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RESEARCH ARTICLE
Comorbidity of Intellectual Disability Confounds
Ascertainment of Autism: Implications for Genetic
Diagnosis
Andrew Polyak,
1
Richard M. Kubina,
2
and Santhosh Girirajan
1,3
*
1
Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802
2
Department of Educational Psychology, Counseling, and Special Education, The Pennsylvania State University, University Park,
Pennsylvania 16802
3
Department of Anthropology, The Pennsylvania State University, University Park, Pennsylvania 16802
Manuscript Received: 11 February 2015; Manuscript Accepted: 17 June 2015
While recent studies suggest a converging role for genetic factors
towards risk for nosologically distinct disorders including au-
tism, intellectual disability (ID), and epilepsy, current estimates
of autism prevalence fail to take into account the impact of
comorbidity of these disorders on autism diagnosis. We aimed
to assess the effect of comorbidity on the diagnosisand prevalence
of autism by analyzing 11 years (2000–2010) of special education
enrollment data on approximately 6.2 million children per year.
We found a 331% increase in the prevalence of autism from 2000
to 2010 within special education, potentially due to a diagnostic
recategorization from frequently comorbid features such as ID.
The decrease in ID prevalence equaled an average of 64.2% of the
increase of autism prevalence for children aged 3–18 years. The
proportion of ID cases potentially undergoing recategorization
to autism was higher (P¼0.007) among older children (75%)
than younger children (48%). Some US states showed significant
negative correlations between the prevalence of autism compared
to that of ID while others did not, suggesting state-specific health
policy to be a major factor in categorizing autism. Further, a high
frequency of autistic features was observed when individuals with
classically defined genetic syndromes were evaluated for autism
using standardized instruments. Our results suggest that current
ascertainment practices are based on a single facet of autism-
specific clinical features and do not consider associated comor-
bidities that may confound diagnosis. Longitudinal studies with
detailed phenotyping and deep molecular genetic analyses are
necessary to completely understand the cause of this complex
disorder. 2015 Wiley Periodicals, Inc.
Key words: neurodevelopmental disorders; prevalence; mi-
crodeletion; syndrome
INTRODUCTION
Autism is a neurodevelopmental disorder characterized by impair-
ments in social reciprocity, speech and communication, and
restricted, repetitive and stereotyped patterns of behavior [Ameri-
can Psychiatric Association, 2000]. Several epidemiological reports
have suggested an apparent increase in the prevalence of autism
[Shattuck, 2006; King and Bearman, 2009; Autism et al., 2012]. A
recent study by the United States Center for Disease Control
estimated the prevalence of autism among 8-year-old children,
within the autism and developmental disabilities monitoring
(ADDM) network sites in 2010, to be one in 68 children [Rice
et al., 2007; Baio et al., 2012]. This estimate was a documented
120% increase in prevalence when compared with the data from
2002 (one in 150 children). Another study, based on a population
screening of 7–12-year-old elementary school children in a South
Korean community in 2006, estimated an overall autism prevalence
of one in 38 children [Kim et al., 2011].
While the rise in autism prevalence has been attributed to
various factors including increased awareness [Prior, 2003] and
broadening of the diagnostic criteria [Shattuck, 2006; King and
Bearman, 2009], significant clinical heterogeneity and the non-
specific molecular etiology of autism have precluded robust esti-
mates of prevalence [Levy et al., 2009; Lai et al., 2014]. Further,
Conflict of interest: The authors have no conflicts of interest to declare.
Correspondence to:
Santhosh Girirajan, M.B.B.S., Ph.D., Department of Biochemistry and
Molecular Biology, and Department of Anthropology, 205A Life Sciences
Building, The Pennsylvania State University, University Park, PA 16802.
E-mail: sxg47@psu.edu
Article first published online in Wiley Online Library
(wileyonlinelibrary.com): 00 Month 2015
DOI 10.1002/ajmg.b.32338
How to Cite this Article:
Polyak A, Kubina RM, Girirajan S. 2015.
Comorbidity of Intellectual Disability
Confounds Ascertainment of Autism:
Implications for Genetic Diagnosis.
Am J Med Genet Part B 9999:1–9.
2015 Wiley Periodicals, Inc. 1
Neuropsychiatric Genetics
there is a documented clinical overlap or comorbidity of nosologi-
cally distinct neurodevelopmental disorders [Mitchell, 2011; Coe
et al., 2012]. For example, premorbid social impairment or perva-
sive developmental disorders (PDD) have been observed in 50–
87% of individuals with childhood-onset schizophrenia [Sporn
et al., 2004; Rapoport et al., 2009; King and Lord, 2011; Gadow,
2012; Cristino et al., 2014]. Similarly, features of intellectual
disability (ID) have been reported in as high as 68% of individuals
with autism [Yeargin-Allsopp et al., 2003], and epilepsy and
attention-deficit hyperactivity disorders (ADHD) have been
reported to co-occur in as high as 38.3% and 59%, respectively,
of children with autism [Tuchman and Rapin, 2002; Levy et al.,
2009; Viscidi et al., 2013]. Accumulating evidence from genomic
studies suggests a converging role for genetic factors towards a
common molecular etiology for these varied neurodevelopmental
disorders [Cristino et al., 2014; Noh et al., 2013; Steinberg and
Webber, 2013; Fromer et al., 2014]. Although the epidemiological
studies, to date, report statistically significant increases in the
prevalence of autism, they fail to take into account the effect of
the comorbidity of other disorders on the diagnosis and prevalence
of autism. To better understand the effect of comorbid features on
autism prevalence, we systematically analyzed 11 years (2000–
2010) of epidemiological data on an average of 6.2 million children
per year from the United States special education enrollment. We
examined the frequency and age-specific prevalence of autism and
frequently comorbid clinical features. Our results suggest that
comorbidity of ID can significantly impact diagnosis and confound
prevalence estimates of autism.
METHODS
Special Education Data
The individuals with disabilities education act (IDEA) is a law
originally enacted in 1975 that ensures services to children with
disabilities, ascertained by 13 disability categories throughout the
United States. We obtained special education enrollment data, i.e.,
the number of children receiving services under various disability
categories, from publicly available databases (IDEA part B) for the
50 US states. The IDEA part B database includes annual state-by-
state counts of children aged 3–21 years documented by their age
and classification of disorder (Supplementary Figure S1). However,
other clinical details (phenotypic scores, gender, etc) were not
available. For the current study, we obtained enrollment data on
approximately 6.2 million children per year aged 3–21 ye ars over an
11-year period spanning 2000–2010 evaluated by special education
and placed under one of the 13 disability categories (Supplemen-
tary Table S1). Children recruited under the special education act
are ascertained under only one disability category. However,
reclassification to a different IDEA category is possible (e.g., an
individual originally identified as having ID can be reevaluated and
then reclassified into the autism category). Notably, school districts
do not always use the Diagnostic and Statistical Manual of Mental
Disorders criteria for classifying any of these diagnostic categories
[Bertrand et al., 2001; Maenner and Durkin, 2010; Boyle et al.,
2011]. Further, the Department of Education’s legal definitions of
disorders under the IDEA categories are generalized allowing for a
broader interpretation, and therefore, the ascertainment for each
disorder may vary between different states [Shattuck, 2006; Kogan
et al., 2009].
Data Analysis
We used the United States intercensal estimates for ages 3–21 years
for 2000–2010 to create proportions (number enrolled out of
10,000 children) for each ascertainment category. To assess the
impact of comorbid features on autism prevalence, we estimated
combined proportions of one or more related diagnostic categories
including autism spectrum disorders (ASD), intellectual disability
(ID), other health impairment (OHI), emotional disturbance
(ED), and specific learning disability (SLD), whose phenotypes
have significant comorbidity with autism [Yeargin-Allsopp et al.,
2003; Levy et al., 2009; Autism et al., 2012; Lai et al., 2014]. We note
that “developmental delay” was included within the intellectual
disability category. Throughout the manuscript, the term “autism”
is used interchangeably with “ASD.” We used simple linear regres-
sion to model the relationship between the year and prevalence of
the phenotypic category. Using a nominal cut-off of P<0.05, we
determined if the slope of each model was statistically significant.
RESULTS
Consistent with recent reports [Yeargin-Allsopp et al., 2003; Au-
tism et al., 2012] we find a significant rise in the prevalence of
autism from 1.2 per 1,000 in 2000 to 5.2 per 1,000 in 2010 (331%
increase; linear regression, P¼4.58 10
10
) within the US special
education population (Fig. 1). However, significant decrease in
prevalence from 8.3 per 1,000 in 2000 to 5.7 per 1,000 in 2010 was
observed for ID (31% decrease, P¼3.1 10
10
). Significant
decreases were also seen for categories of emotional disturbance
(22%, P¼6.1 10
5
), and specific learning disability (19%, P
¼4.1 10
8
) (Supplementary Table S2). However, there was no
significant change (linear regression, P¼0.57) in the overall
proportion of children in special education ascertained under
IDEA from 2000 to 2010 (Supplementary Figure S1). Based on
recent studies suggesting common molecular etiology [Coe et al.,
2012] and documented reports of comorbidity of autism and other
neurodevelopmental disorders [Lai et al., 2014], we hypothesized
that estimates of autism prevalence are confounded by the presence
of comorbid phenotypes. We tested the impact of comorbidities on
the prevalence of autism by first combining all related neuro-
developmental phenotypes whose prevalence changed significantly
over the 11-year period. The combined prevalence of autism, ID,
specific learning disability, and other health impairment did not
change significantly from 2000 to 2010 (51.0 per 1,000 in 2000 to
50.3 per 1,000 in 2010; P¼0.19) suggesting that the prevalence of
autism is influenced by related comorbid phenotypes (Supplemen-
tary Figure S2).
Interestingly, when only the prevalence estimates of autism and
ID were considered together, the combined prevalence increased by
15% (9.5 per 1,000 in 2000 to 10.9 per 1,000 in 2010, P¼1.7 10
6
)
(Fig. 1A). This increase is 22-fold less than the increase seen for
autism prevalence alone (331% vs. 15%), suggesting that a potential
diagnostic recategorization from ID to autism can account for a
2 AMERICAN JOURNAL OF MEDICAL GENETICS PART B
significant amount of autism prevalence. In order to determine the
ages at which these changes were most significant, we analyzed the
age-specific changes in prevalence for autism and ID for the ages
between 3 and 21 years. We found that an increase in autism
prevalence, specifically across ages 3–18 years, corresponded to a
decrease in the prevalence of ID (Fig. 1B). These changes in autism
prevalence compared to those in ID allowed us to calculate the
possible magnitude of diagnostic recategorization from ID to au-
tism. For example, at age 8 years, up to 59% of the increase in autism
prevalence could be attributed to a diagnostic recategorization of ID.
On an average, between the ages of 3 and 18 years the decrease in ID
prevalence equaled 64.2% of the increase of autism prevalence, and
these estimates rise to as high as 97% at age 15 years. The magnitude
of this change, at age 15 years, from 2000 to 2010 in the observed
prevalence of autism is 35-fold greater than that compared to the
expected change if autism and ID were combined as a single category
(Supplementary Table S3 and Figure S3). Further, older children
(ages from 10 to 18 years) with ID were more likely to have a shift of
diagnosis towards autism than younger children (ages from 3 to 9
years) (Mann Whitney test, P¼0.007) (Supplementary Figure S4).
We also found that autism and ID can be distinguished based on age-
prevalence. When evaluated by age, the prevalence of autism peaked
between ages 7 and 9 years, while the age-specific prevalence for ID
peaked between ages 11 and 19 years (Supplementary Figure S5).
To assess if the autism diagnostic criteria are uniform across all
US states, we compared the prevalence of autism with the preva-
lence of other disorders over the 11 year period. We observed
positive correlations, at varying degrees, between the prevalence of
autism and that of OHI (Pearson’s correlation coefficient r ¼0.49,
P¼0), OI (r ¼0.097, P¼0.02), ED (r ¼0.19, P¼2.38 10
15
),
HI (r ¼0.12, P¼0.008), TBI (r ¼0.21, P¼2.54 10
5
), and
multiple disabilities (r ¼0.15, P¼0.001) (Supplementary
Figure S6 and Table S4). Further, negative correlations, at signifi-
cant levels, were observed between the prevalence of autism
compared to that of ID (r ¼0.26, P¼1.10 10
9
), and SLD
(r ¼0.26, P¼1.10 10
9
) when all US states were considered
together (Fig. 2, Supplementary Figure S7). Interestingly, the
correlation estimates were higher (r >0.99) for some US states
than others when prevalence of autism was compared to that of ID
(Table I). US states with a higher prevalence rate for ID were more
likely (Mann Whitney test, P¼0.0476) to show a negative corre-
lation with autism prevalence than those states with a lower
prevalence of ID (Supplementary Figure S8). For example, North
Dakota, Vermont, and Georgia showed the strongest correlation
coefficients (0.999, 0.998, 0.997, respectively). However,
states such as Arizona, New Jersey, and Wyoming showed less
strong correlation coefficients (0.625, 0.621, 0.749, respec-
tively) and certain states, such as California, New Mexico, and
Texas, showed no correlation at all. These results potentially reflect
differences in state specific policies for ascertainment of children
under special education.
DISCUSSION
We analyzed one of the largest cohorts of longitudinal special
education population data, through which we observed a 331%
increase in the 11-year autism prevalence. Due to the unavailability
of standardized phenotypic measures (e.g., IQ scores) to determine
FIG. 1. Prevalence of phenotypes from years 2000 to 2010 ascertained through special education enrollment. (A) Yearly prevalence (out of
10,000) is shown for the 13 special education categories including autism, intellectual disability (ID), specific learning disability (SLD),
developmental delay (DD), other health impairments (OHI), emotional disturbance (ED), speech and language impairments (SLD), multiple
disorders (MI), traumatic brain injuries (TRA), deaf-blindness (DB), deafness (DEA), orthopedic impairments (OI), hearing impairments (HI),
and visual impairments (VI). The combined prevalence of autism and ID (Autism þID) is also shown. US intercensal estimates for ages
between 3 and 21 years were used as denominator in the prevalence calculations. (B) The age-specific changes (2000–2010) in prevalence
of autism (red) and ID (blue) are shown from ages 3 to 21 years. Percentage of autism prevalence that can be attributed to diagnostic
change from ID to autism is shown for each age as numbers above the red bars.
POLYAK ET AL. 3
if autistic individuals also showed features of ID, we performed
analysis under the premise that individuals manifesting both
autism and ID were more likely to be binned into the autism
category than that of ID. Nevertheless, the following observations
suggest that a diagnostic recategorization towards autism is occur-
ring, potentially confounding estimates of autism prevalence. First,
we find no change in the overall proportion of children enrolled in
the special education cohort from 2000 to 2010, suggesting that any
perceived increase in prevalence within the cohort to be due to a
recategorization of ascertainment practices. Second, we find that
the increasing trend in autism prevalence disappears when the
combined prevalence of autism and related comorbid features were
considered (Supplementary Figure S2). Finally, the age-specific
changes in prevalence of autism from 2000 to 2010 closely mirrors
that of ID, with an average of 64.2% of the increase in prevalence of
autism potentially explained by a concomitant decrease in the
prevalence of ID. This phenomenon of diagnostic recategorization
has been noted previously [Shattuck, 2006; King and Bearman,
2009], however, the magnitude of effect from comorbid features
has not been documented. Our study shows a 22-fold drop in
prevalence increase when considering the prevalence of a broader
neurodevelopmental disorder category including both autism and
ID. The proportion of ID cases potentially undergoing diagnostic
recategorization to autism was higher among older children (75%)
than younger children (48%). These results suggest that comorbid
features can confound true prevalence estimates of the autism
disorder. We also found that the disability categories within the
special education data showed distinct age-specific prevalence
rates. For example, prevalence estimates peaked between ages 7
and 9 years for autism and between ages 11 and 18 years for ID.
These prevalence peaks suggest distinct developmental trajectories
and specific diagnostic windows for certain comorbid phenotypes.
Interestingly, one recent study found that older children with
autism were more likely to retain their diagnosis than those
diagnosed at a younger age suggesting the complexities associated
with using one set of identifiable features as diagnostic criteria
[Wiggins et al., 2012]. It is likely that older children are more
severely affected manifesting intellectual disability and other co-
morbid phenotypes at a later age. Other disorders in addition to ID
can also potentially contribute to a diagnostic recategorization to
autism. In fact, a significant negative correlation was observed
between the prevalence of autism and that of SLD (Supplementary
Figure S7), suggesting SLD as another potential contributor to
diagnostic recategorization. Further, we found positive correla-
tions between the prevalence of autism and disorders such as OHI
and ED, suggesting that a complex recategorization of disorders
is occurring independent of autism. However, we were unable to
assess the possible effect of diagnostic recategorization from SLD or
OHI as the change in the number of enrolled children within these
disorders (469,216 and 420,840 individuals, respectively) over the
11-year period was more than five times that of autism (93,624
individuals).
FIG. 2. Negative correlation between the prevalence of autism to that of intellectual disability (r ¼0.26, P¼1.10 10
9
). Pearson
correlation coefficients were used to assess the relationship between the prevalence of autism and each of the comorbid phenotypic
categories within the special education enrollment.
4 AMERICAN JOURNAL OF MEDICAL GENETICS PART B
TABLE I. Pearson’s Correlation Coefficients Between the Prevalence of Autism and That of ID for Each of the 50 US States From Years 2000–
2010 for Individuals Ages 3–21 Within Special Education
State
ASD prevalence
in 2000
ASD prevalence
in 2010
ID prevalence
in 2000
ID prevalence
in 2010 P-value Pearson’s r
Alabama 7.02 35.58 167.48 NA 6.58E-03 0.933
Alaska 11.31 41.99 41.81 31.95 5.62E-04 0.961
Arizona 8.39 43.41 51.55 45.3 3.96E-02 0.625
Arkansas 10.5 37.46 163.46 78.28 3.56E-09 0.991
California 14.32 65.1 40.58 41.65 0.68 0.143
Colorado 4.3 29.09 29.67 22.98 4.83E-08 0.984
Connecticut 15.74 70.63 43.21 27.6 7.17E-06 0.951
Delaware 15.43 43 102.4 72.07 2.93E-04 0.885
Florida 11.52 44.54 105.05 65.57 2.89E-08 0.986
Georgia 9.47 42.53 136.7 68.16 6.03E-12 0.998
Hawaii 11.55 40.28 86.44 37.11 3.46E-11 0.997
Idaho 8.05 45.64 49.11 40.68 8.88E-04 0.851
Illinois 12.61 49.01 83.28 61.73 5.47E-07 0.972
Indiana 18.12 67.46 137.21 101.83 3.26E-06 0.959
Iowa 8.25 9.66 208.84 148.89 0.09 0.54
Kansas 9.24 33.99 72.76 47.46 6.90E-10 0.994
Kentucky 9.61 35.77 167.67 141.23 3.52E-06 0.958
Louisiana 9.62 29.25 91.65 64.05 1.23E-04 0.906
Maine 18.3 87.11 32.74 23.77 1.80E-06 0.964
Maryland 16.28 60.55 48.72 36.36 4.48E-05 0.925
Massachusetts 5.01 75.65 97.78 64.96 7.54E-03 0.752
Michigan 17.03 57.59 90.44 79.17 1.97E-03 0.821
Minnesota 20.25 106.59 74.22 62.81 2.13E-08 0.987
Mississippi 4.97 28.99 68.83 NA 1.84E-03 0.937
Missouri 11.24 47.43 81.95 67.49 7.98E-07 0.97
Montana 7.51 25.57 51.53 40.74 3.15E-03 0.799
Nebraska 7.7 42.85 127.17 81.11 1.86E-07 0.978
Nevada 9.11 52.8 34.06 29.16 2.56E-04 0.889
New Hampshire 11.95 53.6 30.41 NA 7.71E-04 0.977
New Jersey 15.49 61.1 27.52 25.1 4.16E-02 0.621
New Mexico 4.3 27.96 35.57 33.91 0.55 0.201
New York 13.54 48.54 32.66 25.62 1.01E-05 0.947
North Carolina 12.47 48.89 137.44 77.4 8.82E-11 0.996
North Dakota 7.4 NA 68.28 44.9 6.49E-09 0.999
Ohio 8.25 55.51 191.65 90.12 1.87E-06 0.964
Oklahoma 7 30.77 87.89 55.97 3.15E-06 0.959
Oregon 32.47 89.22 48.92 42.54 5.81E-06 0.953
Pennsylvania 12.74 67.61 88.06 66.34 4.66E-10 0.994
Rhode Island 12.81 67.2 44.17 33.54 5.28E-04 0.868
South Carolina 8.84 32.83 157.92 67.07 1.26E-08 0.988
South Dakota 11.88 35.17 66.84 66.54 0.91 0.04
Tennessee 7.25 37.22 97.3 47.23 1.59E-07 0.979
Texas 11.46 45.51 41.33 44.53 0.24 0.39
Utah 8.28 44.6 41.68 36.53 2.16E-07 0.978
Vermont 13.28 56.15 79.25 NA 6.00E-06 0.998
Virginia 11.94 57.84 77.57 51.43 7.01E-07 0.971
Washington 10.44 50.73 40.88 27.22 5.81E-06 0.977
West Virginia 7.32 32.34 208.86 167.39 8.94E-07 0.969
Wisconsin 14.32 57 90.27 65.27 2.92E-10 0.995
Wyoming 8.13 44.69 45.88 38.79 8.04E-03 0.749
NA, data not available for year.
POLYAK ET AL. 5
While a negative correlation between the prevalence of autism
and that of ID was observed for all the US states as a whole, when
assessed individually, not all states showed the same strength of
correlation. While the differential rates of autism prevalence reflect
differences in ascertainment in special education schools across the
US states, documented evidence of inconsistency in ascertainment
even among groups following set diagnostic criteria suggests exten-
sive heterogeneity of the disorder [Lord et al., 2012]. In fact, a recent
study found the prevalence of autism and ID to be associated with
state-related regulatory factors, and even found strong correlations
between smaller, county-related factors and autism and ID preva-
lence [Rzhetsky et al., 2014]. Another report in 2001 found the
prevalence of autism within The Brick Township, New Jersey, to be
significantly higher than that of the US [Bertrand et al., 2001].
Further, Davidovitch and colleagues found a lower prevalence of
autism among an Israeli population compared to the US [Davido-
vitch et al., 2013]. A recent study in the United Kingdom using the
UK General Practice Research Database showed a strikingly similar
incidence of autism over a periodof 10 years suggesting no apparent
increase in prevalence rates [Taylor et al., 2013]. These examples
indicate variability in ascertainment of children with neurodeve-
lopmental disorders across different regions, and highlight the need
for large-scale studiesof autism prevalence to take these health policy
variations into account.
Several clinical studies have documented varying percentages of
comorbid features suggesting that comorbidity in autism is the
norm rather than the exception. Changes in nosology as suggested
by revisions to the DSM have certainly contributed to the devia-
tions from the original description of autism. This is reflected by
the fact that only 81.2% of children previously diagnosed with
autism by DSM-IV met the criteria according to DSM-V [Maenner
et al., 2014]. However, studies estimating prevalence of autism
seem to focus on one dimension of clinical features, often ignoring
other comorbid features. For example, 40.2% of individuals iden-
tified with autism by the ADDM network were actually enrolled
under eight different special education categories other than
autism (Supplementary Figure S9). These rates also varied among
states within the ADDM network, further suggesting a major
impact by state-specific policies. While it would be important to
understand how these comorbidity rates change over time, one
limitation of the IDEA dataset was that individuals were only
placed into a single diagnostic category.
The relatively high rate of comorbidity within autism may be
due to a wide array of common genes implicated in many neuro-
developmental disorders [Pettersson et al., 2013]. Further, core
components in autism diagnosis show a documented overlap in
clinical features such as language impairments [Taylor et al., 2014].
Interestingly, when individuals with classically defined genetic
syndromes were evaluated for autism using standardized instru-
ments, higher frequency of autistic features were observed. In fact,
some of these were never thought to be an autism disorder. For
example, the frequency of autism in Smith–Magenis syndrome, a
TABLE II. Frequency of Autism Features in Classically Defined Genetic Syndromes
Disorder Frequency (%) Reference Autism diagnosis instrument
22q11.2 deletion syndrome 40 Niklasson et al. [2009] DSM-IV
Angelman Syndrome 42 Peters et al. [2004] ADOS/DSM-IV
Beckwith–Wiedemann Syndrome 7 Kent et al. [2008] Previous diagnosis
Charge Syndrome 28 Hartshorne et al. [2005] ABC
Chromosome 2q Terminal Deletion 24 Casas et al. [2004] Previous diagnosis
Cohen Syndrome 79 Howlin et al. [2005] ADOS
Cornelia De Lange Syndrome 83 Srivastava et al. [2014] CARS
Cowden Syndrome 53 Varga et al. [2009] DSM-IV
Down Syndrome 19 Moss et al. [2013] SCQ
Fragile-X Syndrome 63 Garcia-Nonell et al. [2008] ADOS-G/DSM-IV
Inverted 8p Deletion Syndrome 75 Fisch et al. [2011] CARS
Jacobsen Syndrome 47 Akshoomoff et al. [2015] ADOS
Klinefelter Syndrome 27 Bruining et al. [2009] ADI-R
Lujan–Fryns Syndrome 63 Lerma-Carrillo et al. [2006] Unspecified
Moebius Syndrome 40 Johansson et al. [2001] DSM-3R/ICD-10
Neurofibromatosis Type 1 4 Williams and Hers [1998] DSM-IV
Phelan–McDermid Syndrome 94 Phelan et al. [2001] CARS
Potocki–Lupski syndrome 66 Treadwell-Deering et al. [2010] ADI-R and ADOS
Prader–Willi Syndrome 36 Lo et al. [2013] DISCO
Smith Magenis Syndrome 90 Laje et al. [2010] SRS, SCQ
Smith–Lemli–Opitz Syndrome 71–86 Sikora et al. [2006] ADOS
Sotos Syndrome 68 Zafeiriou et al. [2013] SCQ
Timothy Syndrome 80 Splawski et al. [2004] Unspecified
Williams–Beuren Syndrome 93 Klein-Tasman et al. [2009] ADOS
Wolf–Hirschhorn Syndrome 5 Fisch et al. [2010] CARS
ADOS, autism diagnostic observation schedule; ADI-R, autism diagnostic interview-revised; ABC, autism behavior checklist; CARS, childhood autism rating scale; DISCO, the diagnostic interview for social
and communication; SRS, social responsiveness scale, SCQ, social communication questionnaire; DSM, diagnostic and statistical manual of mental disorders.
6 AMERICAN JOURNAL OF MEDICAL GENETICS PART B
disorder characterized by severe intellectual disability/multiple
congenital anomalies, was reported to be as high as 90% [Laje
et al., 2010] (Table II). About 63% of individuals with Fragile-X
syndrome, one of the most common causes of intellectual disabili-
ty, was reported to show features of autism [Garcia-Nonell et al.,
2008]. Further, 93% of individuals with Williams–Beuren Syn-
drome, a disorder characterized by severe developmental delay,
also showed features of autism [Klein-Tasman et al., 2009]. While
these studies suggest that autistic features are pervasive in neuro-
developmental disorders, it is possible that many autism diagnosis
instruments lose specificity when applied to severe intellectual
disability disorders. These factors may create a confounding effect
on autism diagnosis.
In conclusion, we propose that nosologically distinct neuro-
developmental phenotypes are not necessarily independent entities
and can appear during early or late developmental stages and
coexist as comorbid features in an affected individual. Como rbidity
of one or more related neurodevelopmental phenotypes with
autism may confound the diagnosis and affect the perceived
prevalence of autism. This may be due to the emphasis given to
the autism component of their diagnoses, as compared to emphasis
on the comorbid features in the past years. Further, the differences
in the relative severity of each of these comorbid features can
complicate definitive diagnosis. Evidently, because these features
co-occur to a large extent, they transcend diagnostic boundaries
and contribute to the variability and severity as well as confound
disease ascertainment. It is therefore clear that the patterns of
underlying genetic etiology neither map well onto current disease
“models” nor respect the DSM categories [Kendler, 2010; Lichten-
stein et al., 2010]. Large-scale longitudinal studies with detailed
phenotyping and deep molecular genetic analyses are necessary to
completely understand the cause and effect of these “disease
models.” It is important that future studies of autism prevalence
take these factors into account.
ACKNOWLEDGMENTS
We thank Dr. Evan Eichler, Dr. Sarah Elsea, Dr. Paul Medvedev,
Dr. Catarina Campbell, Dr. Karyn Meltz-Steinberg, and Dr. Francesca
Chiaramonte, and members of the Girirajan lab for critical reading
and comments on the manuscript. The authors declare that no conflict
of interest exists in relation to this work.
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