Troy University Infant Siblings Psychology Summary Essay

Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
REVIEW
Open Access
Infant siblings and the investigation of autism
risk factors
Craig J Newschaffer1, Lisa A Croen2, M Daniele Fallin3, Irva Hertz-Picciotto4, Danh V Nguyen4, Nora L Lee1*,
Carmen A Berry3, Homayoon Farzadegan3, H Nicole Hess5, Rebecca J Landa6, Susan E Levy7, Maria L Massolo2,
Stacey C Meyerer3, Sandra M Mohammed4, McKenzie C Oliver4, Sally Ozonoff8, Juhi Pandey7, Adam Schroeder4
and Kristine M Shedd-Wise4
Abstract
Infant sibling studies have been at the vanguard of autism spectrum disorders (ASD) research over the past
decade, providing important new knowledge about the earliest emerging signs of ASD and expanding our
understanding of the developmental course of this complex disorder. Studies focused on siblings of children with
ASD also have unrealized potential for contributing to ASD etiologic research. Moving targeted time of enrollment
back from infancy toward conception creates tremendous opportunities for optimally studying risk factors and risk
biomarkers during the pre-, peri- and neonatal periods. By doing so, a traditional sibling study, which already
incorporates close developmental follow-up of at-risk infants through the third year of life, is essentially
reconfigured as an enriched-risk pregnancy cohort study. This review considers the enriched-risk pregnancy cohort
approach of studying infant siblings in the context of current thinking on ASD etiologic mechanisms. It then
discusses the key features of this approach and provides a description of the design and implementation strategy
of one major ASD enriched-risk pregnancy cohort study: the Early Autism Risk Longitudinal Investigation (EARLI).
Keywords: Autism, Cohort, Epidemiology, Pregnancy, Prospective, Sibling, Study Design
Review
Introduction
In 1957, Pearson and Kley published a prescient paper
asserting that neuropsychiatric research should capitalize
on the “tendency of particular abnormalities of behavior
to run in families” (p. 406) so that “subpopulations
defined in terms of genetic relationship to index cases…
might be studied longitudinally…” (p. 418) [1]. They
went on to note that such studies could be effective and
economical for etiologic research. A 1976 review of the
genetics of infantile autism and childhood schizophrenia
[2] highlighted the potential of Pearson and Kley’s highrisk design for etiologic research, but at that time the
only such studies underway were investigations focusing
on children of parents with schizophrenia (reviewed by
Garmezy [3]). In the 1980s, prompted by the 1977 publication of Folstein and Rutter’s seminal autism twin
* Correspondence: [email protected]
1
Department of Epidemiology and Biostatistics, Drexel School of Public
Health, 1505 Race Street, Mail Stop 1033, Philadelphia, PA 19102, USA
Full list of author information is available at the end of the article
study [4], siblings of autism probands increasingly were
included in research samples; however, these were largely cross-sectional family studies in which researchers
looked at recurrence risk and genetic segregation or
linkage, not at prospective investigations where at-risk
siblings were the subjects of principal interest. The first
consideration of the prospective infant sibling study in
autism, according to Yirmiya and Ozonoff, occurred in
the mid-1980s, when US and UK researchers contemplated but rejected the idea because of concerns over
heterogeneity in index proband diagnosis [5]. Once standard diagnostic tools were developed in the early 1990s,
these projects moved forward with a focus firmly on
phenotypic antecedents and very early signs of autism
spectrum disorders (ASDs). Rogers [6] has since
described the discovery of “the first behavioral characteristics that predict development of autism” as the
“Holy Grail” (p. 126) of autism infant siblings research.
Today there are 25 infant sibling research teams that
are part of the High Risk Baby Siblings Research Consortium (BSRC) (Autism Speaks, Research on High Risk
© 2012 Newschaffer et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
Baby Sibs: http://www.autismspeaks.org/science/initiatives/high-risk-baby-sibs), a voluntary network of projects with funding support from the National Institutes
of Health and Autism Speaks, united in their common
purpose of pursuing early phenotypic predictors of autism. The first papers derived from these efforts began to
appear in 2005 and 2006 [7-10], and findings on emergence and trajectory of a range of developmental outcomes in high-risk infant sibling cohorts have been
published steadily in the literature ever since.
Although discovering robust early phenotypic markers
for autism to facilitate early detection and intervention
remains a major autism research goal and an appropriate
priority for infant sibling studies, the application of the
high-risk infant sibling study design in autism etiology
research has been underexplored. In this paper, we consider the role that infant sibling designs can play in autism
risk factor research in the context of the evolving understanding of autism etiology and describe the design and
methods which are being employed by a major high-risk
sibling cohort study focused on autism etiology: the Early
Autism Risk Longitudinal Investigation (EARLI).
Current thinking on etiologic mechanisms in autism
For decades, multiple lines of evidence have supported a
substantial heritable component of autism etiology, including twin studies [4,11-14], familial risk studies [15-24], segregation analyses [24-27] and reported correlations
between autism phenotypes and other congenital genetic
disorders [28-32]. Modern genomic methods applied
extensively to a variety of autism samples over the past
decade have underscored the complexity of autism inheritance. A number of rare variants, for the most part de
novo or inherited copy number variations (CNVs) [33-35],
have been linked to autism by virtue of their apparent
high penetrance. The teams leading three major autism
genomewide association studies (GWASs) [33,36,37] have
generated additional candidate genes, but have failed to
replicate each other’s findings. Consequently, lists of plausible autism candidate genes now include well over 100
genes [38,39], including common genetic variants likely to
have very small independent effects but potentially contributing to mechanisms with larger effects by interacting
with each other or with rare genetic events [40]. Therefore, ongoing efforts are focused on the use of sophisticated analytic techniques applied to genomic data to
identify common, biologically plausible pathways along
which gene-gene interactions may take place [41-44].
In addition to an emphasis on gene-gene interactions,
nearly all recent comprehensive reviews of autism genetics have cited interplay between genetic mechanisms and
environmental exposures as another plausible contributor to the complexity of autism etiology [45-52]. The
recent twin study conducted by Hallmayer et al. [53],
Page 2 of 16
which was larger than all its predecessors and the first
done with autism cases confirmed using today’s diagnostic tools, suggested a far larger role for environmental
factors than did any earlier twin study. Furthermore, the
fact that dizygotic twin concordance in their study was
substantially larger than nontwin sibling recurrence risk
reported in a recent large study of infant siblings [54]
points to the prenatal period specifically as a period of
special interest with respect to environmental influences.
A potential role for epigenetic mechanisms in autism
etiology [55,56] also suggests additional ways in which
environmental exposures can work in concert with genomic factors [57]. The need to move forward with more
extensive investigation of environmental risk factors in
autism is now widely accepted.
For most of the past 40 years, the investigation of environmental exposures has been sporadic. Several studies
have provided evidence of highly elevated risk arising from
congenital exposure to rubella [58,59] and cytomegalovirus [60]. Similarly, some pharmacologic exposures in the
prenatal period have been linked to autism, including thalidomide [61] and valproic acid [62-64]. More recent epidemiologic research has underscored the prenatal period
as the most relevant etiologic window for autism environmental risk factors. For example, large studies have continued to find associations of autism risk with prenatal
medication use [65,66] and infection [67]. Consistent with
the infection finding, investigators in a small case-control
study who capitalized on banked midpregnancy blood
samples reported more frequent elevations in certain circulating inflammatory cytokines in mothers of children
with autism than controls [68]. The first system-level analysis of the ASD brain transcriptome, in addition to an
expected finding of synaptic dysfunction, has also suggested the presence of immune dysregulation [69], which
is consistent with an earlier finding of neuroinflammation
in the brains of individuals with autism [70]. However,
whether these indications derived from autopsy studies
reflect antecedent and potentially causal immunemediated events or downstream responses to other autism
neuropathology is not yet clear.
Recently published systematic reviews of traditional
obstetric and neonatal risk factors and autism reported
that, for most of the individual risk factors considered
(for example, contraception prior to pregnancy, maternal
obstetric history, bleeding in pregnancy, gestational diabetes), either insufficient data were available or findings
have not been well-replicated in the published literature
[71-73]. This is not entirely unexpected, as many past
studies have been based on small clinical samples without
confirmation of diagnoses. However, the one factor with
the most consistent association with increased autism
risk across multiple studies is advanced parental age
[74-80]. A variety of mechanisms might explain these
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
associations, such as increased maternal complications
during pregnancy or delivery, an accumulation of toxins
affecting either the intrauterine environment or sperm
development, and induced de novo mutation, of particular interest in the case of older fathers.
An interesting obstetric risk factor examined only
recently with respect to autism is interpregnancy interval. An interval of less than one year between pregnancies was found in an initial report to be associated with
more than a threefold increase in autism risk compared
to intervals of three or more years (OR = 3.4, 95% CI =
3.00 to 3.82). If short interpregnancy interval is an autism risk factor, it could implicate the intrauterine environment through nutritional depletion mechanisms [81].
Indeed, researchers in a large case-control investigation
have reported intake of prenatal vitamin supplements in
the periconception period (three months prior and one
month after conception) to confer nearly a 40% reduction in risk (OR = 0.62, 95% CI = 0.42 to 0.93) [82].
This study was also notable because it contains the only
published results to date explicitly supporting a geneenvironment interaction in autism with the apparent
protection from maternal prenatal vitamin use magnified
in the presence of certain genotypes involved in onecarbon metabolism [82].
Several investigations have examined air pollution, a
complex mixture of exposures with wide-ranging toxicities, in relation to autism diagnoses. The designs of
these ranged from a purely ecologic design that focused
on industrial emissions of a single pollutant [83], to
investigations that utilized individual-level diagnostic
information in relation to modeled estimates of 25
hazardous air pollutants [84,85], to distance to freeway,
a strong indicator of ambient traffic-related pollutant
levels [86]. This most recent study, a case-control design
using clinically confirmed cases and individual-level
exposure information, found living within one-quarter
mile of a freeway at the time of delivery was associated
with a 1.9-fold increased ASD risk (95% CI = 1.04 to
3.45). Researchers in earlier studies had used exposures
occurring in the second year of life or later, which
might not be the most etiologically relevant period.
Investigators in a number of other studies have also
explored potential associations between autism diagnosis
or autism-related phenotypes and pesticide exposure in
the prenatal period. Residence in a location where application of organochlorine pesticides reached levels falling
into the highest nonzero quartile during the eight-week
pregnancy period after closure of the cranial neural tube
was associated with a sixfold higher odds ratio of the
child’s developing autism (OR = 6.1, 95% CI = 2.4 to
15.3) [87]. In a cohort study of primarily MexicanAmerican women, higher levels of metabolites for organophosphate pesticides were found to predict higher
Page 3 of 16
scores on an autism-related scale in 24-month-olds [88].
Studies attempting to replicate these findings are
needed, though both types of compounds are plausibly
linked to altered central nervous system development
through endocrine disruption for the long-lasting organochlorines and through direct toxicity to the developing brain for the rapidly cleared organophosphates.
Other commonly used pesticides have been associated
with general neurodevelopmental deficits in prospective
pregnancy cohorts [89-91] but have yet to be studied in
autism.
With epidemiologic evidence consistently pointing to
the prenatal period as a window of vulnerability to
environmental exposures in autism, one might ask
whether this is consistent with known autism neuropathology. Indeed, pathologic changes documented in
autopsied brains of individuals with autism, including
those found in the brainstem [92], cerebellum [93,94]
and cortex [95], are indicative of a pathologic process
originating in utero. Early brain overgrowth in autism,
now documented in two longitudinal brain imaging studies [96,97], also suggests the presence of causal events
occurring prior to birth, as does the recent brain transcriptomics report that autism brains lacked a pattern of
differential gene expression across frontal and temporal
cortical regions [69] that typically emerges during fetal
development [98].
Could prenatal causal events be linked to exogenous
exposures? It has long been established that prenatal brain
development, including the fundamental processes of neuronal proliferation, migration, differentiation, synaptogenesis, gliogenesis, myelination and apoptosis, are susceptible
to disruption by environmental exposures [99,100]. Subsequently, each of these fundamental processes has been
considered in alternative models of autism pathology
[101]. Some of the more recent work geared toward using
autism genomics to identify biologic pathways has implicated synaptic homeostasis as a candidate common biological process in autism [102]. Although synapse formation
begins in the third trimester, with synapse restructuring
and connectivity development continuing well into postnatal life, animal models have shown that environmental
exposures earlier in pregnancy can lead to impaired postnatal synaptic activity without obvious signs of disruption
prenatally [103]. In addition, other genomics efforts
focused on pathway detection, one using GWAS data and
the other CNV data, have independently implicated
impaired neuronal projection and axonal guidance [35,44]
as mechanisms of chief interest. These are environmentally sensitive processes beginning early in brain development [104,105] that could certainly affect synaptic
functioning downstream. Of course, the high likelihood of
an in utero origin of autism in no way rules out the potential for etiologic and prognostic influences after birth, but
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
the design and implementation of etiologic research
focused on the prenatal period would appear strongly
justified.
Expanding the infant siblings approach to study autism
etiology
Infant sibling cohort studies, as implemented by members
of the BSRC, enroll subjects younger than 18 months of
age (many as young as 6 months of age) and carry out
close longitudinal developmental follow-up, typically
through 3 years of age. The design choice was motivated
in part by expected recurrence rates that were many times
higher than population autism prevalence and in part by
the opportunity to observe early behavioral markers and
better understand the complex early natural history of
ASDs afforded by carefully measuring development prospectively. Both of these considerations are also quite germane to etiologic research. Yet, to maximally capitalize on
infant sibling designs for etiologic research, it is necessary
to extend cohort enrollment back to a point where the
mother and the developing fetus can be followed prospectively through windows of potential etiologic vulnerability;
in other words, by transforming the design to a high-risk
pregnancy cohort. Each of these three features, increased
event rate, prospective developmental assessment and shift
to a pregnancy cohort design, is each discussed further
below.
Increased event rates
When the BSRC was formed, published estimates for sibling recurrence risk ranged from 2% to 9% [13,15-22,
24,106]. Almost all of these studies considered recurrence
of the more narrow autistic disorder diagnosis among siblings of a proband with autistic disorder, although the one
study of probands and siblings with any autism spectrum
diagnosis reported recurrence within the same range
(5.3%) [106]. In 2011, the BSRC published their first findings on ASD recurrence among 684 siblings of probands
with an ASD followed from at least 18 months until at
least 36 months [54]. In this large, recently ascertained
sample, recurrence was 18.7% (95% CI = 13.3% to 25.5%).
Even with recent population ASD prevalence estimates
approaching 1% [107], this implied 20-fold increase in sibling risk translates into increased numbers of cases in an
enriched-risk sibling cohort, which increases power to
detect associations between risk factors and ASD case status. In addition, the presence of higher levels of subthreshold impairment in toddler-age siblings of ASD probands
has been documented. For example, Toth et al. [108]
found significant differences in expressive and receptive
language, composite IQ, adaptive behavior and social communication skills when they compared (1) 42 toddler-age
siblings of ASD probands who did not meet ASD criteria
based on the toddler-version Autism Diagnostic Interview-Revised (ADI-R) [109] and the Autism Diagnostic
Page 4 of 16
Observation Schedule (ADOS) [110] to (2) 20 typically
developing toddlers with no ASD family history. This suggests that there is a considerable range of impairment in
the infant sibling cohort which should translate into
increased power for risk factor analyses using dimensional
as opposed to categorical phenotypic outcomes. Last, for
conditions such as ASD where complex genetic mechanisms underlie increased baseline risk in the infant sibling
sample, if a risk factor’s effect is amplified by an unknown
genotype or genotypes, the power to detect that risk factor
is affected favorably [111].
Prospective developmental assessment
BSRC studies prospectively evaluate a range of developmental end points, including motor development, repetitive behaviors and abnormal movement patterns, social
and emotional development, and response to joint attention [6]. The prospective developmental assessment in
infant sibling studies can support etiologic research in two
ways.
First, it allows for careful characterization of autismrelated dimensional phenotypes at early ages. As mentioned above, dimensional end points may prove revealing
in ASD etiologic research, and perhaps studying variance
of traits expressed very early in life could be the most
revealing. To date, the dimensional measures used in etiologic research have been those developed from assessment
of older children [112-114]. Should measures that are now
being used in BSRC studies such as the Autism Observation Scale in Infants (AOSI) [115] provide valid early measurement of quantitative traits related to autism, enriched
risk pregnancy cohort studies could incorporate these and
utilize them as continuous end points in risk factor
analyses.
Second, the longitudinal characterization of development could lead to the identification of distinct developmental trajectories which might themselves be considered
as outcomes or could be used to stratify cases to test
hypotheses that cases with different developmental trajectories could have distinct sets of risk factors. Landa and
Garrett-Mayer [10] have already examined trajectories
within high-risk siblings on a range of items measured by
the Mullen Scales of Early Learning [116,117] among
those meeting or not meeting research criteria for ASD at
24 months and found generally flatter trajectories in the
group meeting these criteria, although they noted different
patterns in different domains, such as the ASD group’s
deviating at 14 months on fine motor performance. Rozga
et al. [118] reported no differences at six months of age in
joint attention and requesting behaviors between high-risk
siblings who went on to meet criteria for ASD and those
who did not, but they found an emergence of differences
at age 12 months. Another recent report, however, found
head lag at 6 months of age to be predictive of social and
communication impairment in high-risk siblings at age
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
36 months [119]. As the size of infant sibling cohorts
grows, BSRC investigative teams will be able to employ
more sophisticated analyses to identify unique developmental trajectories, both within and across groups defined
by whether ASD criteria are met.
Shift to a pregnancy cohort design
The returns from expanding infant sibling research can be
amplified with the shift to a pregnancy cohort design. This
approach allows for the prospective collection of detailed
risk factor data during the critical etiologic windows, as
opposed to retrospective collection that would be necessary if cohorts of siblings enrolled as toddlers were used
for risk factor research. For a number of risk factors of
general interest in the prenatal and neonatal periods, validation studies have demonstrated superiority of prospective versus retrospective data collection. For example,
retrospective recall of depressive symptoms in pregnancy
at just six months postpartum compared to prospective
documentation showed only moderate agreement [120].
Recall of prenatal influenza infection symptoms (for example, persistent cough and fever) at delivery suggest underreporting compared to questionnaires completed between
the 18th and 25th weeks of pregnancy [121]. Furthermore,
there are concerns that parents of affected children will
recall exposures during the prenatal period differently
from parents of unaffected children, a phenomenon documented for certain exposures with respect to birth defects
outcomes [122].
Certain findings that have begun to emerge regarding
potential environmental risk factors for autism have limitations with respect to exposure measurement that could
be obviated through prospective data collection in an
expanded infant sibling study design. For example, one
study of air pollution used modeled estimates covering the
second year after birth [84]. Although the associations that
were observed could contribute to the development of autism, exposures in earlier years (for example, during gestation or the first year after birth) might be of greater
relevance. Other retrospective epidemiologic investigations
have explicitly considered exposure during the prenatal
period but have been limited to maternal residence at the
time of birth as a proxy for exposure. For example, one
retrospective study modeled prenatal exposure to pesticide
based on distance from residence to reported date and
location of agricultural applications of pesticides, but there
was no individual-level measurement of exposure available
[87]. The extent to which modeled exposure reflects actual
exposure is a major question, with factors such as address
changes, time spent at home, wind speed and drift, as well
as absence of data on other exposure sources, contributing
to potential misclassification. In addition, a number of studies have used administrative and medical databases to
examine maternal prenatal use of medications. These data
sources provide unbiased assessment for case-control
Page 5 of 16
comparisons and can address the relevant time periods,
but they do not include data on over-the-counter medication use and do not take into account the fact that not all
prescriptions are filled and not all filled prescription medications are actually taken by the patient. The infant sibling
pregnancy cohort design has the potential to combine selfreport data on actual use of medications with medical
records documentation on prescriptions from all sources.
The shift to a prospective cohort design also creates
opportunities for implementation of cost-effective analytic
strategies. When, for example, laboratory assays need to
be completed on stored biologic specimens to generate
risk factor data, these assays can be done on select subsamples from the cohort to conserve resources. Analyses
can be limited to identified cases contrasted to a sample of
noncases selected from the cohort at the time of case
occurrence (incidence-density matched case-control
design), a sample of noncases selected at the end of follow-up (cumulative incidence case-control design) or a
sample of cohort members at baseline (case-cohort
design). Such designs can often achieve close to comparable statistical power to analyses of data from the entire
cohort [123]. Furthermore, when data on the full cohort
can be used to inform the sampling of cases and controls
(for example, two-stage or countermatching designs),
additional statistical efficiencies can be achieved [124,125].
Alternatively, subsamples can be selected for more
resource-intensive risk factor data collection (for example,
through biomarkers or medical records abstraction), and
the data derived from these subsamples can be used to
correct measures of association based on risk factor data
available in the full cohort or in case-control samples
drawn from the full cohort [126].
The current thinking on autism etiology, where causal
mechanisms are believed to be complex and multifactorial, is that a role for environmental factors is quite likely.
However, research on environmental risk factors for autism faces significant challenges. Critical periods may
occur early in brain development, and accurate measurement of environmental exposure during these critical
periods will be important if causal contributions of these
factors in the context of other contributors are to be
identified. Etiologic heterogeneity in autism is likely, and
identification of phenotypic correlates that mark distinct
etiologies or, perhaps more realistically, can serve as useful endophenotypes for identifying certain causal components, is an active area of ongoing research. Given this
situation, the expansion of infant sibling study designs
for etiologic research where exposure data are captured
prospectively during potentially relevant critical windows,
outcomes are also prospectively characterized in detail,
and event rates are higher than in population-based samples would appear to be one quite useful research
approach. The sections that follow provide an overview
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
of a large multisite investigation now underway that is
implementing this study design.
The EARLI Study as a model for risk factor research using
a high-risk infant sibling design
The EARLI Study was conceived to capitalize on expanding the infant siblings approach to autism etiologic
research to realize many of the benefits described above.
At least one other autism enriched-risk pregnancy cohort
study that has many features in common with EARLI is
now underway (the Markers of Autism Risk in BabiesLearning Early Signs (MARBLES) study: http://marbles.
ucdavis.edu/). Below the EARLI research design, study
population, recruitment and enrollment approach, risk
factor data collection and outcomes evaluation strategy
are described. Other investigators looking to replicate or
incorporate aspects of this approach may benefit from the
information provided herein. The EARLI Study parent
protocol was reviewed and approved by the Drexel University Institutional Review Board (Project no. 71109; Protocol no. 17862). Local IRB approvals were also obtained
at all EARLI Study sites. All study participants have undergone the consent process and signed the relevant consent
or assent forms as appropriate for age and cognitive ability, or have had a parent consent on their behalf.
Study design
The EARLI Study is a multisite prospective pregnancy
cohort study of mothers of children with an ASD diagnosis (autistic disorder, Asperger syndrome or pervasive
developmental disorder not otherwise specified (PDD
NOS)) who have become pregnant. Mothers are followed
through pregnancy and delivery, then the pregnancy
cohort evolves into an infant siblings cohort followed
through age three years. Eligible pregnant women are
enrolled by the 28th week of pregnancy, along with their
biological child who has an ASD (the study proband). The
biological father of the current pregnancy is also invited to
enroll, although his participation is not a requirement for
the participation of the mother and proband. Enrolled
mothers are followed closely with intensive data and biosample collection. During the pregnancy, two to four
study visits occur, which entail collection of serial biological samples from the mother and dust samples from the
home. At the time of delivery, placental tissue, cord blood,
heel stick blood and meconium are collected. Serial biological samples are collected postpartum from both the
mother and the newborn baby (the study sibling). Clinical
evaluations are conducted four times, beginning at six
months of age and concluding when the child is three
years old. The evaluations include autism and behavioral
assessments and dysmorphology examinations. At the
final study visit when the child is 36 months of age, the
sibling’s ASD status is determined for all participants,
although individual diagnoses may have been made earlier,
Page 6 of 16
depending on when symptoms emerged. Throughout participation in the study (prenatally and postdelivery) selfreport data are collected from the mothers by using
EARLI instruments and interviews that cover health behaviors, diet, reproductive and medical history, stress,
depression, environmental and occupational exposures
and medication use. Additional data are collected from the
mother about the sibling during the first three years of life
regarding general health, medications and medical care,
specific symptoms or illnesses, diet, environmental exposures and developmental interventions. The EARLI Study
is on pace to enroll 870 families over a 6-year period with
plans in place to acquire follow-up data through 36
months from 630 of these families. Figure 1 shows key elements in EARLI Study data collection over the course of a
family’s participation
Study population
Women who meet the following criteria are eligible to
participate in the EARLI Study: (1) have a biological
child who has been diagnosed with an ASD, (2) competent to communicate in English (or, at two sites, in
Spanish), (3) 18 years of age or older, (4) live within two
hours of a study clinic and (5) are no more than 28
weeks pregnant. Women who meet the first four criteria
and are not pregnant but trying to become pregnant or
may become pregnant in the future (for example,
unplanned pregnancy) may be followed and contacted
regularly to ascertain their reproductive status. If they
become pregnant during this preenrollment period, they
can be rescreened for eligibility to enroll.
The EARLI Study is being implemented at four field
sites in three distinct locations in the US, representing a
racially, ethnically, and socioeconomically diverse study
population (Table 1). The sites are based in major
metropolitan areas (that is, Philadelphia, Baltimore, San
Francisco Bay Area, and Sacramento) and catchment
areas expand to a 2-hour radius of the study clinic at
each site. Table 1 lists the range of county level demographics within the catchment areas.
Participant recruitment
Recruitment strategies vary by field site to accommodate
the unique resources available to each site. Generally,
the target population is mothers with a young child (2
to 12 years old) with ASD, who would be more likely to
become pregnant again than a mother with an older
child. For example, for the Pennsylvania and Maryland
sites, a primary strategy for reaching potentially eligible
mothers is distribution of information through the early
intervention and special education systems. The northern California site at the University of California, Davis,
identifies and reaches potentially eligible mothers primarily through the state’s Department of Developmental
Services, whereas the Kaiser Permanente site in northern California can identify Kaiser Permanente members
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
Page 7 of 16
Figure 1 Early Autism Risk Longitudinal Investigation Study data collection points over the course of participation.
who become newly pregnant and already have children
with autism. Clinical service providers in the catchment
areas, including ASD evaluation and diagnostic centers,
developmental pediatricians and mental health service
providers, are also engaged at each site to reach potentially eligible women. The researchers in the EARLI
Study have not focused on establishing relationships
with providers serving the general population of pregnant women (for example, obstetricians, nurse midwives) or children (for example, general pediatricians)
for individual-level outreach but will make available general information about the EARLI Study as requested.
All field sites carry out supplementary recruitment
efforts through staffed information tables at autism
events and through advocacy organizations’ websites,
listservs and newsletters. Reinforcing information about
the EARLI Study through multiple channels increases
the chance that mothers of reproductive age who have
children with ASD, at a later time when they are pregnant, will remember and consider enrolling in the
EARLI Study. To that end, EARLI also maintains an
active presence in social media, including Facebook
[127] and YouTube [128] and has a web presence [129]
that includes content geared toward potentially eligible
mothers as well as enrolled participants. Given this
recruitment approach, it is possible that participating
families might differ from their respective area source
population on factors related to the extent of connection
to service systems and the degree of immersion in the
autism community. To introduce bias, such selection
needs to be differential with respect to both exposure
and outcome. Although these selection effects could be
associated with certain exposure profiles of interest,
independent associations with ASD risk, though conceivable, seem less likely. EARLI Study sites, to varying
degrees, will be able to explore differences between participating families and source populations. All sites can
compare basic characteristics of participating families
with families in the region receiving services for a young
child with ASD, but only the Kaiser Permanente site has
the ability to identify the subgroup in the source population who are becoming pregnant.
Enrollment and retention
When a pregnant mother of a child with autism begins
the enrollment process, proband diagnosis first needs to
be confirmed. In families with more than one child with
Table 1 Range of the percentage of demographic characteristics among the counties in EARLI Study field site areasa
Characteristics
County Low and High Percentages within Field Site Catchment Areas
SE Pennsylvania (13 counties in NE Maryland (9
N. California (25
PA, NJ, DE)
counties in MD)
counties in CA)
White
41.0% to 89.2%
19.2% to 92.9%
43.0% to 91.4%
Black or African-American
3.6% to 43.4%
3.2% to 64.5%
0.4% to 14.7%
Asian
0.8% to 8.9%
1.1% to 14.4%
1.1% to 33.3%
Hispanic or Latino
3.0% to 18.8%
2.6% to 17.0%
8.5% to 55.4%
Language other than English spoken at home (5 years
or older)b
7.4% to 24.8%
5.2% to 35.8%
6.5% to 51.2%
Families below povertyc
4.2% to 22.9%
4.5% to 22.9%
7.2% to 24.6%
25+ years old with educational attainment 9th to 12th
grade, no diploma†
7.8% to 28.8%
5.7% to 31.6%
6.6% to 33.0%
a
Data are derived from the 2010 US census. b2005 to 2009 for all counties; 2000 for Philadelphia City and Baltimore City. c2009 for all counties; 1999 for
Philadelphia City and Baltimore City. EARLI, Early Autism Risk Longitudinal Investigation.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
ASD, the study proband is the child who is the closest
biologic relation to the future sibling, or, if both are the
same biologic relation to the future sibling, then a child
with an autistic disorder diagnosis would be enrolled
over a child with Asperger syndrome or PDD NOS. The
mother is enrolled in the study after consenting at the
enrollment clinic visit, and the study proband’s eligibility
is confirmed by a valid ADOS [109] and an age-appropriate IQ test (for example, Mullen Scales of Early
Learning for infancy to 68 months of age; or the Kaufman Brief Intelligence Test, Second Edition, for ages 4
years to 90 years). Fathers may enroll at the enrollment
clinic visit, or during a home visit if they are not at the
enrollment clinic visit.
As Figure 1 illustrates, the EARLI Study involves extensive data collection, so investigators strive to maximize
retention by being as flexible as possible. Both online and
paper versions of most questionnaires and documents are
available, and home visits and flexible visit scheduling is
accommodated when possible. Of the first 177 families
enrolled in the study, 97.2% are still participating. Variability in the time of data collection creates challenges and
opportunities for research but is also a reality of intensive,
prospective follow-up of this study population. Retention
in the EARLI Study is also driven by the prospective developmental follow-up offered for the at-risk siblings. EARLI
Study sites provide families with summaries of research
evaluations, discuss questions with families, provide information on local resources for families concerned with
their child’s development and make referrals for services
for affected siblings. Upon enrollment, the EARLI Study
also provides families with a specially developed social
storybook about the impending arrival of a baby sibling
that parents can use in interactions with the proband.
EARLI Study investigators stay connected with enrolled
families as a group through the study website, Facebook
and study newsletters.
Risk factor data collection
The EARLI Study approach to risk factor data collection is
comprehensive, involving multimodal self-report, records
review, direct observation and biologic and environmental
Page 8 of 16
sample collection. This approach allows for analysis of risk
factor characterization during specific suspected etiologic
windows, comparison of risk factor data from multiple
sources, estimation of risk factor-outcome associations
motivated by specific hypotheses and discovery-oriented
work intended to reveal first evidence for novel risk factors. Table 2 provides a broad overview of data collection
modes by subject. Self-reports are provided by both parents at enrollment and extend back to the preconception
period. During pregnancy, mothers provide reports in
weekly pregnancy diaries regarding exposures that are
more challenging to recall retrospectively and are extensively interviewed twice (approximately 625 items) to collect information retrospectively on less time-sensitive
information in pregnancy. Selected self-report questionnaires are also used to cover specific domains such as diet
and depressive symptoms. Table 3 summarizes the range
of risk factor domains covered by EARLI Study self-report
data collection.
Biosampling in the EARLI Study is comparably extensive. Fathers and probands provide biosamples at enrollment. Venous blood is collected from both, and fathers
are also provided with a home semen collection kit. Biosampling in mothers begins at enrollment with the collection of blood, first void urine and hair. Mothers provide
these samples at least once and as many as three additional times during pregnancy, depending on how early in
the pregnancy enrollment occurred. The EARLI Study
makes efforts to work closely with mothers, obstetricians
and/or birth hospitals to facilitate the collection of delivery
samples. Umbilical cord blood and placental samples are
collected as close to delivery as possible. Four placental
punch biopsies, two from the maternal and two from the
fetal side, are taken and placed into cryovials of RNAlater™ (QIAGEN, Valencia, CA, USA). The remaining placental tissue is fixed in formalin. Heel stick cards are left
at the hospital, and newborn blood is collected by hospital
staff after neonatal screening whenever possible without
an additional heel stick. Mothers are provided with collection kits for breast milk, meconium and diaper urine.
Manually expressed breast milk is collected at one week
Table 2 EARLI Study data collection modes by subjecta
Mother
Data collection mode
Preconception
Self-report retrospective
X
Prenatal
Perinatal
X
Father
Proband
Sibling
X
X
X
X
X
X
Self-report prospective
Biologic sampling
X
X
Direct observation (home environment)
X
X
Environmental sampling
X
X
Medical recordsa
a
X
X
X
X
X
X
X
X
The EARLI Study secures medical record releases for each participant and will pursue abstraction as needed to support individual analyses. EARLI, Early Autism
Risk Longitudinal Investigation.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
Table 3 Domains of risk factor data collected from
interviews, diaries and other self-report forms
Risk factor domains
Interviews Diaries Other self-report
formsa
Demographics
M, F
Medication exposure
M, F
M, S
M, F, S
Medical conditions
M
M
M, F, S
M
S
S
M, S
M
M
M, S
M, F
S
M, F
M
M
M, F, S
Personal product use
M
M, S
Anthropometrics
M
M
M
Medical procedures
M
M, S
M, F
Pesticides
Diet
Home environmental
exposures
Health behaviors/lifestyle
Mental condition/history/
symptoms
Vaccine history
F
M, S
Occupational history
M
M, F
a
Includes the following forms: Home Walkthrough Survey, Maternal Interview
Update, CHARGE Family Medical History Form, Dietary History Questionnaires
(preconception, 1 to 20 weeks, 21 to 36 weeks and postnatal), Health
Behaviors Questionnaires (preconception, pregnancy and paternal), Paternal
Interview, 24-Hour Recalls (food and environment), Stress and Depression
Surveys, Postnatal Diaries, Blood Draw Information Form, Maternal Medical
History, Post-Partum Environmental Exposures and Dust Field Log. M, mother;
F, father; S, sibling.
(postcolostrum) and twelve weeks. Nighttime diaper urine
is collected from a sterile gauze pad at one week. Study
staff visit the family at three months and pick up biologic
samples the family has retained in the home freezer and
also collect clean-catch urine from the infant sibling at
that time. At the six-month clinic visit, mothers again provide blood, urine and hair samples, and a first venous
blood sample, another diaper urine sample and a hair sample are taken from the infant sibling. Biosampling concludes with infant siblings’ providing venous blood and
diaper/pull-up pad urine samples during the 12- and 24month follow-up clinic visits. This continued longitudinal
Page 9 of 16
sampling of blood and urine in siblings provides opportunities for assessment of early life exposures and also creates the potential to investigate peripheral biomarkers of
early outcome. Biosampling time points by participant and
sample type are summarized in Table 4.
Biosample-processing decisions in EARLI were made
by balancing timeliness, logistics and cost while being
mindful of the nature of each sample. Processing on-site
is minimal for venous blood, limited to centrifuging as
dictated by tube type, with samples shipped next-day
delivery to the central laboratory and biorepository
(CLBR). The complement of tube types used varies
slightly by subject and visit, but generally ethylenediaminetetraacetic acid (EDTA) and serum separator tubes
(SSTs) are used during each draw with the PAXgene
Blood RNA Kit (QIAGEN, Valencia, CA, USA), prescreened metal EDTA and cell preparation tubes (CPTs)
interspersed. Maternal first void and infant sibling urine
are aliquoted and frozen on-site and shipped monthly on
dry ice to the CLBR. Diaper pad urine, meconium and
breast milk are also batch-shipped frozen to the CLBR.
Semen samples are collected by the father and frozen at
home for a minimum of 24 hours, then shipped directly
to the CLBR. Dried heel stick cards are sent back to the
sites, where they are stored at ambient temperature and
batch-shipped, as are hair samples, to the CLBR. Blood
sample processing at the CLBR generates a repository of
multiple aliquots of stored plasma, serum, whole blood,
extracted DNA and peripheral blood mononuclear cells
(PBMCs), including aliquots processed and saved to
allow for establishment of cell lines.
Researchers in the EARLI Study also assess the home
environment once during pregnancy and at the threemonth postpartum home visit. The home assessment
includes a walk-through survey with questions related to
how the family distributes their indoor time across
rooms in the home, characteristics of the principal
rooms where the mother and infant sibling spend most
Table 4 EARLI biosampling time points by biosample and participant typea
Sample
Mother
Father Proband Infant sibling
Blood
E, pre-2nd, pre-3rd, post-6
months
E
Hair
E, pre-2nd, pre-3rd, post-6
months
Post-6 months, post-12 months, post-24 months
Urine
E, pre-2nd, pre-3rd, post-6
months
Post-1 week, post-3 months, post-6 months, post-12 months, post-24
months
Semen
Placenta, cord blood
a
Post-6 months, post-12 months, post-24 months
E
D
Heel stick blood,
meconium
Breast milk
E
D
Post-1 week, post-3 months
D, delivery; E, enrollment; EARLI, Early Autism Risk Longitudinal Investigation; pre-2nd, second prenatal visit; pre-3rd, third prenatal visit; post-1 week, 1 week
postnatal; post-3 months, 3 months postnatal; post-6 months, 6 months postnatal; post-12 months, 12 months postnatal; post-24 months, 24 months postnatal.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
of their time, cleaning product use, and indoor and outdoor spray and pesticide use. A dust sample is also collected from the main living area by using a Eureka
Mighty Mite vacuum cleaner (Eureka Co, Charlotte, NC,
USA) following a protocol used in multiple previous
studies [130-134]. House dust is an easily collected
reservoir comprising compounds such as pesticides,
plasticizers and flame retardants and has served as a
marker of exposure in several epidemiologic studies
[135-139].
Finally, the members of the EARLI Study team obtain
medical record release forms from all participants, and
they plan to abstract records as needed to assess exposure related to clinical domains where self-report data
have inherent limitations and/or where medical records
data might validate recall. Items of particular interest
include specific clinical tests or results in mothers (for
example, type of ultrasound, blood pressure, blood glucose levels) and newborns (for example, oxygen saturation values, fetal heart rate tracings, newborn screening
results) and details regarding indications and dates for
procedures and medications.
Outcome data collection
Infant siblings are followed to age 36 months, with clinical assessment of ASD-related behaviors and other
developmental domains occurring at 6, 12, 18, 24 and
36 months. Behavioral outcome assessment tools are
summarized in Table 5. The assessment protocol was
Page 10 of 16
designed to measure core autism and related phenotypes, enabling investigation of dichotomous end points,
continuous outcomes and developmental trajectories.
Direct observation, interview and parent-report measures are all used. The autism-specific direct observation
tool used at ages 6 and 12 months is the AOSI [115].
Initial evaluation of this tool suggested that total scores
are the most robust predictor of autism at 24 months of
age [115], and work is ongoing to assess the dimensional
measure utility of the AOSI as well as the predictive
ability of both total score and specific items [140]. At
ages 24 and 36 months, the ADOS is administered. At
these ages, the ADOS has high sensitivity for both autism and ASD, along with moderate specificity, using the
revised scoring algorithm [141]. An algorithm has also
recently been developed for converting raw ADOS
scores to a 10-point severity measure (with scoring also
dependent on ADOS module, classification and age)
[142]. At 36 months, the ADI-R is also administered.
The addition of the ADI-R to ADOS results for determining a final classification markedly improves classification specificity without major sacrifices in sensitivity
[141]. The Social Responsiveness Scale (SRS) [143,144]
has been shown to have useful dimensional scale properties in first-degree relatives of affected probands
[145,146]. The EARLI researchers administer the Preschool Version (for 3-year-olds) of the SRS to infant siblings at age 36 months. At enrollment, the Adult
Table 5 EARLI behavioral outcome assessments by infant sibling follow-up pointa
Assessments
6-month clinic
visit
Autism assessments
AOSI (Autism Observation Scale for Infants) X
12-month clinic
visit
18-month
mailing
24-month clinic
visit
36-month clinic
visit
X
ADI-R (Autism Diagnostic InterviewRevised)
ADOS (Autism Diagnostic Observation
Schedule)
SRS (Social Responsiveness Scale)
X
X
X
X
Other behavioral assessments
CSBS-DP (Communication and Symbolic
Behavior Scales Developmental Profile)
Infant/Toddler Checklist
X
X
X
X
X
X
X
X
CBCL (Child Behavior Checklist)
X
MCDI (MacArthur Communicative
Development
Inventories)
M-CHAT(Modified Checklist for Autism in
Toddlers)
Mullen Scales of Early Learning
X
Rothbart Temperament Questionnaires
X
SEQ (Sensory Experiences Questionnaire)
Vineland II
(Vineland Adaptive Behavior Scales, 2nd
edition)
a
EARLI, Early Autism Risk Longitudinal Investigation.
X
X
X
X
X
X
X
X
X
X
X
X
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
Research Version of the SRS is administered to the parents, who each report on their spouse, and the Preschool Version or the Autoscore Form Parent Report is
administered to the proband, depending on the proband’s age.
In addition, EARLI incorporates other behavioral measures that have been demonstrated to have value in phenotyping high-risk infant siblings. The Mullen Scales of
Early Leaning, in addition to providing data on subdomains of particular interest, such as nonverbal IQ [117]
and motor functioning [147], has also been used more
broadly to characterize developmental trajectory in highrisk siblings [10,148]. The Vineland Adaptive Behavior
Scales [149,150] have been employed effectively with
infant sibling data to differentiate functional phenotypes
[148]. In addition, other tools can improve the richness of
available data on early language and communication
(Communication and Symbolic Behavior Scales Developmental Profile Infant/Toddler Checklist (CSBS DP ITC)
[151,152] and MacArthur-Bates [153,154]), sensory
impairments [155], temperament (Rothbart) [156,157] and
emergent maladaptive behavioral and emotional problems
(Child Behavior Checklist (CBCL) [158,159]).
The EARLI Study also incorporates data collection
regarding physical features and medical comorbidities. At
age 36 months, a dysmorphology assessment is completed
following a protocol adapted from the Study to Explore
Early Development (SEED) [160]. This examination
involves direct measurement of growth parameters
(height, weight, head circumference and body mass index)
and evaluation of dysmorphology. A trained member of
the research team photographs the child’s face and ears
(front of face, right and left profiles of ears, and left and
right three-quarters images showing each ear and the
face), hands (both sides and a hand scan of the palms),
feet (weight-bearing and not), teeth, any skin findings and
two posterior views of the head to identify hair whorl and
hairline. For the facial photographs, a size reference sticker
is included. Parents are queried about the presence of physical anomalies and whether the child has ever had corrective surgery, has been diagnosed with any syndromes or
has had any genetic testing. This same assessment is administered to the proband at the time of enrollment. At the
6-, 12- and 24-month study visits, the sibling also receives
a brief physical examination to capture infant growth parameters (length, weight, head circumference and weight-tolength ratio), and the parents are asked the same set of
questions on genetic testing, anomaly or syndrome diagnoses and corrective surgeries. Finally, a comprehensive
medical history questionnaire completed by the mother
addresses any medical problems and procedures that have
occurred during the course of the infant sibling’s first
three years of life. As mentioned above, medical records
Page 11 of 16
releases are obtained for the infant sibling to allow followup for more details on any problems or procedures noted.
The design, recruitment strategy and data collection
approach of the EARLI Study are intended to build a data
platform upon which a wide range of prenatal and early
life risk factor investigations will be launched. The rich
combination of prospectively collected exposure and outcome data should allow for analyses that incorporate
strong confounder control and limit exposure misclassification and have a range of data sufficient to approach
complex questions of effect modification and mediation
along risk pathways. Although EARLI’s sample size is large
in relation to other infant sibling studies, there will no
doubt be challenges related to sample size, and, as mentioned previously, attention to designing analytic contrasts
in ways that maximize efficiency and incorporation of
dimensional as well as categorical outcomes will likely
prove helpful in this regard.
Conclusions
Infant sibling studies have already played a major role in
autism research over the past decade, improving our
understanding of the complex early developmental trajectory of autism, providing exciting leads on approaches for
early detection and documenting recurrence risk under
today’s diagnostic standards. Extension of the infant siblings design to intervention studies is already underway,
with behavioral interventions being tested in high-risk
siblings with very early signs of developmental issues
(see, for example, the Infant Start Study [161]).
As described above, the potential for the extension of
the design to autism risk factor research is great. The
EARLI Study has substantial potential to contribute to risk
factor research on its own; however, there is also added
potential through collaborations and extensions of the
EARLI project. The EARLI Study team is working with
researchers in the Infant Brain Imaging Study (IBIS) [162],
another extension of the infant siblings design adding prospective brain imaging to developmental follow-up, to
conduct coordinated genomics on both EARLI and IBIS
study samples to undertake pooled analyses of genetic variants and developmental phenotypes. Because both IBIS
and EARLI are collecting phenotype data on infant siblings
longitudinally from ages 6 to 36 months, they have a
unique opportunity to examine genetic relationships with
developmental trajectories in addition to autism per se.
Moreover, the genetic data will support independent analyses of genotypes and brain imaging in the IBIS sample
and gene-environment interaction in EARLI. EARLI has
also partnered with experts in epigenetics to explore the
potential role of epigenetic mechanisms in autism and the
possible link between epigenetics and environmental risk
factors. Through a National Institutes of Health Roadmap
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
program award, EARLI data will be analyzed in parallel
with data from two other birth cohort studies to examine
relationships between DNA methylation (DNAm) and
prenatal exposures, as well as between DNAm, birth outcomes and early childhood developmental milestones. As
the EARLI cohort develops, other opportunities to take
advantage of the rich available data in this unique sample
are sure to arise.
Last, in addition to enriched risk pregnancy cohorts
such as EARLI, it should be noted that worldwide there
are several population-based pregnancy cohort studies, in
which recruitment is not geared to enriched-risk families,
that have made autism an identified outcome of interest
[163-167]. These studies range in size from the 1,200subject Hamamatsu Birth Cohort in Japan [168] to the
110,000-subject Autism Birth Cohort Study [167] that
has recently been incorporated into the Norwegian
Mothers and Babies Study [169]. Researchers in population-based cohort studies can explore the generalizability
of findings that emerge from enriched-risk designs and
could also become engaged with enriched-risk cohorts in
coordinated analytic efforts to study rare prenatal exposures or complex etiologic mechanisms.
As research on autism risk factors and risk biomarkers
during the pre-, peri- and neonatal periods intensifies
during the coming decade, enriched-risk cohort designs,
along with large case-control studies [170,171] and population-based cohort designs, can be expected to play an
important role. This expansion of autism infant sibling
studies, which have emerged during the past five years as
extremely valuable tools with which to improve understanding of the early-life autism phenotype, to address
etiologic questions will, we hope, mark an important step
toward identification of avoidable or modifiable factors
that will ultimately help reduce the population morbidity
and impact of autism on quality of life.
Abbreviations
ADI-R: Autism Diagnostic Interview-Revised; ADOS: Autism Diagnostic
Observation Schedule; AOSI: Autism Observation Scale for Infants; ASD:
Autism spectrum disorder; BSRC: Baby Siblings Research Consortium; CBCL:
Child Behavior Checklist; CLBR: Central laboratory and biorepository; CNV:
Copy number variation; CPT: Cell preparation tube; CSBS DP: Communication
and Symbolic Behavior Scales Developmental Profile; DNAm: DNA
methylation; EARLI: Early Autism Risk Longitudinal Investigation; EDTA:
Ethylenediaminetetraacetic acid; GWAS: Genomewide association study; IBIS:
Infant Brain Imaging Study; MARBLES: Markers Of Autism Risk In BabiesLearning Early Signs; PBMC: Peripheral blood mononuclear cell; PDD NOS:
Pervasive developmental disorder not otherwise specified; SEED: Study to
Explore Early Development; SST: Serum separator tube; SRS: Social
Responsiveness Scale.
Acknowledgements
The EARLI Study is funded by the National Institute of Environmental Health
Sciences, the National Institute of Mental Health, the National Institute of
Child Health and Human Development, and the National Institute of
Neurologic Disease and Stroke (R01 ES016443), with additional funding from
Autism Speaks (AS 5938).
Page 12 of 16
Author details
Department of Epidemiology and Biostatistics, Drexel School of Public
Health, 1505 Race Street, Mail Stop 1033, Philadelphia, PA 19102, USA. 2Kaiser
Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, USA.
3
Department of Epidemiology, Johns Hopkins Bloomberg School of Public
Health, 615 N Wolfe Street, Baltimore, MD 21205, USA. 4Department of
Public Health Sciences, University of California, Davis, CA 95616, USA. 5Kaiser
Permanente San Jose Medical Center, 6620 Via Del Oro, San Jose, CA 95119,
USA. 6Kennedy Krieger Institute, 3901 Greenspring Avenue, 2nd Floor,
Baltimore, MD 21211, USA. 7Center for Autism Research, The Children’s
Hospital of Philadelphia, 3535 Market Street, Suite 860, Philadelphia, PA
19104, USA. 8The MIND Institute, UC Davis Medical Center, 2825 50th Street,
Sacramento, CA 95817, USA.
1
Authors’ contributions
CJN, LAC, MDF, and IHP made substantial contributions to the conception
and design of the study, and drafted and revised the manuscript. DVN,
SMM, and AS participated in data coordination, collection, and analysis, and
revised the manuscript. NLL, CAB, MLM, MCO, and KMSW contributed to the
implementation of the study and contributed to manuscript revisions. HF
and SCM participated in the design and coordination of biosampling
aspects of study implementation, and revised the manuscript. HNH, SEL, RJL,
SO, and JP contributed to the study design and clinical data collection, and
contributed to the manuscript draft and revisions. All authors read and
approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 6 November 2011 Accepted: 18 April 2012
Published: 18 April 2012
References
1. Pearson JS, Kley IB: On the application of genetic expectancies as agespecific base rates in the study of human behavior disorders. Psychol Bull
1957, 54:406-420.
2. Hanson DR, Gottesman II: The genetics, if any, of infantile autism and
childhood schizophrenia. J Autism Child Schizophr 1976, 6:209-234.
3. Garmezy N: Children at risk: the search for the antecedents of
schizophrenia. Part II: ongoing research programs, issues, and
intervention. Schizophr Bull 1974, 9:55-125.
4. Folstein S, Rutter M: Infantile autism: a genetic study of 21 twin pairs. J
Child Psychol Psychiatry 1977, 18:297-321.
5. Yirmiya N, Ozonoff S: The very early autism phenotype. J Austim Dev
Disord 2007, 37:1-11.
6. Rogers SJ: What are infant siblings teaching us about autism in infancy?
Autism Res 2009, 2:125-137.
7. Zwaigenbaum L, Bryson S, Rogers T, Roberts W, Brian J, Szatmari P:
Behavioral manifestations of autism in the first year of life. Int J Dev
Neurosci 2005, 23:143-152.
8. Yirmiya N, Gamliel I, Pilowsky T, Feldman R, Baron-Cohen S, Sigman M: The
development of siblings of children with autism at 4 and 14 months:
social engagement, communication, and cognition. J Child Psychol
Psychiatry 2006, 47:511-523.
9. Mitchell S, Brian J, Zwaigenbaum L, Roberts W, Szatmari P, Smith I, Bryson S:
Early language and communication development of infants later
diagnosed with autism spectrum disorder. J Dev Behav Pediatr 2006, 27:
S69-S78.
10. Landa R, Garrett-Mayer E: Development in infants with autism spectrum
disorders: a prospective study. J Child Psychol Psychiatry 2006, 47:629-638.
11. Folstein S, Rutter M: Genetic influences and infantile autism. Nature 1977,
265:726-728.
12. Bailey A, Le Couteur A, Gottesman I, Bolton P, Simonoff E, Yuzda E,
Rutter M: Autism as a strongly genetic disorder: evidence from a British
twin study. Psychol Med 1995, 25:63-77.
13. Ritvo ER, Spence MA, Freeman BJ, Mason-Brothers A, Mo A, Marazita ML:
Evidence for autosomal recessive inheritance in 46 families with
multiple incidences of autism. Am J Psychiatry 1985, 142:187-192.
14. Steffenburg S, Gillberg C, Hellgren L, Andersson L, Gillberg IC, Jakobsson G,
Bohman M: A twin study of autism in Denmark, Finland, Iceland, Norway
and Sweden. J Child Psychol Psychiatry 1989, 30:405-416.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
15. Chakrabarti S, Fombonne E: Pervasive developmental disorders in
preschool children. JAMA 2001, 285:3093-3099.
16. Smalley SL, Asarnow RF, Spence MA: Autism and genetics: a decade of
research. Arch Gen Psychiatry 1988, 45:953-961.
17. Bailey A, Palferman S, Heavey L, Le Couteur A: Autism: the phenotype in
relatives. J Autism Dev Disord 1998, 28:369-392.
18. Szatmari P, Jones MB, Zwaigenbaum L, MacLean JE: Genetics of autism:
overview and new directions. J Autism Dev Disord 1998, 28:351-368.
19. Chudley AE, Gutierrez E, Jocelyn LJ, Chodriker BN: Outcomes of genetic
evaluation in children with pervasive developmental disorder. J Dev
Behav Pediatr 1998, 19:321-325.
20. Veenstra-Vanderweele J, Cook EH Jr, Lombroso PJ: Genetics of childhood
disorders: XLVI. Autism, part 5: genetics of autism. J Am Acad Child
Adolesc Psychiatry 2003, 42:116-118.
21. Bolton PF, Pickles A, Murphy M, Rutter M: Autism, affective and other
psychiatric disorders: patterns of familial aggregation. Psychol Med 1998,
28:385-395.
22. Piven J, Palmer P, Jacobi D, Childress D, Arndt S: Broader autism
phenotype: evidence from a family history study of multiple-incidence
autism families. Am J Psychiatry 1997, 154:185-190.
23. Ritvo ER, Jorde LB, Mason-Brothers A, Freeman BJ, Pingree C, Jones MB,
McMahon WM, Petersen PB, Jenson WR, Mo A: The UCLA-University of
Utah epidemiologic survey of autism: recurrence risk estimates and
genetic counseling. Am J Psychiatry 1989, 146:1032-1036.
24. Jorde LB, Hasstedt SJ, Ritvo ER, Mason-Brothers A, Freeman BJ, Pingree C,
McMahon WM, Petersen B, Jenson WR, Mo A: Complex segregation
analysis of autism. Am J Hum Genet 1991, 49:932-938.
25. Ritvo ER, Freeman BJ, Mason-Brothers A, Mo A, Ritvo AM: Concordance for
the syndrome of autism in 40 pairs of afflicted twins. Am J Psychiatry
1985, 142:74-77.
26. Bolton P, Macdonald H, Pickles A, Rios P, Goode S, Crowson M, Bailey A,
Rutter M: A case-control family history study of autism. J Child Psychol
Psychiatry 1994, 35:877-900.
27. Risch N, Spiker D, Lotspeich L, Nouri N, Hinds D, Hallmayer J, Kalaydjieva L,
McCague P, Dimiceli S, Pitts T, Nguyen L, Yang J, Harper C, Thorpe D,
Vermeer S, Young H, Hebert J, Lin A, Ferguson J, Chiotti C, Wiese-Slater S,
Rogers T, Salmon B, Nicholas P, Petersen PB, Pingree C, McMahon W,
Wong DL, Cavalli-Sforza LL, Kraemer HC, Myers RM: A genomic screen of
autism: evidence for a multilocus etiology. Am J Hum Genet 1999,
65:493-507.
28. Steffenburg S, Gillberg CL, Steffenburg U, Kyllerman M: Autism in
Angelman syndrome: a population-based study. Pediatr Neurol 1996,
14:131-136.
29. Smalley SL: Autism and tuberous sclerosis. J Autism Dev Disord 1998,
28:407-414.
30. Folstein SE, Rutter ML: Autism: familial aggregation and genetic
implications. J Autism Dev Disord 1988, 18:3-30.
31. Brown WT, Jenkins EC, Cohen IL, Fisch GS, Wolf-Schein EG, Gross A,
Waterhouse L, Fein D, Mason-Brothers A, Ritvo E: Fragile X and autism: a
multicenter survey. Am J Med Genet 1986, 23:341-352.
32. Folstein SE, Piven J: Etiology of autism: genetic influences. Pediatrics 1991,
87:767-773.
33. Bucan M, Abrahams BS, Wang K, Glessner JT, Herman EI, Sonnenblick LI,
Alvarez Retuerto AI, Imielinski M, Hadley D, Bradfield JP, Kim C, Gidaya NB,
Lindquist I, Hutman T, Sigman M, Kustanovich V, Lajonchere CM,
Singleton A, Kim J, Wassink TH, McMahon WM, Owley T, Sweeney JA,
Coon H, Nurnberger JI, Li M, Cantor RM, Minshew NJ, Sutcliffe JS, Cook EH,
et al: Genome-wide analyses of exonic copy number variants in a familybased study point to novel autism susceptibility genes. PLoS Genet 2009,
5:e1000536.
34. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B,
Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J,
Spence SJ, Lee AT, Puura K, Lehtimäki T, Ledbetter D, Gregersen PK,
Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King MC,
Skuse D, Geschwind DH, Gilliam TC, et al: Strong association of de novo
copy number mutations with autism. Science 2007, 316:445-449.
35. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J,
Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD,
Bailey AJ, Baird G, Battaglia A, Berney T, Bolshakova N, Bolte S, Bolton PF,
Bourgeron T, Brennan S, Brian J, Bryson SE, Carson AR, Casallo G, Casey J,
Chung BH, Cochrane L, Corsello C, et al: Functional impact of global rare
Page 13 of 16
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
copy number variation in autism spectrum disorders. Nature 2010,
466:368-372.
Miller DT, Shen Y, Weiss LA, Korn J, Anselm I, Bridgemohan C, Cox GF,
Dickinson H, Gentile J, Harris DJ, Hegde V, Hundley R, Khwaja O, Kothare S,
Luedke C, Nasir R, Poduri A, Prasad K, Raffalli P, Reinhard A, Smith SE,
Sobeih MM, Soul JS, Stoler J, Takeoka M, Tan WH, Thakuria J, Wolff R,
Yusupov R, Gusella JF, et al: Microdeletion/duplication at 15q13.2q13.3
among individuals with features of autism and other neuropsychiatric
disorders. J Med Genet 2009, 46:242-248.
Anney R, Klei L, Pinto D, Regan R, Conroy J, Magalhaes TR, Correia C,
Abrahams BS, Sykes N, Pagnamenta AT, Almeida J, Bacchelli E, Bailey AJ,
Baird G, Battaglia A, Berney T, Bolshakova N, Bolte S, Bolton PF,
Bourgeron T, Brennan S, Brian J, Carson AR, Casallo G, Casey J, Chu SH,
Cochrane L, Corsello C, Crawford EL, Crossett A, et al: A genome-wide scan
for common alleles affecting risk for autism. Hum Mol Genet 2010,
19:4072-4082.
Scherer SW, Dawson G: Risk factors for autism: translating genomic
discoveries into diagnostics. Hum Genet 2011, 130:123-148.
Betancur C: Etiological heterogeneity in autism spectrum disorders: more
than 100 genetic and genomic disorders and still counting. Brain Res
2011, 1380:42-77.
Devlin B, Melhem N, Roeder K: Do common variants play a role in risk for
autism? Evidence and theoretical musings. Brain Res 2011, 1380:78-84.
Hu VW, Addington A, Hyman A: Novel autism subtype-dependent genetic
variants are revealed by quantitative trait and subphenotype association
analyses of published GWAS data. PLoS One 2011, 6:e19067.
Anney RJ, Kenny EM, O’Dushlaine C, Yaspan BL, Parkhomenka E,
Buxbaum JD, Sutcliffe J, Gill M, Gallagher L, Bailey AJ, Fernandez BA,
Szatmari P, Scherer SW, Patterson A, Marshall CR, Pinto D, Vincent JB,
Fombonne E, Betancur C, Delorme R, Leboyer M, Bourgeron T,
Mantoulan C, Roge B, Tauber M, Freitag CM, Poustka F, Duketis E,
Klauck SM, The Autism Genome Project, et al: Gene-ontology enrichment
analysis in two independent family-based samples highlights
biologically plausible processes for autism spectrum disorders. Eur J Hum
Genet 2011, 19:1082-1089.
Yaspan BL, Bush WS, Torstenson ES, Ma D, Pericak-Vance MA, Ritchie MD,
Sutcliffe JS, Haines JL: Genetic analysis of biological pathway data
through genomic randomization. Hum Genet 2011, 129:563-571.
Hussman JP, Chung RH, Griswold AJ, Jaworski JM, Salyakina D, Ma D,
Konidari I, Whitehead PL, Vance JM, Martin ER, Cuccaro ML, Gilbert JR,
Haines JL, Pericak-Vance MA: A noise-reduction GWAS analysis implicates
altered regulation of neurite outgrowth and guidance in autism. Mol
Autism 2011, 2:1.
Geschwind DH: Genetics of autism spectrum disorders. Trends Cogn Sci
2011, 15:409-416.
Cook EH Jr, Scherer SW: Copy-number variations associated with
neuropsychiatric conditions. Nature 2008, 455:919-923.
State MW: The genetics of child psychiatric disorders: focus on autism
and Tourette syndrome. Neuron 2010, 68:254-269.
Abrahams BS, Geschwind DH: Advances in autism genetics: on the
threshold of a new neurobiology. Nat Rev Genet 2008, 9:341-355.
Freitag CM, Staal W, Klauck SM, Duketis E, Waltes R: Genetics of autistic
disorders: review and clinical implications. Eur Child Adolesc Psychiatry
2010, 19:169-178.
Merikangas AK, Corvin AP, Gallagher L: Copy-number variants in
neurodevelopmental disorders: promises and challenges. Trends Genet
2009, 25:536-544.
O’Roak BJ, State MW: Autism genetics: strategies, challenges, and
opportunities. Autism Res 2008, 1:4-17.
Eapen V: Genetic basis of autism: is there a way forward? Curr Opin
Psychiatry 2011, 24:226-236.
Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J,
Fedele A, Collins J, Smith K, Lotspeich L, Croen LA, Ozonoff S, Lajonchere C,
Grether JK, Risch N: Genetic heritability and shared environmental factors
among twin pairs with autism. Arch Gen Psychiatry 2011, 68:1095-1102.
Ozonoff S, Young GS, Carter A, Messinger D, Yirmiya N, Zwaigenbaum L,
Bryson S, Carver LJ, Constantino JN, Dobkins K, Hutman T, Iverson JM,
Landa R, Rogers SJ, Sigman M, Stone WL: Recurrence risk for autism
spectrum disorders: a Baby Siblings Research Consortium study.
Pediatrics 2011, 128:e488-e495.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
55. Schanen NC: Epigenetics of autism spectrum disorders. Hum Mol Genet
2006, 15(Spec No 2):R138-R150.
56. Grafodatskaya D, Chung B, Szatmari P, Weksberg R: Autism spectrum
disorders and epigenetics. J Am Acad Child Adolesc Psychiatry 2010,
49:794-809.
57. Bjornsson HT, Cui H, Gius D, Fallin MD, Feinberg AP: The new field of
epigenomics: implications for cancer and other common disease
research. Cold Spring Harb Symp Quant Biol 2004, 69:447-456.
58. Chess S: Autism in children with congenital rubella. J Autism Child
Schizophr 1971, 1:33-47.
59. Chess S: Follow-up report on autism in congenital rubella. J Autism Child
Schizophr 1977, 7:69-81.
60. Yamashita Y, Fujimoto C, Nakajima E, Isagai T, Matsuishi T: Possible
association between congenital cytomegalovirus infection and autistic
disorder. J Autism Dev Disord 2003, 33:455-459.
61. Rodier PM, Ingram JL, Tisdale B, Croog VJ: Linking etiologies in humans
and animal models: studies of autism. Reprod Toxicol 1997, 11:417-422.
62. Christianson AL, Chesler N, Kromberg JG: Fetal valproate syndrome:
clinical and neuro-developmental features in two sibling pairs. Dev Med
Child Neurol 1994, 36:361-369.
63. Williams PG, Hersh JH: A male with fetal valproate syndrome and autism.
Dev Med Child Neurol 1997, 39:632-634.
64. Moore SJ, Turnpenny P, Quinn A, Glover S, Lloyd DJ, Montgomery T,
Dean JCS: A clinical study of 57 children with fetal anticonvulsant
syndromes. J Med Genet 2000, 37:489-497.
65. Croen LA, Connors SL, Matevia M, Qian Y, Newschaffer C, Zimmerman AW:
Prenatal exposure to β2-adrenergic receptor agonists and risk of autism
spectrum disorders. J Neurodev Disord 2011, 3:307-315.
66. Croen LA, Grether JK, Yoshida CK, Odouli R, Hendrick V: Antidepressant use
during pregnancy and childhood autism spectrum disorders. Arch Gen
Psychiatry 2011, 68:1104-1112.
67. Atladóttir HO, Thorsen P, Østergaard L, Schendel DE, Lemcke S, Abdallah M,
Parner ET: Maternal infection requiring hospitalization during pregnancy
and autism spectrum disorders. J Autism Dev Disord 2010, 40:1423-1430.
68. Goines PE, Croen LA, Braunschweig D, Yoshida CK, Grether J, Hansen R,
Kharrazi M, Ashwood P, Van de Water J: Increased midgestational IFN-γ,
IL-4 and IL-5 in women bearing a child with autism: a case-control
study. Mol Autism 2011, 2:13.
69. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J,
Cantor RM, Blencowe BJ, Geschwind DH: Transcriptomic analysis of
autistic brain reveals convergent molecular pathology. Nature 2011,
474:380-384.
70. Vargas DL, Nascimbene C, Krishnan C, Zimmerman AW, Pardo CA:
Neuroglial activation and neuroinflammation in the brain of patients
with autism. Ann Neurol 2005, 57:67-81.
71. Gardener H, Spiegelman D, Buka SL: Prenatal risk factors for autism:
comprehensive meta-analysis. Br J Psychiatry 2009, 195:7-14.
72. Gardener H, Spiegelman D, Buka SL: Perinatal and neonatal risk factors for
autism: a comprehensive meta-analysis. Pediatrics 2011, 128:344-355.
73. Kolevzon A, Gross R, Reichenberg A: Prenatal and perinatal risk factors for
autism: a review and integration of findings. Arch Pediatr Adolesc Med
2007, 161:326-333.
74. Sasanfar R, Haddad SA, Tolouei A, Ghadami M, Yu D, Santangelo SL:
Paternal age increases the risk for autism in an Iranian population
sample. Mol Autism 2010, 1:2.
75. Shelton JF, Tancredi DJ, Hertz-Picciotto I: Independent and dependent
contributions of advanced maternal and paternal ages to autism risk.
Autism Res 2010, 3:30-39.
76. Grether JK, Anderson MC, Croen LA, Smith D, Windham GC: Risk of autism
and increasing maternal and paternal age in a large North American
population. Am J Epidemiol 2009, 170:1118-1126.
77. King MD, Fountain C, Dakhlallah D, Bearman PS: Estimated autism risk and
older reproductive age. Am J Public Health 2009, 99:1673-1679.
78. Durkin MS, Maenner MJ, Newschaffer CJ, Lee LC, Cunniff CM, Daniels JL,
Kirby RS, Leavitt L, Miller L, Zahorodny W, Schieve LA: Advanced parental
age and the risk of autism spectrum disorder. Am J Epidemiol 2008,
168:1268-1276.
79. Croen LA, Najjar DV, Fireman B, Grether JK: Maternal and paternal age and
risk of autism spectrum disorders. Arch Pediatr Adolesc Med 2007,
161:334-340.
Page 14 of 16
80. Reichenberg A, Gross R, Weiser M, Bresnahan M, Silverman J, Harlap S,
Rabinowitz J, Shulman C, Malaspina D, Lubin G, Knobler HY, Davidson M,
Susser E: Advancing paternal age and autism. Arch Gen Psychiatry 2006,
63:1026-1032.
81. Cheslack-Postava K, Liu K, Bearman PS: Closely spaced pregnancies are
associated with increased odds of autism in California sibling births.
Pediatrics 2011, 127:246-253.
82. Schmidt RJ, Hansen RL, Hartiala J, Allayee H, Schmidt LC, Tancredi DJ,
Tassone F, Hertz-Picciotto I: Prenatal vitamins, one-carbon metabolism
gene variants, and risk for autism. Epidemiology 2011, 22:476-485.
83. Palmer RF, Blanchard S, Stein Z, Mandell D, Miller C: Environmental
mercury release, special education rates, and autism disorder: an
ecological study of Texas. Health Place 2006, 12:203-209.
84. Windham GC, Zhang L, Gunier R, Croen LA, Grether JK: Autism spectrum
disorders in relation to distribution of hazardous air pollutants in the
San Francisco Bay area. Environ Health Perspect 2006, 114:1438-1444.
85. Kalkbrenner AE, Daniels JL, Chen JC, Poole C, Emch M, Morrissey J:
Perinatal exposure to hazardous air pollutants and autism spectrum
disorders at age 8. Epidemiology 2010, 21:631-641.
86. Volk HE, Hertz-Picciotto I, Delwiche L, Lurmann F, McConnell R: Residential
proximity to freeways and autism in the CHARGE study. Environ Health
Perspect 2011, 119:873-877.
87. Roberts EM, English PB, Grether JK, Windham GC, Somberg L, Wolff C:
Maternal residence near agricultural pesticide applications and autism
spectrum disorders among children in the California Central Valley.
Environ Health Perspect 2007, 115:1482-1489.
88. Eskenazi B, Huen K, Marks A, Harley KG, Bradman A, Barr DB, Holland N:
PON1 and neurodevelopment in children from the CHAMACOS study
exposed to organophosphate pesticides in utero. Environ Health Perspect
2010, 118:1775-1781.
89. Rauh VA, Garfinkel R, Perera FP, Andrews HF, Hoepner L, Barr DB,
Whitehead R, Tang D, Whyatt RW: Impact of prenatal chlorpyrifos
exposure on neurodevelopment in the first 3 years of life among innercity children. Pediatrics 2006, 118:e1845-e1859.
90. Engel SM, Wetmur J, Chen J, Zhu C, Barr DB, Canfield RL, Wolff MS:
Prenatal exposure to organophosphates, paraoxonase 1, and cognitive
development in childhood. Environ Health Perspect 2011, 119:1182-1188.
91. Bouchard MF, Chevrier J, Harley KG, Kogut K, Vedar M, Calderon N,
Trujillo C, Johnson C, Bradman A, Barr DB, Eskenazi B: Prenatal exposure to
organophosphate pesticides and IQ in 7-year-old children. Environ Health
Perspect 2011, 119:1189-1195.
92. Rodier PM, Ingram JL, Tisdale B, Nelson S, Romano J: Embryological origin
for autism: developmental anomalies of the cranial nerve motor nuclei. J
Comp Neurol 1996, 370:247-261.
93. Bauman ML, Kemper TL: Neuroanatomic observations of the brain in
autism: a review and future directions. Int J Dev Neurosci 2005,
23:183-187.
94. Kemper TL, Bauman M: Neuropathology of infantile autism. J Neuropathol
Exp Neurol 1998, 57:645652.
95. Acosta MT, Pearl PL: The neurobiology of autism: new pieces of the
puzzle. Curr Neurol Neurosci Rep 2003, 3:149-156.
96. Schumann CM, Bloss CS, Barnes CC, Wideman GM, Carper RA,
Akshoomoff N, Pierce K, Hagler D, Schork N, Lord C, Courchesne E:
Longitudinal magnetic resonance imaging study of cortical
development through early childhood in autism. J Neurosci 2010,
30:4419-4427.
97. Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet C,
Piven J: Early brain overgrowth in autism associated with an increase in
cortical surface area before age 2 years. Arch Gen Psychiatry 2011,
68:467-476.
98. Johnson MB, Kawasawa YI, Mason CE, Krsnik Z, Coppola G, Bogdanović D,
Geschwind DH, Mane SM, State MW, Sestan N: Functional and
evolutionary insights into human brain development through global
transcriptome analysis. Neuron 2009, 62:494-509.
99. Rice D, Barone S Jr: Critical periods of vulnerability for the developing
nervous system: evidence from humans and animal models. Environ
Health Perspect 2000, 108(Suppl 3):511-533.
100. Rodier PM: Environmental causes of central nervous system
maldevelopment. Pediatrics 2004, 113:1076-1083.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
101. Persico AM, Bourgeron T: Searching for ways out of the autism maze:
genetic, epigenetic and environmental clues. Trends Neurosci 2006,
29:349-358.
102. Toro R, Konyukh M, Delorme R, Leblond C, Chaste P, Fauchereau F,
Coleman M, Leboyer M, Gillberg C, Bourgeron T: Key role for gene dosage
and synaptic homeostasis in autism spectrum disorders. Trends Genet
2010, 26:363-372.
103. Connors SL, Levitt P, Matthews SG, Slotkin TA, Johnston MV, Kinney HC,
Johnson WG, Dailey RM, Zimmerman AW: Fetal mechanisms in
neurodevelopmental disorders. Pediatr Neurol 2008, 38:163-176.
104. Royland JE, Kodavanti PR: Gene expression profiles following exposure to
a developmental neurotoxicant, Aroclor 1254: pathway analysis for
possible mode(s) of action. Toxicol Appl Pharmacol 2008, 231:179-196.
105. Augustine-Rauch KA, Zhang QJ, Posobiec L, Mirabile R, DeBoer LS,
Solomon HM, Wier PJ: SB-236057: Critical window of sensitivity study and
embryopathy of a potent musculoskeletal teratogen. Birth Defects Res A
Clin Mol Teratol 2004, 70:773-788.
106. Szatmari P, Jones MB, Tuff L, Bartolucci G, Fisman S, Mahoney W: Lack of
cognitive impairment in first-degree relatives of children with pervasive
developmental disorders. J Am Acad Child Adolesc Psychiatry 1993,
32:1264-1273.
107. Autism and Developmental Disabilities Monitoring Network Surveillance
Year 2006 Principal Investigators, Centers for Disease Control and
Prevention (CDC): Prevalence of autism spectrum disorders: Autism and
Developmental Disabilities Monitoring Network, United States, 2006.
MMWR Surveill Summ 2009, 58(SS10):1-20.
108. Toth K, Dawson G, Meltzoff AN, Greenson J, Fein D: Early social, imitation,
play, and language abilities of young non-autistic siblings of children
with autism. J Autism Dev Disord 2007, 37:145-157.
109. Lord C, Rutter M, Le Couteur A: Autism Diagnostic Interview-Revised: a
revised version of a diagnostic interview for caregivers of individuals
with possible pervasive developmental disorders. J Autism Dev Disord
1994, 24:659-685.
110. Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC,
Pickles A, Rutter M: The Autism Diagnostic Observation Schedule-Generic:
a standard measure of social and communication deficits associated
with the spectrum of autism. J Autism Dev Disord 2000, 30:205-223.
111. Weinberg CR, Shore DL, Umbach DM, Sandler DP: Using risk-based
sampling to enrich cohorts for endpoints, genes, and exposures. Am J
Epidemiol 2007, 166:447-455.
112. Constantino JN, Todd RD: Autistic traits in the general population: a twin
study. Arch Gen Psychiatry 2003, 60:524-530.
113. Ronald A, Happe F, Price TS, Baron-Cohen S, Plomin R: Phenotypic and
genetic overlap between autistic traits at the extremes of the general
population. J Am Acad Child Adolesc Psychiatry 2006, 45:1206-1214.
114. Skuse DH, Mandy W, Steer C, Miller LL, Goodman R, Lawrence K, Emond A,
Golding J: Social communication competence and functional adaptation
in a general population of children: preliminary evidence for sex-byverbal IQ differential risk. J Am Acad Child Adolesc Psychiatry 2009,
48:128-137.
115. Bryson SE, Zwaigenbaum L, McDermott C, Rombough V, Brian J: The
Autism Observation Scale for Infants: scale development and reliability
data. J Autism Dev Disord 2008, 38:731-738.
116. Mullen EM: Mullen Scales of Early Learning San Antonio, TX: Pearson
Education; 1995.
117. Bishop SL, Guthrie W, Coffing M, Lord C: Convergent validity of the
Mullen Scales of Early Learning and the differential ability scales in
children with autism spectrum disorders. Am J Intellect Dev Disabil 2011,
116:331-343.
118. Rozga A, Hutman T, Young GS, Rogers SJ, Ozonoff S, Dapretto M,
Sigman M: Behavioral profiles of affected and unaffected siblings of
children with autism: contribution of measures of mother-infant
interaction and nonverbal communication. J Autism Dev Disord 2011,
41:287-301.
119. Flanagan J, Landa R, Bauman M: Head lag in infants at risk for autism: a
preliminary study. Am J Occup Ther .
120. Newport DJ, Brennan PA, Green P, Ilardi D, Whitfield TH, Morris N,
Knight BT, Stowe ZN: Maternal depression and medication exposure
during pregnancy: comparison of maternal retrospective recall to
prospective documentation. BJOG 2008, 115:681-688.
Page 15 of 16
121. Voldsgaard P, Schiffman J, Mednick S, Rodgers B, Christensen H, Bredkjær S,
Schulsinger F: Accuracy of retrospective reports of infections during
pregnancy. Int J Methods Psychiatr Res 2002, 11:184-186.
122. Werler MM, Pober BR, Nelson K, Holmes LB: Reporting accuracy among
mothers of malformed and nonmalformed infants. Am J Epidemiol 1989,
129:415-421.
123. Langholz B, Thomas DC: Nested case-control and case-cohort methods of
sampling from a cohort: a critical comparison. Am J Epidemiol 1990,
131:169-176.
124. Steenland K, Deddens JA: Increased precision using countermatching in
nested case-control studies. Epidemiology 1997, 8:238-242.
125. Schaubel D, Hanley J, Collet JP, Bolvin JF, Sharpe C, Morrison HI, Mao Y:
Two-stage sampling for etiologic studies: sample size and power. Am J
Epidemiol 1997, 146:450-458.
126. Spiegelman D, Gray R: Cost-efficient study designs for binary response
data with Gaussian covariate measurement error. Biometrics 1991,
47:851-869.
127. Early Autism Risk Longitudinal Investigation (EARLI) Study. [https://www.
facebook.com/The.EARLI.Study].
128. All about the EARLI Study. [http://www.youtube.com/watch?
v=FdFxehVpZH4&lr=1].
129. Early Autism Risk Longitudinal Investigation. [http://www.earlistudy.org].
130. Julien R, Adamkiewicz G, Levy JI, Bennett D, Nishioka M, Spengler JD:
Pesticide loadings of select organophosphate and pyrethroid pesticides
in urban public housing. J Expo Sci Environ Epidemiol 2008, 18:167-174.
131. Allen JG, McClean MD, Stapleton HM, Webster TF: Critical factors in
assessing exposure to PBDEs via house dust. Environ Int 2008,
34:1085-1091.
132. Vojta PJ, Friedman W, Marker DA, Clickner R, Rogers JW, Viet SM,
Muilenberg ML, Thorne PS, Arbes SJ Jr, Zeldin DC: First National Survey of
Lead and Allergens in Housing: survey design and methods for the
allergen and endotoxin components. Environ Health Perspect 2002,
110:527-532.
133. Rudel RA, Camann DE, Spengler JD, Korn LR, Brody JG: Phthalates,
alkylphenols, pesticides, polybrominated diphenyl ethers, and other
endocrine-disrupting compounds in indoor air and dust. Environ Sci
Technol 2003, 37:4543-4553.
134. Wu N, Herrmann T, Paepke O, Tickner J, Hale R, Harvey LE, La Guardia M,
McClean MD, Webster TF: Human exposure to PBDEs: associations of
PBDE body burdens with food consumption and house dust
concentrations. Environ Sci Technol 2007, 41:1584-1589.
135. Colt JS, Davis S, Severson RK, Lynch CF, Cozen W, Camann D, Engels EA,
Blair A, Hartge P: Residential insecticide use and risk of non-Hodgkin’s
lymphoma. Cancer Epidemiol Biomarkers Prev 2006, 15:251-257.
136. Colt JS, Rothman N, Severson RK, Hartge P, Cerhan JR, Chatterjee N,
Cozen W, Morton LM, De Roos AJ, Davis S, Chanock S, Wang SS:
Organochlorine exposure, immune gene variation, and risk of nonHodgkin lymphoma. Blood 2009, 113:1899-1905.
137. Colt JS, Severson RK, Lubin J, Rothman N, Camann D, Davis S, Cerhan JR,
Cozen W, Hartge P: Organochlorines in carpet dust and non-Hodgkin
lymphoma. Epidemiology 2005, 16:516-525.
138. Thorne PS, Kulhankova K, Yin M, Cohn R, Arbes SJ Jr, Zeldin DC: Endotoxin
exposure is a risk factor for asthma: the national survey of endotoxin in
United States housing. Am J Respir Crit Care Med 2005, 172:1371-1377.
139. Bornehag CG, Sundell J, Weschler CJ, Sigsgaard T, Lundgren B,
Hasselgren M, Hägerhed-Engman L: The association between asthma and
allergic symptoms in children and phthalates in house dust: a nested
case-control study. Environ Health Perspect 2004, 112:1393-1397.
140. Georgiades S, Szatmari P, Zwaigenbaum L, Bryson SE, Brian J, Roberts W,
Smith IM, Vaillancourt T, Roncadin C: A prospective study of sub-threshold
autistic-like traits in unaffected siblings of children with autism
spectrum disorder [abstract 128.004]. Presented at the International
Meeting for Autism Research, 13 San Diego USA: CA 2011.
141. Kim SH, Lord C: Combining information from multiple sources for the
diagnosis of autism spectrum disorders for toddlers and young
preschoolers from 12 to 47 months of age. J Child Psychol Psychiatry 2012,
53:143-151.
142. Gotham K, Pickles A, Lord C: Standardizing ADOS scores for a measure of
severity in autism spectrum disorders. J Autism Dev Disord 2009,
39:693-705.
Newschaffer et al. Journal of Neurodevelopmental Disorders 2012, 4:7
http://www.jneurodevdisorders.com/content/4/1/7
143. Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL,
Metzger LM, Shoushtari CS, Splinter R, Reich W: Validation of a brief
quantitative measure of autistic traits: comparison of the Social
Responsiveness Scale with the Autism Diagnostic Interview-Revised. J
Autism Dev Disord 2003, 33:427-433.
144. Bolte S, Poustka F, Constantino JN: Assessing autistic traits: cross-cultural
validation of the Social Responsiveness Scale (SRS). Autism Res 2008,
1:354-363.
145. Virkud YV, Todd RD, Abbacchi AM, Zhang Y, Constantino JN: Familial
aggregation of quantitative autistic traits in multiplex versus simplex
autism. Am J Med Genet B Neuropsychiatr Genet 2009, 150B:328-334.
146. Constantino JN, Zhang Y, Frazier T, Abbacchi AM, Law P: Sibling recurrence
and the genetic epidemiology of autism. Am J Psychiatry 2010,
167:1349-1356.
147. Akshoomoff N: Use of the Mullen Scales of Early Learning for the
assessment of young children with autism spectrum disorders. Child
Neuropsychol 2006, 12:269-277.
148. Szatmari P, Merette C, Emond C, Zwaigenbaum L, Jones MB, Maziade M,
Roy MA, Palmour R: Decomposing the autism phenotype into familial
dimensions. Am J Med Genet B Neuropsychiatr Genet 2008, 147B:3-9.
149. Sparrow SS, Balla DA, Cicchetti DV, Bella DA: Vineland Adaptive Behavior
Scale-II San Antonio, TX: Pearson Education; 2005.
150. Fenton G, D’Ardia C, Valente D, Del Vecchio I, Fabrizi A, Bernabei P:
Vineland Adaptive Behavior profiles in children with autism and
moderate to severe developmental delay. Autism 2003, 7:269-287.
151. Wetherby AM, Prizant BM: Communication and Symbolic Behavior Scales
Developmental Profile (CSBS DP) Baltimore: Paul H. Brookes Publishing Co;
2002.
152. Eadie PA, Ukoumunne O, Skeat J, Prior MR, Bavin E, Bretherton L, Reilly S:
Assessing early communication behaviours: structure and validity of the
Communication and Symbolic Behaviour Scales-Developmental Profile
(CSBS-DP) in 12-month-old infants. Int J Lang Commun Disord 2010,
45:572-585.
153. Luyster R, Lopez K, Lord C: Characterizing communicative development in
children referred for autism spectrum disorders using the MacArthurBates Communicative Development Inventory (CDI). J Child Lang 2007,
34:623-654.
154. MacArthur-Bates Communicative Development Inventories. [http://www.
sci.sdsu.edu/cdi/].
155. Little LM, Freuler AC, Houser MB, Guckian L, Carbine K, David FJ,
Baranek GT: Psychometric validation of the Sensory Experiences
Questionnaire. Am J Occup Ther 2011, 65:207-210.
156. Garon N, Bryson SE, Zwaigenbaum L, Smith IM, Brian J, Roberts W,
Szatmari P: Temperament and its relationship to autistic symptoms in a
high-risk infant sib cohort. J Abnorm Child Psychol 2009, 37:59-78.
157. Rothbart MK: Measurement of temperament in infancy. Child Dev 1981,
52:569-578.
158. Achenbach TM: Achenbach System of Empirically Based Assessment (ASEBA):
Development, Findings, Theory, and Applications Burlington, VT: University of
Vermont, Research Center for Children, Youth, & Families; 2009 [http://www.
aseba.org].
159. Pandolfi V, Magyar CI, Dill CA: Confirmatory factor analysis of the Child
Behavior Checklist 1.5-5 in a sample of children with autism spectrum
disorders. J Autism Dev Disord 2009, 39:986-995.
160. Centers for Disease Control and Prevention (CDC): Autism Spectrum
Disorders: (ASDs): Study to Explore Early Development (SEED).[http://
www.cdc.gov/ncbddd/autism/seed.html].
161. UC Davis MIND Institute: Current Research Studies.[http://www.ucdmc.
ucdavis.edu/mindinstitute/research/esdm/currentprojects.html].
162. Brain Development in Autism: Infant Siblings. [http://www.ibisnetwork.
org].
163. Torrey EF, Dhavale D, Lawlor JP, Yolken RH: Autism and head
circumference in the first year of life. Biol Psychiatry 2004, 56:892-894.
164. Heikura U: Intellectual Disability in the Northern Finland Birth Cohort 1986
Oulu, Finland: Oulu University Press; 2008 [http://herkules.oulu.fi/
isbn9789514287114/isbn9789514287114.pdf].
165. Williams E, Thomas K, Sidebotham H, Emond A: Prevalence and
characteristics of autistic spectrum disorders in the ALSPAC cohort. Dev
Med Child Neurol 2008, 50:672-677.
166. Eliasen M, Tolstrup JS, Nybo Andersen AM, Grønbæk M, Olsen J,
Strandberg-Larsen K: Prenatal alcohol exposure and autistic spectrum
Page 16 of 16
167.
168.
169.
170.
171.
disorders: a population-based prospective study of 80,552 children and
their mothers. Int J Epidemiol 2010, 39:1074-1081.
Stoltenberg C, Schjølberg S, Bresnahan M, Hornig M, Hirtz D, Dahl C, Lie KK,
Reichborn-Kjennerud T, Schreuder P, Alsaker E, Oyen AS, Magnus P, Surén P,
Susser E, Lipkin WI, ABC Study Group: The Autism Birth Cohort: a
paradigm for gene-environment-timing research. Mol Psychiatry 2010,
15:676-680.
Tsuchiya K, Matsumoto K, Suda S, Miyachi T, Itoh H, Kanayama N, Hirano K,
Ohzeki T, Takei N, HBC Study Team: Searching for very early precursors of
autism spectrum disorders: the Hamamatsu Birth Cohort for Mothers
and Children (HBC). J Dev Orig Health Dis 2010, 1:158-173.
Magnus P, Irgens LM, Haug K, Nystad W, Skjærven R, Stoltenberg C, MoBa
Study Group: Cohort profile: the Norwegian Mother and Child Cohort
Study (MoBa). Int J Epidemiol 2006, 35:1146-1150.
Hertz-Picciotto I, Croen LA, Hansen R, Jones CR, van de Water J, Pessah IN:
The CHARGE study: an epidemiologic investigation of genetic and
environmental factors contributing to autism. Environ Health Perspect
2006, 114:1119-1125.
Schendel DE, DiGuiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA,
Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, PintoMartin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P,
Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H,
Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, et al: The
Study to Explore Early Development (SEED): a multisite epidemiologic
study of autism by the Centers for Autism Developmental Disabilities
Research and Epidemiology (CADDRE) Network1. J Autism Dev Disord ,
doi:10.1007/s10803-012-1461-8.
doi:10.1186/1866-1955-4-7
Cite this article as: Newschaffer et al.: Infant siblings and the
investigation of autism risk factors. Journal of Neurodevelopmental
Disorders 2012 4:7.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit

Purchase answer to see full
attachment