Minor physical anomalies in autism: a meta-analysis
HM Ozgen1,2, JW Hop1, JJ Hox3, FA Beemer4and H van Engeland1,2
1Department of Child and Adolescent Psychiatry, University Medical Centre, Utrecht, The Netherlands;
2Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, Utrecht, The Netherlands;
3Department of Methodology and Statistics, Utrecht, The Netherlands and4Department of Medical Genetics,
University Medical Centre, Utrecht, The Netherlands
Autism is a complex neurodevelopmental disorder in which the interactions of genetic,
epigenetic and environmental influences play a causal role. Despite the compelling evidence
for a strong heritability, the etiology and molecular mechanisms underlying autism remain
unclear. High phenotypic variability and genetic heterogeneity confounds the identification of
susceptibility genes. The lack of robust indicators to tackle this complexity in autism has led
researchers to seek for novel diagnostic tools to create homogenous subgroups. Several
studies have indicated that patients with autism have higher rates of minor physical anomalies
(MPAs) and that MPAs may serve as a diagnostic tool; however, the results have been
inconsistent. Using the cumulative data from seven studies on MPAs in autism, this meta-
analysis seeks to examine whether the aggregate data provide evidence of a large mean effect
size and statistical significance for MPAs in autism. It covers the studies using multiple
research methods till June 2007. The current results from seven studies suggested a
significant association of MPAs in autism with a robust pooled effect size (d=0.84), and
thereby provide the strongest evidence to date about the close association between MPAs and
autism. Our results emphasize the importance of MPAs in the identification of heterogeneity in
autism and suggest that the success of future autism genetics research will be exploited by the
use of MPAs. Implications for the design of future studies on MPAs in autism are discussed
and suggestions for further investigation of these important markers are proposed. Clarifying
this relation might improve understanding of risk factors and molecular mechanisms
Molecular Psychiatry (2008) 0, 000–000. doi:10.1038/mp.2008.75
Keywords: autistic disorder; heterogeneity; etiology; congenital abnormalities; classification;
genetics; genotype; phenotype; case–control studies; biological marker
Autism (OMIM 209850) is a severe neurodevelop-
mental disorder, characterized by qualitative impair-
ments in social interaction and communication,
accompanied by repetitive and stereotyped behaviors
and interests. These symptoms manifest in the first 3
years of age with a lifelong persistence.1
prevalence is estimated to be approximately 1 in
150, making it one of the most prevalent medical
conditions of childhood.2,3Boys are affected approxi-
mately four times more than girls, with an even higher
ratio in milder forms of the broad spectrum.4
The vulnerability for developing autism is highly
genetically determined. Twin studies indicated sub-
stantially greater concordance for autism in mono-
zygotic than in dizygotic twins.5–10Moreover, family
studies revealed a recurrence risk of 5–6% among
siblings of affected individuals, which is much higher
than the prevalence rate in the general population.11,12
Together, these data show that autism is a strongly
genetically influenced multifactorial childhood psy-
The prevailing view is that its etiology involves a
complex interaction between multiple genes and
possibly environmental insults, leading to an aberrant
neurodevelopment.16–18Researchers have attempted
to overcome the challenges posed by such a complex-
ity with reliable diagnostic tools, including the study
of head circumference and other morphological
characteristics.19–22Excessive head growth found in
the first year of life, in children later diagnosed
with autism, has been one of the most promising
As to other morphological characteristics, an excess
of minor physical anomalies (MPAs) in autistic
individuals received specific attention. MPAs are
defined as slight morphological deviations that have
no serious medical or cosmetic significance to an
individual. However, they are of great value to the
clinician because they can be utilized as indicators
of underlying disease susceptibility or disturbed
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Received 12 December 2007; revised 30 May 2008; accepted 9
Correspondence: Dr HM Ozgen, Department of Child and
Adolescent Psychiatry, UMC Utrecht, B01.201, PO Box 85500,
3508 GA, Utrecht, The Netherlands.
Molecular Psychiatry (2008) 0, 1–8
& 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00
additional studies. In addition, individuals with
expertise in the area of dysmorphology were asked
for studies which were in press or any other papers of
particular interest. Abstracts of studies identified by
the search strategies were then scrutinized to deter-
mine whether they could be included or not. This
search returned 78 potential hits of which the
development (for example, they are found to be more
common in individuals with an obvious major
embryonic defect).28–31The presence of such MPAs
in autism has been suggested to be related to the
shared genetic risk of developing autism.32,33
Although, in the psychiatric literature ‘MPAs’ is
generally accepted, many different terms are used to
describe them, including dysmorphic features, minor
congenital anomalies or minor malformations. The
majority of the studies that have assessed the
incidence of MPAs in autism used the Waldrop scale
(vide infra), with occasional modifications and omis-
sions of items.34–36MPAs in that list originated from
an unpublished study by Goldfarb and Botstein37to
classify schizophrenic patients. However, although
the Waldrop scale is able to dissociate patients from
controls, it has been criticized for inherent limitations
regarding both content and form, its restricted range
of 18 items, low sensitivity, subjective nature, ethnic
effects and inter-rater reliability.38–41
To date, there are a number of studies that have
examined the prevalence of MPAs in children with
autism.32,42Although most of these studies showed
excess MPAs in autistic individuals as compared to
healthy controls, the findings are inconsistent regard-
ing the magnitude and extent of the case–control
differences. There is substantial variation in MPA
scores across studies, for autistics as well as for the
control groups. Moreover, effect sizes in the indivi-
dual studies have not been quantitatively reviewed
and integrated in a meta-analytical way.
The aim of the present meta-analysis was to
produce a synthesized effect-size estimate that has
considerably more power than the individual stu-
dies.43In addition, in order to identify the sources of
variation across the studies, effects of the study
characteristics on the findings were analyzed.
Materials and methods
An extensive bibliographic search was conducted to
identify relevant articles that examined the incidence
of MPAs in autism. Pubmed, Cinahl, PsycINFO and
the Cochrane library were searched from inception to
June 2007. For the query translation, Medical Subject
Headings terms were used where they were available.
The thesaurus for index terms was also checked to
identify possible synonyms. The keywords used in
the computerized search were ‘clinical morphology’,
‘minor physical anomalies’, ‘dysmorphology’ and
‘autism’. The reference lists cited in these studies
and published reviews were examined to identify
abstracts were evaluated and 16 articles were found
to be relevant to the study.
Studies were considered eligible for inclusion if: (1)
they were designed as a case–control study where the
controls were healthy children; (2) they had used the
Waldrop scale or some variant of it in the MPA
assessment; (3) they had presented sufficient data to
compute effect size in the form of a standardized
difference between means (that is, means, standard
deviations, exact P, t or F values); (4) they were
written in English. As the primary focus of this study
was on MPAs that are listed on a standard scale
reports that examined only head
circumference or major abnormalities were all ex-
cluded. Studies that reported previously published
data were also excluded. Selection of the studies for
inclusion was completed by two authors (HMO and
JWH). Authors of the identified studies were con-
tacted if there were queries regarding their studies.
Data were independently extracted by two authors
(HMO and JWH), using a structured pro forma. For
each eligible study, recorded data variables were
authors, year and country of publication; demo-
graphic variables (mean age, male/female ratio and
ethnicity); diagnostic criteria (if applicable); study
size (number of participants and controls) and rating
methodology (whether raters were trained to recog-
nize dysmorphic features, blinding of the authors and
inter-rater reliability). The assessment scale used to
identify and quantify MPAs was classified as ‘Wal-
drop scale’ or ‘Waldrop scale modified’. The number
of MPA scale items used in each study was also
recorded. Any discrepancy between ratings was
discussed and resolved by consensus.
The key to meta-analysis is defining an effect size
capable of representing the quantitative findings of a
set of research studies in a standardized form that
permits meaningful comparison and analyses across
the studies.43Therefore, for each individual study, an
unbiased standardized mean difference (d) was
calculated. This effect size was computed as the
difference between the mean of the autistic group and
that of the control group, divided by the pooled
standard deviation. The resulting effect size was
corrected for upwardly biased estimation due to small
sample size by using Hedges’ formula.43,44When
means and standard deviations were not available, d
values were calculated from the reported t or F values.
After computing individual effect sizes for each
study, a weighted mean effect size (g) was obtained
which indicated the magnitude of the association
across all studies.44Each effect size was weighted by
the inverse of its sampling variance when calculating
the pooled effect size, to account for the different
sample sizes on which each effect size was based.45
Minor physical anomalies in autism
HM Ozgen et al
An effect size between 0.2 and 0.5 is considered to be
weak, between 0.4 and 0.6 moderate, and greater than
0.8 is considered to be a large effect size.46
To investigate the significance level of the effect, a
95% confidence interval (CI) and z-value was calcu-
lated. Once the effect size for each study was
obtained, the variance across effect sizes was as-
sessed. The homogeneity statistic Q was calculated to
test whether the observed variability in the distribu-
tion of effect size estimates is greater than that
expected from sampling error.43As published re-
search findings suggest a number of variables that
may influence effect size, a weighted regression
analysis was performed to determine the extent to
which selected study characteristics might explain
between-study variations in effect size. Potential
moderator variables for analysis were year of publica-
tion, the number of Waldrop scale items used and the
use of siblings as control group. Other variables with a
potential influence on effect size such as IQ, case–
control sex ratio, autistic symptoms and types of
autism could not be analyzed because of the small
number of studies reporting results for these para-
meters. Data analyses were performed using random-
effects framework. A random-effects model assumes
that each observed effect size differs from the
population mean by differences in sample sizes plus
a value that represents other sources of variability
assumed to be randomly distributed. To obtain a good
estimate of the random effects variance component,
we chose the noniterative method based on the
method of moments.43Subsequently, a sensitivity
analysis was performed to explore the influence of
each study’s effect size on the overall effect size, by
deleting each study sequentially. The pooled effect
size for the remaining studies is recomputed each
time with the removal of each study, along with their
95% CI. This analysis allowed testing the overall
robustness of the meta-analysis as well as detection of
the most influential studies.
For computations of the mean effect size and the
meta-regression, SPSS macros developed by Wilson43
were used. All other analyses were carried out using
the Meta.Win 2.0 statistical package.47
To investigate the possibility of publication bias,
Rosenthal’s fail-safe N statistic was computed.48
Publication bias implies that studies with no effect
may not be published, posing a threat to the stability
of the obtained effect size. This method determines
the number of unpublished studies with null results
that would be required to reduce the overall effect
size to a nonsignificant level. A large fail-safe
number makes the ‘file-drawer’ problem negligible.
Furthermore, publication bias was also assessed using
Description of studies
The combined literature search yielded 78 references.
After eliminating overlapping references and those
that clearly did not meet the criteria, 16 studies were
identified and retrieved for further scrutiny. Of those,
one was excluded because it was investigating major
congenital anomalies, rather than MPAs,50two be-
cause controls were not included33,51and two because
the Waldrop scale or a variant of it in their assessment
was not used.52,53Two more studies were excluded
because of absence of sufficient data to compute a
mean effect size even after contacting the investiga-
tors.54,55Two final studies were excluded because of
the absence of relevant data in the published articles
and no response from the investigators.56,57
In the end, a total of seven studies, published
between 1975 and 2005, met our inclusion criteria
and contributed to the meta-analysis.5,58–63Each
sample was included independently into analysis.
These studies included 330 patients with autism
(mean age: 9.75, 80% male) and 382 healthy controls
(mean age: 10.3, 70% male). In two studies only boys
were included,60,62and three studies had mixed
ethnicities.58–60Three studies used siblings as their
control group;5,58,60four of the seven selected studies
were conducted in the United States,58,59,62,63two
were carried out in Canada60,61and one in the United
Kingdom.5The main characteristics of the studies are
listed in Table 1.
Effect sizes were calculated of all studies that
provided mean MPA scores on the basis of the
Waldrop scale. As graphically presented in Figure 1,
the results of our meta-analysis indicate that mean
MPA scores in patients with autism differ from those
of healthy controls.
In all seven studies the direction for the effect size
indicates that autism patients show higher MPA
scores than controls. None of the seven studies had
a negative effect size or included the value zero,
indicating a statistically significant effect for each
study. Finally, a single pooled effect size of 0.84
(P<0.0001) was found, with 95% CI ranging from
0.47 to 1.21. The results of our meta-analysis indicate
a significant difference in the mean number of MPA
scores between patients with autism and healthy
controls. The pooled effect size is in the range of a
robust effect.43,46There was considerable heterogene-
ity across studies (Q=36.90, d.f.=6, P<0.01) and
distributional analysis of effect size estimates indi-
cated one positive outlier. Without this potential
outlying case, the effect size distribution was no
longer heterogeneous. However, the new combined
effect size was still statistically significant. We were
reluctant to remove this study from the analysis
because removal may lead to an underestimation of
the estimated mean effect size. In addition, the study
completely fit our inclusion criteria, and closer
examination revealed nothing unusual about the
outlying study. Thus, the size and statistical signifi-
cance of the pooled effect size is not strongly
influenced by the outlying study. Results of the
sensitivity analysis revealed that removing any single
Minor physical anomalies in autism
HM Ozgen et al
The largest negative shift occurred following re-
moval of the study by Gualtieri,59which was expected
based on the large effect size reported in this study.
The largest positive shift occurred following removal
of Soper et al.61In both cases, however, there was a
negligible net effect on the overall pooled estimate.
This indicates that all seven studies were similarly
study failed to result in a significant shift in the
pooled effect size estimate (Figure 2).
influential and that the meta-analysis is generally
robust. Because of the large CI (0.47–1.21; see Table 1)
and the small number of studies, there was sufficient
variability to warrant further analysis. Therefore, we
performed weighted regression analysis where the
relationship between quantitative study characteris-
tics and effect size was explored. Neither the number
of MPA scale items nor the use of siblings as controls,
or gender rates accounted for a significant proportion
Characteristics of included studies
Reference YearCountry Dominant
DiagnosisCase N Control
% Male MPA scale
1975USACaucasian Clinical charts
2831 10018 0.95
Campbell et al.581978USAMixed522980180.73
Links et al.60
45 52 71180.92
Gualtieri et al.59
Bailey et al.5
Soper et al.61
2005 Canada Caucasian 721007517NA
Abbreviations: MPA, minor physical anomaly; N, number of subjects with autism; n, number of healthy controls; NA, not
Here the main characteristics of the seven studies included in this meta-analysis are listed.
pooling results across studies. The black square and horizontal line correspond to weighted mean effect size and 95% CI for
each study. The summary diamond bar (at the bottom of the figure) represents the pooled effect size estimate and 95% CI.
Meta-analysis indicates significant association between MPAs and autism (P<0.05). CI indicates confidence interval; MPAs,
minor physical anomalies.
Meta-analyses of case–control studies investigating the relationship between MPAs and autism. Meta-analysis
Minor physical anomalies in autism
HM Ozgen et al
the small range. Moreover, the pooled effect size
calculated from all seven studies was 0.84 (P<0.001),
which is considered to be a large effect. This
compelling, robust effect showed strong consistency
by sensitivity analysis, regardless of the dataset
removed. The meta-analysis supports the conclusion
that patients with autism have significantly more
of the between-study variance in effect size. None of
the regression models were statistically significant
(P>0.5). However, the failure to find a moderator is
not surprising given the small number of studies
Publication-bias analysis indicated a fail-safe number
of 43 that means that at least 43 studies reporting no
effect need to be found before the mean results are no
more significant, large enough to credence to our
findings. The estimated bias with the Egger’s test was
0.22 (95% CI 14.2–14.64), P=0.97, which also
indicates an absence of publication bias.43
This meta-analysis integrated the results of seven
studies that compared MPAs of 330 patients with
autism with those of 388 healthy controls. Each
original study consisted of a relatively small sample
of autistic cases ranging from 20 to 74. The results
indicate that the mean total MPA scores in children
with autism differ from those of healthy controls. In
each of the seven studies the autistic sample had
significantly higher rates of MPAs than those of the
the magnitude of the case–control difference was in
the individual studies,
MPAs than those of normal controls; this finding
is consistent with the findings of the individual
Despite this finding, a high degree of variability in
the magnitude of the effect size magnitude was
observed among these seven studies. In order to
identify potential factors that could be responsible for
constraining this variation, we performed regression
analyses. None of the earlier mentioned moderator
factors (that is, year of publication, number of
Waldrop scale items used and the use of siblings as
control group) were able to account for a statistically
significant relationship to the observed between-
study variation in effect sizes (P>0.05). This failure
to identify a moderating factor is not surprising given
the small number of studies included in this meta-
analysis. The effect of other potentially important
moderator variables (for example, sex, IQ, familiarity)
could not be analyzed because of a lack of information
on these characteristics in most of the studies as well
as the small number of studies.
Although this meta-analysis clearly shows a robust
effect for significant excess of MPAs in the autistic
population, the results are complicated by a number
of methodological issues. Although having the ad-
vantage of being short, there have been concerns
about Waldrop scale’s limitations. A major methodo-
logical issue is the unblended nature of the measure-
ment. Although a good inter-rater reliability can be
achieved, one should be cautious that patients with
autism might be different during even the minimal
interaction needed to measure some of the Waldrop
Although few studies have been carried out blinded,
complete blinding is very difficult given the close
personal interaction involved when assessing MPAs
and facial measurements. In addition, one should
keep in mind the subjectivity regarding the validity of
certain Waldrop items (for example, hair quality,
hypertelorism, clinodactyly). In addition, we believe
that other methodological variability among the
studies may explain some of the variation in MPA
scores: inconsistencies in sample size and composi-
tion, differences in MPA items, lack of consensus in
the terminology, and ethnic diversity. For instance,
the majority of cases were Caucasian, however, three
studies used mixed ethnicities in their population
As we confirmed the higher rate of MPAs in autistic
patients as compared to controls, we are faced with
new challenging questions. First of all, why do
autistic patients have higher rates of MPAs? Appar-
ently, a common genetic vulnerability for developing
autism is reflected in MPAs. Several developmental
genes have recently been identified that play a
paramount role in shaping body structures.64More-
over, new insights into craniofacial morphogenesis
have indicated that a rapidly increasing number of
genes are known to regulate cerebro-craniofacial
development.65,66It can be speculated that the genes
that determine the craniofacial morphology overlap
removing each study sequentially, on the estimated pooled
effect size. The central baseline (þ0.84) represents the
original pooled effect size based on all seven studies. The
upper and lower bounds (1.21 and 0.47, respectively)
represent the 95% confidence intervals of the original
pooled estimate. Squares represent the recalculated pooled
effect size for the remaining six studies. The sensitivity
analysis shows that the results from this meta-analysis are
robust to the choice of the statistical method and the
included studies. It also suggests that publication bias is
unlikely to have distorted its findings.
Sensitivity analyses demonstrate the effect of
Minor physical anomalies in autism
HM Ozgen et al
and autism? Findings indicating overlapping markers
could provide important clues regarding the under-
lying genetic bases of these disorders. Some evidence
for such an overlap comes from the observation that
individuals with autism spectrum disorders may also
be at greater risk for schizophrenia.71And, recent
findings indicate that most complex disorders are
with candidate genes for autistic disorders. MPAs
could also be related to prenatal infection, or other
environmental exposures which are associated with
autism. Prenatal or postnatal exposure to infections,
such as rubella, herpes simplex virus and cytomega-
lovirus, has been reported in several patients with
autism. Thalidomide exposure during pregnancy is
consensually associated with autism.67In addition,
both de novo and familial cytogenetic abnormalities
may be associated with an increased number of
MPAs.68Copy number variation (CNV) including
deletion and duplication, translocation, inversion of
chromosomes has been identified in some individuals
with autism.14,69In fact, Engels et al.70showed a direct
association between the severity of physical anoma-
lies and the chance to find mutant CNVs. The results
of this meta-analysis suggest that MPAs in autism are,
at least in part, related to the risk of developing the
disease and that these MPAs may therefore precede
the clinical onset of the disorder.
Another critical question is whether these physical
anomalies in autism are broad population character-
istic of all patients in the spectrum, or whether
patient–control differences derive from overrepresen-
tation of those abnormalities among only a specific
subgroup of patients. Some evidence for the latter
comes from Miles et al.33who hypothesized that
autistic patients with high MPA scores represent
‘nonfamilial or sporadic’ autism due to single envir-
onmental insults or nontransmitted genetic events,
whereas autistic patients with low MPA scores
represent ‘familial’ autism (where genetic clustering
of psychiatric disturbance reflects variable expression
of the underlying genotype).33,42These findings were
confirmed by Links et al.60and replicated in a later
study by Miles et al.33
Another fundamental issue to be addressed is
whether sets of certain physical anomalies are related
to specific phenotypic behavioral characteristics in
children with autism, and whether clustering of
certain anomalies to groups of patients would yield
homogenous subgroups. Except one early study by
Walker,63no other study in the literature dealt with
clustering. However, he found random association
and heterogeneity in distribution with few exceptions
(for example, high palate and low setting ears).
A final, more speculative, question is about the
specificity of MPAs. Are the MPAs seen in autism
different from those in other disorders? In a recent
meta-analysis, a higher prevalence of MPAs was also
established in schizophrenia.31Do MPAs seen in
autism have a different etiology than those in
schizophrenia, or do disorders associated with MPAs
share a common etiological basis with schizophrenia
probably rooted in genetic variation that is signifi-
cantly shared by multiple disease phenotypes.72
Robust diagnostic specificity is often lacking for
several other disease markers as well as MPAs and
reflects the fact that different disorders may share
genes, and also share partially overlapping neural
substrate dysfunction and clinical features.73
Limitations and strengths of this meta-analysis
There are certain limitations that should be borne in
mind when interpreting the results.
First, as with all meta-analysis studies, the results
depend on the quality of the individual studies.
Although we used well-defined inclusion criteria, we
had to accept some methodological diversity among
the studies in order to compare a sufficient number of
studies. We should also mention that the inclusion or
exclusion of a given study in this analysis was not
based on the scientific value of the publication. We
had to exclude some valuable publications, as they
did not meet the specific goal of the present study.
Second, the diagnosis of autism was occasionally
problematic in the early studies, written before the
introduction of the fourth edition of the Diagnostic
and Statistical Manual of Mental Disorders (DSM)
classification. Nevertheless, those studies were in-
cluded because they met all our inclusion criteria.
Sensitivity analysis confirms that the change in the
type of criteria used for autism diagnosis does not
appear to influence the effect size. Moreover, perhaps,
in part, due to a true increase in the prevalence rate
or, in part, due to the introduction of DSM 4 or greater
awareness of the syndrome, the prevalence of autism
has significantly increased over the last decades.
Therefore, we cannot exclude that the effect size of
the association today may somewhat differ from that
of the earlier studies included in the meta-analysis.
However, the effect size reported by the most recent
study (Soper et al.60) is not different from that of the
older studies. And again sensitivity analyses show
that the publication year does not influence the effect
Third, three studies were included that measured
MPAs in sibling controls, which may introduce a
confounding factor. However, because of the small
number of studies in the meta-analysis, these studies,
which met all criteria, were included. Interestingly,
there were no significant differences in MPA scores
when compared to either a sibling or nonfamily
Fourth, we were unable to examine topography of
dysmorphic features in autism, because we had too
little information comparing these across studies. Yet,
such information is fundamental to understanding
the timing and nature of dysmorphic events. Interest-
ingly, increased head circumference, which is a well-
documented finding in autistic children, was not
consistently reported in these seven MPA studies.
Fifth, although the age of the participants has been
thought to facilitate differences in effect size among
the studies, the results of moderator variable analysis,
Minor physical anomalies in autism
HM Ozgen et al
and predicting prognosis.
possibly, in part, due to the small number of studies
failed to confirm this hypothesis. Additionally,
although gender is known to affect the incidence of
autism, the studies included in this meta-analysis did
not provide enough data to examine gender effects.
Thus the possibility that some of the effects found in
this meta-analysis study were caused by confounding
factors such as age and sex cannot be ruled out.
Despite these limitations, the present results offer
several methodological advantages for future inquiry.
This is the first report studying the association
between MPAs and autism in a meta-analytic way.
This study provides evidence that MPAs are signifi-
cantly increased in the autistic population and that
some, unknown biological mechanism is likely
responsible for producing these anomalies which
may yield further knowledge about the developmen-
tal origins of the disease.
Recommendations for future research
It is obvious that the assessment of MPAs in
autism require further study. With the aforemen-
tioned caveats in mind, we have the following
Although MPA measurement is considered simple,
noninvasive and inexpensive, we should caution that
their genetic architecture may be as complex as that of
autism itself. This does not mean there is no
advantage to use them for genetic studies.74,75More
and larger studies in ethnically homogenous popula-
tions are needed to search for a possible correlation
between MPAs and family history as well as to
achieve sufficient power to search the potential role
of moderating variables such as gender, autism
symptoms and IQ.
Our results provide strong support for the associa-
tion between MPAs and autism. This meta-analysis
emphasizes the importance of MPAs in the identifica-
tion of heterogeneity in autism and suggests that the
success of future autism genetics research will be
exploited by the use of MPAs. Although these
findings reflect a vulnerability to developing autism,
it is still unclear how and to what extent genes and/or
environment are involved. Future studies should
focus on the search for susceptibility genes, chromo-
somal alterations (for example, mutations, duplica-
tions, deletions or CNVs) as well as different
environmental factors in relation to morphological
characteristics by using detailed definitions of the
phenotype and an internationally accepted classify-
ing list to enable comparison of the results. MPAs
might serve as a helpful instrument in autism
research, delineating subgroups which provide a
more homogenous basis for unraveling the etiology
We thank Dr Marcus Munafo for critically reading and
helpful discussion of the article. We are grateful to Dr
Henry Soper, who provided data from his studies for
the meta-analysis and Dr Judith Rapoport and Dr Tom
Gualtieri for answering specific questions regarding
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