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sing the Autism Diagnostic Interview—Revised toncrease Phenotypic Homogeneity in Genetic Studiesf Autism
anessa Hus, Andrew Pickles, Edwin H. Cook, Jr., Susan Risi, and Catherine Lordackground: Many chromosomal regions for susceptibility to autism spectrum disorders (ASDs) have been identified, but few haveeached genomewide significance. In response, researchers have attempted to increase the power of their analyses by stratifyingamples to increase phenotypic homogeneity. Although homogeneity has typically been defined by a single variable, resultant groupsften differ in other dimensions that may be directly pertinent. Group differences in age, gender, IQ, and measures of autism severityre examined as related to Autism Diagnostic Interview—Revised (ADI-R) domains previously used for subsetting or Quantitative Traitnalysis (QTL).ethods: Participants were research participants and clinic referrals for assessment of possible autism. Assessments included theDI-R, Autism Diagnostic Observation Schedule, Vineland Adaptive Behavior Scales, and a developmental or cognitive test. Data wereollected for 983 individuals, ages 4 to 52 years, with diagnoses of autism and ASDs.esults: Findings suggest that, of several potential grouping variables, only restricted and repetitive behaviors associated with
nsistence on Sameness were independent of age, IQ, and autism severity.onclusions: Results emphasize the potential unintended effects of stratification and the importance of understanding such
nterrelationships between phenotypic characteristics when defining subgroups or performing QTL.
ey Words: Autism spectrum disorders, genetics, phenotype
utism is a complex neurodevelopmental disorder definedby a triad of qualitative impairments in communicationand social interaction and by restricted, repetitive, and
tereotyped behaviors and patterns of interest (American Psychi-tric Association 1994; World Health Organization 1992). Whenxperienced clinicians are given multiple sources of information,utism is one of the most reliably diagnosable disorders insychiatry (Volkmar et al 2005). Severity of symptoms variesreatly, however, as does the nature of symptoms with respect toge, intellectual disabilities, and language delay. In twin studiesf autism, concordance between monozygotic (MZ) twins for aarrow diagnosis of autism has been as low as 36% (Folstein andutter 1977). When individuals with fewer or less severe impair-ents, as seen in the broader classification of autism spectrumisorders (ASDs; including pervasive developmental disorder,ot otherwise specified [PDD-NOS] and Asperger’s disorder), orhe even more broadly defined broader autism phenotype (BAP;awson et al 2002; LeCouteur et al 1996) are considered affected,
he concordance rate rises to 82% or even higher (Bailey et al995; Steffenburg et al 1989). Thus, what is transmitted geneti-ally is unlikely to be narrowly defined autistic disorder. Whetherhere is a final common pathway or many different routes toomplete or partial forms of the ASDs is not clear.
Several loci have been suggested as potential autism suscep-ibility genes, including (but not limited to) different regions onhromosome 2, 7, 13, 15, and 17 (see Veenstra-VanderWeele andook 2004). Although researchers have found suggestive or
rom the University of Michigan Autism and Communication DisordersCenter (VH, SR, CL), Ann Arbor, Michigan; School of Epidemiology andHealth Science (AP), University of Manchester, Manchester, United King-dom; Institute for Juvenile Research (EHC), Department of Psychiatry,University of Illinois, Chicago, Illinois.
ddress reprint requests to Catherine Lord, University of Michigan Autismand Communication Disorders Center, 1111 East Catherine Street, Room217, Ann Arbor, MI 48109-2054; e-mail: [email protected].
eceived March 16, 2006; revised July 26, 2006; accepted August 21, 2006.
006-3223/07/$32.00oi:10.1016/j.biopsych.2006.08.044
significant genomewide linkages to these regions, relatively fewresults have been replicated across samples. Polygenic mecha-nisms have been proposed, with estimates of the number ofgenes contributing to autism susceptibility from 2 to greater than15 (Pickles et al 1995; Risch et al 1999), which would imply thatnonreplications are not immediately interpretable. In response tothese findings, research groups have sought to improve thepower of their analyses by increasing sample size, stratifyinggroups to improve phenotypic homogeneity, and performinglinkage analysis to quantitative traits related to phenotypiccomponents of autism. One strength of autism research is thestandardized instruments used to inform diagnosis, including theAutism Diagnostic Interview—Revised (ADI-R; LeCouteur et al2003) and the Autism Diagnostic Observation Schedule (ADOS;Lord et al 1999). These instruments provide data for diagnosticthresholds, domain scores, and specific items. The ADI-R alsoprovides subdomain scores comparable to the Diagnostic andStatistical Manual of Mental Disorders (4th ed.; DSM-IV) and theInternational Classification of Diseases (10th ed.; ICD-10). Be-cause the ADI-R and its previous versions (see Lord 1994), wereused as inclusion criteria for many genetic studies, these standarddata are widely available and researchers have sought ways ofusing them to select cases with increased homogeneity.
Using Familiality to Identify Homogeneous Phenotypes
One approach has been to assume that if a variable showshigh familiality–broad heritability (through sibling–relative cor-relations), using it to subset families will increase genetic homo-geneity. Spiker et al (1994) found concordance for the ADIdomain Restricted and Repetitive Stereotyped Behaviors (RRSBs)within sibling pairs, in contrast to high intrafamilial variabilityand low concordance for IQ, verbal ability, and other autisticsymptoms. Silverman et al (2002) found reduced variabilitywithin sibships (compared with between families) for ADI-RRRSB and Nonverbal Communication domains, as well as bothonset and presence of phrase speech. Recently, Szatmari et al(2006) found moderate familial aggregation of an Insistence onSameness (IS) factor in affected siblings. LeCouteur and col-
leagues (1996) found minimal concordance within monozygoticBIOL PSYCHIATRY 2007;61:438–448© 2007 Society of Biological Psychiatry
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MZ) twins for RRSBs on the ADI but found familial clustering ofonverbal IQ and verbal/nonverbal status. Nevertheless, mark-dly different levels of impairment were common. In contrast,olevzon et al (2004) found that ADI-R Communication andocial domains showed significantly decreased variance withinZ twins compared with other sibships. Szatmari et al (1996)
ound concordance in IQ and level of adaptive functioning inffected siblings and then, using a larger sample (MacLean et al999), reported a moderate degree of family resemblance foronverbal IQ and social and communicative adaptation, withonverbal Communication and verbal–nonverbal status being
he only ADI/ADI-R measures to show familial aggregation.
tratifying Samples by Language Acquisition
One construct commonly used to stratify samples is age ofanguage acquisition, based on age of first words or phrases.elayed language is defined on the ADI-R by age of first words 24 and age of first phrases � 33–36 months. As shown in Table
, several research groups have found increased logarithm of thedds (LOD) scores for various chromosomal areas using sub-amples of families with phrase speech delay (PSD; Bradford etl 2001; Buxbaum et al 2001; Shao et al 2002). Wassink et al2004) found evidence for linkage in the nondelayed, but not theSD, subsample. Although each research group used PSD as atratification variable to form subgroups, their initial samplesaried by study. For example, Shao and colleagues’ (2002)ample was restricted to families with children with autism,hereas Buxbaum et al’s (2001) sample included families whoet less stringent criteria for an ASD. Furthermore, Bradford et
l’s (2001) findings only emerged when they also incorporated aistory of language-related difficulties in the parents.
dentifying Quantitative Trait Loci
Rather than stratifying families based on probands’ characteris-
able 1. Findings of Genetic Studies Using Phenotypic Characteristics
uthors Year Variable Used
uxbaum et alc 2001 PSDradford et alc 2001 PSDassink et alb 2004 PSD
hao et alc 2002 PSDlarcón et ala 2002 WD
PSDlarcón et ala 2005 WD
PSDpence et ala 2006 WD PSDhao et alc 2003 IScCauley et alc 2004 Compulsions
utcliffe et alb 2005 Compulsionsulder et alb 2005 Compulsionsurmi et alc 2003 Savant Skillsa et alc 2005 Savant Skills
ordjman et alb 2001 ADI-R algorithm subdomains
uxbaum, et ala 2004 ADI-R algorithm subdomainsrune et alb In press ADI-R algorithm subdomains
ADI-R, Autism Diagnostic Interview-Revised; IS, Insistence on Sameneselay; QTL, quantitative trait loci; WD, word delay.
aGenome scan.bCandidate gene study.cFine-mapping study.
ics, some researchers have attempted to identify loci that affect
endophenotypes of specific behaviors related to autism. As shownin Table 1, using nonparametric linkage analysis in sibships, quan-titative trait loci (QTLs) have been identified for age of first word,age of first phrase, and RRSBs (based on ADI-R items). Alarcón andcolleagues (2002, 2005) have found chromosomal regions associ-ated with these QTLs, the most significant being with age of firstwords and age of first phrases. An ordered-subset analysis (OSA; seeShao et al 2003) identified a chromosomal region that would nothave been considered significant enough to analyze further withoutranking families according to age of first words. This language QTLwas attributable to a subset of families with the earliest languagedevelopment, suggesting that the loci was not necessarily associatedwith susceptibility to language delay, but with more general varia-tion in language acquisition.
Use of Empirically Derived Factors
Several research groups have found two distinct factors(although what they are called and which variables they includehas varied slightly) within the RRSB domain of the ADI-R:Insistence on Sameness (IS) and Repetitive Sensory-Motor Ac-tions (RSMA: Cuccaro et al 2003; Bishop et al 2006; Shao et al2003; Szatmari et al 2006); see Table 2. Tadevosyan-Leyfer et al(2003) identified Compulsions and Sensory Aversions factors,which had some overlap with IS but also differed in several ways.As shown in Table 1, several research groups have identifiedchromosomal regions potentially related to autism using subsetsof samples with high IS (Shao et al 2003) or Compulsions scores(McCauley et al 2004; Mulder et al 2005; Sutcliffe et al 2005).
Another of Tadevosyan’s factors, the Savant Skills Factor, hasalso been associated with increased LOD scores (Nurmi et al2003). Ma et al (2005) constructed a similar savant skills score butdid not replicate Nurmi’s finding (see Table 1). In addition toconducting factor analyses, research groups have used DSM-IV/ICD-10 based groupings of ADI-R scores from the diagnostic
Result Area Implicated
Increased LOD & NPL 2qIncreased LOD 7q, 13qIncreased LOD AVPR1aIncreased LOD 2qQTL 7qQTL 10, 11, 20QTL 3q, 17q, 7q35QTL 17No significant increase in NPL scoresIncreased LOD 15q11-q13Increased LOD 17q11.2Increased LOD SLC6A4Higher mean scores Intron 2 VNTRIncreased LOD 15q11-q13No linkage in a subset with higher savant skills 15q11-q13Higher short allele frequency with more
severe social impairment5-HTTLPR
Increased NPL 1q24, 6q, 19pHigher mean scores 5-HTTLPR
, logarithm of the odds; NPL, nonparametric linkage; PSD, phrase speech
s; LODalgorithm, such as relative failure to initiate or sustain conversa-
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ional interchange and lack of varied spontaneous make-believer social imitative play (Tordjman et al 2001), failure to useonverbal communication to regulate social interaction, andtereotyped and repetitive motor mannerisms (Brune et al, inress), and encompassing preoccupation or circumscribed pat-ern of interest, and apparently compulsive adherence to non-unctional routines or rituals (Buxbaum et al 2004; see Table 1).
urpose
Research has suggested that many specific aspects of ASD,ncluding some of the stratifications and approaches to languager behavioral phenotypes just described, are strongly correlatedith variables such as age, verbal and nonverbal IQ, and general
everity of autistic symptoms (Szatmari et al 1996). The impact oftratification on a single measure, such as phrase speech delay,n other, more general variables, such as IQ, has rarely beeneported in genetic studies (for exceptions, see Cuccaro et al003; Silverman et al 2002; Szatmari et al 2006). Knowledgebout the impact of subsetting based on major demographic andackground proband variables could improve understanding ofhenotypes and should increase replicability across investiga-
able 2. Factor Analyses of Restricted, Repetitive, and Stereotyped Behavi
DI-R ItemCuccaro et al
2003
AMSRsmsirennaMregniFdnadnaAMSRsmsirennaMxelpmoCrehtAMSRstcejbOfoesUevititepeAMSRstseretnIyrosneSlausun
—snoitapuccoerPlausunAMSRgnikco
—stnemhcattAlausun
—hceepSdepytoeret
esistance to Trivial Change in Environment ISSIslautiR/snoislupmo
ifficulties with Change in Routine or Environment IS
—esioNotytivitisneSlareneGeudn—ilumitSyrosneSotesnopseRevitage
—stseretnIdebircsmucri—slautiRlabre
This table shows five factor/principal components analyses of repetitiroup’s respective factors. Items are outlined across studies to highligh
--, SA. ADI-R, Autism Diagnostic Interview-Revised; IS, Insistence on Samen
able 3. Demographics of Sample
Male Subjects
Autism (n � 557) PDD-NOS (n � 247) Asperg
ge (years)M (SD) 7.65 (4.45) 7.93 (4.05) 16.4Range 4.00-45.42 4.00-30.17 10.9
erbal IQM (SD) 48.08 (30.07) 83.98 (28.20) 117.6Range 2-151 13-151 9
onverbal IQM (SD) 64.81 (29.01) 87.65 (25.11) 108.7Range 7-153 23-144 7
PDD-NOS, pervasive developmental disorder-not otherwise specified. N � 98
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tions. A better understanding of the consequences of groupingaccording to a single measure (e.g., PSD) or factor (e.g., ADI-RNonverbal Communication) would not negate previous geneticfindings but could provide important insights into potentialinteractions (e.g., IQ by RRSB) that might decrease measurementerror and allow better interpretation of both extremes of a subset(e.g., both earlier and later talkers). This possibility is made moretimely by Alarcón et al’s (2005) recent finding that it was notlanguage delay but early talkers who accounted for the variancein their analyses. Likewise, some background variables mayaccount more directly for results attributed to increased pheno-typic homogeneity or a specific ADI-R item factor.
The present study examines group differences in chronolog-ical age, gender, Verbal and Nonverbal IQ, and measures ofautism severity, as related to ADI-R items and domains previ-ously used for subsetting or QTL analysis.
Methods and Materials
SampleAs shown in Table 3, participants were 983 individuals (812
male subjects), 4 to 52 years old (M age � 7.75 years, SD � 4.58),
s on the ADI-R
hao et al2003
Bishop et al2006
Szatmari et al2006
Tadevosyan et al2003
rehtOAMSRAMSRAMSR—AMSRAMSRAMSR—AMSRAMSRAMSR—AMSRAMSRAMSR
snoislupmoC—AMSRAMSR—AMSR—AMSR
snoislupmoC——AMSR
snoislupmoC———
IS IS/SA IS CompulsionssnoislupmoCSIAS/SISI
IS IS/SA IS —
AS—AS/SIrehtOAS—AS/SIrehtO
———rehtOrehtO———
reotyped behaviors items on the ADI-R and which items loaded on eachcorrespondence between similar factors: —, RSMA;A, IS/Compulsions;
MSA, Repetitive Sensory-Motor Actions; SA, Sensory Aversions.
Female Subjects
� 8) Autism (n � 106) PDD-NOS (n � 63) Asperger (n � 2)
4) 7.36 (6.11) 7.51 (3.59) 9.00 (1.30)7 4.00-51.67 4.00-20.25 8.08-9.92
75) 44.38 (31.77) 77.6 (32.70)5 111.00 (21.21)2-129 13-143 96-126
85) 58.10 (30.12) 80.05 (25.96) 93 (.00)2-135 22-153 93
or Item
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5 (17.9-138
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V. Hus et al BIOL PSYCHIATRY 2007;61:438–448 441
nd mostly Caucasian (N � 760). Based on standardized assess-ents, 663 were diagnosed with autism, and 320 with an ASD
including 10 with Asperger syndrome). Verbal and nonverbal IQcores ranged from 2 to 153.
ata CollectionParticipants were research participants and clinic referrals for
ssessment of possible autism to clinics in Illinois, Michigan, andorth Carolina. Each participant was evaluated using the ADI-R,DOS, Vineland Adaptive Behavior Scales, a cognitive assess-ent, a clinical interview and observation, and received a best
stimate diagnosis from a psychologist–child psychiatrist teamsee Risi et al 2006). Written informed consent was obtained fromll individuals or their parents, in accordance with internaleview board–approved protocols.
easuresThe ADI-R (LeCouteur et al 2003) is a standardized, semistruc-
ured parent interview designed to assess behaviors related toutism or ASDs. Results are available at three levels: Items,omains (summed totals of select items corresponding to theSM-IV domains: Social Reciprocity, Communication, and Re-
tricted, Repetitive Behaviors), and a diagnostic algorithm, inhich a child meets criteria for classification of autism if scores inll domains meet or exceed the cutoff scores. There are separateommunication totals and cutoffs for verbal and nonverbalhildren. Nonverbal and Verbal Communication items werelways analyzed separately. In the RRSB domain, one item is onlycored for verbal participants. To account for the possibility ofhis resulting in lower RRSB totals for nonverbal participants, thisubdomain score was prorated for nonverbal participants.DI-Rs were administered by trained examiners who met stan-ard reliability criteria (see Rutter et al 2003).
The ADOS (Lord et al 1999) is a standardized, semistructuredbservational assessment instrument that is organized into fourodules, based on an individual’s expressive language level,
anging from preverbal to verbally fluent. Scores are available forndividual items, DSM-IV domains (only Social Reciprocity andommunication), and a diagnostic algorithm with cutoff scores
or autism and ASD. Possible totals vary across modules and soere prorated to be comparable. The ADOS was administered by
rained examiners who met standard reliability criteria (see Lordt al 1999).
A standard developmental hierarchy of measures, includinghe Mullen Scales of Early Learning (Mullen 1995) and theifferential Ability Scales (Elliot 1990) were used to determineerbal and nonverbal IQs. If standard scores were not availableecause of severity of delay, ratio IQ scores were calculated.
haracterizing Phenotype GroupsBased on previous research, we formed the following groups:1. Language Acquisition Groups defined based on ADI-R
tems 9 (Age of First Words) and 10 (Age of First Phrases).ndividuals were grouped as follows:
● NDW (not delayed words): acquired words � 24 months● DW (delayed words): acquired words � 24 months● NW (no words): no words at time of ADI-R● NDP (not delayed phrases): acquired phrases � 33 months)● DP (delayed phrases): acquired phrases � 33 months)● NP (no phrases): no phrases at time of ADI-R)
2. Restricted and Repetitive Behaviors Groups were
efined based on Cuccaro et al’s (2003) factors: RepetitiveSensory Motor Actions (RSMA) and Resistance to Change. Forclarity, we refer to the latter as Insistence on Sameness (IS)throughout this article (Shao et al 2003). Eight ADI-R items weresummed to yield RSMA (five items) and IS (three items) scores.Higher scores indicate greater levels of impairment. Participantsmissing any item were excluded, slightly reducing sample sizes.Confirmatory factor analyses using MPlus 3.0 replicated Cucca-ro’s factors in this sample (see Bishop et al 2006). Groups wereas follows:
● LRSMA (low RSMA): score � 4● MRSMA (medium RSMA): score � 5 or 6● HRSMA (high RSMA): score � 6● LIS (Low IS): score � 0● MIS (medium IS): score � 1 or 2● HIS (high IS): score � 2
3. The Savant Skills Factor was based on Tadevosyan-Leyfer et al’s (2003) savant factor (used in Nurmi et al 2003), withcurrent and ever scores of four ADI-R items: visuospatial ability,memory skill, musical ability, and computational ability. Itemscores were summed and divided by total number of items togenerate a score between 0 and 1. Higher scores indicate moresavant skills. Participants were then divided into two groups:Savant-positive and Savant-negative (see Nurmi et al 2003).Savant-positive indicates presence of at least one savant skill.Missing scores were coded 0.
Statistical AnalysesUsing SPSS 13.0, gender, race/ethnicity, chronologic age,
verbal and nonverbal IQ, ADI-R scores, and ADOS scores werecompared for each set of groups using analysis of variance(ANOVA) and independent sample t tests for continuous vari-ables and chi-square analyses for categoric variables. Post hocTukey analyses were used to further examine between-groupdifferences. For Language Acquisition and RRSB groups, analyseswere conducted dividing groups both by median and tertilesplits. Significant differences emerged in both sets of analyses,and thus tertile splits are reported because they yield moreinformation. Because of the number of analyses of correlatedvariables (14 standard variables, 5 sets of analyses), significancelevels were set at p � .001.
Results
Preliminary AnalysesCorrelations. Relationships between phenotypic group (age
of first words, age of first phrases, RSMA, IS, and Savant Skills)and descriptive measures (age, IQ, ADOS scores, and ADI-Rscores) were examined with Pearson correlations, as in Table 4.We found that IS was only weakly correlated with the Social andVerbal Communication Domains on the ADI-R. Other groupsshowed significant correlations with the majority of areas, withRSMA and age of first phrases exhibiting the strongest relation-ships with IQ, ADOS, and ADI-R scores.
Gender, Race and Ethnicity. Preliminary analyses indicatedno significant differences between male and female participantsin any of our measures, as in Table 5. Also shown in Table 5,however, there were significant differences between racial/ethnic categories in both language acquisition and RSMA groups.To address these, analyses involving these phenotypic measureswere rerun separately by African American (the largest minoritygroup) and others (including Caucasian, Hispanic, Asian Amer-
icans, and persons of mixed race). The primary difference waswww.sobp.org/journal
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hat verbal and nonverbal IQ and scores on the ADI-R and ADOSor African American individuals with delays (DW, DP) tended toe more like scores for individuals without delays (NDW, NDP),ather than like those of nonverbal children (NW, NP). Withimited sample sizes, these results are difficult to interpret.
anguage Acquisition GroupsAge of First Words: NW versus DW versus NDW. Age at first
ords was significantly associated with diagnosis, �2(2, 983) �4.95, p � .001, see Table 5. ANOVAs indicated significantssociations with verbal and nonverbal IQs, and no associationith chronological age at assessment (Table 6). Post hoc Tukeynalyses revealed that for both verbal and nonverbal IQ, the DWroup was significantly lower than the NDW, and NW wasignificantly lower than both.
As shown in Table 6, there were significant differencesetween word acquisition groups for all ADI-R domains andubdomains. Post hoc analyses showed that the NDW group hadignificantly less impaired mean scores on Social Reciprocity andRSB than the DW and NW groups, with DW scores significantly
ower than NW in both domains. In Nonverbal Communication,W scores were significantly higher (indicating more impair-ent) than both DW and NDW groups. In Verbal Communica-
ion, the DW group scored significantly higher than the NDW.A similar pattern occurred for ADOS scores. In the Social
omain, NW mean scores were higher than DW or NDW, andW mean scores were higher than the NDW group. In the areasf Play and RRSBs, the NW group had significantly higher scoreshan both the NDW and DW, whereas in Communication, NDWcores were significantly lower than DW and NW.
Using logistic regression, total ADI-R algorithm score anderbal IQ each independently predicted age of first wordsdelayed or not delayed, with NW included in the delayed). Aget time of the ADI-R, ADOS total, nonverbal IQ, and gender didot make significant contributions (see Table 7).
Age of First Phrases: NP versus DP versus NDP. Results forrouping by age at first phrase were similar to word acquisition
able 4. Intercorrelations Between Age of First Words, Age of First Phrasescores and Verbal and Nonverbal IQ, Age, and ADOS and ADI-R domain sco
1 2 3 4 5 6
. Age Words — .51a .16a �.07 �.10a .11a �.
. Age Phrases — .25a �.10 �.17a �.02 �.
. Repetitive SensoryMotor Actions
— .31a .01 .02 �.
. Insistence on Sameness — .20a .08 .
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N � 983, but varied because of missing data. ADI-R, Autism Diagnostic Iap � .001.
roupings, indicating an association with diagnosis, �2(2, 983) �
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128.78, p � .001. One-Way ANOVAs showed significant differ-ences between phrase acquisition groups in mean Verbal andNonverbal IQs, as in Table 6. Post hoc Tukey analyses indicatedthat for both Verbal and Nonverbal IQ, DP was significantlylower than NDP, and NP was significantly lower than both. Inaddition, the NP group was significantly younger than DP andNDP, and the DP group was significantly younger than NDP.
When examining the ADI-R scores by domain, there weresignificant differences between phrase acquisition groups for alldomains. Post hoc analyses indicated that NDP had significantlylower scores in the Social, Nonverbal Communication, and RRSBdomains than did DP and NP, and DP scores were significantlylower than NP. In Verbal Communication, DP was higher thanNDP. As shown in Table 6, a similar pattern occurred in ADOStotal and domain scores, with the NP group’s mean scoresconsistently higher than the DPs or NDPs, and the DP group’smean scores higher than the NDP group’s, except in Play, wherethere was no significant difference between DP and NDP.
Logistic regression indicated that, like age of first words,ADI-R algorithm score and Verbal IQ independently predictedage of first phrases (delayed or not delayed, with NP included inthe delayed). Unlike age of first words, age of the individual atthe time of the ADI-R was also a significant predictor of age offirst phrases. ADOS total, Nonverbal IQ, and gender did notmake significant contributions (see Table 7).
Restricted and Repetitive Behavior FactorsRepetitive Sensory Motor Actions. As with the language
acquisition groups, a relationship between RSMA and diagno-sis was found, �
2 (2, 625) � 59.84, p � .001 (see Table 5).ANOVAs and post hoc tests also indicated that verbal andnonverbal IQs differed significantly for all groups, with thelowest IQ scores in the very repetitive (HRSMA) group andhighest in the less repetitive (LRSMA) group (see Table 8). Agewas not significant.
There were significant differences among RSMA groups for allADI-R and ADOS domains (see Table 8). Post hoc analyses
titive Sensory Motor Actions, Insistence on Sameness, and Savant Skills
8 9 10 11 12 13 14 15 16
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— �.36a �.14a �.37a �.42a �.47a �.32a �.43a �.51a
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V. Hus et al BIOL PSYCHIATRY 2007;61:438–448 443
significantly lower for the LRSMA group compared with bothothers, and the MRSMA group was significantly less impairedthan the HRSMA. For the remaining domains (Verbal and Non-verbal Communication, RRSB), the LRSMA had significantlylower scores than the other groups. Similar patterns of significantdifferences emerged for ADOS scores.
Insistence on Sameness (IS) Factor. Analyses conducted toexamine the differences between IS groups found that groupswere only significantly different in their ADI-R RRSB scores,which would be expected. As shown in Tables 5 and 8, therewere no significant differences between IS groups in gender,race, diagnosis, IQ, ADI-R Social or Communication scores, orADOS scores.
Savant Skills Factor. Chi-square analyses showed no signif-icant differences in diagnostic category between Savant-Negative(SN) and Savant-Positive (SP). t tests indicated that participants inthe SN group were significantly younger and had significantlylower verbal and nonverbal IQs compared with SP; see Table 9.SN had significantly higher ADI-R RRSB and Nonverbal Commu-nication scores than SP, with no other significant differences.
Restricting SamplesOne question that arose was whether restricting the sample to
participants with narrowly defined autism diagnoses rather thanbroader ASDs would minimize some of the relationships be-tween a specific target phenotype (e.g., delayed phrase speech)and the other cognitive and behavioral characteristics associatedwith it. Results based on only participants meeting the strictdiagnosis of autism indicated few significant differences betweenNDW and DW or NDP and DP groups on ADI-R and ADOSdomains, with differences remaining between NW and NP andthe verbal participants. Differences between language acquisi-tion and RSMA groups on ADI-R and ADOS Communicationitems were also no longer significant. Differences remainedbetween high (HRSMA) and low (LRSMA) groups, but thosebetween MRSMA groups and others were no longer significantfor the ADI-R or ADOS domains.
Discussion
As results from more association and linkage studies emerge,it seems less likely that a single, polygenic pattern will accountfor the majority of cases of ASD. Consequently, interest inincreasing the homogeneity of samples has grown steadily. Asshown in Table 1, several groups have successfully increasedLOD scores by segregating samples using measures from theADI-R. Findings from the analyses summarized in Table 10suggest a number of ways that we can build on this approach toproduce more interpretable and potentially more replicablefindings, and this might yield even more successful depictions ofphenotypes.
It is clear that phenotypic factors with similar names (e.g.,Insistence on Sameness vs. Compulsions) from similar instru-ments (e.g., the original ADI, the ADI-R) are not necessarily thesame. As shown in Table 2, the two most commonly citedmethods for categorizing repetitive behaviors on the ADI-R(Cuccaro et al 2003; Tadevosyan-Leyfer et al 2003) are notidentical. Even within very similar RSMA factors, “unusual pre-occupations” has varied across studies in loading, in part perhapsdue to age differences in samples (see Bishop et al 2006).Researchers with a hypothesis about a phenotypic characteristicthat might be transmitted familially need to carefully investigatewhich factor or domain from the diagnostic instruments best
reflects their interests before deciding on a subsetting strategy.Tab
Ge M F
Rac C A O
Dia A A
NW
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444 BIOL PSYCHIATRY 2007;61:438–448 V. Hus et al
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Language history is a theoretically compelling and convenientay of subsetting samples. Doing so has yielded important
nformation, particularly regarding chromosomes 2 and 7. Strat-fications by language delay, however, result in samples thatiffer in many more characteristics than onset of first words orhrases. Participants with delayed words or phrases consistentlyave lower verbal and nonverbal IQs and higher symptom scoresn all defining domains of autism, as measured on both the
able 6. Age, Level of Functioning, and Autism Severity by Language Acqu
Word Acquis
NDW (n � 484) DW (n � 403)
ge (months)d–f
M (SD) 95.90 (52.72) 91.91 (52.27)Range 48-545 48-620
erbal IQa–f
M (SD) 69.12 (34.57) 57.33 (30.80)Range 3-151 2-151
onverbal IQa–f
M (SD) 76.98 (28.90) 72.14 (28.26)Range 13-144 3-153
DI-R Domain ScoresSocial,a–f M (SD) 17.48 (7.72) 20.24 (7.15)Communication, Nonverbal,a,c–f M (SD) 8.11 (4.20) 9.35 (3.71)Communication, Verbalb,e M (SD) 13.86 (5.59) 15.80 (5.25)RRSBa–f M (SD) 3.07 (1.26) 4.97 (.85)
DOS Domain ScoresSociala–f M (SD) 8.52 (2.36) 9.16 (3.30)Communicationb–f M (SD) 5.46 (2.33) 5.96 (2.17)Playa,c,d,f M (SD) 2.34 (1.39) 2.45 (1.35)RRSBa,c–f M (SD) 3.13 (2.51) 3.59 (2.43)
ns vary because of missing data and verbal and nonverbal status. ADI-chedule; ASD, autism spectrum disorder; DW, delayed words; DP, delayed po words; RRSB, Restricted and Repetitive Stereotyped Behaviors.
aNW vs. DW significantly different.bDW vs. NDW significantly different.cNW vs. NDW significantly different.dNP vs. DP significantly different.eDP vs. NDP significantly different.fNP vs. NDP significantly different.gp � .001.
able 7. Effects of Age, Gender, IQ, and Autism Severity on Languagecquisition Groups
� SE
ge of First Words (months) .001 .001ender .105 .198DI-R total .033a .007DOS total �.023 .016onverbal IQ .006 .004erbal IQ �.023a .004ge of First Phrasesge (months) �.006a .002ender �.064 .272DI-R total .046a .009DOS total �.023 .021onverbal IQ .004 .006erbal IQ �.045a .006
ADI-R, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnosticbservation Schedule.
ap � .001.
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ADI-R and ADOS. Higher scores on the ADOS are particularlyimportant, because they indicate that the effect is not due tomethod variance associated with parent reports, but to differ-ences in the symptoms as observed by an independent clinician.This was the case for nonverbal (NW, NP) children versus verbalchildren, even when samples were restricted to participants whohad narrowly defined autism diagnoses.
Stratifications by delay in acquisition in phrases were also, notsurprisingly, associated with the participants’ chronologic age atthe time of the ADI-R, such that participants without phraseswere significantly younger than other individuals with ASD.Stratifications for all the language measures were also associatedwith race, with fewer African American children in the no delayand more in the no words or phrases groups. Age and race effectsmay partially reflect recruitment differences both within and acrosssites in our sample. Particular attention to age when the ADI-R isadministered is important when considering language measures;researchers interested in language phenotypes may want to restrictthe ages of their samples or exclude participants with no words orno phrases (see Alarcón et al 2005) to increase sensitivity to detectgenetic factors of particular interest.
For example, in following up on recent findings regardinglinkage to a relatively small region of chromosome 2, whensubsetting sib pairs with phrase speech delay, one might want toexclude older children with no phrases and use a sample with arelatively narrow autism diagnosis with a lower age of at least 5years. This would increase the likelihood that subsetting by age offirst phrases would produce samples that differed primarily in
n Groups
Phrase Acquisition
(n � 96) F NDP (n � 243) DP (n � 504) NP (n � 236) F
7 (44.39) 2.14 112.44 (69.63) 90.93 (45.92) 77.68 (49.70) 26.02g
8-380 48-545 48-453 48-620
8 (13.67) 99.99g 89.78 (28.82) 61.82 (27.25) 22.88 (15.56) 416.81g
2-67 14-151 8-151 2-70
6 (19.91) 77.51g 90.37 (24.41) 75.72 (26.45) 41.92 (19.99) 236.69g
2-102 20-144 14-153 2-107
8 (5.41) 44.58g 14.98 (7.20) 19.48 (7.39) 23.43 (5.92) 87.17g
0 (3.03) 42.73g 8.85 (3.90) 11.50 (3.02) 92.98g
10.25g 12.88 (5.57) 15.63 (5.27) 19.45g
0 (.72) 455.80g 2.29 (1.11) 4.41 (.98) 5.29 (0.87) 604.07g
1 (3.40) 28.83g 7.40 (3.17) 8.75 (3.29) 11.45 (2.29) 99.06g
9 (1.68) 7.82g 4.90 (2.42) 5.89 (2.20) 6.47 (1.65) 26.57g
8 (0.91) 23.22g 2.10 (1.38) 2.29 (1.35) 3.44 (0.92) 62.46g
8 (2.55) 25.68g 2.39 (2.29) 3.30 (2.38) 5.46 (2.18) 86.90g
tism Diagnostic Interview-Revised; ADOS, Autism Diagnostic Observations; NDP, not delayed phrases; NDW, not delayed words; NP, no phrases; NW,
isitio
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aw
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V. Hus et al BIOL PSYCHIATRY 2007;61:438–448 445
utistic symptoms. These steps would confirm that such a findingas related to speech delay in contrast to other impairments.Results for RSMA followed very similar patterns as for lan-
uage delay, with groups defined by RSMA scores differing onerbal and nonverbal IQ and all symptom areas on the ADI-R andDOS. In contrast, IS was relatively independent of gender, race,iagnosis, chronological age, nonverbal and verbal IQ, andutism symptom domains on the ADI-R and ADOS. These resultsre consistent with those reported by Cuccaro et al (2003) andishop et al (2006) who found high correlations between RSMA,ut not IS scores, with adaptive behavior or IQ. It is interesting to
able 8. Age, Level of Functioning and Autism Severity by RRSB Groups
RSMA
Low(n � 227)
Medium(n � 158)
High(n � 240)
ge (months)M 86.67 92.73 86.55(SD) (44.09) (59.91) (36.82)Range 48–388 48–620 48–279
erbal IQa–c
M 66.31 56.17 41.82(SD) (32.85) (34.06) (30.70)Range 8–141 2–141 3–129
onverbal IQa–c
M 79.45 69.46 56.74(SD) (28.21) (29.43) (28.65)Range 15–153 3–129 10–150
DI-R Domain ScoresSociala,b,c
M 16.87 20.34 22.84(SD) (7.59) (6.75) (6.74)
ommunication, Nonverbala,c
M 7.81 9.39 10.61(SD) (4.12) (3.86) (3.51)
ommunication — Verbala,c
M 13.27 15.61 16.72(SD) (5.52) (5.09) (5.03)
RSBa,c–f
M 4.41 6.28 6.73(SD) (2.23) (2.28) (2.24)
DOS Domain ScoresSociala–c
M 8.18 9.29 10.10(SD) (3.62) (3.10) (3.28)Commc
M 5.39 6.03 6.52(SD) (2.38) (2.18) (1.84)Playb,c
M 2.25 2.35 2.89(SD) (1.43) (1.42) (1.34)RRSBa–c
M 2.53 3.82 4.74(SD) (2.18) (2.46) (2.44)
ns vary because of missing data and verbal and nonverbal status. ADI-Rchedule; IS, Insistence on Sameness; RRSB, Restricted and Repetitive Stere
aLow RSMA vs. medium RSMA significantly different.bMedium RSMA vs. high RSMA significantly different.cLow RSMA vs. high RSMA significantly different.dLow IS vs. medium IS significantly different.eMedium IS vs. high IS significantly different.fLow IS vs. high IS significantly different.gp � .001.
ote that although proposed as a central characteristic of autism
by Kanner (1943) and Rutter (1978), insistence on sameness wasnot included in DSM-IV (APA 1994) and ICD-10 (WHO 1992) inpart because research (Lord et al 1993) suggested that ADI-Ritems regarding insistence on sameness were not specific toASDs. In fact, for this reason, rather than despite these findings,the IS factor may be useful in stratifying an ASD sample becauseit offers a relatively independent dimension that varies consider-ably within ASD and other populations. Several studies havefound evidence of familiality specifically for the IS factor (Shao etal 2003; Szatmari et al 2006), which increases its potential valuefor use in stratification, given the likelihood that it is a genetically
IS
Low(n � 160)
Medium(n � 190)
High(n � 230) F
5 93.41 91.05 106.30 3.75(60.16) (62.84) (61.24)48–620 48–545 48–407
3g 55.94 56.39 63.04 2.49(36.88) (35.20) (36.59)2–143 6–141 2–151
1g 68.20 68.94 73.02 1.39(29.85) (31.26) (31.66)3–142 10–144 2–153
5g 18.29 19.26 20.36 3.76(7.69) (7.27) (7.35)
3g 8.82 8.97 9.28 .64(4.23) (4.12) (3.83)
0g 13.44 14.37 15.55 4.71(6.10) (5.23) (5.42)
2g 4.27 5.39 7.21 84.30g
(2.17) (2.23) (2.35)
1g 9.04 9.43 9.03 .81(3.40) (3.28) (3.47)
6g 5.60 5.84 5.61 .51(2.33) (2.11) (2.33)
4g 2.51 2.32 2.50 .87(1.37) (1.46) (1.36)
9g 3.44 3.81 3.83 .94(2.71) (2.56) (2.61)
ism Diagnostic Interview—Revised; ADOS, Autism Diagnostic Observationd Behaviors; RSMA, Repetitive Sensory Motor Actions
F
1.0
39.0
35.3
41.8
31.1
15.7
66.0
17.9
13.1
10.9
41.6
, Autotype
driven construct.
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446 BIOL PSYCHIATRY 2007;61:438–448 V. Hus et al
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The savant skills domain followed another pattern withifferences between its two groups strongly associated with age,onverbal and verbal IQs. However, it was not associated witheverity of autism symptoms. What this means is not clear, but itmplies that stratifying on IQ before savant skills may provide aore specific measure of the presence of specific strengths.One of the arguments in favor of using factor or principal
omponents analyses is that factor scores reduce measurementrror and so may improve power. Nonetheless, it is important toote that, for the sake of transparency, a number of studies haveot used actual factor scores but have simply added ADI-R itemcores. Most factor analyses have not adjusted for ordinality, andany have not followed standard guidelines such as minimumumber of items per factor, minimal loadings, and adherence to
Table 9. Age, Level of Functioning and Autism Severity
Savant Posi
Age (months)M (SD) 107.61Range 48
Verbal IQM (SD) 73.34Range 7
Nonverbal IQM (SD) 83.41Range 14ADI-R Domain Scores
Social M (SD) 18.27Communication, Nonverbal M (SD) 8.35Communication, Verbal M (SD) 14.99RRSB M (SD) 3.73
ADOS Domain ScoresSocial M (SD) 8.66Communication M (SD) 5.55Play M (SD) 2.34RRSB M (SD) 3.50
ns vary because of missing data and verbal and noADOS, Autism Diagnostic Observation Schedule; IS, Insityped Behaviors; RSMA, Repetitive Sensory Motor Actio
ap � .001.
able 10. Summary of Variables Associated with Potential Constructssed for Stratification
Age ofFirst
Words
Age ofFirst
Phrases
Rep. SensoryMotorAction
Insistence onSameness
SavantSkills
ge X X Xerbal IQ X X X Xonverbal IQ X X X XDI-RSocial X X XComm, NV X X X XComm, V X X XRRSB X X X X X
DOSSocial X X XComm X X XPlay X X XRRSB X X X
ADI-R, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnosticbservation Schedule; Comm, communication; NV, nonverbal; RRSB, Re-
tricted and Repetitive Stereotyped Behaviors; V, verbal.
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a standard time frame (e.g., not including “current” and “ever”items within the same factor, without some theoretical justifica-tion; Tabachnick and Fidell 1989). Thus, the degree to which allfactors reduce measurement error is not clear.
LimitationsThis article presents relatively straightforward analyses of a
number of variables to illustrate simple points and to providedata comparable to that presented in previous genetics studies(e.g., Shao 2003; Sutcliffe 2005). Nevertheless, more complexmultivariate analyses would provide more accurate representa-tions of interactions between both the predictor and criterionvariables for this particular dataset. Bishop et al (2006) is anexample of such analyses carried out for different purposes, andmore research is underway. Data from other parent-reportmeasures of autistic symptoms (e.g., the Social ResponsivenessScale, Constantino and Gruber 2005; the Pervasive Developmen-tal Disorders Behavior Inventory, Cohen et al 2003) would alsoprovide information on the degree to which these findings arespecific to the ADI-R. A more diverse sample would haveallowed us to better address the effects of race/ethnicity.
ImplicationsIncreasing homogeneity of variance in ASD samples is an
important response to the likely complexity of the genetics ofautism. Our findings here should not detract from the value ofthe subsetting approach in the search for consistent linkages torelatively small regions in a complex genetic disorder. Whensamples are stratified, however, it is essential to provide infor-mation on how that stratification affects both basic demographics(e.g., gender, race, recruitment site, chronologic age) and othermeasures that are assumed to be relatively independent, such asIQ and other autism symptom domains. Researchers may want toconsider restricting their samples (e.g., as in Alarcón et al’sexclusion of nonverbal children) or further stratifying samples byother features, such as chronologic age for language measures or
vant Skills Groups
� 282) Savant Negative (n � 701) t
1) 87.22 (46.76) �5.34a
48-620
4) 53.83 (33.36) �8.24a
2-148
3) 66.28 (83.41) �8.18a
2-153
) 19.74 (7.61) 2.75) 9.26 (4.04) 3.20a
) 14.47 (5.58) �1.16) 4.24 (1.46) .10a
) 9.23 (3.40) 2.28) 5.84 (2.19) 1.65) 2.56 (1.37) 2.10) 3.54 (2.59) .18
al status. ADI-R, Autism Diagnostic Interview-Revised;e on Sameness; RRSB, Restricted and Repetitive Stereo-
by Sa
tive (n
(69.3-545
(33.3-151
(29.1-153
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IQ for measures of repetitive behaviors or savant skills, to test
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V. Hus et al BIOL PSYCHIATRY 2007;61:438–448 447
ore specifically the effect of the variable in which they are mostnterested. Another option would be to make age and/or IQegression covariates in the QTL. Covarying verbal IQ in aenetic analysis of social-communication scores, however, es-entially creates a scale of the discrepancy between ADI-Rommunication scores and IQ, which may or may not be whatesearchers have in mind. The robustness of findings for Insis-ence on Sameness suggests that it is possible to identify aelatively independent set of behaviors. With the availability ofarger samples and more careful methodologies, identification ofther similar factors seems likely. Although such stratificationay result in substantially reduced sample sizes, with the
ollaboration of research groups and the advent of publiclyvailable repositories such as those from Autism Genetic Re-ource Exchange and National Institutes of Mental Health, ithould be possible to achieve the sufficiently large and homog-nous sample subsets necessary to yield greater sensitivity toetect relevant genetic factors (Le Couteur 1996, Le Couteur003, Tabachnick 1989).
This research was supported in part by Grant Nos. NIMH25MH067723 and R01MH066496. The authors thanks Shan-ing Qiu for compiling our datasets; the families who partici-ated in these research projects; and the staff at the Northarolina TEACCH centers, the University of Chicago Departmentf Psychiatry, and UMACC. Disclosure: Authors S.R. and C.L.eceive royalties for the ADI-R and/or ADOS; profits accrued fromhis study were donated to charity.
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