![Chromosome 2 HLOD scores for the dominant model as a function of phenotype. IS, insistence on sameness; RSMA, repetitive sensory-motor actions; IS-Adj, IS adjusted for RSMA; RSMA-Adj, RSMA adjusted for IS. Autism spectrum disorder (ASD) HLOD scores are based on previously reported linkage analyses with the same pedigrees [31].](https://www.researchgate.net/profile/Hilary-Coon/publication/45460426/figure/fig3/AS:195400737792002@1423598574218/Chromosome-2-HLOD-scores-for-the-dominant-model-as-a-function-of-phenotype-IS_Q320.jpg)
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Cannon et al. Molecular Autism 2010, 1:3
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Open Access
RESEARCH
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Research
Genome-wide linkage analyses of two repetitive
behavior phenotypes in Utah pedigrees with
autism spectrum disorders
Dale S Cannon
†
, Judith S Miller, Reid J Robison, Michele E Villalobos, Natalie K Wahmhoff, Kristina Allen-Brady,
William M McMahon and Hilary Coon*
†
Abstract
Background: It has been suggested that efforts to identify genetic risk markers of autism spectrum disorder (ASD)
would benefit from the analysis of more narrowly defined ASD phenotypes. Previous research indicates that 'insistence
on sameness' (IS) and 'repetitive sensory-motor actions' (RSMA) are two factors within the ASD 'repetitive and
stereotyped behavior' domain. The primary aim of this study was to identify genetic risk markers of both factors to
allow comparison of those markers with one another and with markers found in the same set of pedigrees using ASD
diagnosis as the phenotype. Thus, we empirically addresses the possibilities that more narrowly defined phenotypes
improve linkage analysis signals and that different narrowly defined phenotypes are associated with different loci.
Secondary aims were to examine the correlates of IS and RSMA and to assess the heritability of both scales.
Methods: A genome-wide linkage analysis was conducted with a sample of 70 multiplex ASD pedigrees using IS and
RSMA as phenotypes. Genotyping services were provided by the Center for Inherited Disease Research using the 6 K
single nucleotide polymorphism linkage panel. Analysis was done using the multipoint linkage software program
MCLINK, a Markov chain Monte Carlo (MCMC) method that allows for multilocus linkage analysis on large extended
pedigrees.
Results: Genome-wide significance was observed for IS at 2q37.1-q37.3 (dominant model heterogeneity lod score
(hlod) 3.42) and for RSMA at 15q13.1-q14 (recessive model hlod 3.93). We found some linkage signals that overlapped
and others that were not observed in our previous linkage analysis of the ASD phenotype in the same pedigrees, and
regions varied in the range of phenotypes with which they were linked. A new finding with respect to IS was that it is
positively associated with IQ if the IS-RSMA correlation is statistically controlled.
Conclusions: The finding that IS and RSMA are linked to different regions that only partially overlap regions previously
identified with ASD as the phenotype supports the value of including multiple, narrowly defined phenotypes in ASD
genetic research. Further, we replicated previous reports indicating that RSMA is more strongly associated than IS with
measures of ASD severity.
Background
Although it is generally accepted that genetic factors play
a major role in the etiology of autism spectrum disorders
(ASDs)[1], identification of specific genetic risk markers
is complicated by the phenotypic complexity of clinical
diagnoses. For example, the Diagnostic and Statistical
Manual of Mental Disorders 4thed. (DSM-IV)[2] diagnos-
tic criteria for autistic disorder (AD) require impairments
in three domains: social interaction, communication and
repetitive and stereotyped behavior. Each of these three
domains has been shown to be heritable, but their covari-
ation in the general population is modest, and genetic
modeling suggests distinct genetic influences for each [3-
5]. Thus, it has been argued that the ability to identify
susceptibility loci for ASD would be increased if specific
ASD/AD traits were used as phenotypes [3,6].
* Correspondence: hilary.coon@hsc.utah.edu
1 Utah Autism Research Project, Department of Psychiatry, University of Utah,
650 Komas Drive, Suite 206, Salt Lake City, UT, 84108-3528, USA
† Contributed equally
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Specific ASD/AD traits have been employed in genetic
studies most often either to stratify pedigrees for linkage
analysis or as the dependent variable in association tests
for specific alleles. For example, the first approach has
found stronger ASD linkage signals in pedigrees with
more abnormal levels of phrased speech delay [7,8],
repetitive behavior [9-11] and savant skills [12], but there
have been failures in replication [13]. The second
approach has resulted in significant genotype associa-
tions with repetitive behavior [14-16]. A third, less com-
mon approach has been to use the specific trait as a
quantitative or qualitative phenotype in linkage analyses.
For example, we used the Social Reciprocity Responsive-
ness Scale (SRS) [17] score as the phenotype in linkage
analyses of multiplex ASD pedigrees (Coon et al.,
Genome-wide linkage using the Social Responsiveness
Scale (SRS) in Utah autism pedigrees, submitted).
Although each of these methods has merit, it should be
noted that the first method attempts to reduce heteroge-
neity of the diagnostic phenotype by stratification on a
specific trait, whereas the second and third approaches
seek to identify risk markers for the trait itself.
Repetitive and stereotyped behavior is a promising can-
didate for further genetic study because it probably com-
prises at least two even more specific phenotypes that
differ in their behavioral correlates, familiality, and rela-
tion to genetic linkage with ASD. The 'restricted and
repetitive stereotyped behavior' (RRSB) domain of the
Autism Diagnostic Interview--Revised (ADI-R) [18,19] is
a well-accepted measure of the repetitive behavior phe-
notype. To uncover the factor structure of RRSB, a variety
of factor analytic techniques have been used with differ-
ent subsets of RRSB items and with study populations
that differ in ASD severity and ethnicity [11,20-25].
Remarkably, in spite of their methodological differences,
these analyses converge on a two-factor solution com-
prising 'repetitive sensory-motor actions' (RSMA) and
'insistence on sameness' (IS). RSMA items investigate
repetitive physical mannerisms and unusual sensory
interests, whereas IS items investigate compulsive behav-
iors. There are two exceptions to the common two-factor
solution. First, an exploratory factor analysis of RRSB
items [26] recovered essentially the same RSMA and IS
factors but also found a third factor ('circumscribed inter-
ests'). This finding does not detract from the conclusion
that RRSB comprises RSMA and IS, but rather suggests
that RRSB may measure additional factors as well. Sec-
ond, a principal components analysis of all ADI-R items
identified six factors, including a 'compulsions' factor that
contained some items from both the IS and RSMA fac-
tors, and a 'social intent' factor that combined social
interaction items with the RSMA item of 'hand and finger
mannerisms' [27]. Despite this, the preponderance of sta-
tistical evidence indicates that RSMA and IS are distinct
factors within the RRSB domain.
It is well established that IS and RSMA have different
patterns of relationship with other ASD traits. Specifi-
cally, RSMA, but not IS, has been reported to be associ-
ated with lower IQ, less adaptive behavior, and later age of
appearance of first words and phrases [6,20,21], which
suggests that RSMA may be more correlated with ASD
severity [6]. These findings support the validity of treat-
ing IS and RSMA as different phenotypes.
There is more empirical support for a genetic effect on
IS than on RSMA. Whereas modest evidence of familial
concordance occurs for IS, no reported concordance
occurs for RSMA [21,25]. Thus, the IS factor may account
for earlier findings that RRSB is familial [28,29]. Indeed,
Silverman et al. [28] reported that RRSB categories that
include IS items were familial, whereas those that include
RSMA items were not. Further, a linkage analysis across
the 15q11-q13 region in a subset of families with the
highest IS scores resulted in increased LOD scores for
AD [11] over scores obtained without stratification. By
contrast, stratification on RRSB or RSMA did not
increase lod scores. Finally, obsessive compulsive disor-
der (OCD) features in parents were associated with IS,
but not RSMA, in children with AD [30], which suggests
that IS may be part of a broader autism phenotype of
obsessive behavior.
We are not aware of previous genetic linkage studies
with either IS or RSMA as the phenotype. The primary
aim of the present study was to perform a genome-wide
linkage analysis with both IS and RSMA as phenotypes
using large extended ASD pedigrees. Thus, our goal was
to identify genetic risk regions for IS and RSMA in ASD
cases rather than to stratify on IS and RSMA to reduce
ASD heterogeneity. Because IS and RSMA data were
available only for ASD cases rather than for all pedigree
members, we focused our analyses on these specific phe-
notypes in ASD cases and did not include clinically unaf-
fected family members in this study. Signals obtained
with these phenotypes were compared with those found
in the same set of pedigrees using ASD diagnosis [31].
Contrasting results obtained with IS and RSMA with
those obtained by ASD categorical diagnosis addresses
empirically the possibilities that more narrowly defined
phenotypes improve linkage analysis signals, and that dif-
ferent narrowly defined phenotypes are associated with
different loci. Secondary aims were to examine the corre-
lates of IS and RSMA and to assess the heritability of both
scales.
Methods
This study has ongoing approval from the University of
Utah institutional review board (IRB). All adults partici-
pating in the research signed informed consent docu-
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ments. All subjects under the age of 18 signed assent
documents and their parents or guardians signed paren-
tal permission documents. These documents were
approved by the University of Utah IRB.
Subjects
Subjects were members of 70 pedigrees having at least
two family members with ASD. In total, 653 subjects
were genotyped, 192 of whom had a study diagnosis of
ASD. Study diagnosis was based in almost all instances on
both the ADI-R [18,19] and the Autism Diagnostic
Observation Schedule-Generic (ADOS-G) [32]. These
pedigrees were used in our recent genome-wide linkage
analyses of ASD [31]. All of the families studied are part
of the Utah collection of multiplex ASD pedigrees. We
did not include pedigrees from other collections or
repositories. Additional sample characteristics including
pedigree sizes, ascertainment and assessment methods
were reported previously [31].
Phenotypes
RSMA and IS scales
RSMA and IS scales were derived from the RRSB domain
of the ADI-R, which was available for 183 subjects with a
study diagnosis of ASD. RSMA and IS items were ADI-R
items that reliably loaded on one scale or the other in pre-
vious factor analytic studies [11,20-25]. For both scales,
scores were the unweighted sum of ADI-R item 'ever' rat-
ings of 0-3. We believe this method of scoring the two
scales is less susceptible to chance inter-item correlations
in our data than would be factor scales derived from our
data alone. RSMA items included 'hand and finger man-
nerisms', 'unusual sensory interests', 'repetitive use of
objects', 'complex mannerisms' and 'rocking'. IS items
included 'difficulties with minor changes in personal rou-
tine or environment', 'resistance to trivial changes in
environment' and 'compulsions/rituals'.
Language delay
Items from the ADI-R ('age of first words' and 'age of first
phrases') were used to assess language delay in ASD
cases. For parents who indicated normal onset but who
could not remember the exact ages, values were set to 23
months for words and 32 months for phrases (acquiring
language after these ages is considered abnormal on the
ADI-R). For parents who indicated delayed onset but
could not remember the exact ages, values were set to 1.5
standard deviations above the mean. For subjects who
never acquired language, values were set to 3 standard
deviations above the mean.
Intellectual function
IQ was measured in subjects with ASD using an assess-
ment instrument appropriate for the subject's age and
developmental level. IQ measures included the Wechsler
Intelligence Scale for Children, 3rd revision (WISC-III)
[33], the Wechsler Adult Intelligence Scale, 3rd revision
(WAIS-III) [34], the Differential Abilities Scale (DAS)
[35] and the Mullen Scales of Early Development [36].
SRS
The SRS is a quantitative measure of social ability ranging
continuously from significantly impaired to above-aver-
age social abilities [17]. Although the SRS can be used
with a general population, in our study the SRS was used
only with ASD cases. The SRS mannerisms scale, which
contains items that measure stereotypical behaviors and
restricted interests, was used to determine whether IS or
RSMA was more highly associated with another accepted
measure of repetitive behavior in ASD cases.
Genotyping
Genotyping services were provided by the Center for
Inherited Disease Research (CIDR), using the 6 K single
nucleotide polymorphism (SNP) linkage panel. Methods
and quality control procedures have been described in
detail previously [31]. After quality control, there were
genotypes from 6,044 SNPs on 653 pedigree members
who were members of 67 informative families. Eliminat-
ing linkage disequilibrium (LD) between markers in link-
age studies has been strongly recommended, as false-
positive results can occur in the presence of LD, particu-
larly with extended multigenerational pedigrees for
which ancestral genotypes are unavailable [37]. Recom-
mended thresholds of acceptable LD vary, but a pair-wise
r2 value of 0.05 between SNPs has been supported with
extensive simulation studies [37]. Therefore, before link-
age analysis, we screened SNPs for LD using the PLINK
software package [38], which recursively removes SNPs
within a sliding window. We set a window size of 50
SNPs, shifted the window by 5 SNPs at each step, and
used a variance inflation factor (VIF) of 1.5, which is
equivalent to an r2 of 0.33 regressed simultaneously over
all SNPs in the selected window. This r2 considers not
only the correlations between SNPs but also between lin-
ear combinations of SNPs [38], and corresponds in our
data to a pair-wise r2 value of approximately 0.05. This
screening for LD removed 1,207 SNPs. As part of the val-
idation procedure, we also removed 115 SNPs with a
minor allele frequency < 0.10 and 4 SNPs that were not in
Hardy-Weinberg equilibrium (standard 1 degree of free-
dom test failed at the 0.05 level). The total number of
SNPs left after this phase was 4,718.
Analyses
Heritability
The heritability (proportion of variance in the trait due to
genetic influences) of IS and RSMA was estimated using
SOLAR software [39]. For discrete traits, SOLAR uses a
threshold model to estimate polygenic heritability [40].
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Estimates were also computed using jPAP software [41];
no substantive differences were found.
Linkage analysis
We used the genetic map provided by CIDR based on the
deCODE genetic map [42]. Base pair positions were
obtained from the March 2006 human reference
sequence (hg18) assembly. Analysis was performed using
the multipoint linkage software MCLINK, a Markov
chain Monte Carlo (MCMC) method that allows for mul-
tilocus linkage analysis on large extended pedigrees [43].
Using blocked Gibbs sampling, MCLINK generates
inheritance vectors from the Markov chain. Each state in
this chain is an inheritance state, indicating the grandpa-
ternal or grandmaternal origin of an allele at each marker
locus, with changes in the origin of alleles along the
inheritance vector indicating points of recombination.
MCLINK then estimates the log-likelihood function link-
age statistics. Internally, MCLINK runs the analysis five
times to ensure a consistent solution. MCLINK has been
used previously to identify candidate genomic regions for
a number of complex diseases [44-48]. Results from
MCLINK have shown a high degree of similarity to other
MCMC linkage methods [49], and to exact linkage meth-
ods and variance components linkage methods as applied
to extended pedigrees [50]. Allele frequencies for the
MCLINK analysis were estimated using all of the
observed data.
We performed nonparametric and general parametric
model-based analyses. Although nonparametric methods
are the standard analytic approach for complex psychiat-
ric disorders, parametric methods have some advantages
in the analysis of a complex trait such as ASD, particularly
when using large extended pedigrees. Parametric models,
which are based on assumptions about the genotype-phe-
notype relationship, simplify the parameter space and
allow for more powerful and efficient analyses without
leading to false-positive results [51,52]. We decided to
use two simple dominant and recessive models based on
an extensive set of simulation analyses in which the
results of various simple inheritance models were com-
pared with the results of analyses based on a specified
true model of inheritance [53]. Those simulation analyses
found that the power to reach a given lod score using the
simple models was approximately 80% that of the true
model, and that the expected lod scores for the simple
models approached the true expected lod scores. The
multipoint hlod score allows for unlinked pedigrees and
variation in the recombination fraction. The HLOD pro-
vided by MCLINK is robust to model mis-specification,
and may reflect the true position of linkage regions more
accurately under conditions of appreciable heterogeneity
[54]. HLOD scores have been shown to be more powerful
than homogeneity LOD scores or model-free methods
under these conditions [55,56]. The HLOD has been
shown to produce scores consistent with other published
methods [57,58].
For both IS and RSMA, the phenotype was coded as
unknown if the measure was not available, unaffected if
the score was in the lowest tertile for the scale, and
affected if the score was in the upper two tertiles. This
approach re-codes affection status for all subjects rather
than selecting a subset of subjects with high values on the
traits. For IS, raw score tertile bins were 0-1, 2-3 and > 3;
for RSMA, they were 0-3, 4-6 and > 6. The tertiles were
given different liability classes (penetrances) to weight
those in the upper tertile more strongly. Our recessive
model assumed a disease allele frequency of 0.05 and
penetrances of each of the three genotypes of 0.0014,
0.0014 and 0.8 in the upper tertile, and 0.01, 0.01 and 0.5
in the middle tertile. For the dominant model, the disease
allele frequency was 0.0025. The penetrances were
0.0014, 0.8 and 0.8 in the upper tertile, and 0.01, 0.5 and
0.5 in the middle tertile. These model parameters roughly
reproduce the reported population frequency of ASDs
[1].
Linkage analyses were repeated on the basis of residual
scale scores to determine whether signals could be repli-
cated using measures of IS and RSMA phenotypes that
were statistically independent of each scale's correlation
with the other. Thus, for each scale, residual scores were
computed using the other scale as a covariate (that is, IS-
Adj = IS adjusted for RSMA and RSMA-Adj = RSMA
adjusted for IS). Then, residual scores were divided into
tertiles, and phenotype and liability values were coded in
the same manner as were raw scores, that is, the lowest
tertile was coded as unaffected and the top two tertiles
were coded as affected, and the penetrance of the highest
tertile was greater than that of the lower two tertiles.
For HLOD scores, results are presented using the
Lander and Kruglyak [59] genome-wide criteria. Sugges-
tive linkage evidence was defined by a LOD score ≥ 1.86
and significant genome-wide linkage evidence was
defined by a LOD score ≥ 3.30.
Results
Interscale correlation
The zero-order correlation between RSMA and IS was r
= 0.32 (P < 0.001), indicating that they share 10% of their
variance (r2 = 0.322 = 0.10). Consequently, residual scores
were closely correlated with the raw score of the same
scale (correlations = 0.95, P-values < 0.001), and 90% of
the variance of each scale was unique (r2 = 0.952 = 0.90).
Scale correlates
RSMA was more strongly associated than IS with other
ASD features (Table 1). Both IS and RSMA raw scores
were correlated with ADI-R domain scores and SRS man-
nerisms scale, but the RSMA correlations with ADI-R
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social and SRS mannerisms scales were significantly
greater than those for IS. RSMA but not IS was correlated
with ADOS score (after controlling for the effect of
ADOS module scale), age of first phrases and IQ mea-
sures. With the exception of IQ measures, criterion vari-
ables significantly associated with raw scale scores tended
to have lower correlations with residual scores, which
suggests that the variance that IS and RSMA have in
common may reflect a broader ASD trait. IQ measures,
which were negatively correlated with RSMA, tended to
be even more negatively associated with RSMA-Adj,
although this trend was nominally significant (P < 0.01)
only for non-verbal IQ. IS-Adj was positively correlated
with IQ measures even though raw score IS was not, and
IS-IQ correlations were significantly higher with residual
than with raw scores. Thus, the unique variance of both
IS and RSMA was less strongly associated with ASD but
more strongly associated with IQ, although the direction
of the relations with IQ was opposite (Table 1).
Heritability
The heritability of both scales was significant. For IS, H2
was 0.85 (P < 0.0004, SE = 0.21), and for RSMA, H2 was
0.51 (P < 0.03, SE = 0.26). Because the scales were signifi-
cantly correlated, we also estimated the heritability of
each with the other as a covariate. With RSMA as a cova-
riate, IS was still significant (H2 = 0.69, P < 0.004, SE =
0.23) and RSMA was a significant covariate (P = 0.003).
By contrast, when IS was entered as a covariate for
RSMA, RSMA was not significantly heritable (H2 = 0.31,
P = 0.13, SE = 0.27), but IS was a significant covariate (P <
0.0001).
Linkage
Table 2 lists all regions with at least suggestive evidence
of linkage (HLOD ≥ 1.86 for parametric tests [59] or P <
0.005 for nonparametric tests). There was strong corre-
spondence between regions for which there was evidence
of linkage with the recessive model and nonparametric
linkage (NPL), which suggests that these linkage findings
are resistant to model mis-specification. Fewer tests of
the dominant model, compared with the recessive model,
were suggestive or significant. Thus, to simplify presenta-
tion of genome-wide results, Figures 1 and 2 display the
genome-wide distribution of HLOD scores for the reces-
sive model only (Table 2, Figures 1 and 2).
Evidence of linkage reached genome-wide significance
levels (HLOD > 3.30) for two regions, 2q37.1-q37.3 and
15q13.1-q14 (Table 2), so we examined the linkage evi-
dence for these regions in greater detail (Table 3). For
2q37.1-q35.3, the linkage evidence was greater for the
dominant model, so dominant model HLOD scores
across chromosome 2 are shown in Figure 3 along with
ASD HLOD scores from our earlier work [31]. The evi-
dence of linkage to 2q37.1-q37.3 was greater for IS than
for IS-Adj, RSMA and RSMA-Adj. Note too that we
observed no evidence of ASD linkage to this region in our
earlier study [31]. Taken together, these findings suggest
Table 1: Correlations of IS and RSMA with ADI-R, ADOS, SRS and IQ measures.
Criterion IS RSMA t-Test
Raw Res Raw Res IS vs. RSMA IS vs. IS-Adj RSMA vs. RSMA-Adj
ADI-R Social 0.30* 0.12 0.57* 0.50* 3.75* 8.98* 3.62*
ADI-R Comm 0.41* 0.27* 0.49* 0.40* 0.98 6.09* 4.19*
ADI-R RRSB 0.63* 0.46* 0.61* 0.42* 0.46 10.95* 11.77*
ADOS Score† - 0.01 - 0.10 0.29* 0.31* 3.60* 3.88* 0.87
SRS Mannerisms 0.29* 0.11 0.57* 0.51* 3.70* 8.60* 3.03*
First words -0.02 - 0.06 0.12 0.14 1.43 1.51 0.52
First phrases 0.10 0.03 0.22* 0.21* 1.18 2.26 0.36
VIQ 0.10 0.23* - 0.38* - 0.43* 5.52* 5.48* 2.09
NVIQ 0.17 0.30* - 0.37* - 0.45* 6.42* 5.68* 3.31*
FSIQ 0.11 0.25* - 0.41* - 0.47* 6.26* 6.25* 2.56
ADI-R Comm, verbal communication; ADOS, Autism Diagnostic Observation Schedule; FSIQ, full scale IQ; IS, insistence on sameness; IS-Adj,
IS adjusted for RSMA; NVIQ, nonverbal IQ; RRSB, restricted and repetitive stereotyped behavior; RSMA, repetitive sensory-motor actions;
RSMA-Adj, RSMA adjusted for IS; SRS, Social Reciprocity Scale; VIQ, verbal IQ.
*P < 0.01.
†For ADOS score, partial correlations with ADOS 'module' effects removed are reported.
The t-tests are two-tailed tests of the difference between correlations with the same criterion variable [70]. Sample sizes ranged from 131
to181, depending criterion variable.
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2q37.1-q35.3 may harbor a genetic risk marker for repeti-
tive behavior, particularly IS, which is not strongly associ-
ated with ASD (Table 3, Figure 3).
Linkage results for chromosome 15 were of particular
interest, both because of the different pattern of signals
for IS and RSMA, and the linkage magnitude. Linkage
evidence for both IS and RSMA at 15q13.1-q14 was
greater for the recessive than for the dominant model
(Table 3). Because there also was suggestive evidence
with the recessive model of IS linkage to 15q21.1-q22.2
(Table 3), Figure 4 shows HLOD scores for the recessive
model across chromosome 15. The linkage evidence at
15q13.1-q14 was greater for RSMA than for IS, but none-
theless the evidence for IS was suggestive. A different pat-
tern of findings was observed at 15q21.1-q22.2. Not only
was there no RSMA signal this location, but the IS-Adj
signal was much stronger than the unadjusted IS signal
(HLOD = 3.03 and 1.88, respectively; NPL = 3.10 and
2.60, respectively). This was the largest difference in link-
age values between adjusted and unadjusted phenotypes
Figure 1 Genome-wide distribution of recessive model HLOD scores for insistence on sameness (IS).
12 3 4 5 678 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Chromosome
0
1
2
3
4
HLOD
Figure 2 Genome-wide distribution of recessive model HLOD scores for repetitive sensory-motor actions (RSMA).
12 3 4 5 678 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Chromosome
0
1
2
3
4
HLOD
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for any locus at which at least suggestive linkage evidence
was observed for both raw and residual data. Thus, it
appears that the shared variance between IS and RSMA
actually dampened the IS signal at 15q21.1-q22.2. Finally,
note in Figure 4 that 15q13.1-q14 and 15q21.1-22.2 both
lie within a broader region in which we found evidence at
genome-wide significance levels of linkage with ASD in
our previous study with the same pedigrees [31]. Linkage
evidence for ASD in the 15q13.1-q14 region is compara-
ble with that for the two RSMA variables, but even stron-
ger evidence of ASD linkage was observed in the 15q21.1-
q22.2 region (Figure 4).
Discussion
In a large sample of multiplex ASD pedigrees, we found
evidence that IS and RMSA are distinct phenotypes that
Table 2: Linkage signals for insistence on sameness (IS) and repetitive sensory-motor actions (RSMA).
Chromosome Marker Boundary (Mb) Phenotype Rec Dom NPL
2p25.3-p25.1 rs309276 4.11 to 8.13 IS-Adj 2.12 - 2.56
2p25.2-p25.1 rs1560382 4.58 to 9.97 IS 2.00 - 2.56
2q37.1-q37.3 rs1569125 230.89 to 241.89 RSMA -- 2.15 -
2q37.1-q37.3 rs1198823 235.61 to 239.02 IS-Adj -- 2.02 2.57
2q37.1-q37.3 rs1198823 235.61 to 240.61 IS 2.22 3.42** 2.99
3q13.31-q22.1 rs13975 115.07 to 133.09 RSMA-Adj -- 2.02 -
4q31.22-q32.2 rs538317 146.67 to 162.95 IS-Adj 2.39 - 2.91
4q31.23-q32.2 rs2090870 150.39 to 163.91 IS 2.35 - -
6q22.31-q24.3 rs1041480 125.57 to 148.01 IS -- - 2.69
8q13.2-q22.1 rs1025908 68.59 to 97.25 RSMA-Adj -- 1.93 -
8q13.2-q22.1 rs2016354 70.19 to 96.31 RSMA -- 2.34 -
9p24.3-p24.1 rs1532309 0.59 to 4.80 IS 2.71 - -
13q12.12-q12.3* rs306395 22.86 to 30.07 IS 2.15 - 2.76
15q13.1-q14† rs904951 27.94 to 31.72 RSMA 3.93 2.68 4.54
15q13.1-q14 rs904951 27.94 to 31.72 RSMA-Adj 4.35 2.19 4.11
15q13.1-q15.1 rs965471 27.94 to 39.04 IS-Adj 2.05 - -
15q13.3-q15.1 rs965471 29.46 to 38.23 IS 2.00 - -
15q21.1-q22.2 rs11856 43.47 to 60.20 IS 1.88 - 2.60
15q21.2-q22.2 rs11856 50.79 to 59.13 IS-Adj 3.03 - 3.10
17p13.2-p13.1 rs1848550 5.23 to 9.00 RSMA 2.05 - -
17q23.2-q24.2 rs1874087 58.03 to 65.40 RSMA-Adj 2.40 - -
22q13.1-q13.33 rs132817 37.83 to 48.44 RSMA-Adj -- 1.98 -
Xp11.4-q21.33 rs763554 40.14 to 97.88 RSMA 2.61 - -
Xq13.1-q21.33 rs763554 70.24 to 96.70 RSMA-Adj 3.07 - -
Xq27.3-q28 rs17318101 142.53 to 154.55 RSMA 1.86 - -
Xq27.3-q28 rs473491 144.27 to 154.55 IS 1.97 - -
Dom, dominant; Rec, recessive; IS, insistence on sameness; IS-Adj, IS adjusted for RSMA; RSMA, repetitive sensory-motor actions; RSMA-Adj,
RSMA adjusted for IS.
*The nonparametric linkage signal on chromosome 13 for IS shifted slightly downstream: 13q12.12-q13.1, marker = rs1886204, region
boundaries = 22.86 to 31.54 Mb.
†The dominant model signal on chromosome 15 for RSMA shifted slightly downstream: 15q13.1-q14, marker = rs2 596156, region boundaries
= 25.85 to 35.33 Mb.
'Adj', scale adjusted for the other scale. Signals that are least suggestive [60] are shown for parametric models; for nonparametric linkage
(NPL), signals shown are regions where P < 0.005. Bold font for HLOD scores indicates genome-wide significance [60] and for NPL indicates
P < 2E-05. Signal boundaries were defined as 1 HLOD or 1 NPL drops from the peak. If linkage at a locus was observed for more than one
analysis, boundaries shown are for the recessive model.
Cannon et al. Molecular Autism 2010, 1:3
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Page 8 of 13
can be differentiated by both their phenotypic and geno-
typic relations. Further, the results suggest that ASD sus-
ceptibility loci vary in the breadth of their phenotypic
effects. Finally, the results illustrate the value of using
narrowly defined phenotypes to detect the specific con-
tribution of implicated susceptibility loci to the heteroge-
neous ASD phenotype.
IS and RSMA as distinct phenotypes
The overall pattern of relations of the two RRSB scales
and their residuals with other ADI-R and ADOS mea-
Figure 3 Chromosome 2 HLOD scores for the dominant model as a function of phenotype. IS, insistence on sameness; RSMA, repetitive sensory-
motor actions; IS-Adj, IS adjusted for RSMA; RSMA-Adj, RSMA adjusted for IS. Autism spectrum disorder (ASD) HLOD scores are based on previously
reported linkage analyses with the same pedigrees [31].
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 50 100 150 200 250
HLOD
Position (Mb)
IS
IS-Adj
RSMA
RSMA-Adj
ASD
Table 3: HLOD values for both recessive and dominant parametric models for both unadjusted and adjusted measures of
IS and RSMA for selected regions.
Region IS IS-Adj RSMA RSMA-Adj
Rec Dom Rec Dom Rec Dom Rec Dom
2q37.1-q37.3 2.22* 3.42† 1.34 2.02* 1.33 2.15* 0.70 0.60
15q13.1-q14 2.00* 0.29 2.05* 0.91 3.93† 2.68 4.35† 2.19*
15q21.1-q22.2 1.88* 0.42 3.03* 1.02 1.65 0.94 1.02 0.92
Dom, dominant; Rec, recessive; IS, insistence on sameness; IS-Adj, IS adjusted for RSMA; RSMA, repetitive sensory-motor actions; RSMA-Adj,
RSMA adjusted for IS.
*HLOD values reaching the level of suggestive evidence [60].
†HLOD values reaching genome-wide level of significance [60].
Cannon et al. Molecular Autism 2010, 1:3
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Page 9 of 13
sures suggests that although both RSMA and IS are indi-
ces of ASD severity, the relation with ASD severity is
greater for RSMA than for IS and is in part a function of
the shared variance between IS and RSMA. This general
conclusion that RSMA is more closely associated with
ASD severity is consistent with a previous report of the
correlates of these scales [6]. The negative correlation
between RSMA and IQ and the absence of a significant
correlation between IS and IQ are consistent with previ-
ous reports [6,20], but the finding that the absolute mag-
nitude of IQ correlations with both RSMA-Adj and IS-
Adj is greater than IQ correlations with the raw scale val-
ues has not been reported previously. Taken together,
these correlational findings suggest that the shared vari-
ance between IS and RSMA is associated with ASD
severity but not with IQ.
The hypothesis that the positive relation between IS-
Adj and IQ is mediated by anxiety is offered for further
investigation. Anxiety, which is a common comorbid con-
dition for ASD [60-62], has been reported to be positively
correlated with IQ in children and adolescents with ASD
[60,61]. If obsessive behaviors are attempts to regulate
anxiety [63], then perhaps the positive relation between
IS-Adj and IQ we observed is in part a consequence of
the positive relation that others have reported between
anxiety and IQ. Given that no data are available to sup-
port an association between the IS-Adj scale and anxiety,
the hypothesis that the relation between IQ and IS-Adj is
mediated by anxiety remains to be tested empirically.
Our results indicate that whereas both IS and RSMA
are heritable, the estimated heritability was greater for IS.
Further, the heritability of RSMA may not be indepen-
dent of its relation with IS. Our findings are consistent
with previous reports of significant heritability for IS
[21,25], but in our families we find significantly positive
heritability for RSMA as well. It is possible that the
weaker RSMA heritability effect was not detected in
those earlier reports.
Finally, we found different linkage patterns for IS and
RSMA. There were many instances of suggestive signals
Figure 4 Chromosome 15 HLOD scores for the recessive model as a function of phenotype. IS, insistence on sameness; RSMA, repetitive senso-
ry-mo tor actions; IS -Adj, IS a djusted f or RSMA; RSMA-A dj, RSMA a djusted for IS. Autis m spectru m disorde r (ASD) HL OD scores are based on previously
reported linkage analyses with the same pedigrees [31].
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
20 30 40 50 60 70 80 90 100
HLOD
Position (Mb)
IS
IS-Adj
RSMA
RSMA-Adj
ASD
Cannon et al. Molecular Autism 2010, 1:3
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linked to one but not the other phenotype, including dif-
ferential linkage of IS at 9p24.3-p24.1 and 15q21.2-q22.2
(Table 2). The only signals that reached genome-wide sig-
nificance were at different loci for each scale: 2q37.1-37.3
for IS and at 15q13.1-q14 for RSMA. It is true that at both
2q37.1-37.3 and 15q13.1-q14 there was suggestive evi-
dence of linkage with the other scale (Table 3, Figure 3,
Figure 4), but consideration of linkage results for residual
scales and linkage results for ASD at the two loci suggests
different interpretations of these suggestive signals. At
2q37.1-37.3, where there was a significant signal for IS,
the suggestive signal for RSMA was not observed with
RSMA-Adj and there was no linkage with ASD. Thus, it is
possible that this region is relatively specific to IS, and
that the suggestive signal for RSMA can be attributed to
correlation of RSMA with IS. By contrast, at 15q13.1-q14,
where there was a significant signal for RSMA, suggestive
signals were found for both IS and IS-Adj, indicating that
the IS signal was not due to the RSMA-IS correlation; the
region was also linked to ASD in our earlier study. Thus,
it seems likely that RSMA, being more strongly correlated
with other ASD criteria, was more strongly linked to
15q13.1-q14, which appears to harbor risk markers for a
broad range of ASD traits.
Implications for studies of narrow phenotypes
Some of the IS- and RSMA-specific findings not repli-
cated in our affected status analyses (for example, the sig-
nificant signal specific to IS at 2q37.1-37.3) may be
examples of the hoped-for outcome of identifying suscep-
tibility loci that are specific to narrowly defined pheno-
types [6]. Given that ASD is probably caused by many
genes, each with relatively small effects [64,65], increas-
ing our ability to detect such genes is crucial. Thus, these
findings encourage further research with narrowly
defined phenotypes to uncover linkage signals not
observed with broader diagnostic categories.
Further, our findings provide an example of increased
knowledge of the nature of genetic effects that may be
possible with more homogeneous phenotypes. Previ-
ously, we reported possibly distinct ASD regions with evi-
dence of linkage at 15q13.1-q14, 15q14-q21.1 and
15q21.1-q22.2 [31]. We now report that 15q13.1-q14 is
linked to both RSMA and IS, but is linked more strongly
to RSMA and that 15q21.1-q22.2 is linked to IS but not to
RSMA. Thus, these two loci appear to affect different
aspects of repetitive behavior, a possibility that was
missed in our analysis of affected status.
The variability observed in this study in the phenotypic
scope of linkage regions leads us to suggest that multiple
ASD phenotypes should be used in future genetic studies
to characterize the nature and breadth of the phenotypic
linkage or association of risk variants. It is possible that
variants with broad phenotypic effects may affect the
root causes of ASD, whereas variants with narrow effects
contribute to phenotypic heterogeneity among individu-
als with ASD. The use of multiple phenotypes emphasizes
the importance of additional research aimed at develop-
ing an empirical model of the relations and interactions
between specific features of ASD. Such a model should
lead to identification of a set of phenotype measures that
assess all the key specific features of ASD. The work of
previous investigators to identify IS and RSMA as distinct
features of repetitive behavior is a substantial contribu-
tion to this goal.
We note that our results are again consistent with the
well-replicated finding of complexity and heterogeneity
in ASD genetics. Our lod scores showed inter- and intra-
family heterogeneity. For extended pedigrees, the scores
expected under an assumption of a shared haplotype
across all affected members exceeded by several lod units
those actually found, depending upon the pedigree and
model assumptions. Homogeneity clearly did not exist
across all pedigrees in our sample; for any given region,
multiple pedigrees showed no evidence of linkage.
Previous genetic studies of repetitive and stereotyped
behavior
Shao et al. [13] reported the only linkage study of which
we are aware that stratified pedigrees on either IS or
RSMA. That study differs from the present study in sev-
eral regards. First, they limited their linkage analysis to
the 15q11-q13 region, whereas we did a genome-wide
scan. Second, they used nuclear families rather than
extended pedigrees. Third, they used the diagnosis of AD
as the phenotype, whereas we used IS and RSMA as phe-
notypes. Finally, they used ordered-subset analysis and
we did not. Shao et al. did not find significant evidence of
linkage in the 15q11-q13 region across all 81 families they
studied but they did find significant evidence of linkage in
the region of marker GABRB3 in the subset of 23 families
with the highest mean IS scores. Stratifying families by
RSMA or RRSB did not enhance the signal. GABRB3 is
located at 24.4 Mb, which is upstream of the lower
boundary (27.94 Mb) of 15q13.1-q14. We did not choose
subsets of our sample, but rather re-defined affection sta-
tus based on IS or RSMA phenotypic information, using
information from all ASD members of the pedigrees. The
methodological differences between our study and that of
Shao et al. preclude firm conclusions about why they
found that stratifying on IS but not RSMA enhanced the
AD linkage signal, whereas we found both RSMA and IS,
but particularly RSMA, to be associated with a region just
downstream.
Studies that stratified pedigrees by other repetitive
behavior measures, including individual RRSB items and
the 'compulsions' factor examined by Tadevosyan-Lefer et
al. [27], report increased HLOD scores for AD at chro-
Cannon et al. Molecular Autism 2010, 1:3
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Page 11 of 13
mosome 1 [7] and at 17q11.2 [10]. Significant associa-
tions between SLC25A12 alleles (2q31.1) have been
reported for both the RRSB 'routines and rituals' category
(similar to IS) [15] and the compulsions factor [16]. None
of these loci overlaps signals that we obtained for IS or
RSMA linkage. These differences may again be due in
part to methodological differences between choosing
subsets versus re-defining phenotypes.
The suggestive evidence of IS linkage that we observed
on chromosome 9 for IS spans a region implicated as a
susceptibility locus for OCD in two studies [66,67]. This
replication is noteworthy because the earlier two studies
did not include subjects with ASD. We did not find evi-
dence of linkage for ASD diagnosis in this region using
our full set of families, although we did find a evidence of
linkage for ASD in this region in our analysis of a single
large extended pedigree [68]. Previous research has indi-
cated that OCD features in parents of children with AD
are correlated with scores for IS but not RSMA in
probands [30]. Thus, this region at the chromosome 9
telomere may underlie a repetitive behavior broader
autism phenotype rather than ASD.
Limitations
Our sample was a cohort of multiplex ASD pedigrees,
and IS and RSMA data were collected only on subjects
thought to have ASD. We believe our method is appropri-
ate to the valid aim of uncovering susceptibility loci for
ASD and related phenotypes within extended families
containing multiple members with ASD. However, we
acknowledge that our method limits the generalizability
of our findings to other research aims. For example, the
absence of repetitive behavior phenotype data for family
members without ASD limits our ability to answer the
question of whether repetitive behavior is a broader
autism phenotype that occurs in unaffected relatives
[30,69]. Further, because our sample is not population-
based, we cannot generalize our findings to the search for
genetic markers for repetitive behavior in the general
population [3]. Finally, our study includes analyses of the
IS and RSMA phenotypes under two simple dominant
and recessive models. If we conservatively assume that
these models and phenotypes are not correlated, then sig-
nificance thresholds would be adjusted by log10(4) = 0.6
lod score units. Our thresholds would then be 2.26 for
suggestive evidence and 3.9 for significant evidence. With
this adjustment, results on chromosome 15 remain sig-
nificant and many other results remain suggestive, but
other results would be considered as nominal.
Conclusions
IS and RSMA, two factors within the ADI-RRSB domain,
were found to be linked to largely non-overlapping chro-
mosomal regions. Genome-wide significance was
observed for IS at 2q37.1-q37.3 (dominant model HLOD
= 3.42) and for RSMA at 15q13.1-q14 (recessive model
HLOD = 3.93). Regions varied in the range of phenotypes
with which they were linked. These findings support the
value of including multiple, narrowly defined phenotypes
in ASD genetic research.
Competing interests
HC, WMM and JMM received partial salary support from Lineagen Inc. http://
www.lineagen.com from 12/1/07 to 12/31/08. This salary support is not ongo-
ing.
Authors' contributions
DSC conducted the statistical analyses and drafted the manuscript. JMM con-
firmed research diagnoses of ASD, supervised collection of all phenotype data
and made significant contributions to the interpretation of results. RJR and KAB
assisted with interpretation of results and helped draft the manuscript. MEV
and NKW verified the accuracy of data extracted from a research database and
contributed to the interpretation of results. WMM participated in the design of
the study and helped draft the manuscript. HC conceived of the study, partici-
pated in its design, directed the statistical analyses and helped to draft the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by R01 MH06359, the Utah Autism Foundation, the
Carmen B. Pingree School for Children with Autism and the University of Utah
General Clinical Research Center, which is funded by NCRR grant RR025764.
Partial support for all datasets within the Utah Population Database (UPDB)
was provided by the University of Utah Huntsman Cancer Institute. We thank
our staff whose countless hours of work have made this study possible. We also
greatly appreciate the time and effort given by the family members who par-
ticipated in this study.
Author Details
Utah Autism Research Project, Department of Psychiatry, University of Utah,
650 Komas Drive, Suite 206, Salt Lake City, UT, 84108-3528, USA
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doi: 10.1186/2040-2392-1-3
Cite this article as: Cannon et al., Genome-wide linkage analyses of two
repetitive behavior phenotypes in Utah pedigrees with autism spectrum dis-
orders Molecular Autism 2010, 1:3
