U IIn enoVa an RBa ism srea attesam y haoft up dare DI-RAnMe rralsAD Behcol f autRe riabIns severCo ts ofint g su
AsteatrexpaupsygreageofnaRumedisnot otherwise specified [PDD-NOS] and Aspergers disorder), orthe even more broadly defined broader autism phenotype (BAP;Dathe199calthecom
sigresnisge15nothepogrolincostaAu20LothrproStaInternational Classification of Diseases (10th ed.; ICD-10). Be-
000dowson et al 2002; LeCouteur et al 1996) are considered affected, concordance rate rises to 82% or even higher (Bailey et al5; Steffenburg et al 1989). Thus, what is transmitted geneti-ly is unlikely to be narrowly defined autistic disorder. Whetherre is a final common pathway or many different routes toplete or partial forms of the ASDs is not clear.Several loci have been suggested as potential autism suscep-ility genes, including (but not limited to) different regions onomosome 2, 7, 13, 15, and 17 (see Veenstra-VanderWeele andok 2004). Although researchers have found suggestive or
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 familialitybroad heritability (through siblingrelative 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 monozygotic
m the University of Michigan Autism and Communication DisordersCenter (VH, SR, CL), Ann Arbor, Michigan; School of Epidemiology andHealth Science (AP), University ofManchester, Manchester, United King-dom; Institute for Juvenile Research (EHC), Department of Psychiatry,University of Illinois, Chicago, Illinois.dress reprint requests to Catherine Lord, University of Michigan AutismandCommunicationDisorders Center, 1111 East Catherine Street, Room217, Ann Arbor, MI 48109-2054; e-mail: firstname.lastname@example.org March 16, 2006; revised July 26, 2006; accepted August 21, 2006.
BIOL PSYCHIATRY 2007;61:4384486-3223/07/$32.00i:10.1016/j.biopsych.2006.08.044 2007 Society of Biological Psychiatrysing the Autism Diagnosticcrease Phenotypic Homogf Autismnessa Hus, Andrew Pickles, Edwin H. Cook, Jr., Susckground: Many chromosomal regions for susceptibility to autched genomewide significance. In response, researchers haveples to increase phenotypic homogeneity. Although homogeneit
en differ in other dimensions that may be directly pertinent. Groexamined as related to Autism Diagnostic InterviewRevised (Aalysis (QTL).thods: Participants were research participants and clinic refeI-R, Autism Diagnostic Observation Schedule, Vineland Adaptivelected for 983 individuals, ages 4 to 52 years, with diagnoses osults: Findings suggest that, of several potential grouping vaistence on Sameness were independent of age, IQ, and autismnclusions: Results emphasize the potential unintended effecerrelationships between phenotypic characteristics when definin
yWords: 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
reotyped behaviors and patterns of interest (American Psychi-ic Association 1994; World Health Organization 1992). Whenerienced clinicians are given multiple sources of information,
tism is one of the most reliably diagnosable disorders inchiatry (Volkmar et al 2005). Severity of symptoms variesatly, however, as does the nature of symptoms with respect to, intellectual disabilities, and language delay. In twin studiesautism, concordance between monozygotic (MZ) twins for arrow diagnosis of autism has been as low as 36% (Folstein andtter 1977). When individuals with fewer or less severe impair-nts, as seen in the broader classification of autism spectrumorders (ASDs; including pervasive developmental disorder,nterviewRevised toeity in Genetic Studies
isi, and Catherine Lordpectrum disorders (ASDs) have been identified, but few havempted to increase the power of their analyses by stratifyings typically been defined by a single variable, resultant groupsifferences in age, gender, IQ, and measures of autism severity) domains previously used for subsetting or Quantitative Trait
for assessment of possible autism. Assessments included theavior Scales, and a developmental or cognitive test. Data wereism and ASDs.les, only restricted and repetitive behaviors associated withity.stratification and the importance of understanding suchbgroups or performing QTL.
nificant genomewide linkages to these regions, relatively fewults have been replicated across samples. Polygenic mecha-ms have been proposed, with estimates of the number ofnes contributing to autism susceptibility from 2 to greater than (Pickles et al 1995; Risch et al 1999), which would imply thatnreplications are not immediately interpretable. In response tose findings, research groups have sought to improve thewer of their analyses by increasing sample size, stratifyingups to improve phenotypic homogeneity, and performingkage analysis to quantitative traits related to phenotypicmponents of autism. One strength of autism research is thendardized instruments used to inform diagnosis, including thetism Diagnostic InterviewRevised (ADI-R; LeCouteur et al03) and the Autism Diagnostic Observation Schedule (ADOS;rd et al 1999). These instruments provide data for diagnosticesholds, domain scores, and specific items. The ADI-R alsovides subdomain scores comparable to the Diagnostic andtistical Manual of Mental Disorders (4th ed.; DSM-IV) and the
lanDe 1, sodsamal (20PSstravarsamwhmealshis
Table 1. Findings of Genetic Studies Using Phenotypic Characteristics
Bux IBra IWa ISha IAla Q
QSpe NSha IMc ISut IMu HNu IMa NTor H
Bux IBru H
V. Hus et al BIOL PSYCHIATRY 2007;61:438448 439Z) twins for RRSBs on the ADI but found familial clustering ofnverbal IQ and verbal/nonverbal status. Nevertheless, mark-ly different levels of impairment were common. In contrast,levzon et al (2004) found that ADI-R Communication andcial domains showed significantly decreased variance within twins compared with other sibships. Szatmari et al (1996)nd concordance in IQ and level of adaptive functioning inected siblings and then, using a larger sample (MacLean et al9), reported a moderate degree of family resemblance fornverbal IQ and social and communicative adaptation, withnverbal Communication and verbalnonverbal status beingonly ADI/ADI-R measures to show familial aggregation.
atifying Samples by Language Acquisition
One construct commonly used to stratify samples is age ofguage acquisition, based on age of first words or phrases.layed language is defined on the ADI-R by age of first words24 and age of first phrases 3336 months. As shown in Tableeveral research groups have found increased logarithm of theds (LOD) scores for various chromosomal areas using sub-ples of families with phrase speech delay (PSD; Bradford et
2001; Buxbaum et al 2001; Shao et al 2002). Wassink et al04) found evidence for linkage in the nondelayed, but not theD, subsample. Although each research group used PSD as atification variable to form subgroups, their initial samplesied by study. For example, Shao and colleagues (2002)ple was restricted to families with children with autism,ereas Buxbaum et als (2001) sample included families whot less stringent criteria for an ASD. Furthermore, Bradford et (2001) findings only emerged when they also incorporated atory of language-related difficulties in the parents.
ntifying Quantitative Trait Loci
Rather than stratifying families based on probands characteris-, some researchers have attempted to identify loci that affect
hors Year Variable Used
baum et alc 2001 PSDdford et alc 2001 PSDssink et alb 2004 PSDo et alc 2002 PSDrcn et ala 2002 WD
PSDrcn et ala 2005 WD
PSDnce et ala 2006 WD PSDo et alc 2003 ISCauley et alc 2004 Compulsionscliffe et alb 2005 Compulsionslder et alb 2005 Compulsionsrmi et alc 2003 Savant Skillset alc 2005 Savant Skillsdjman et alb 2001 ADI-R algorithm subdomains
baum, et ala 2004 ADI-R algorithm subdomainsne et alb In press ADI-R algorithm subdomains
ADI-R, Autism Diagnostic Interview-Revised; IS, Insistence on Sameness; Lay; QTL, quantitative trait loci; WD, word delay.aGenome scan.bCandidate gene study.cFine-mapping study.dophenotypes of specific behaviors related to autism. As shownTable 1, using nonparametric linkage analysis in sibships, quan-tive trait loci (QTLs) have been identified for age of first word, of first phrase, and RRSBs (based on ADI-R items). Alarcn andleagues (2002, 2005) have found chromosomal regions associ-d with these QTLs, the most significant being with age of firstrds and age of first phrases. An ordered-subset analysis (OSA; seeao et al 2003) identified a chromosomal region that would notve been considered significant enough to analyze further withoutking families according to age of first words. This language QTLs attributable to a subset of families with the earliest languagevelopment, suggesting that the loci was not necessarily associatedth susceptibility to language delay, but with more general varia-n in language acquisition.
e of Empirically Derived Factors
Several research groups have found two distinct factorsthough what they are called and which variables they includes varied slightly) within the RRSB domain of the ADI-R:istence on Sameness (IS) and Repetitive Sensory-Motor Ac-ns (RSMA: Cuccaro et al 2003; Bishop et al 2006; Shao et al03; Szatmari et al 2006); see Table 2. Tadevosyan-Leyfer et al03) identified Compulsions and Sensory Aversions factors,ich had some overlap with IS but also differed in several ways. shown in Table 1, several research groups have identifiedromosomal regions potentially related to autism using subsetssamples with high IS (Shao et al 2003) or Compulsions scorescCauley et al 2004; Mulder et al 2005; Sutcliffe et al 2005).Another of Tadevosyans factors, the Savant Skills Factor, haso been associated with increased LOD scores (Nurmi et al03). Ma et al (2005) constructed a similar savant skills score but not replicate Nurmis finding (see Table 1). In addition tonducting factor analyses, research groups have used DSM-IV/-10 based groupings of ADI-R scores from the diagnosticorithm, such as relative failure to initiate or sustain conversa-
Result Area Implicated
ncreased LOD & NPL 2qncreased LOD 7q, 13qncreased LOD AVPR1ancreased LOD 2qTL 7qTL 10, 11, 20TL 3q, 17q, 7q35TL 17o significant increase in NPL scoresncreased LOD 15q11-q13ncreased LOD 17q11.2ncreased LOD SLC6A4igher mean scores Intron 2 VNTRncreased LOD 15q11-q13o linkage in a subset with higher savant skills 15q11-q13igher short allele frequency with moresevere social impairment
ncreased NPL 1q24, 6q, 19pigher mean scores 5-HTTLPR
ogarithm of the odds; NPL, nonparametric linkage; PSD, phrase speechdewitio
Table 2. Factor Analyses of Restricted, Repetitive, and Stereotyped Behavior Items on the ADI-R
stereogro he co---, ; RMS
440 BIOL PSYCHIATRY 2007;61:438448 V. Hus et al
wwnal interchange and lack of varied spontaneous make-believesocial imitative play (Tordjman et al 2001), failure to usenverbal communication to regulate social interaction, andreotyped and repetitive motor mannerisms (Brune et al, inss), and encompassing preoccupation or circumscribed pat-n of interest, and apparently compulsive adherence to non-ctional routines or rituals (Buxbaum et al 2004; see Table 1).
Research has suggested that many specific aspects of ASD,luding some of the stratifications and approaches to languagebehavioral phenotypes just described, are strongly correlatedth variables such as age, verbal and nonverbal IQ, and generalerity of autistic symptoms (Szatmari et al 1996). The impact oftification on a single measure, such as phrase speech delay,other, more general variables, such as IQ, has rarely beenorted in genetic studies (for exceptions, see Cuccaro et al3; Silverman et al 2002; Szatmari et al 2006). Knowledgeout the impact of subsetting based on major demographic andckground proband variables could improve understanding ofenotypes and should increase replicability across investiga-
I-R ItemCuccaro et al
istance to Trivial Change in Environment ISSIslautiR/snoislup
ficulties with Change in Routine or Environment IS
This table shows five factor/principal components analyses of repetitiveups respective factors. Items are outlined across studies to highlight tSA. ADI-R, Autism Diagnostic Interview-Revised; IS, Insistence on Sameness
le 3. Demographics of Sample
Autism (n 557) PDD-NOS (n 247) Asperger
e (years)(SD) 7.65 (4.45) 7.93 (4.05) 16.45 (4
ange 4.00-45.42 4.00-30.17 10.92-2bal IQ(SD) 48.08 (30.07) 83.98 (28.20) 117.63 (1
ange 2-151 13-151 91-1nverbal IQ(SD) 64.81 (29.01) 87.65 (25.11) 108.75 (1
ange 7-153 23-144 79-1
PDD-NOS, pervasive developmental disorder-not otherwise specified. Nw.sobp.org/journalns. A better understanding of the consequences of groupingording to a single measure (e.g., PSD) or factor (e.g., ADI-Rnverbal Communication) would not negate previous geneticdings but could provide important insights into potentialeractions (e.g., IQ by RRSB) that might decrease measurementor and allow better interpretation of both extremes of a subsetg., both earlier and later talkers). This possibility is made moreely by Alarcn et als (2005) recent finding that it was notguage delay but early talkers who accounted for the variancetheir analyses. Likewise, some background variables mayount more directly for results attributed to increased pheno-ic homogeneity or a specific ADI-R item factor.The present study examines group differences in chronolog-l age, gender, Verbal and Nonverbal IQ, and measures oftism severity, as related to ADI-R items and domains previ-sly used for subsetting or QTL analysis.
mpleAs shown in Table 3, participants were 983 individuals (812le subjects), 4 to 52 years old (M age 7.75 years, SD 4.58),
o et al003
Bishop et al2006
Szatmari et al2006
Tadevosyan et al2003
IS IS/SA IS CompulsionssnoislupmoCSIAS/SISI
IS IS/SA IS
typed behaviors items on the ADI-R and which items loaded on eachrrespondence between similar factors: , RSMA;A, IS/Compulsions;A, Repetitive Sensory-Motor Actions; SA, Sensory Aversions.
) Autism (n 106) PDD-NOS (n 63) Asperger (n 2)
7.36 (6.11) 7.51 (3.59) 9.00 (1.30)4.00-51.67 4.00-20.25 8.08-9.92
44.38 (31.77) 77.6 (32.70)5 111.00 (21.21)2-129 13-143 96-126
58.10 (30.12) 80.05 (25.96) 93 (.00)2-135 22-153 93
but varied because of missing data.auou
and mostly Caucasian (N 760). Based on standardized assess-ments, 663 were diagnosed with autism, and 320 with an ASD(insco
Sensory Motor Actions (RSMA) and Resistance to Change. Forclarity, we refer to the latter as Insistence on Sameness (IS)thrsumHimiCoroas
V. Hus et al BIOL PSYCHIATRY 2007;61:438448 441cluding 10 with Asperger syndrome). Verbal and nonverbal IQres ranged from 2 to 153.
ta CollectionParticipants were research participants and clinic referrals foressment of possible autism to clinics in Illinois, Michigan, andrth Carolina. Each participant was evaluated using the ADI-R,OS, Vineland Adaptive Behavior Scales, a cognitive assess-nt, a clinical interview and observation, and received a bestimate diagnosis from a psychologistchild psychiatrist teame Risi et al 2006). Written informed consent was obtained fromindividuals or their parents, in accordance with internaliew boardapproved protocols.
asuresThe ADI-R (LeCouteur et al 2003) is a standardized, semistruc-ed parent interview designed to assess behaviors related totism or ASDs. Results are available at three levels: Items,mains (summed totals of select items corresponding to theM-IV domains: Social Reciprocity, Communication, and Re-cted, Repetitive Behaviors), and a diagnostic algorithm, inich a child meets criteria for classification of autism if scores indomains meet or exceed the cutoff scores. There are separatemmunication totals and cutoffs for verbal and nonverballdren. Nonverbal and Verbal Communication items wereays analyzed separately. In the RRSB domain, one item is onlyred for verbal participants. To account for the possibility ofs resulting in lower RRSB totals for nonverbal participants, thisdomain score was prorated for nonverbal participants.I-Rs were administered by trained examiners who met stan-rd reliability criteria (see Rutter et al 2003).The ADOS (Lord et al 1999) is a standardized, semistructuredservational assessment instrument that is organized into fourdules, based on an individuals expressive language level,ging from preverbal to verbally fluent. Scores are available forividual items, DSM-IV domains (only Social Reciprocity andmmunication), and a diagnostic algorithm with cutoff scoresautism and ASD. Possible totals vary across modules and sore prorated to be comparable. The ADOS was administered byined examiners who met standard reliability criteria (see Lordal 1999).A standard developmental hierarchy of measures, including Mullen Scales of Early Learning (Mullen 1995) and theferential Ability Scales (Elliot 1990) were used to determinebal and nonverbal IQs. If standard scores were not availablecause of severity of delay, ratio IQ scores were calculated.
aracterizing Phenotype GroupsBased on previous research, we formed the following groups:1. Language Acquisition Groups defined based on ADI-Rms 9 (Age of First Words) and 10 (Age of First Phrases).ividuals were grouped as follows:
NDW (not delayed words): acquired words 24 monthsDW (delayed words): acquired words 24 monthsNW (no words): no words at time of ADI-RNDP (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 werefined based on Cuccaro et als (2003) factors: Repetitiveoughout this article (Shao et al 2003). Eight ADI-R items weremed to yield RSMA (five items) and IS (three items) scores.
gher scores indicate greater levels of impairment. Participantsssing any item were excluded, slightly reducing sample sizes.nfirmatory factor analyses using MPlus 3.0 replicated Cucca-s factors in this sample (see Bishop et al 2006). Groups werefollows:
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-fer et als (2003) savant factor (used in Nurmi et al 2003), withrrent and ever scores of four ADI-R items: visuospatial ability,mory skill, musical ability, and computational ability. Itemres were summed and divided by total number of items tonerate a score between 0 and 1. Higher scores indicate moreant skills. Participants were then divided into two groups:ant-positive and Savant-negative (see Nurmi et al 2003).ant-positive indicates presence of at least one savant skill.ssing scores were coded 0.
tistical AnalysesUsing SPSS 13.0, gender, race/ethnicity, chronologic age,rbal and nonverbal IQ, ADI-R scores, and ADOS scores werempared for each set of groups using analysis of varianceNOVA) and independent sample t tests for continuous vari-les and chi-square analyses for categoric variables. Post hockey analyses were used to further examine between-groupferences. For Language Acquisition and RRSB groups, analysesre conducted dividing groups both by median and tertileits. Significant differences emerged in both sets of analyses,d thus tertile splits are reported because they yield moreormation. Because of the number of analyses of correlatediables (14 standard variables, 5 sets of analyses), significanceels were set at p .001.
liminary AnalysesCorrelations. Relationships between phenotypic group (agefirst words, age of first phrases, RSMA, IS, and Savant Skills)d descriptive measures (age, IQ, ADOS scores, and ADI-Rres) were examined with Pearson correlations, as in Table 4.found that IS was only weakly correlated with the Social and
rbal Communication Domains on the ADI-R. Other groupswed significant correlations with the majority of areas, withMA and age of first phrases exhibiting the strongest relation-ps with IQ, ADOS, and ADI-R scores.Gender, Race and Ethnicity. Preliminary analyses indicatedsignificant differences between male and female participantsany of our measures, as in Table 5. Also shown in Table 5,wever, there were significant differences between racial/nic categories in both language acquisition and RSMA groups.address these, analyses involving these phenotypic measuresre rerun separately by African American (the largest minorityup) and others (including Caucasian, Hispanic, Asian Amer-ns, and persons of mixed race). The primary difference waswww.sobp.org/journal
Table 4. Intercorrelations Between Age of First Words, Age of First Phrases, Repetitive Sensory Motor Actions, Insistence on Sameness, and Savant SkillsScores and Verbal and Nonverbal IQ, Age, and ADOS and ADI-R domain scores.
1. A a 2. A a 3. R
4. I5. S a
6. A a
7. V8. N 9. A10.
442 BIOL PSYCHIATRY 2007;61:438448 V. Hus et al
wwt verbal and nonverbal IQ and scores on the ADI-R and ADOSAfrican American individuals with delays (DW, DP) tended tomore like scores for individuals without delays (NDW, NDP),her than like those of nonverbal children (NW, NP). Withited sample sizes, these results are difficult to interpret.
nguage Acquisition GroupsAge of First Words: NW versus DW versus NDW. Age at firstrds was significantly associated with diagnosis, 2(2, 983) 95, p .001, see Table 5. ANOVAs indicated significantociations with verbal and nonverbal IQs, and no associationth chronological age at assessment (Table 6). Post hoc Tukeyalyses revealed that for both verbal and nonverbal IQ, the DWup was significantly lower than the NDW, and NW wasnificantly lower than both.As shown in Table 6, there were significant differencestween word acquisition groups for all ADI-R domains anddomains. Post hoc analyses showed that the NDW group hadnificantly less impaired mean scores on Social Reciprocity andSB than the DW and NW groups, with DW scores significantlyer than NW in both domains. In Nonverbal Communication,scores were significantly higher (indicating more impair-
nt) than both DW and NDW groups. In Verbal Communica-n, the DW group scored significantly higher than the NDW.A similar pattern occurred for ADOS scores. In the Socialmain, NW mean scores were higher than DW or NDW, andmean scores were higher than the NDW group. In the areas
Play and RRSBs, the NW group had significantly higher scoresn both the NDW and DW, whereas in Communication, NDWres were significantly lower than DW and NW.Using logistic regression, total ADI-R algorithm score andrbal IQ each independently predicted age of first wordslayed or not delayed, with NW included in the delayed). Agetime of the ADI-R, ADOS total, nonverbal IQ, and gender didt make significant contributions (see Table 7).Age of First Phrases: NP versus DP versus NDP. Results foruping by age at first phrase were similar to word acquisitionupings, indicating an association with diagnosis, 2(2, 983)
1 2 3 4 5 6 7
ge Words .51a .16a .07 .10a .11a .27ge Phrases .25a .10 .17a .02 .52epetitive Sensoryotor Actions
.31a .01 .02 .38
nsistence on Sameness .20a .08 .08avant Skills .15a .25ge .17erbal IQ onverbal IQDI-R SocialADI-R VerbalommunicationADI-R NonverbalommunicationADI-R RRSBADOS SocialADOS CommunicationADOS PlayADOS RRSB
N 983, but varied because of missing data. ADI-R, Autism Diagnostic Inteap .001.w.sobp.org/journal8.78, p .001. One-Way ANOVAs showed significant differ-ces between phrase acquisition groups in mean Verbal andnverbal IQs, as in Table 6. Post hoc Tukey analyses indicatedt for both Verbal and Nonverbal IQ, DP was significantlyer than NDP, and NP was significantly lower than both. In
dition, the NP group was significantly younger than DP andP, and the DP group was significantly younger than NDP.When examining the ADI-R scores by domain, there werenificant differences between phrase acquisition groups for allmains. Post hoc analyses indicated that NDP had significantlyer scores in the Social, Nonverbal Communication, and RRSBmains than did DP and NP, and DP scores were significantlyer than NP. In Verbal Communication, DP was higher thanP. As shown in Table 6, a similar pattern occurred in ADOSal and domain scores, with the NP groups mean scoresnsistently higher than the DPs or NDPs, and the DP groupsan scores higher than the NDP groups, except in Play, wherere was no significant difference between DP and NDP.Logistic regression indicated that, like age of first words,I-R algorithm score and Verbal IQ independently predictede of first phrases (delayed or not delayed, with NP included indelayed). Unlike age of first words, age of the individual attime of the ADI-R was also a significant predictor of age oft phrases. ADOS total, Nonverbal IQ, and gender did notke significant contributions (see Table 7).
stricted and Repetitive Behavior FactorsRepetitive Sensory Motor Actions. As with the languageuisition groups, a relationship between RSMA and diagno- was found, 2 (2, 625) 59.84, p .001 (see Table 5).OVAs and post hoc tests also indicated that verbal andnverbal IQs differed significantly for all groups, with theest IQ scores in the very repetitive (HRSMA) group andhest in the less repetitive (LRSMA) group (see Table 8). Ages not significant.There were significant differences among RSMA groups for allI-R and ADOS domains (see Table 8). Post hoc analysesicated that the scores on the ADI-R Social domain were
8 9 10 11 12 13 14 15 16
.28 .16a .03 .18a .20a .17a .06 .17a .16a
.49 .26a .03 .30a .34a .35a .15a .35a .31a
.38 .38a .33a .34a .33a .27a .25a .26a .44a
.08 .14a .20a .06 .02 .03 .03 .02 .06
.29 .08 .07a .09a .14a .05 .04 .06 .02
.07 .07 .04a .01 .21a .05 .17a .05 .19a
.79a .48a .28a .46a .56a .57a .40a .50a .52a
.36a .14a .37a .42a .47a .32a .43a .51a
.79a .80a .43a .46a .33a .26a .32a
.91a .30a .38a .36a .14a .23a
.39a .48a .34a .29 .31a
.04 .26a .24a .36a
.68a .53a .48a
Revised; ADOS, Autism Diagnostic Observation Schedule.comethe
V. Husn the HRSMA. For the remaining domains (Verbal and Non-rbal Communication, RRSB), the LRSMA had significantlyer scores than the other groups. Similar patterns of significantferences emerged for ADOS scores.Insistence on Sameness (IS) Factor. Analyses conducted toamine the differences between IS groups found that groupsre only significantly different in their ADI-R RRSB scores,ich would be expected. As shown in Tables 5 and 8, therere no significant differences between IS groups in gender,e, diagnosis, IQ, ADI-R Social or Communication scores, orOS scores.Savant Skills Factor. Chi-square analyses showed no signif-nt differences in diagnostic category between Savant-Negative) and Savant-Positive (SP). t tests indicated that participants inSN group were significantly younger and had significantlyer verbal and nonverbal IQs compared with SP; see Table 9.had significantly higher ADI-R RRSB and Nonverbal Commu-ation scores than SP, with no other significant differences.
stricting SamplesOne question that arose was whether restricting the sample torticipants with narrowly defined autism diagnoses rather thanader ASDs would minimize some of the relationships be-een a specific target phenotype (e.g., delayed phrase speech)d the other cognitive and behavioral characteristics associatedth it. Results based on only participants meeting the strictgnosis of autism indicated few significant differences betweenW and DW or NDP and DP groups on ADI-R and ADOSmains, with differences remaining between NW and NP andverbal participants. Differences between language acquisi-
n and RSMA groups on ADI-R and ADOS Communicationms were also no longer significant. Differences remainedtween high (HRSMA) and low (LRSMA) groups, but thosetween MRSMA groups and others were no longer significantthe ADI-R or ADOS domains.
As results from more association and linkage studies emerge,eems less likely that a single, polygenic pattern will accountthe majority of cases of ASD. Consequently, interest in
reasing the homogeneity of samples has grown steadily. Aswn in Table 1, several groups have successfully increasedD scores by segregating samples using measures from theI-R. Findings from the analyses summarized in Table 10gest a number of ways that we can build on this approach toduce more interpretable and potentially more replicabledings, and this might yield even more successful depictions ofenotypes.It is clear that phenotypic factors with similar names (e.g.,istence on Sameness vs. Compulsions) from similar instru-nts (e.g., the original ADI, the ADI-R) are not necessarily thee. As shown in Table 2, the two most commonly citedthods for categorizing repetitive behaviors on the ADI-Rccaro et al 2003; Tadevosyan-Leyfer et al 2003) are notntical. Even within very similar RSMA factors, unusual pre-cupations has varied across studies in loading, in part perhapse to age differences in samples (see Bishop et al 2006).searchers with a hypothesis about a phenotypic characteristict might be transmitted familially need to carefully investigateich factor or domain from the diagnostic instruments bestlects their interests before deciding on a subsetting strategy.significantly lower for the LRSMA group compared with bothothers, and the MRSMA group was significantly less impairedat
.2 .8 .8 .7 .5 .0 .0 ases;
et al BIOL PSYCHIATRY 2007;61:438448 443www.sobp.org/journal
Table 6. Age, Level of Functioning, and Autism Severity by Language Acquisition Groups
AgM 3.67 (R 48-3
VerM 9.38 (R 2-6
NoM 7.06 (R 2-1
ADS 4.68 (C 2.00 (CR 5.60 (
ADS 1.11 (C 6.39 (P 3.48 (R 5.38 (
444 BIOL PSYCHIATRY 2007;61:438448 V. Hus et al
wwLanguage history is a theoretically compelling and convenienty of subsetting samples. Doing so has yielded importantormation, particularly regarding chromosomes 2 and 7. Strat-ations by language delay, however, result in samples thatfer in many more characteristics than onset of first words orrases. Participants with delayed words or phrases consistentlyve lower verbal and nonverbal IQs and higher symptom scoresall defining domains of autism, as measured on both the
NDW (n 484) DW (n 403) N
(SD) 95.90 (52.72) 91.91 (52.27) 8ange 48-545 48-620bal IQaf
(SD) 69.12 (34.57) 57.33 (30.80) 1ange 3-151 2-151nverbal IQaf
(SD) 76.98 (28.90) 72.14 (28.26) 3ange 13-144 3-153I-R Domain Scoresocial,af M (SD) 17.48 (7.72) 20.24 (7.15) 2ommunication, Nonverbal,a,cf M (SD) 8.11 (4.20) 9.35 (3.71) 1ommunication, Verbalb,e M (SD) 13.86 (5.59) 15.80 (5.25)RSBaf M (SD) 3.07 (1.26) 4.97 (.85)OS Domain Scoresocialaf M (SD) 8.52 (2.36) 9.16 (3.30) 1ommunicationbf M (SD) 5.46 (2.33) 5.96 (2.17)laya,c,d,f M (SD) 2.34 (1.39) 2.45 (1.35)RSBa,cf M (SD) 3.13 (2.51) 3.59 (2.43)
ns vary because of missing data and verbal and nonverbal status. ADI-R,edule; ASD, autism spectrumdisorder; DW, delayedwords; DP, delayed phrwords; 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.
le 7. Effects of Age, Gender, IQ, and Autism Severity on Languageuisition Groups
e of First Words (months) .001 .001der .105 .198I-R total .033a .007OS total .023 .016nverbal IQ .006 .004bal IQ .023a .004e of First Phrasese (months) .006a .002der .064 .272I-R total .046a .009OS total .023 .021nverbal IQ .004 .006bal IQ .045a .006
ADI-R, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnosticservation Schedule.ap .001.w.sobp.org/journalI-R and ADOS. Higher scores on the ADOS are particularlyportant, because they indicate that the effect is not due tothod variance associated with parent reports, but to differ-ces in the symptoms as observed by an independent clinician.is was the case for nonverbal (NW, NP) children versus verbalildren, even when samples were restricted to participants whod narrowly defined autism diagnoses.Stratifications by delay in acquisition in phrases were also, notprisingly, associated with the participants chronologic age attime of the ADI-R, such that participants without phrases
re significantly younger than other individuals with ASD.atifications for all the language measures were also associatedth race, with fewer African American children in the no delayd more in the no words or phrases groups. Age and race effectsy partially reflect recruitment differences both within and acrosss in our sample. Particular attention to age when the ADI-R isministered is important when considering language measures;earchers interested in language phenotypes may want to restrictages of their samples or exclude participants with no words or phrases (see Alarcn et al 2005) to increase sensitivity to detectetic factors of particular interest.For example, in following up on recent findings regardingkage to a relatively small region of chromosome 2, whensetting sib pairs with phrase speech delay, one might want tolude older children with no phrases and use a sample with aatively narrow autism diagnosis with a lower age of at least 5rs. This would increase the likelihood that subsetting by age oft phrases would produce samples that differed primarily inguage onset, rather than age, cognitive measures, or severity of
96) F NDP (n 243) DP (n 504) NP (n 236) F
44.39) 2.14 112.44 (69.63) 90.93 (45.92) 77.68 (49.70) 26.02g
80 48-545 48-453 48-620
13.67) 99.99g 89.78 (28.82) 61.82 (27.25) 22.88 (15.56) 416.81g
7 14-151 8-151 2-70
19.91) 77.51g 90.37 (24.41) 75.72 (26.45) 41.92 (19.99) 236.69g
02 20-144 14-153 2-107
5.41) 44.58g 14.98 (7.20) 19.48 (7.39) 23.43 (5.92) 87.17g
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
.72) 455.80g 2.29 (1.11) 4.41 (.98) 5.29 (0.87) 604.07g
3.40) 28.83g 7.40 (3.17) 8.75 (3.29) 11.45 (2.29) 99.06g
1.68) 7.82g 4.90 (2.42) 5.89 (2.20) 6.47 (1.65) 26.57g
0.91) 23.22g 2.10 (1.38) 2.29 (1.35) 3.44 (0.92) 62.46g
2.55) 25.68g 2.39 (2.29) 3.30 (2.38) 5.46 (2.18) 86.90g
m Diagnostic Interview-Revised; ADOS, Autism Diagnostic ObservationNDP, not delayed phrases; NDW, not delayedwords; NP, no phrases; NW,ADimmeenThchha
Table 8. Age, Level of Functioning and Autism Severity by RRSB Groups
V. Hus et al BIOL PSYCHIATRY 2007;61:438448 445istic symptoms. These steps would confirm that such a findings related to speech delay in contrast to other impairments.Results for RSMA followed very similar patterns as for lan-age delay, with groups defined by RSMA scores differing onbal and nonverbal IQ and all symptom areas on the ADI-R andOS. In contrast, IS was relatively independent of gender, race,gnosis, chronological age, nonverbal and verbal IQ, andtism symptom domains on the ADI-R and ADOS. These results consistent with those reported by Cuccaro et al (2003) andhop et al (2006) who found high correlations between RSMA,t not IS scores, with adaptive behavior or IQ. It is interesting tote that although proposed as a central characteristic of autism
e (months)86.67 92.73 86.55
SD) (44.09) (59.91) (36.82)ange 48388 48620 48279bal IQac
66.31 56.17 41.82 3SD) (32.85) (34.06) (30.70)ange 8141 2141 3129nverbal IQac
79.45 69.46 56.74 3SD) (28.21) (29.43) (28.65)ange 15153 3129 10150I-R Domain Scoresociala,b,c
16.87 20.34 22.84 4SD) (7.59) (6.75) (6.74)munication, Nonverbala,c
7.81 9.39 10.61 3SD) (4.12) (3.86) (3.51)munication Verbala,c
13.27 15.61 16.72 1SD) (5.52) (5.09) (5.03)Ba,cf
4.41 6.28 6.73 6SD) (2.23) (2.28) (2.24)OSDomain Scoresocialac
8.18 9.29 10.10 1SD) (3.62) (3.10) (3.28)ommc
5.39 6.03 6.52 1SD) (2.38) (2.18) (1.84)layb,c
2.25 2.35 2.89 1SD) (1.43) (1.42) (1.34)RSBac
2.53 3.82 4.74 4SD) (2.18) (2.46) (2.44)
ns vary because of missing data and verbal and nonverbal status. ADI-R, Aedule; IS, Insistence on Sameness; RRSB, Restricted and Repetitive StereotyaLow 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. Kanner (1943) and Rutter (1978), insistence on sameness wast included in DSM-IV (APA 1994) and ICD-10 (WHO 1992) inrt because research (Lord et al 1993) suggested that ADI-Rms regarding insistence on sameness were not specific toDs. In fact, for this reason, rather than despite these findings,IS factor may be useful in stratifying an ASD sample becauseffers a relatively independent dimension that varies consider-ly within ASD and other populations. Several studies havend evidence of familiality specifically for the IS factor (Shao et2003; Szatmari et al 2006), which increases its potential valueuse in stratification, given the likelihood that it is a geneticallyven construct.
High(n 230) F
93.41 91.05 106.30 3.75(60.16) (62.84) (61.24)48620 48545 48407
55.94 56.39 63.04 2.49(36.88) (35.20) (36.59)2143 6141 2151
68.20 68.94 73.02 1.39(29.85) (31.26) (31.66)3142 10144 2153
18.29 19.26 20.36 3.76(7.69) (7.27) (7.35)
8.82 8.97 9.28 .64(4.23) (4.12) (3.83)
13.44 14.37 15.55 4.71(6.10) (5.23) (5.42)
4.27 5.39 7.21 84.30g
(2.17) (2.23) (2.35)
9.04 9.43 9.03 .81(3.40) (3.28) (3.47)
5.60 5.84 5.61 .51(2.33) (2.11) (2.33)
2.51 2.32 2.50 .87(1.37) (1.46) (1.36)
3.44 3.81 3.83 .94(2.71) (2.56) (2.61)
Diagnostic InterviewRevised; ADOS, Autism Diagnostic Observationehaviors; RSMA, Repetitive Sensory Motor ActionsbynopaiteAStheit oabfoual fordri
Table 9. Age, Level of Functioning and Autism Severity by Savant Skills Groups
446 BIOL PSYCHIATRY 2007;61:438448 V. Hus et al
wwThe savant skills domain followed another pattern withferences between its two groups strongly associated with age,nverbal and verbal IQs. However, it was not associated witherity of autism symptoms. What this means is not clear, but itplies that stratifying on IQ before savant skills may provide are specific measure of the presence of specific strengths.One of the arguments in favor of using factor or principalponents analyses is that factor scores reduce measurement
or and so may improve power. Nonetheless, it is important tote that, for the sake of transparency, a number of studies havet used actual factor scores but have simply added ADI-R itemres. Most factor analyses have not adjusted for ordinality, andny have not followed standard guidelines such as minimummber of items per factor, minimal loadings, and adherence to
Age (months)M (SD) 107.61 (6Range 48-54
Verbal IQM (SD) 73.34 (3Range 7-15
Nonverbal IQM (SD) 83.41 (2Range 14-15ADI-R Domain Scores
Social M (SD) 18.27 (7Communication, Nonverbal M (SD) 8.35 (4Communication, Verbal M (SD) 14.99 (5RRSB M (SD) 3.73 (1
ADOS Domain ScoresSocial M (SD) 8.66 (3Communication M (SD) 5.55 (2Play M (SD) 2.34 (1RRSB M (SD) 3.50 (2
ns vary because of missing data and verbal and nonvADOS, Autism Diagnostic Observation Schedule; IS, Insistetyped Behaviors; RSMA, Repetitive Sensory Motor Actions.
le 10. Summary of Variables Associated with Potential Constructsd for Stratification
e X X Xbal IQ X X X Xnverbal IQ X X X XI-Rocial X X Xomm, NV X X X Xomm, V X X XRSB X X X X XOSocial X X Xomm X X Xlay X X XRSB X X X
ADI-R, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnosticservation Schedule; Comm, communication; NV, nonverbal; RRSB, Re-cted and Repetitive Stereotyped Behaviors; V, verbal.w.sobp.org/journaltandard time frame (e.g., not including current and everms within the same factor, without some theoretical justifica-n; Tabachnick and Fidell 1989). Thus, the degree to which alltors reduce measurement error is not clear.
itationsThis article presents relatively straightforward analyses of amber of variables to illustrate simple points and to provideta comparable to that presented in previous genetics studiesg., Shao 2003; Sutcliffe 2005). Nevertheless, more complexltivariate analyses would provide more accurate representa-ns of interactions between both the predictor and criterioniables for this particular dataset. Bishop et al (2006) is anample of such analyses carried out for different purposes, andre research is underway. Data from other parent-reportasures of autistic symptoms (e.g., the Social Responsivenessle, Constantino and Gruber 2005; the Pervasive Developmen- Disorders Behavior Inventory, Cohen et al 2003) would alsovide information on the degree to which these findings arecific to the ADI-R. A more diverse sample would haveowed us to better address the effects of race/ethnicity.
plicationsIncreasing homogeneity of variance in ASD samples is anportant response to the likely complexity of the genetics oftism. Our findings here should not detract from the value ofsubsetting approach in the search for consistent linkages to
atively small regions in a complex genetic disorder. Whenples are stratified, however, it is essential to provide infor-tion on how that stratification affects both basic demographicsg., gender, race, recruitment site, chronologic age) and otherasures that are assumed to be relatively independent, such asand other autism symptom domains. Researchers may want tonsider restricting their samples (e.g., as in Alarcn et alsclusion of nonverbal children) or further stratifying samples byer features, such as chronologic age for language measures orfor measures of repetitive behaviors or savant skills, to test
282) Savant Negative (n 701) t
87.22 (46.76) 5.34a
53.83 (33.36) 8.24a
66.28 (83.41) 8.18a
19.74 (7.61) 2.759.26 (4.04) 3.20a
14.47 (5.58) 1.164.24 (1.46) .10a
9.23 (3.40) 2.285.84 (2.19) 1.652.56 (1.37) 2.103.54 (2.59) .18
status. ADI-R, Autism Diagnostic Interview-Revised;n Sameness; RRSB, Restricted and Repetitive Stereo-exmomeScatalprospeall
more specifically the effect of the variable in which they are mostinterested. Another option would be to make age and/or IQreggensenCorestenrellarothmacolavasoushoende200
Dawson G, Webb S, Schellenberg G, Dager S, Friedman S, Aylward E, et al(2002): Defining the broader phenotype of autism: Genetic, brain, and
V. Hus et al BIOL PSYCHIATRY 2007;61:438448 447ression covariates in the QTL. Covarying verbal IQ in aetic analysis of social-communication scores, however, es-tially creates a scale of the discrepancy between ADI-Rmmunication scores and IQ, which may or may not be whatearchers have in mind. The robustness of findings for Insis-ce on Sameness suggests that it is possible to identify aatively independent set of behaviors. With the availability ofger samples and more careful methodologies, identification ofer similar factors seems likely. Although such stratificationy result in substantially reduced sample sizes, with thelaboration of research groups and the advent of publiclyilable repositories such as those from Autism Genetic Re-rce Exchange and National Institutes of Mental Health, ituld be possible to achieve the sufficiently large and homog-ous sample subsets necessary to yield greater sensitivity totect relevant genetic factors (Le Couteur 1996, Le Couteur3, Tabachnick 1989).
This research was supported in part by Grant Nos. NIMH5MH067723 and R01MH066496. The authors thanks Shan-g Qiu for compiling our datasets; the families who partici-ted in these research projects; and the staff at the Northrolina TEACCH centers, the University of Chicago DepartmentPsychiatry, and UMACC. Disclosure: Authors S.R. and C.L.eive royalties for the ADI-R and/or ADOS; profits accrued froms study were donated to charity.
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448 BIOL PSYCHIATRY 2007;61:438448 V. Hus et al
Using the Autism Diagnostic InterviewRevised to Increase Phenotypic Homogeneity in Genetic Studies of AutismUsing Familiality to Identify Homogeneous PhenotypesStratifying Samples by Language AcquisitionIdentifying Quantitative Trait LociUse of Empirically Derived FactorsPurposeMethods and MaterialsSampleData CollectionMeasuresCharacterizing Phenotype Groups1. Language Acquisition Groups2. Restricted and Repetitive Behaviors Groups3. The Savant Skills Factor
ResultsPreliminary AnalysesCorrelationsGender, Race and Ethnicity
Language Acquisition GroupsAge of First Words: NW versus DW versus NDWAge of First Phrases: NP versus DP versus NDP
Restricted and Repetitive Behavior FactorsRepetitive Sensory Motor ActionsInsistence on Sameness (IS) FactorSavant Skills Factor