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Decomposing the autism phenotype into familial dimensions.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 147B:3 –9 (2008)
Decomposing the Autism Phenotype Into
Familial Dimensions
Peter Szatmari,1* Chantal Mérette,4 Claudia Emond,4 Lonnie Zwaigenbaum,2 Marshall B. Jones,3
Michel Maziade,4 Marc-Andre Roy,4 and Roberta Palmour5
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
Department of Behavioral Science, Pennsylvania State University College of Medicine, Hershey, Pennsylvania
Department of Psychiatry, Laval University, Quebec City, PQ, Canada
Department of Psychiatry, McGill University, Montreal, PQ, Canada
The objective of this article is to decompose the
level of functioning phenotype in autism to see if
it can be conceptualized as two simpler, but still
familial, dimensional phenotypes of language and
non-verbal IQ. We assembled 80 sibpairs with
either autism, Asperger syndrome or atypical
autism. To see whether the familial correlation
on language scores was accounted for by the
familial correlation on non-verbal IQ, residual
language scores were calculated for each member
of the sibpair based on a multiple regression
equation using their IQ score as an explanatory
or independent variable and controlling for
the age and gender of the affected individual.
These residual scores were then used to calculate
intraclass correlations between affected sibs. This
process was repeated using IQ as the dependent
variable and language as a covariate. Within
affected individuals there was a strong relation
between non-verbal IQ (as measured by the Leiter
performance scale) and language (as measured by
the Vineland Communication Scale). In addition,
there was familial correlation between sibs on
both measures. Evidence of familial aggregation
on both non-verbal IQ and language remained
even after partialling out the effect of the covariates by regression analysis and by generalized
estimating equation. These findings suggest that
non-verbal IQ and language in PDD may arise
from independent genetic mechanisms. The implications of this finding for linkage analysis and for
identifying genetically informative phenotypes
are discussed.
ß 2007 Wiley-Liss, Inc.
autism; genetics; phenotype; sibpairs familiality
Please cite this article as follows: Szatmari P, Mérette C,
Emond C, Zwaigenbaum L, Jones MB, Maziade M,
Roy M-A, Palmour R. 2008. Decomposing the Autism
Grant sponsor: Canadian Institutes of Health Research.
*Correspondence to: Peter Szatmari, M.D., Offord Centre for
Child Studies, Hamilton, Ontario, Canada L8N 3Z5.
Received 22 March 2006; Accepted 2 April 2007
DOI 10.1002/ajmg.b.30561
ß 2007 Wiley-Liss, Inc.
Phenotype Into Familial Dimensions. Am J Med Genet
Part B 147B:3–9.
Autism is a disorder that shows considerable variation in
clinical expression. Some children can have many symptoms,
others just a few, and the number and type of symptoms may
change with development [Piven et al., 1996; Starr et al., 2003].
There are also different ‘‘pervasive developmental disorders’’
(PDDs) that share Wing’s triad of social impairments in
reciprocal interaction, in verbal and non-verbal communication and in a preference for repetitive activities rather than
social forms of play [Wing, 1996]. But many in the field see the
distinctions between these ‘‘subtypes’’ as largely artificial and
not all that useful in efforts to find autism-related genes. Many
prefer to think of a ‘‘spectrum’’ of disorders [Wing, 1996] that
differ on a continuum of severity. The factors that account for
this variation, however, remain poorly understood.
Rapid advances have been made recently in understanding
the etiology of autism, notably the fact that genetic mechanisms account for a large part of etiology [Cook, 2001]. Older
studies reported that the risk to siblings is between 50 and 100
times the population prevalence and the twin studies provided
estimates of heritability of greater than 90% [Szatmari et al.,
1998]. While these figures are widely quoted [Folstein and
Rosen-Sheidley, 2001] they may need to be revised downward
given the evidence that the disorder is more common in the
general population than previously thought [Chakrabarti and
Fombonne, 2001]. While it should be relatively easy to identify
susceptibility genes given these estimates, there are now
several genome scans published, none of which have been able
to identify regions of genome-wide significance [for reviews see
Wassink et al., 2004; Bacchelli and Maestrini 2006; Coon,
2006]. The most recent scan with the largest sample size has
perhaps the most promising results [Szatmari et al., 2007a] but
still the most significant result is part of sub-group analyses.
There are of course many possible reasons why this situation
may exist [Szatmari et al., 1998]. Foremost among these is the
possibility that the phenotypes used in these genome scans
may not always be the most informative for genetic purposes
[see Baron, 1995; Tsuang and Faraone, 2000 for similar
arguments]. For the most part, the phenotype used in current
genetic studies consists of a diagnosis of autism (broad or
narrow) as conceptualized in DSM-IV and as operationalized
by the Autism Diagnostic Interview (ADI). This may be helpful
in some circumstances but it still includes a very wide range of
symptoms and a level of functioning (LOF) that can range from
profound developmental delay to functioning well above
average. It is certainly possible, given the enormous range of
clinical variation seen in autism, that cases at the ends of the
Szatmari et al.
spectrum might arise from separate genetic mechanisms. It is
also possible that different components of the phenotype are
associated with different genetic mechanisms between
families. Affected individuals from the same family may be
concordant for some aspect of the phenotype but not others,
possibly reflecting intrafamily locus heterogeneity. This would
be especially true if the genes causing the disorder were
relatively common.
Thus, searching for more genetically informative phenotypes in autism is an essential prerequisite to the detection
of susceptibility genes [Szatmari et al., 2007b]. Attempts to
subtype autism have a long and checkered history [Beglinger
and Smith, 2001]. The problem is that the process for defining
subtypes has not been guided by genetic epidemiologic
methods [Tsuang, 2001]. If the goal is to identify susceptibility
genes, then the subtypes, or component phenotypes, must be
defined with that purpose in mind. A genetically informative
phenotype is one where the risk of that phenotype at a specific
genetic locus is very high [Szatmari et al., 2006]. As a result,
the mode of transmission of that specific phenotype within a
pedigree may be simpler, and more Mendelian, than other
definitions of the phenotype. Some attempts to use more
informative phenotypes in autism such as those based on
quantitative traits have indeed been more successful in
garnering marginally higher linkage signals than traditional
categorical-diagnostic approaches, a strategy that supports the
potential utility of this approach [Alarcon et al., 2002, 2005;
Buxbaum et al., 2004; Chen et al., 2006]. The key issue in this
context, though, is which dimension to use since there are so
many from which to choose.
Thus, the search for genetically informative phenotypes in
autism may be complicated by the possibility that the disorder
is a multivariate phenotype; that is, made up of two or more
dimensions that may or may not be correlated with each other.
Ronald et al. [2006] have recently argued, using twin data from
the general population, that autistic symptoms are composed
of two independent dimensions; social reciprocity and repetitive behaviors. In a similar vein, we have recently shown using
factor analysis on a sample of affected subjects that measures
of LOF and autistic symptoms represent two domains where
the observable variables are psychometrically correlated but
the underlying factors are essentially orthogonal [Szatmari
et al., 2002]. LOF refers in this context to the actual
achievement of important developmental milestones in everyday functioning. It is usually assessed using IQ and measures
of socialization and language from the Vineland Adaptive
Behavior Scales [VABS; Volkmar et al., 1993; Carter et al.,
1998]. We have begun to use LOF to identify more informative
phenotypes in PDDs. For example, we have shown that both
non-verbal IQ and language skills are the most familial
characteristics among sibships with multiple cases of PDD
[MacLean et al., 1999]. Significant familial aggregation
indicates that the variance between sibships on these dimensions is greater than the variance within sibships. Insistence
on sameness also shows familial aggregation in our, and in
other, data sets [Silverman et al., 2002; Szatmari et al., 2006],
however, the magnitude of the estimate appears lower
suggesting that language and non-verbal IQ might be more
useful traits to use in linkage analysis. The problem is that,
intuitively, non-verbal IQ and communication skills are likely
to be highly correlated within a population. Thus, it is not clear
that the familial aggregation observed on IQ is independent of
the familial correlation on communication. If it is the genetic
architecture underlying one dimension may be different from
the architecture underlying the other, so combining them into
one domain (such as LOF) may not be appropriate. This would
also have important implications for linkage analysis in that it
would suggest that genes linked to one dimension may not be
linked to the other. Searching for linkage to these dimensions
separately may be a more powerful strategy than searching for
genes linked to autism as a whole.
Multiple incidence or multiplex (MPX) families were
recruited systematically from parent support groups and
mental health and social service agencies across Canada that
serve children with developmental disorders, [see MacLean
et al., 1999 for complete description of recruiting]. We
ascertained families with two or more siblings with any form
of PDD (including autism, atypical autism, PDD (NOS) not
otherwise specified, Asperger syndrome, and disintegrative
disorder, but not Rett’s syndrome). No restriction was placed
on PDD subtype or on LOF. After a full description of the study
was given to the families, written informed consent was
obtained from the parents as well as from children over
18 years who were able to give informed consent. Seventy-six
MPX families with 156 affected children were recruited
(Table I). Of these 76 MPX families, 72 consisted of sibpairs
and 4 families contained 3 affected children. For each of these
latter families, only two independent pairs among the 3
possible sibpairs were selected to yield a total of 80 affected
sibpairs in the first analysis reported below. The sibpairs
reported in the Maclean et al.’s article [1999] are a subset of
this sample. We identified the proband as the first child to be
identified as affected.
The following additional inclusion criteria were applied if
the proband received a diagnosis of PDD by DSM-IV criteria:
(1) age greater than two years for the affected children,
(2) English being the language most often spoken in the home,
and (3) no neurological disease or known chromosomal disorder. Medical records were checked to ensure that all probands
and affected siblings had undergone medical evaluations
including metabolic screens, EEGs, DNA testing for the
TABLE I. Sample Characteristics for the Affected Multiplex Families
Number of males
Leiter IQ
ADI-R domains; Social interaction
Verbal Comm
NV Comm
Repetitive B
Mean (SD) or
Mean (SD) or
67 (28)
64 (26)
24 (4)
18 (4)
13 (1)
7 (2)
65 (31)
62 (20)
23 (5)
17 (5)
13 (2)
6 (3)
Comm, communication; NV, non-verbal; B, Behavior.
Decomposing the Autism Phenotype
fragile-X syndrome and routine karyotyping to rule out known
neurologic and chromosomal disorder. The mean age of the
probands was 112 months (SD ¼ 75; range 28–482); and of the
sibs, the mean was 90 (SD ¼ 71; range 28–359).
Diagnosis and Assessments on Probands
and Affected Siblings
For all but 13 families, the proband was the oldest of the
siblings with PDD. Subsequent to the identification of a
potential ‘‘proband,’’ clinical assessments were carried out on
all children in the sibship to distinguish affected and
unaffected children. Parts of the Family History Interview
for Developmental Disorders of Cognition and Social Functioning [Folstein and Rutter, 1991] were used to screen all children.
If any PDD behaviors were observed, the ADI and later the
ADI-R [Lord et al., 1994] was used to collect information to
make a more definite diagnosis. The Autism Diagnostic
Observation Scale [ADOS; Lord et al., 1989] and its newer
version [Lord et al., 2000] were also used to assess the social,
language, and play skills of the child based on direct
observation. Finally, clinical records were obtained but
information on previous diagnoses was deleted. An independent blind diagnosis was then made, according to DSM-IV
criteria, [American Psychiatric Association, 1994] using the
ADI-R, the ADOS and all available clinical information by
three clinicians (none of whom had seen the subject) with an
average of 20 years experience in the diagnosis of PDD
children. If there was any disagreement, the case was discussed and a best-estimate consensus diagnosis was reached. A
sample of non-PDD cases were also diagnosed by the panel so
that each multiplex case was diagnosed blind to the status of
that individual’s siblings and to avoid any expectation bias (see
Mahoney et al. [1998] for a full description of diagnostic
procedures, reliability and accuracy estimates). Of the
156 cases, 115 were given a diagnosis by best estimate of
autism, 18 had atypical autism, and 23 were given a diagnosis
of AS. The mean and standard deviation of the age, IQ, and ADI
scores of the sample are given in Table I and indicate that this is
a representative sample of children with PDDs with a typically
wide distribution of scores. Seventy-three subjects were verbal
and 83 were non-verbal.
The Leiter IQ scale was also completed on affected children
and the VABS was completed either in face-to-face interviews
or over the phone for those who lived at a distance. In all
cases, the primary care giver who completed the ADI-R was
the mother. The same interviewer conducted the interview
for all siblings within a family but the ADI-R and the VABS
were done by separate interviewers blind to each other’s
Autism Diagnostic Interview-Revised (ADI-R)
This is an investigator-based interview administered by a
trained interviewer to the primary care giver(s) of the child
[Lord et al., 1994]. It is designed to obtain detailed descriptions
of behaviors necessary for the diagnosis of PDD, especially
autism [Lord et al., 1994]. The interview focuses on the key
diagnostic features described in the International Classification of Diseases-10th edition (ICD-10) and DSM-IV. The
questions are designed to distinguish qualitative impairments
from developmental delays by assessing behaviors at an
appropriate age, identifying behaviors that would be considered deviant at any age and examining current and most
abnormal behaviors for those strongly influenced by maturational age.
Autism Diagnostic Observation Schedule (ADOS)
The Autism Diagnostic Observation Schedule (ADOS) is a
semi-structured assessment of communication, social interaction and play or imaginative use of materials, for individuals
suspected of having autism or other PDDs. The ADOS consists
of four modules, each of which is appropriate for children and
adults of differing language levels, ranging from non-verbal to
verbally fluent [Lord et al., 2000].
Leiter Performance Scales
This is the standard measure of non-verbal problem solving
and learning ability [Levine, 1986]. It is especially appropriate
to this population of PDD children because it does not require
verbal instructions or responses for administration and
correlates highly with WISC-R IQ. It is commonly used with
PDD and other language impaired children. Both the old and
the newer versions of the Lieter were used. All scores from the
older version were adjusted to be consistent with the new
The Vineland Adaptive Behavior Scales
The VABS is a semistructured interview administered to a
parent. This scale is designed to assess adaptive behavior in the
domains of socialization, language and communication, motor
and daily living skills [Sparrow et al., 1984]. The communication and socialization scores, and their relation to parameters
such as IQ, are seen as very sensitive measures of impairment
in children with PDD [Volkmar et al., 1993; Carter et al., 1998].
We report here on the communication scale (VABSCOM) alone
which is standardized to a mean of 100 and a standard
deviation of 15, with high scores indicating a higher language
skills. It is composed of three sub-domains; receptive, expressive, and written language, but we used only the total score to
ensure greater reliability.
To evaluate the genetic structure of language skills
(VABSCOM) and non-verbal IQ we used two approaches first
suggested by Raskind et al. [2000]. In our first approach, we
used regression analyses on the independent sibpairs to
estimate two models. In one model, IQ was included as a
covariate (as well as age and gender) in the analysis where the
subject’s VABSCOM is treated as the dependent variable.
Then, in the second model, an analysis was done where the role
of the measures were reversed (dependent variable ¼ IQ,
covariate ¼ VABSCOM, age and gender). In each of these
analyses, the residual values, which are calculated as observed
values minus predicted values, were extracted. The residual
value is the variability due to the response variable that
remains once the variability of the covariates is removed.
Using the residual values from both models (Model 1:
VABSCOMi ¼ b0 þ b1 IQi þ b2 agei þ b3 sexi þ ei; Model 2:
IQi ¼ b4 þ b5 VABSCOMi þ b6 agei þ b7 sexi þ ei), we then
estimated familial aggregation through intraclass correlation
coefficients [Armitage and Berry 1994] with corresponding
95% confidence intervals [Rosner and Bernard, 2006]. Residual
values were used to estimate correlation coefficients instead of
the raw data because we wanted to know if the familial
aggregation remained once the variability of the covariates
was removed.
There are three possible outcomes (see Fig. 1); if including
the covariate does not reduce or eliminate this familial
aggregation pattern, then the covariate probably does not
Szatmari et al.
Fig. 1. Theoretical illustration of three possible genetic systems underlying the expression of VABSCOM and IQ in autism: (a) VABSCOM and IQ have a
completely different genetic influence, (b) VABSCOM and IQ share the same genetic factor, and (c) the IQ trait has its own genetic influence (Gene 2) in
addition to a genetic factor shared with VABSCOM (Gene 1). The opposite is also possible but not illustrated.
uniquely account for the familiality of the other measure (as in
Fig. 1a). In Figure 1b the covariate would reduce or account for
the variation in both directions suggesting a common genetic
mechanism. However, if the familial aggregation pattern is
reduced or eliminated in one direction but not in the reverse
(see Fig. 1c), this would suggest that one of the two covariates
has its own genetic influence in addition to a genetic factor
shared with the other covariate. In the example shown in
Figure 1c, IQ shares a genetic influence with VABSCOM,
represented by Gene 1. Thus, when IQ is a covariate, it
accounts for the familial aggregation of VABSCOM. However,
when VABSCOM is the covariate, then IQ still displays
familial aggregation due to its own genetic influence (Gene 2
in Fig. 1c). We also introduced age and gender as covariates in
the regression analyses, since the observed familial aggregation may be due to an age or a gender resemblance in the
As our second approach, a generalized estimating equation
(GEE) was also used to estimate correlations between sibs [Hsu
et al., 2002] while taking into account the correlation between
language skills and non-verbal IQ. Two sets of estimating
equations are computed. The first set estimates the marginal
effects of covariates such as age and gender on each of IQ and
VABS. The second set estimates the correlations between
related pairs after adjusting for the covariates in the first set.
These sets of equations are then solved simultaneously to
obtain the estimates of intraclass correlation coefficients as
well as regression coefficients of covariates [Hsu et al., 2002].
GEE can be conceptually written k DTk Wk1 Fk ¼ 0 where the
summation is over all K independent sibships (k ¼ 1, . . ., K), Fk
is the deviation of observed measure (VABSCOM in one model
and IQ in the second model) and correlation functions from
their expectations under the assumed mean and correlation
regression models, Dk is a matrix of derivatives of the assumed
marginal mean and correlation regression models with respect
to unknown parameters, and finally, Wk is a matrix of weights
[Raskind et al., 2000]. Here, the linear link function was used to
model the marginal means and the marginal correlation. For
this approach, we used the EE analysis program written and
assembled by the Quantitative Genetic Epidemiology group at
the Fred Hutchinson Cancer Research Center (see Q.G.E.
Technical Report 126), and available at
software.php. This second approach offered the advantage of
estimating within a single analysis, two ICCs while controlling
for all covariates. It also offered the advantage of analyzing the
original sample of 76 MPX families without having to select two
different pairs in each of the 4 three-children families.
Our objective was to evaluate the structure of underlying
shared and unique genetic contributions of communication
skills and non-verbal IQ among the sample of 80 sibpairs
affected by autism spectrum disorder. The two measures were
highly correlated (r ¼ 0.70; P-value < 0.0001) within affected
subjects. The objective, however, was to determine if these two
variables have a completely different genetic influence
(Fig. 1a), if they share the same genetic factor (Fig. 1b) or if
one of them has its own genetic influence (Fig. 1c).
Familial Aggregation of VABSCOM and IQ
The variables were highly correlated with each other
supporting the need for taking the covariates into account.
The covariates IQ, age and gender explained 50% of the
variance of VABSCOM, while the covariates VABSCOM, age
and gender explained 49% of the variance of IQ.
The intraclass correlation coefficient for the VABSCOM and
IQ was calculated (correcting only for age and gender). There
was positive evidence of familial aggregation on both these
variables (i.e., VABSCOM: r1 ¼ 0.25; IQ: r1 ¼ 0.28, see Table II)
but the 95% CI on these values was very wide. The intraclass
correlation coefficients were then calculated using (instead of
the scores only adjusted for age and gender) the residual values
from the two regression models above. The ICCs remained
surprisingly highly significant (VABSCOM: rIad ¼ 0.36; IQ
rIad ¼ 0.39, see Table II) and in fact increased slightly.
The second approach used the GEE. The correlation
coefficients which estimate familial aggregation of a variable
accounting for the residual effects of the other variables was
still moderately high and statistically significant (VABSCOM:
rIad ¼ 0.36; IQ rIad ¼ 0.40, see Table III). Though the confidence
intervals on these estimates were still rather wide.
Hence, the two approaches came to the same conclusion.
The estimates of familial aggregation of VABSCOM and
IQ between sibs remain relatively high even after removing
the variability due to the covariates and despite the high
TABLE II. Intraclass Correlation Coefficients (ICC) Corrected for Covariates
Outcome variables
Age, gender
Age, gender
Age, gender, IQ
Age, gender, VABSCOM
VABSCOM, Vineland Communication Score.
95% CI
Decomposing the Autism Phenotype
TABLE III. Marginal Correlation Coefficients Adjusted for the Residual Effects of the Other
Measure, Age and Gender Using the GEE-Based Approach
Outcome variable
Correlation coefficient
Robust SE
95% CI
VABSCOM, Vineland Communication Score.
correlation between the two measures within affected subjects
(r ¼ 0.70). Since including the covariates does not eliminate the
familial aggregation pattern; IQ and VABSCOM appear to
have largely distinct genetic foundations (as in Fig. 1a).
The results indicate that there are at least two familial LOF
dimensions in the PDDs; non-verbal IQ and language. Even
though these are highly correlated with each other within a
population of affected individuals, the familial aggregation of
one seems to be largely independent of the other. Presumably
the high-observed correlation between these dimensions
within a subject is due to the measures themselves which rely
to some extent on a common trait of general cognitive ability.
Although the magnitude of the familial estimates look modest,
it is worthwhile pointing out that the ICC estimates we
obtained make biological sense; they are roughly half what
Kolevzon et al. [2004] obtained with PDD MZ twins on
language skills. In essence, the data seem to support the model
presented in Figure 1a; that the genetic mechanism underlying
one dimension (non-verbal IQ) is largely independent of the
genetic mechanisms underlying the second dimension (language skills). Of course, this conclusion is based on sibpair data
not twin data, so the assumption that the familial effects reflect
underlying genetic mechanisms remains to be proven. However, there are no known factors that account for familial
aggregation other than genetic factors [Newschaffer et al.,
2002] though environmental effects may interact with
genetic susceptibility, so the assumption may be a safe one at
this point in time. It is possible that children from the same
family would receive similar types of early intervention which
have been shown to influence IQ and language [Smith et al.,
2000]. However, how this would make sibpairs more alike and
so bias the results is not clear. It is also important to point out
that the familial aggregation reported among affected family
members is not driven by the mechanisms that account for
familial resemblance in IQ and communication skills among
non-affected family members. In a previous analysis, we have
shown that the correlation between parents and unaffected
sibs on cognitive measures is greater (and similar to that seen
in typical families) than the correlation observed between
parents and affected children, which was low and nonsignificant [see also Fombonne et al., 1997; Folstein et al.,
1999; Szatmari et al., 2003].
The strengths of the study include the sampling of MPX
subjects, the ascertainment scheme which ensured that a
sample with wide variation in LOF and type of PDD would be
included, the blind assessments, and the extensive diagnostic
protocol. This study also has some limitations that should be
kept in mind. The number of sibpairs was relatively small and
thus the confidence intervals around the estimates of familial
aggregation may be large. However, the estimates are very
similar to those obtained by Silverman et al. [2002] and Spiker
et al. [2002]. It is also true that the same informant reported on
both children using the VABS. Though this potential rater bias
would have no impact on the non-verbal IQ measure. It was our
impression, however, that, if anything, parents tended to
exaggerate the differences between affected children on the
VABS rather than see them as more similar. More importantly,
the coding of the VABS asks for observable behavior that may
overcome this potential bias in reporting. It is possible however
that the two assessments are administered in different ways
and that this might account for the lack of one co-variate
reducing the ICC of the other. It is also true that the age
range of the subjects was quite large and the Leiter and
the VABSCOM may not be measuring the same concept over
the entire age range, particularly for a child with autism who
may have unusual skills in one particular cognitive area. These
are all potentially complicated measurement issues when
testing subjects with autism over a wide range of abilities and
ages but how this might influence the results is unclear,
especially since we used age as a covariate. We would once
again anticipate that such examples of potential measurement
error would reduce the observed correlation rather than bias it
upwards. It will be important to attempt to replicate these
results not only with a different sample but also using different
instruments measuring the same constructs and the same
types of instruments (structured or parent administered)
measuring different constructs.
These results have two important implications. The first is
that it may be possible now to decompose the autism phenotype
into simpler dimensions, some of which are familial within
sibpairs. The PDD phenotype is first differentiated into a
dimension that measures LOF and one that defines autistic
symptoms. We can now further decompose the LOF phenotype
into non-verbal IQ and communication/language skills which
may represent more genetically informative phenotypes for
linkage and association studies. It would be helpful, as a result,
for genetic studies to report these separately when commenting on LOF.
The dimensions of non-verbal IQ and language can be used in
two ways; as quantitative traits or as covariates. It may be
that the genes that increase risk for autism are not the same
genes that influence variation in non-verbal IQ or language
in that disorder. Identifying genes that influence dimensions in
a quantitative trait linkage analysis may be an extremely
important avenue of investigation as it may be easier to
identify genes associated with a simpler dimension than genes
that increase risk for a complex phenotype such as autism.
Indeed, we have shown that polymorphisms in the MAO and
DBH genes in the mother (and possibly in the fetus) influence
non-verbal IQ in a substantial way [Jones et al., 2004] but do
not increase risk for autism. Several studies have also shown
that using communication as a quantitative trait, increases the
linkage signals at several loci [Alarcon et al., 2002; Chen et al.,
In addition, these dimensions can be used as covariates in
linkage analysis. This can be done through stratification,
ordered subset analysis [Hauser et al., 2004], and mixture
models [Devlin et al., 2002]. For example, sibships could be
stratified into at least two subgroups prior to analysis using
measures of non-verbal IQ. Isolating sibpairs where both have
a low or high non-verbal IQ score may make the sibpairs more
genetically homogeneous. In fact, focusing on the high
functioning sibpairs may be quite useful. The point is not that
the low functioning sibpairs are not familial. They may well be,
however, the autism in these lower functioning sibpairs may be
Szatmari et al.
due to the presence of several different mental retardation
syndromes, which are still familial, but are likely to be much
more heterogeneous. Language scores could be used in a
similar way to identify families where both affected sibs have
language impairment and where unaffected relatives also
segregate difficulties in language development. Several studies have reported promising results using this strategy
[Buxbaum et al., 2001; Shao et al., 2002; Nurmi et al., 2003].
In general, the results of our analysis indicate that it may be
useful to further explore the possibility of decomposing the
autism phenotype into simpler but still familial dimensions
and focus on these for genetic studies.
Dr. Szatmari and Dr. Zwaigenbaum were supported by
Fellowship awards from the Ontario Mental Health Foundation. Dr. Roy and Dr. Mérette were supported by scientist
awards from the Fonds de la Recherche en Sante du Quebec.
We would like to thank the children and the families who
participated in the study.
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