenotypic variance attributable to the
biometrical factors. This approach is similar to that
of the classical twin study (Neale & Cardon, 1992;
Given that all phenotypes were categorical, thresh-
olds were estimated instead of means for all manifest
variables. Finally, age at the last wave of data collec-
tion was included as a covariate for all phenotypes.
All behavioral genetic analyses were conducted using
Mplus version 6.1 (Muthe ´n & Muthe ´n, 2010).
Bivariate behavioral genetic analyses
Finally, SEMs were fitted to estimate the degree to
which covariances between SDP and co-occurring risk
factors are associated with the biometrical factors.
The bivariate model was based on the Cholesky de-
composition approach (see Fig. 1), from which three
triangular matrices containing parameter estimates for
the biometrical factors are derived (Neale & Cardon,
1992; Loehlin, 1996). In bivariate models, each matrix
contains three elements, two on the diagonal account-
ing for the variance in each phenotype (e.g. SDP and a
1550 J. M. Ellingson et al.
co-occurring risk factor) and one on the off-diagonal
accounting for the covariance between both pheno-
types. Thus, the variances and covariance are decom-
posed into the biometrical factors. Model constraints
and parameterizations were similar to those used in
the univariate model, and age was again included as a
covariate for both phenotypes.
The frequencies of all co-occurring risk factors by level
of SDP engagement and the corresponding odds ratios
(ORs) obtained from logistic regression models are
shown in Table 1. The ORs indicated that the risk
factors were significantly more likely to occur in
mothers engaging in moderate or heavy SDP, relative
to those engaging in no SDP. The largest effects were
for crimes and hospitalizations related to substance
abuse (OR 7.5–13.6), and the smallest effects were for
other crimes (i.e. non-drug, non-violent convictions;
OR 2.2). Of note, teen pregnancy was more strongly
associated with moderate SDP than heavy SDP, which
may be due to teenagers having less time to acquire
more severe smoking habits than older mothers.
Polychoric correlations of SDP with co-occurring
risk factors are shown in Table 2 using within-sister
(e.g. correlations of a mother’s engagement in SDP
with her own criminal convictions) and cross-sister
phenotypic correlations for full- and half-sister pairs
(e.g. correlations of a mother’s engagement in SDP
with her sister’s criminal convictions). The strongest
within-sister correlations were for drug-related con-
victions (r=0.43), driving convictions (r=0.38) and
substance-related psychiatric hospitalizations (r=0.37–
0.39). As expected, the strongest cross-sister correlate
of each mother’s SDP was her sister’s engagement
in SDP (r=0.45 for full, r=0.24 for half). All cross-
sister correlations were higher for full- than half-sister
pairs, suggesting that genetic factors influence SDP
and the association between SDP and each risk factor.
Univariate behavioral genetic analyses
Estimates of the proportion of variance associated
with genetic and environmental factors in SDP and co-
occurring risk factors are presented in Table 3. The
variance in SDP was influenced primarily by additive
genetic (45%) and unshared environmental factors
(53%), with shared environment having a non-
significant influence (2%). All co-occurring risk factors
were most strongly associated with unshared en-
vironmental factors (i.e. environmental factors affect-
ing sisters differently), which accounted for at least
50% of the variance in all phenotypes. In addition, a
substantial amount of variance in all co-occurring
risk factors was due to genetic factors, ranging from
19% (non-cohabitation) to 42% (low level of edu-
cational attainment). Shared environmental factors
(i.e. environmental factors affecting siblings similarly)
were associated with any convictions (6%), teen
pregnancy (7%), non-cohabitation (8%) and low level
of educational attainment (4%), but showed negligible
influences on all other phenotypes. Notably, common
environmental factors were near 10% for some
phenotypes (e.g. violence- and drug-related convict-
ions), but these were low base rate occurrences and
had large standard errors resulting in estimates that
were not significantly different from zero.
Fig. 1. Bivariate behavioral genetic model of genetic and
environmental factors accounting for covariance in maternal
smoking during pregnancy (SDP) and behavioral correlates.
In this study we examined bivariate behavioral genetic
models using the Cholesky decomposition approach. Both
SDP and the co-occurring risk factor entered into each model
were regressed on mother’s age, to account for risk/
opportunity for the phenotype to occur (e.g. criminal
conviction, psychiatric hospitalization). Models were used
to obtain estimates of the proportion of covariance between
SDP and each co-occurring risk factor associated with genetic
and environmental factors. The variances for all latent
variables (A, C, and E) were fixed to 1. Standard errors were
derived from the following equations, which were entered
into a Model Constraint command in Mplus. Adapted from
the Mplus User’s Guide, examples 5.19 and 7.28 (Muthe ´n &
Muthe ´n, 2010). Shared Environment (C) was fixed to zero
in the bivariate models. Subscripts refer to latent variables
and parameters for SDP (s) and the co-occurring risk factor
(c), as well the bivariate parameters (bv). Parameter estimates
were calculated as follows: A=VAcrVAbv, E=VEcrVEbv,
Covariance=A+C(0), H2=A/(A+E)=proportion of
covariance attributed to genetic factors, E2=E/(A+E)=
proportion of covariance attributed to environmental
Disentangling SDP and co-occurring risk factors1551
Bivariate behavioral genetic analyses
Estimates of the proportion of covariance between
SDP and co-occurring risk factors associated with
genetic and environmental factors are presented in
Table 4. Shared environment was fixed to zero for
all models because of its negligible influence on SDP
and to ensure interpretable parameter estimates across
all models (e.g. sums of the biometrical parameters
would account for 100% of the covariance in each
Unshared environmental factors accounted for
the largest proportion of covariance between SDP and
all co-occurring risk factors. Additive genetic factors
were also associated with a significant proportion
of the covariance between SDP and all phenotypes,
as these estimates ranged from 21% (non-violence-,
convictions). The proportion of covariance attributed
to genetic factors was particularly high for substance-
related phenotypes(30–35%), including driving-related
convictions (i.e. driving under the influence) and
substance-related psychiatric hospitalizations. Shared
genetic liability also accounted for a relatively large
proportion of the covariance between SDP and
other co-occurring risk factors (28–35%), such as low
maternal educational attainment. In sum, the genetic
factors that influence a woman’s criminal behavior,
substance abuse and the environment she provides for
her offspring also influence SDP.
The current study used multivariate behavioral gen-
etic models to elucidate the relationship between
Table 1. Frequencies and odds ratios (ORs) of co-occurring risk factors as a function of smoking during pregnancy (SDP) in mothers
Frequency (%) OR (95% CI)
No SDPModerate SDP Heavy SDP Moderate SDPHeavy SDP
Other maternal characteristics
Low educational attainment
CI, Confidence interval.
ORs are calculated relative to the non-SDP group.
Table 2. Cross-trait polychoric correlations of smoking during pregnancy (SDP) with co-occurring risk factors, using within- and cross-
Other maternal risk factors
ViolentDrugDriving Other AnyDrug Alcohol Any
All correlations were conducted with the three-category SDP measure.
1552 J. M. Ellingson et al.
maternal SDP and co-occurring familial risk factors,
which previous studies have indicated are familial in
nature. The co-occurrence of SDP and these risk fac-
tors was largely attributed to environmental factors
(accounting for 65–80% of the covariance), with gen-
etic factors playing a significant role and accounting
for the remaining covariance. As expected, genetic
factors were associated with a smaller proportion of
covariance between SDP and co-occurring risk factors
(21–35%) than previously shown for SDP and nicotine
dependence (42%; Agrawal et al. 2008).
Maternal SDP may be a proxy of behavioral dysre-
gulation, such as problems with delayed gratification
(Metcalfe & Mischel, 1999) or an inability to control
one’s own behavior (e.g. dyscontrol; Widiger &
Sankis, 2000), which may manifest in SDP and the
other risk factors included in the current study (e.g.
CD, substance abuse; Lau et al. 1995; Barkley, 1997).
These results are consistent with previous research
showing that externalizing disorders cluster under a
single latent factor (Krueger & Markon, 2006; Lahey
et al. 2009). Notably, an effect of SDP has not extended
to emotional dysregulation (e.g. internalizing dis-
orders; Wakschlag et al. 2006; Brion et al. 2010), sug-
gesting that the risk factors associated with SDP are
specific to behavioral dysregulation (Thapar et al.
2003; Huijbregts et al. 2008). Furthermore, poor inhi-
bition and difficulty delaying gratification may be
similar to the cognitive deficits found in ADHD
(Solanto et al. 2001) and low levels of educational at-
tainment. Therefore, many of the genetic effects in-
volved in SDP and adverse psychological phenotypes
are probably acting on executive functioning (e.g.
decision making, planning), and the environmental
effects may be counter to the benefits of social and
emotional learning programs (Payton et al. 2000).
Table 3. Parameter estimates of genetic and environmental factors accounting for the
variance of smoking during pregnancy (SDP) and co-occurring risk factors from univariate
behavioral genetic models
0.45 (0.02) 0.02 (0.01)N.S.
Other maternal characteristics
Low educational attainment
Standard errors are given in parentheses.
N.S. denotes parameter estimate non-significant from zero.
Table 4. Parameter estimates of genetic and environmental
factors accounting for the covariance for smoking during
pregnancy (SDP) with co-occurring risk factors from bivariate
behavioral genetic models
Other maternal characteristics
Low educational attainment
Standard errors are given in parentheses.
Disentangling SDP and co-occurring risk factors1553
To our knowledge, only one other multivariate
behavioral genetic study involving SDP has been
conducted to date. Agrawal et al. (2008) conducted a
twin study in which 42% of the covariance between
the SDP and nicotine dependence was attributed to
common genetic factors. Those findings are compar-
able to the substance-related phenotypes analyzed
in the current study, in which the proportion of co-
variance attributed to genetic factors was slightly
smaller (30–35%). Notably, multiple substance-related
phenotypes in the current study were of those most
strongly associated with the genetic influences of SDP.
Although a common factor representing liability for
substance abuse has been identified (as opposed to
several, drug-specific factors; Han et al. 1999), the co-
variance attributed to genetic factors in Agrawal et al.
(2008) may include influences on nicotine sensitivity
and, thus, contribute to a larger proportion of co-
An important strength of the current analyses stems
from using a large, population-based sample, in which
numerous co-occurring risk factors of public interest
are available. Many of the co-occurring risk factors
included in the current study are rare events, requir-
ing a large sample for adequate power in identifying
effects. For example, violent criminal acts are rare oc-
currences in females, as are substance-related psychi-
atric hospitalizations (e.g. 1.9% of the current sample).
Notably, even with a large sample of 225676 families
in the current study, phenotypes with a low base rate
occurrence had relatively large standard errors for
biometrical factors (e.g. see Table 3).
Multiple measures were also available across the
domains of interest in the current study: criminal
convictions, psychiatric hospitalizations and other
characteristics of an offspring’s rearing environment.
This allows parameter comparisons to be made to
identify consistencies or outliers. For example, all
maternal characteristics (maternal teen pregnancy,
cohabitation, educational attainment) had similar
degrees of genetic influence shared with SDP. By
contrast, there was an unexpectedly large difference
between the proportions of covariance attributed to
genetic factors for other crimes (i.e. non-violence-,
non-drug-related; 21%) relative to violence- (34%)
and drug-related crimes (35%). Whether this finding
is anomalous or indicative of differences in causal
underpinnings warrants further investigation.
An inherent characteristic of studies on maternal
SDP, which is a limitation to the current study, is
the use of a sample restricted to females. Specifically,
there are gender differences for the heritability of
externalizing behaviors, with genetic factors having a
stronger influence on these phenotypes in men (Hicks
et al. 2007). Given these differences, the estimates of the
genetic and environmental contributions to the co-
variance between SDP and externalizing phenotypes
may not apply to males. Additional research is there-
fore needed to disentangle how the intergenerational
transmission of maternal SDP may confer risk for
adverse outcomes in male offspring.
These findings also do not identify specific genetic
or environmental factors that contribute to SDP
and/or co-occurring risk factors, and research in the
fields of molecular genetics, neuroscience and the
social sciences is needed to further the progress in this
area. For example, several gene–environment inter-
actions have been identified that involve SDP and may
indicate genes that influence both SDP and these
adverse psychological phenotypes (Neuman et al.
2007; Lotfipour et al. 2009; Wiebe et al. 2009). A basic
knowledge of how these and other genes influence
SDP and behavioral dysregulation is important for
advancing understanding in this area.
Gene–environment interactions may also involve
specific environmental factors, which moderate the
heritability of phenotypes and/or the covariance
among phenotypes associated with genetic factors. For
example, a polymorphism protecting against alcohol
dependence in Japanese populations has been ident-
ified (i.e. aldehyde dehydrogenase, ALDH), but this
protective effect has declined as per capita alcohol
consumption has increased in Japan (Higuchi et al.
1994). In the current study, data span a period when
SDP became increasingly deviant (as the public
awareness of the consequences of SDP increased),
and, consequently, the relationship between SDP
and co-occurring risk factors may have changed. We
investigated this possibility, and the associations be-
tween SDP and co-occurring risk factors were moder-
ated by secular changes. This interaction may be due
to marked changes in the prevalence of SDP over this
period (decreasing from 32% in 1982 to 7% in 2009).
However, there was no difference in the heritability of
SDP when our sample was split into two cohorts:
those giving birth in 1982–1991 (47%) and those giving
birth in 1992–2009 (46%).
A final limitation concerns the phenotypes used in
the current study. Behavioral dysregulation manifests
in numerous phenotypes that range greatly in sever-
ity, but many phenotypes in the current study reflect
severe dysregulation (e.g. violent crime). The current
findings, therefore, cannot be applied to less severe
co-occurring risk factors, and future research should
cover these areas.
Alternative models of SDP
Slotkin (1998) proposed three pathways by which
SDP may influence a developing organism: (1) direct
1554J. M. Ellingson et al.
effects on the maternal–fetal unit, such as hypoxia,
vascular effects, placental effects and malnutrition in
offspring; (2) neurodevelopmental insults causing be-
havioral dysregulation (for instance, through nicotine
exposure); and (3) environmental risk factors co-
occurring with SDP, including lower parental edu-
cational attainment. Much evidence has supported a
causal link between SDP and effects on the maternal–
fetal unit (e.g. increased pregnancy-related problems;
Cnattingius, 2004; Johansson et al. 2009). Rigorously
controlling for familial factors (e.g. the co-occurring
environmental risk factors that Slotkin described),
however, has attenuated the effect of SDP on neuro-
behavioral outcomes, which is inconsistent with the
presence of neurodevelopmental effects (e.g. psycho-
logical problems; Knopik, 2009). The current study
suggests another pathway through which SDP is
associated with offspring psychological problems: co-
occurring genetic risk factors that influence SDP and
the environment in which offspring are reared. Thus,
what Slotkin posited to be an effect of neurodevelop-
mental insults may be, at least partially, due to shared
genetic liability. However, the lack of a direct effect of
SDP on psychological outcomes does not negate the
fact that SDP can lead to fatal consequences for off-
spring through direct effects on the maternal–fetal
The aim of the current study was to further disen-
tangle the relationship between SDP and co-occurring
familial risk factors. The current results suggest that
the genetic factors that influence a woman’s criminal
behavior, substance abuse, and the environment she
provides for her offspring also influence SDP. Thus, it
is possible that the intergenerational transmission of
genes conferring risk for ASB and substance misuse,
at least partially, influence the associations between
maternal SDP and adverse offspring outcomes.
Funding for the study was provided by the National
Institute of Child and Human Development (Grant:
Declaration of Interest
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