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Autism in African American Families Clinical-phenotypic findings.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 144B:1022 –1026 (2007)
Autism in African American Families:
Clinical-Phenotypic Findings
Michael L. Cuccaro,1* Jason Brinkley,2 Ruth K. Abramson,3 Alicia Hall,3 Harry H. Wright,3 John P. Hussman,4
John R. Gilbert,1 and Margaret A. Pericak-Vance1
1
Duke University Medical Center, Durham, NC
North Carolina State University, Raleigh, NC
3
University of South Carolina School of Medicine, Columbia, SC
4
Hussman Foundation, Ellicott City, MD
2
Unlike other complex diseases, the study of autism has been almost exclusively limited to Caucasian families. This study represents a first effort
to examine clinical and phenotypic findings in
individuals with autism from African American
families. Drawing from an ongoing genetic study
of autism we compared African American (N ¼ 46,
mean age ¼ 118 months) and Caucasian (N ¼ 298,
mean age ¼ 105 months) groups on autism symptoms and developmental language symptoms. The
African American group showed greater delays in
language but did not differ from the Caucasian
group on core autism symptoms. These findings,
while suggestive of a more severe phenotype, may
reflect an ascertainment bias. Nonetheless, we
believe that more studies of racial-ethnic groups
should be conducted with several goals in mind
including strengthening recruiting strategies
to include more ethnic-racial groups and more
thoughtful evaluation of phenotypic traits.
Such considerations will aid greatly in the
search for genetic variants in autism.
ß 2007 Wiley-Liss, Inc.
KEY WORDS: autism; phenotype; African American; racial-ethnic; genetics
Please cite this article as follows: Cuccaro ML, Brinkley
J, Abramson RK, Hall A, Wright HH, Hussman JP,
Gilbert JR, Pericak-Vance MA. 2007. Autism in African
American Families: Clinical-Phenotypic Findings. Am J
Med Genet Part B 144B:1022–1026.
INTRODUCTION
Autism is a neurodevelopmental disorder characterized
by social-communicative impairments and the presence of
repetitive and restricted behaviors [American Psychiatric
Association, 2000]. Prevalence studies suggest that autism is
more common than originally believed and recent estimates
cite a rate of 1/300 [Fombonne, 2003]. While there has been
substantial scientific inquiry of clinical, etiologic, and neuro-
Grant sponsor: National Institute of Neurological Disorders
and Stroke; Grant numbers: P01NS026630, R01NS036768; Grant
sponsor: National Alliance of Autism Research.
*Correspondence to: Michael L. Cuccaro, Ph.D., Miami Institute for Human Genomics, Miller School of Medicine, University
of Miami, Miami, FL. E-mail: mcuccaro@med.miami.edu
Received 3 August 2006; Accepted 23 February 2007
DOI 10.1002/ajmg.b.30535
ß 2007 Wiley-Liss, Inc.
biological aspects of autism [Santangelo and Tsatsanis,
2005] only a handful of studies have been extended to nonCaucasians.
For instance, there have been few studies of prevalence
which have included African Americans. Those studies which
have indirectly or directly examined prevalence in this
group have yielded mixed results. It has been proposed that
autism occurs equally across race and culture [Fombonne,
2003]. Support for this hypothesis was found in a large scale
population based study of the Atlanta region which reported
similar occurrence rates of autism in Caucasian and African
American children [Yeargin-Allsopp et al., 2003]. However,
two studies noted higher prevalence rates in African American
children [Croen et al., 2002; Hillman et al., 2000]. While these
results may be a consequence of ascertainment bias (e.g.,
African American families may only come to services if the
child with autism is more severely affected or have difficulties
accessing the health care system), at a minimum, prevalence
rates in African Americans appear to be at least comparable to
those in Caucasians.
Studies of autism in African Americans have focused on
differences in clinical practices and service utilization [Mandell and Novak, 2005]. For instance, African American
children are more likely to be identified with autism based on
school records versus clinic based evaluations [YearginAllsopp et al., 2003]. Mandell et al. [2002] report that relative
to Caucasian children, African American children are more
likely to be diagnosed significantly later. According to Dyches
et al. [2004], in a review of multicultural issues in autism,
current research has not fully investigated the relationships, if
any, between genetic or biological etiologies of autism and race.
The autism phenotype, while well defined, is highly variable
[Santangelo and Tsatsanis, 2005]. It is believed that this
clinical heterogeneity may reflect underlying genetic differences. Again, while autism affects all racial and ethnic
groups, a survey of autism genetic studies conducted in the
last 10 years in the United States reveals that race is either not
mentioned or restricted to families or individuals from
Caucasian families [Cantor et al., 2005; Rabionet et al., 2006;
Ramoz et al., 2006]. The underlying rationale for studying
genetic variations in different racial and ethnic groups is that a
complex disorder may have differential liability alleles for
different populations; what is learned from one population
(e.g., Caucasians) may not transfer seamlessly to another
population (e.g., African Americans). There is an abundance of
research that points to phenotypic and genotypic differences in
African American individuals in complex diseases believed to
have a strong genetic component. Genetic differences in
African Americans have been identified in cardiovascular
disease [Reiner et al., 2005], prostate cancer [Rebbeck, 2005],
Alzheimer Disease, and schizophrenia For instance, in Alzheimer Disease, the APOE4 allele causes increased risk among
Caucasian subjects, while apolipoprotein D might be a more
important risk factor in African Americans [Desai et al., 2003].
Autism in African American Families
Studies of the NOTCH4 gene at 6p22-6p24, reveal both a
susceptibility haplotype (1725G/25T) for schizophrenia in
African Americans [Luo et al., 2004] and excess transmission
of the 8 and 13 repeat alleles of the (TAA)n marker in
the NOTCH4 gene in African American participants with
schizophrenia [Skol et al., 2003]. While such diseases are
highly influenced by environmental factors, such results
are consistent with the position that risk alleles may have
differential effects in different racial groups [Risch et al., 2002].
Phenotype differences have been found in African American
versus Caucasian patients in complex genetic disorders
including Prader–Willi syndrome on dimensions such as
growth, hand and foot lengths, and facial appearance [Hudgins
et al., 1998], and Attention Deficit/Hyperactivity Disorder on
measures of AD/HD symptom severity [Epstein, 2002].
This study is a preliminary investigation of clinical features
of autism in a sample of African American families. The
primary objective of this study was to determine if there
are meaningful clinical phenotypic differences between the
African American and Caucasian autism participants. Clearly,
defining phenotypic subgroups in genetic analyses enhances
efforts to identify genes in complex disorders such as autism
[Coon, 2006]; the study of clinical variation in different
populations can inform subgroup development. Identifying
clinical differences in different populations represents an
approach to subgrouping that has the potential to point to
variations at the genetic level. Finally, this study is an
important first step in alerting clinicians to the significance
of specific symptoms (e.g., severe language deficits) in African
American patients where autism may be suspected. Subsequent studies that clarify the relationship, if any, between
phenotypic differences and genetic variations may be an aid in
developing intervention strategies aimed at specific symptoms
not only in African Americans but across all individuals with
autism spectrum disorders.
METHOD
Participants
A total of 344 individuals with autism (n ¼ 46 African
American; 298 Caucasian) were drawn from a large multi-site
study of autism genetics conducted in the southeastern United
States. Participants and families were recruited via support
groups, advertisements, and clinical and educational settings.
Participants were from both multiple incidence (more than one
affected individual) and single incidence families. In multiple
incidence families only results from one affected individual
were used in order to maintain independence of observations.
Core inclusion criteria included chronological age between
3 and 21 years of age, a presumptive clinical diagnosis of
autism, and expert clinical determination of autism using
DSM-IV criteria and supported by the Autism Diagnostic
Interview-R [Rutter et al., 2003] or Autism Diagnostic
Observation Schedule [Lord et al. 1999]. Exclusion criteria
were developmental level <18 months of age, severe sensory
problems (e.g., visual impairment or hearing loss), significant
motor impairments (e.g., failure to sit by 12 months or walk by
24 months), or identified metabolic, genetic, or progressive
neurological disorders based on screening by clinical staff.
Race was self-reported by families. Families were defined as
African American if one or both parents were of AfricanAmerican descent and they indicated that their child was of
African American descent. Three families with children of
mixed races were included in the African American sample.
The groups were evenly distributed with respect to male:
female ratio (M:F AA ¼ 4.7:1, CA ¼ 4.1:1). Table I provides an
overview of the demographic characteristics. The groups did
not significantly differ on age of onset and age of walking. Also,
1023
TABLE I. Sample Characteristics and Distribution of Functional
Language Level (ADI-R q19 Rating) by Race
Sample characteristics
Number of males
Number of females
Total participants
Mean (SD) age of examb
Mean (SD) age walkingb
Mean (SD) age of onsetb
Mean (SD) age at first wordb,c
Mean (SD) age at phrase
speechb,c
Level of language
Functional language
Non-functional language
African
Americana
Caucasian
American
38
8
46
117.7 (50.6)
12.6 (3.0)
19.2 (8.5)
38.6 (17.6)
59.4 (31.1)
240
58
298
104.7 (47.0)
13.0 (3.5)
19.7 (13.1)
26.7 (15.0)
41.3 (20.0)
23 (50%)
23 (50%)
227 (76%)
71 (24%)
a
African American sample includes three families of mixed race.
Age measured in months.
Includes only individuals with functional language.
b
c
while there is a likelihood that the groups were different with
respect to age at exam (P ¼ 0.08), this difference was not
statistically significant.
Procedure
All families were recruited through an ongoing autism
genetics study. Informed consent was obtained from parents/
caregivers for minor participants. As part of the study, all
participants were evaluated using a standard set of diagnostic
and clinical assessments.
Measurement of Clinical Features
Caregivers were administered the Autism Diagnostic Interview-Revised (ADI-R) as part of the ascertainment assessment
battery. The ADI-R [Lord et al., 1994, 1997; Rutter et al.,
2003] is an investigator-based, semi-structured interview for
caregivers that is used to assess diagnostic symptoms of
autism. The ADI-R uses a diagnostic algorithm for autism
based on DSM-IV diagnostic criteria [American Psychiatric
Association, 1994; Filipek et al., 2000] and serves as a clinical
research diagnostic standard. Diagnostic algorithms are based
on total scores for the Reciprocal Social Interaction, Communication, and Restricted and Repetitive Behaviors or Interests
domains using empirically identified item sets. Total scores for
these domains are derived based on the presence and severity
of behaviors occurring either ever early in development or at
any point in development. Individual items constituting the
domain scores are coded 0 (not present) to 3 (present and
severe). In constructing the algorithm, items scored as 3 are
recoded as 2 to reduce undue weight attached to any one item
[Rutter et al., 2003]. In addition to ADI-R domain scores, age of
first single words (q12), age of first phrases (q13), and overall
level of language (q19) were examined.
Statistical Analysis
Data analyses were designed to compare the African
American and Caucasian participants on variables constructed from the ADI-R. All statistical analyses were
conducted using SAS 8.02 [SAS Institute, 2000]. The analyses
were conducted in stages. In the first stage, the variables of
interest were the ADI-R Reciprocal Social Interaction (RSI),
Non-Verbal Communication (NVC), Repetitive Behavior
(REP) domain scores and Evidence of Abnormalities Before
36 Months of Age (ABN) score. Group differences were
examined using MANOVA. The next stage of analysis
1024
Cuccaro et al.
examined only participants who had functional language as
indicated by a score of 0 on ADI-R question 19 (Overall Level
of Language). A score of 0 is defined as ‘‘functional use of
spontaneous, echoed or stereotyped language that, on a daily
basis, involves phrases of three words or more that at least
sometimes include a verb and is comprehensible to other
people.’’ The groups were compared on the Verbal Communication domain using ANOVA as well as chi-square analyses
to examine differences on six individual questions that
compose the Verbal Communication domain. The final stage
of analysis compared the African American and Caucasian
participants on ADI-R measures of language development—
age of first single words (q12) and age of first phrases (q13).
These data were analyzed using ANOVA while accounting for
overall level of language (ADI-R question 19). This step was
necessary as the two groups differed with respect to level of
functional language (50% of the AA sample vs. 76% of CA had a
rating of 0 on the overall level of language item of 0, P ¼ 0.0002;
See Table I).
The final portion of the analysis examined differences in
language development between the African American and
Caucasian groups using ADI-R questions 12 and 13 (age at first
words and age at first phrases) while accounting for overall
level of language (ADI-R item 19). This strategy was necessary
as the groups differed with respect to functional language level
(as noted earlier with P-value of 0.0002). Table III shows
ANOVA results that model questions age at first words (ADI-R
q12) and age at phrase speech (ADI-R q13) using both
dichotomized functional language (responses from question
19) and race. Both models are significant (Q12 F(2, 299) ¼ 10.7,
P < 0.0001; Q13 F(2, 267) ¼ 11.08, P < 0.0001). Type 1 analyses
show that in both models there are significant race effects in
the development of first words and first phrases, even after
accounting for the differences in the level of language. As with
the individual language items, there are variations in sample
size due to coding conventions. There were no significant
interactions between race and level of language for either of the
above models (results not shown).
RESULTS
DISCUSSION
MANOVA on the domain scores (RSI, NVC, REP, ABN),
using a single test statistic (Wilks’ Lambda) resulted in a Pvalue of 0.19 indicating no significant differences between
Caucasian and African American scores on the ADI-R domain
scores. We then restricted our analyses only to individuals with
functional language as indicated by a score of 0 on NVC overall
level of language (N ¼ 250; Caucasian N ¼ 227; African American N ¼ 23). Repeating the MANOVA for the RSI, NCV, REP,
and ABN domains yielded similar results (P ¼ 0.20). Given our
interest in the language functioning of the groups, we
examined the Verbal domain separately in this second group
of individuals and noted a marginally significant result when
race was used to model the ADI-R Verbal domain score
(F(1,248) ¼ 3.01, P ¼ 0.08). Despite the lack of statistically
significant findings we explored the marginally significant
ANOVA finding—examining the six ADI-R items (item
numbers 16, 18, 20, 22, 23, 24) that sum to provide the ADI—
Verbal Communication domain score. Individual item scores
were recoded into low (0–1) response versus high (2–3)
response and chi-square tests were used to determine if there
were any differences between the two groups on these six
questions (Table II). No significant differences were apparent
in the distribution of scores between the two groups as P-values
ranged from 0.06 to 0.46. Note that variations in sample size
between items are the result of coding responses such as not
appropriate or not known on select items. Since no test met the
threshold of significance, retrospective power calculations of
likelihood ratio chi-square tests were calculated (see Table II)
using SAS [SAS Institute, 2000]. As seen in Table II it is clear
that our power to detect significant effects in these analyses
were severely limited by our sample size suggesting that a
larger sample size of African Americans is needed.
Genotypic and phenotypic differences in African American
participants have been found in complex neuropsychiatric
diseases such as schizophrenia, AD/HD, and Alzheimer’s
disease. This is the first study of phenotypic features in
African American participants with autism. Based on detailed
caregiver report over a variety of symptom domains from the
ADI-R, the African American participants showed significantly later acquisition of first words and phrase speech. This
developmental difference stands in contrast to no differences in
the mean scores on the ADI-R social and repetitive behavior
domain scores. Even the ADI-R language composite scores
were not significantly different. However, the presence of
functional language was very different in our groups as was the
age at acquisition suggesting the possibility of a more severe
language phenotype in our African American participants.
Phenotypic differences in African American participants
with autism may reflect underlying differences in genetic
susceptibility unique to this population. The parent reported
presence of a more severe language phenotype in the
African American group assumes greater significance in light
of studies that suggest acquisition of language may index
genetically meaningful subgroups [Tager-Flusberg and
Joseph, 2003; Alarcon et al., 2005]. While no population
specific genetic differences have been identified in African
American families, Collins et al. [2006] have found evidence
of significant association to autism in SNPS in GABRA4
and GABRB1 in African-Americans (rs2280073, P ¼ 0.0287,
and rs16859788, P ¼ 0.0253) confirming results previously
detected in a Caucasian dataset. Efforts to determine if
different genetic variations are present in African American
autism families can use information about phenotypic differences to explore this further. For instance, selecting both
TABLE II. Distribution of Item Scores on ADI-R Language Items for the African American and Caucasian Participants
Caucasian
ADI—R item (#)
Social vocalization/chat (Q16)
Reciprocal conversation (Q20)
Stereotype utterances/delayed echolalia (Q18)
Inappropriate questions/statements (Q22)
Pronominal reversal (Q23)
Neologisms/idiosyncratic language (Q24)
African American
Low
(0–1)
High
(2–3)
Low
(0–1)
High
(2–3)
Chi-square
P-value
Retrospective
power
162
110
92
157
115
194
60
113
130
63
97
30
12
6
7
17
11
20
9
15
14
3
6
1
2.20
3.43
0.53
1.91
0.71
1.61
0.14
0.06
0.46
0.17
0.40
0.20
0.32
0.46
0.11
0.28
0.13
0.25
Autism in African American Families
TABLE III. Comparisons of Age at First Words (ADI-R q12) and
Age at Phrase Speech (ADI-R q13) by Race Accounting for
Differences in Language Level
Sum of
squares
Q12 (age at first words)
Model
Level of language
Race
Error
Total
Q13 (age at phrase speech)
Model
Level of language
Race
Error
Total
a
F-statistic
P-value
5,394.45a
2,492.40a
14.63
6.76
110,267.99
118,156.84
0.0002
0.0098
4,598.77a
6,029.97a
9.59
12.57
128,073.56
138,702.30
0.0022
0.0005
Type I sum of squares.
African American and Caucasian families with severe language impairments one could examine whether there are
genetic differences in these subsets.
Other explanations for phenotypic differences should be
entertained. An important consideration is whether the
observed differences in language severity reflect a clinical or
ascertainment bias (i.e., for the African American participants
only those with the more severe phenotype came to the
attention of clinical providers or enrolled in the study).
Further, it is not known if this bias is unique to the current
study or more characteristic of African American autism
families. In each case, the bias explanation holds that only
the African American families with the more severely affected
children were participants in this study. The bias could be the
result of differences in referral patterns or help-seeking and as
a result only those families in which children have severe
impairments make their way to medical professionals.
A related possibility is that severity level of the child
influences the willingness of caregivers to participate in
genetic studies of autism. While we are not able to discount
such a bias, it is important to note that the severity effect was
not evident across all domains of functioning. Further bias may
be related to access to services that can ameliorate early
language problems. As noted above we did not find that the
African American participants had greater impairments in
social and repetitive domains—independent features of the
autism phenotype. Also, while several studies have suggested
that African American individuals with autism are diagnosed
later than Caucasian individuals we found that the groups
did not differ with respect to age at which the parents
recognized the initial symptoms. While this is less tied into
the discussion at hand, it is an important point to consider in
the clinical realm (i.e., if parents are noting these symptoms at
about the same age, why are the African American participants
being identified much later). Also, it would be interesting to
examine the type of symptoms that first arouse interest in the
groups.
A third possibility is that the African American group
demonstrates a more severe language phenotype, that is,
independent of autism (i.e., not autism specific). For instance,
in the case of language, it may be that the delay in language
acquisition occurs in the African American population and we
are capturing these same phenomena in our African American
autism participants. While all hypotheses should be entertained, there is no evidence to suggest that there are
differences among the various populations with respect to
acquisition of single words or phrase speech. To the contrary,
these milestones seem to be universal and occur with
1025
regularity among the various ethnic-racial groups. Further,
in a study of preschoolers with speech delay, the factors such as
family history and maternal education were significant risk
predictors while race was not associated with increased risk
[Campbell et al., 2003]. However, recent evidence suggests that
there may be differences in how parents report on language
skills as a function of SES and race [Roberts et al., 1999].
Further, there is evidence that language experiences vary
according to SES. For instance, Hoff and colleagues have
reported in a series of studies that the language experiences
of children significantly impact language development [HoffGinsberg, 1991; Hoff, 2003]. Specifically, there appear to be
SES related differences in the speech that children hear
which in turn affects their language skills [Hoff, 2003]. This
strong relationship between SES, language experiences, and
language development underscores the potential significance
of SES in any studies of language in autism spectrum
disorders.
As this study represents the first effort to examine clinical
phenotypic features in African American individuals with
autism it should be considered preliminary. Certainly such
results warrant careful examination of other non-Caucasian
populations as well as larger scale studies of African American
individuals with autism. Given the preliminary nature of
the work, it is important to note that are a number of
limitations which should be recognized and incorporated into
future studies as appropriate. First, the absence of control
participants reduces our confidence in whether the findings—
particularly with respect to language—are autism specific. A
second concern is the limited assessment battery. The data
reported are all derived from parent/caregiver report on
behavior. Direct assessment of language and other domains
may serve to further elucidate the autism phenotype in African
American participants. Further, ADOS data was available for
only a limited number of participants in the African American
sample. This information would have allowed us to have an
index of behavior, including language, independent of parent
report. A third limitation is the lack of information about SES
which may also influence symptom presentation—particularly
with respect to reporting on language abilities. A final
limitation concerns age at diagnosis of autism which has been
shown to be significantly later for African American children
[Mandell et al., 2005]. While we did not directly compare the
two groups on age at diagnosis, we did find that the age at
which parents had first concerns about development was later
for the African American children with autism although this
difference was not statistically significant (P ¼ 0.74).
Recruiting families from different racial-ethnic groups for
autism genetics research is crucial to our understanding of the
complexity of this disorder. It is common practice to separate
racial-ethnic groups based on the premise that there are
different genetic or environmental causes in different populations. While preliminary, the current study raises questions
about how to integrate different populations into genetic
studies of autism. Our findings suggest that those studies
which include mixed populations may be prone to difficulties
due to ascertainment related influences. For instance, constructing subsets based on phenotypes that are potentially
influenced by ascertainment differences may be confounding
(e.g., only African American families whose children have
severe language or behavior problems may access services or
enroll) and introduce a nonrandom element into the study.
Correcting for such a situation may be as simple as analyzing
the groups separately. However, it does speak to the importance of seeking to understand and explain the full range of
contributions to phenotype. One approach which will enhance
understanding of environmental and genetic contributions to
clinical phenomena in autism (e.g., language) is a thorough
examination of phenotype and course over time. This could be
1026
Cuccaro et al.
accomplished by careful collection of historical data as well as
attention to prospective methods to examine the trajectory of
clinical variables. For the most part, the study of phenotype has
focused on single points in time with little consideration of the
persistence of traits over time. In conjunction with a strengthening of recruiting strategies to ensure diverse representation
we can dissect the contributions to phenotype and discern more
clearly whether phenotypic differences are relevant to genetic
variation.
ACKNOWLEDGMENTS
We thank the patients with autism, and family members
who agreed to participate in this study, as well as the personnel
of the Center for Human Genetics at Duke University Medical
Center where this research was conducted. We also thank
Dr. Robert Delong and Dr. Gordon Worley of Duke University
Medical Center for referring patients and their families to the
study. This research was supported in part by grants from the
National Institute of Neurological Disorders and Stroke
(P01NS026630, R01NS036768); funding from the National
Alliance of Autism Research; and a gift from the Hussman
Foundation. The research conducted in this study complies
with current U.S. laws.
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