Runs of Homozygosity Associated with Speech Delay in
Autism in a Taiwanese Han Population: Evidence for the
Ping-I Lin1,2, Po-Hsiu Kuo3, Chia-Hsiang Chen4,5, Jer-Yuarn Wu6,7, Susan S-F. Gau2,3,4,8*, Yu-Yu Wu9, Shih-
1Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America, 2Department of Psychiatry, National
Taiwan University Hospital, Taipei, Taiwan, 3Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health, Taipei,
Taiwan, 4Department of Psychiatry, National Taiwan University College of Medicine, Taipei, Taiwan, 5Center for Neuropsychiatric Research, National Health Research
Institutes, Zhunan, Taiwan, 6Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, 7School of Chinese Medicine, China Medical University, Taichung, Taiwan,
8Graduate Institute of Brain and Mind Sciences, Graduate Institute of Clinical Medicine, Department of Psychology, and School of Occupational Therapy, National Taiwan
University, Taipei, Taiwan, 9Department of Psychiatry, Chang Gung Memorial Hospital- Linkou Medical Center, Chang Gung University College of Medicine, Tao-Yuan,
Taiwan, 10Department of Child and Adolescent Psychiatry, Taoyuan Mental Hospital, Department of Health, Executive Yuan, Tao-Yuan, Taiwan
Runs of homozygosity (ROH) may play a role in complex diseases. In the current study, we aimed to test if ROHs are linked
to the risk of autism and related language impairment. We analyzed 546,080 SNPs in 315 Han Chinese affected with autism
and 1,115 controls. ROH was defined as an extended homozygous haplotype spanning at least 500 kb. Relative extended
haplotype homozygosity (REHH) for the trait-associated ROH region was calculated to search for the signature of selection
sweeps. Totally, we identified 676 ROH regions. An ROH region on 11q22.3 was significantly associated with speech delay
(corrected p=1.7361028). This region contains the NPAT and ATM genes associated with ataxia telangiectasia characterized
by language impairment; the CUL5 (culin 5) gene in the same region may modulate the neuronal migration process related
to language functions. These three genes are highly expressed in the cerebellum. No evidence for recent positive selection
was detected on the core haplotypes in this region. The same ROH region was also nominally significantly associated with
speech delay in another independent sample (p=0.037; combinatorial analysis Stouffer’s z trend=0.0005). Taken together,
our findings suggest that extended recessive loci on 11q22.3 may play a role in language impairment in autism. More
research is warranted to investigate if these genes influence speech pathology by perturbing cerebellar functions.
Citation: Lin P-I, Kuo P-H, Chen C-H, Wu J-Y, Gau SS-F, et al. (2013) Runs of Homozygosity Associated with Speech Delay in Autism in a Taiwanese Han Population:
Evidence for the Recessive Model. PLoS ONE 8(8): e72056. doi:10.1371/journal.pone.0072056
Editor: Balraj Mittal, Sanjay Gandhi Medical Institute, India
Received May 2, 2013; Accepted July 5, 2013; Published August 16, 2013
Copyright: ? 2013 Lin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was mainly supported by grants from National Science Council (NSC96-3112-B-002-033, NSC97-3112-B-002-009, NSC98-3112-B-002-004, and
NSC 99-3112-B-002-036), National Taiwan University (AIM for Top University Excellent Research Project: 10R81918-03, 101R892103, 102R892103), and National
Taiwan University Hospital (NTUH101-N2017) and partially supported by Academia Sinica Genomic Medicine Multicenter Study (Academia Sinica 40-05-GMM) and
National Center for Genome Medicine at Academia Sinica (NCGM, NSC-101-2319-B-001-001) of National Core Facility Program for Biotechnology (NCFPB) and
Translational Resource Center for Genomic Medicine (TRC, NSC-101-2325-B-001-035) of National Research Program for Biopharmaceuticals (NRPB), National
Science Council, Taiwan. The authors gratefully acknowledge the resources provided by the AGRE consortium and the participating AGRE families. AGRE is a
program of Autism Speaks and is supported, in part, by grant 1U24MH081810 from the National Institute of Mental Health to Clara M. Lajonchere (PI). The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Autistic disorder (henceforth denoted as autism) is a neurode-
velopmental disorder characterized by deficits in communication,
social interaction, and behavioral patterns. Family and twin studies
have strongly suggested that genetic factors contribute to the
development of autism . Most genome-wide association studies
(GWAS) have investigated the impact of genetic variants on the
risk of autism one at a time [2–4]. However, many of these
GWAS-derived findings could not be successfully replicated across
different populations . The failure to replicate previous findings
may be, at least in part, attributed to the negligence of multi-locus
effects . To evaluate all possible multi-locus effects in the
context of hypothesis-free GWAS, one has to overcome the
computational and statistical burden. Some of prior studies have
focused on genes with relevant biological functions to investigate
multi-locus effects on the risk of autism [7–9]. Additionally, whole-
genome scans also suggest that a cluster of rare variants across
different genes may collectively predict the risk of autism [10,11].
Therefore, systemic approaches to investigating the effect of
clusters of multiple loci from the whole genome may lead to
discoveries that complement the GWAS-derived findings.
Runs of homozygosity (ROHs) may play a role in neuropsy-
chiatric diseases, such as schizophrenia [12,13] and Alzheimer’s
disease . A recent study also identified several novel candidate
genes characterized by ROHs associated with the risk of autism
. Compared to the number of SNPs in the whole genome, the
number of ROHs is apparently more tractable, and hence requires
a less stringent significance threshold to search for significant
findings. Therefore, a ROH-based approach may provide
PLOS ONE | www.plosone.org1August 2013 | Volume 8 | Issue 8 | e72056
opportunities of revealing multi-locus effects on phenotypes. The
link between common ROHs and diseases may reflect several
different non-mutually exclusive mechanisms. First, a haplotype at
high frequency with high homozygosity spanning over a large
region is a sign of an incomplete selective sweep. Under such
circumstances, an individual may carry consecutive homozygous
SNPs due to identical-by-descent haplotypes that harbor ancestral
alleles with an advantageous effect . In case-control studies, an
ROH over-represented in cases may be attributed to a disease-
linked variant with an advantageous effect, while an ROH over-
represented in controls may stem from a protective effect of recent
mutation. On the other hand, selection pressure may not purge all
deleterious mutations and hence inbreeding might cause the
accumulation of multiple variants of adverse effects, which leads to
a multi-locus recessive disease model. Alternatively, a disease-
associated ROH may arise when a deleterious mutation is in
linkage disequilibrium with another variant that undergoes recent
positive selection . Second, an ROH over-represented in cases
may simply stem from a multi-locus recessive disease model.
Third, a disease-associated ROH may indicate the difference in
relatedness between cases and controls . The ROH-based
analysis is a novel approach to identifying clustering patterns of
variants to unmask ambiguous disease-genotype associations. To
explore the relationships between ROHs and autism, we
conducted a genome-wide association study in a Taiwanese Han
population. Our core hypothesis posits that several novel genes
characterized by ROHs are associated with autism and its related
language impairment. We selected speech delay as the primary
clinical feature as previous evidence suggests that language
impairment is the most important predictor for the prognosis
and developmental course of autism [19,20].
The descriptive analysis results for demographic and clinical
features are summarized in Table 1. Verbal IQ and Performance
IQ had the highest percentage of missing data, and hence we
compared the association test results with and without Verbal IQ/
Performance IQ in the regression model. Since the effect of IQ on
the association between ROH markers and traits was limited, the
missing data of IQ might not pose a great concern in the current
study. The case-control association analysis did not yield genome-
wide significant findings after multiple-testing correction (Table 2).
There was no statistically significant difference in the ROH length
between cases and controls (mean length: 658 kb vs. 645 kb;
z=0.62, p=0.229). We also calculated the value of Froh (total
length of all their ROHs in the autosome and divided by the total
SNP-mappable autosomal distance) for the ROH burden analysis
, and did not find any significant difference in Froh between
and cases and controls. The genome-wide significant finding
(p=0.05/676=7.461025) was obtained from the association
analysis for speech delay (surrogated by ‘‘age-of-first-phrase or
AFP’’). The association analysis findings across the whole
autosome are illustrated in Figure 1. We also used k-means
clustering algorithm to classify the population into two subgroups,
and identified the early-AFP group and late-AFP group with the
cutoff at 45 months of age. The results suggest that the
distributions of ROHs of early-AFP versus late-AFP groups
appeared to be similar to each other (Figure 2). One ROH region
on chromosome 11q22.3 was significantly associated with risk of
speech delay (Bonferroni-corrected p=1.7361028). The signifi-
cant results (corrected with Bonferroni method) for AFP are
summarized in Table 3. This ROH marker was found to be
positively associated with AFP as a continuous variable. This result
remained statistically genome-wide significant after we adjusted for
IQ, gender, and education levels of parents. We also assessed the
relationship between this ROH region and AFP as a dichotomous
variable using the logistic regression model, and the association
remained significant (p,0.0001). This region contains nine genes,
none of which has been found to be associated with the risk of
autism in previous studies or specific language disorders.
We further examined if the ROH region on 11q22.3 might arise
from selection sweeps. The distributions of relative extended
haplotype homozygosity (REHH) of the early-AFP and late-AFP
groups (classified using the k-means clustering algorithm) appeared
to be similar to each other (Figure 3A versus Figure 4A). The
distributions of REHH (the factor by which EHH decays on the
tested five-SNP core haplotype ‘‘rs1074014-rs1072877-rs1564582-
rs11212724-rs11211725’’ in the genes on 11q22.3) of early-AFP
and late-AFP groups seemed to differ by the core haplotype with
strongest evidence for incomplete selection sweep (Figure 3C
versus Figure 4C). Additionally, these two groups might have
different ancestral haplotypes (Figure 3E versus 4E), although their
frequency distributions of core haplotypes were similar (Figure 3B,
3D versus Figure 4B, 4D). The Nevertheless, none of these core
haplotypes appeared to have remarkable signatures of recent
positive selection based on the REHH distributions (i.e., REHH
exceeding 2 at 200 Kb away from the core haplotype). We also
searched for the signature of selection sweeps in genes proximal to
11q22.3, and found that the CWF19L2 (CWF19-like 2, cell cycle
control) gene located 1 Mb upstream to this region based on the
phase-I Hapmap Asian-descent population (CHB+JPT) has an
iHS (Integrated Haplotype Score ) score=1.7 (p=0.0237) based
on the query using the webtool Haplotter . We hence
calculated the linkage disequilibrium (LD) coefficients D9 between
the ROH region and CWF19L2 gene, and found that a locus
(rs1046094, a 39 UTR variant) within the CWF19L2 gene was
correlated with another locus (rs4754276, an intronic variant)
within the RAB39 (ras-related protein Rab-39A) gene (D9=0.9)
The inbreeding coefficient F value was ,0.01 based on the SNP
data on chromosome 11 in either the early-AFP or late-AFP
groups. Therefore, the ROH markers associated with speech delay
might not be caused by the difference in the degree of
consanguinity between these two subpopulations. Additionally,
we queried the CNV data generated by the same SNP arrays in
the discovery sample, and did not find any deletions or
duplications in this 11q region. Therefore, the ROHs on
11q22.3 were not likely attributed to hemizygous deletions.
The recruitment of subjects under the auspice of Autism
Genetic Resource Exchange (AGRE) has been described else-
where . Briefly, AGRE is a joint effort of the Cure Autism
Now (CAN) Foundation and the Human Biological Data
Interchange (HBDI). The diagnosis was made by all of the NIH
autism collaborative groups using the Autism Diagnostic Inter-
view–Revised (ADI-R)  and the Autism Diagnostic Observa-
tional Schedule (ADOS) . We have downloaded the clinical
and SNP data (generated by the Affymetrix SNP 5.0 platform) for
all probands. We implemented the same data-cleaning algorithm
used in the discovery sample. A total of 325,971 valid SNPs for
1,387 subjects diagnosed with autism were obtained. The age of
first phrase (AFP) distributions of the AGRE sample and our
discovery sample are shown in Supporting Information (Figure
S1). We did not find significant difference in the distributions of
AFP between the discovery population (Taiwan) and replication
sample (AGRE) (Mann-Whitney U test p.0.05). We attempted to
Runs of Homozygosity Associated with Speech Delay
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replicate the association between the ROH region on 11q22.3 and
AFP in another independent population. The SNP data on
chromosome 11 were retrieved from 1,387 individuals affected
with autism recruited through multi-site collaborative efforts of
Autism Genetic Resource Exchange (AGRE). We performed the
same statistical methods as what we used in the discovery sample
described in the Methods section and identified 31 ROH regions
on chromosome 11. When AFP was treated as a continuous
outcome, no significant association was detected on 11q22.3.
However, when we chose 49 months as a cutoff using the k-means
clustering algorithm to define the presence of ‘‘speech delay,’’ we
found that the ROH region on 11q22.3 (117.5 Mb-113.1 Mb) was
nominally significantly associated with speech delay (P=0.0377).
We then calculated the combined p-values from these two samples
Figure 1. The association findings for age of first phrase (AFP) are presented as –log10p-values (unadjusted by multiple tests)
across the whole autosome. The arrow indicates the ROH region at 11q22.3.
Figure 2. The distribution of runs of homozygosity (ROH) regions (by length of the ROH region) is shown. Age of first phrase (AFP) was
classified into early-AFP and late-AFP groups by the k-means clustering algorithm.
Runs of Homozygosity Associated with Speech Delay
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based on the Stouffer method, and obtained Stouffer z value and z
trend of 0.0007 and 0.0005, respectively. Note that these SNP data
were based on Affymetrix SNP 5.0 platform that had lower marker
density than Affymetrix SNP 6.0 data. The AGRE sample had a
European origin, which might also contribute to different ROH
patterns from our sample with an Asian origin.
There has been limited research on the role of ROHs in autism
in Asian populations. A recent study identified several novel
candidate genes in ROH regions associated with the risk of autism
in a European-descent population . However, most of these
loci reported by this study would not remain to be significantly
associated with the disease risk after multi-testing corrections.
Implementing stricter correction methods, we failed to detect
significant disease-associated ROHs at a genome-wide level in our
population. We speculate that the effect size of single ROH region
associated with the risk of autism might be too small to be detected
in a genome-wide scan. Another recent study reported that the
length and number of ROHs in autistic cases were higher than
controls in a southern European-descent population . How-
ever, our study shows that either lengths of ROHs or Froh values
were similar in cases and controls. The inconsistent findings may
stem from the difference in the population history of different
samples. Additionally, consanguinity is unlikely to explain the
relationship between ROHs and speech delay, as our findings do
not reveal a remarkable difference in the degree of inbreeding
between subgroups with speech delay and without speech delay.
Furthermore, recent positive selection may play a limited role in
the ROHs associated with speech delay in autism, as none of the
candidate genes were found to have a strong signature of selection
sweeps. However, we found that the patterns of extended
homozygosity decay from the core haplotype on 11q22.3 might
vary by the presence of speech delay. Our results suggest the
variant within the RAB39 gene might be associated with the
variant within the CWF19L2 gene under recent positive selection.
The current findings reveal a few novel candidate genes on
11q22.3 associated with speech delay in a Taiwanese Han
population of autism. Among these genes, NPAT and ATM
genes are associated with ataxia telangiectasia, one of the most
frequent autosomal recessive cerebellar ataxias. Ataxia telangiec-
Table 1. Demographic features of cases in the discovery population.
Variable MinimumMedianMean Maximum
Deviation# Missing# Non-missing
14 3.75 0.760 315
14 3.95 0.770 315
32 97.0 99.4183 26.6311 304
3 1818.19367.03 59256
3 18 18.66376.7658 257
Age of First Phrase5
5 36.039.6156 18.2985 230
44 100.095.2 148 24.24114 201
41 98.0 97.1145 20.71114 201
1Maternal and paternal education: 1#6 years, 2=729 years; 3=10212 years, 4=13216 years, 5=.16 years.
2SRST: Total social responsiveness score assessed by the Autism Diagnostic Interview-revised.
3SCQ: social communication quotient total score.
4FSTBEH: Stereotype behavior/interest score assessed by the Autism Diagnostic Interview-revised.
5The unit of age of phrase is month.
6VIQ=verbal IQ; PIQ=performance IQ.
7The unit of paternal/maternal age is year.
Table 2. Case-control association test results for 4 runs of homozygosity (ROH) regions nominally associated with the risk of
autism (unadjusted p-value ,0.01) in the discovery sample.
Start positionEnd positionLength
208,824,998209,180,938355,94020.00082720.320.0180.001IDH1, PIP5K3, PTH2R
46,630,74347,455,038824,295200.0022215.100.0270.005PREX1, ARFGEF2, FKSG61, CSE1L,
CSE1, STAU1, DDX27, ZNFX1,
120,782,861121,320,700537,8398 0.0064516.760.018 0.003TAF2, DSCC1, DEPDC6, COL14A1
*RCase=prevalence rate of the ROH marker in cases; RCtrl=prevalence rate of the ROH marker in controls;
DUnadjusted p-values based on the Fisher’s exact tests.
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tasia is also characterized by impairment in verbal fluency.
Individuals affected with Ataxia telangiectasia often show weak
oral motor performance . It is unclear whether ataxia
telangiectasia and autism has similar defects in the speech
pathologies. Another gene located in the same region, EXPH5
(exophilin 5) is a cerebellum-expressed gene . Cerebellum
modulates motor coordination that also regulates the speech
function. Additionally, individuals with autism and speech delay
and individuals with autism without speech delay have marked
difference in metabolic ratio in cerebellar regions . Therefore,
the ATM, NPAT, and EXPH5 genes may influence some neural
correlates associated with the cerebellum. Variants in these three
genes may thus influence the language function linked to the
cerebellum in autism.
Additionally, the CUL5 (culin 5) gene in the region has been
found to regulate cortical layering by modulating the neuronal
migration process [30,31]. The protein culin 5 encoded by the
CUL5 gene plays a pivotal role if degradation of an intracellular
signaling molecule, Disabled-1, which is activated by reelin
encoded by the RELN gene. Previous studies have shown mixed
evidence for the association between the RELN gene and the risk
of autism [32,33]. It has been shown that subtle dysregulated
neuronal migration, such as perisylvian polymicrogyria, is
associated with the developmental language disorder . An
animal study also showed that homozygous mutants for the CUL5
variant is defective in Notch signaling as indicated by the impaired
expression of Notch target genes, which affects the initiation of
Notch signaling during neurogenesis . These findings may
comprise the lines of indirect evidence for the relationship between
the CUL5 gene and speech delay.
The association of chromosome 11q structural variants with
language impairment has been documented by several studies. For
instance, at least half of the individuals afflicted by 11q terminal
deletion syndrome might be affected by mild to moderate
impairment in expressive language . A case report documents
a girl with a 11q21–22.3 deletion manifested multiple congenital
abnormalities, including speech delay . Two case studies also
report the association between 11q24 deletion and developmental
speech delay in Jacobson syndrome . Mosaic 11q deletions
have also been noted in metopic synostosis associated with an
increased risk of speech delay . Additionally, the deletion of
11q23.3 might be associated with speech delay [40,41], while the
duplication of the 11q23.3 region might also lead to speech delay
. Taken together, these findings suggest that the chromosome
11q21–q24 might harbor genes that play a role in language
Speech delay has been regarded as an endophenotype of
autism. Some prior studies used speech delay as a clinical marker
to identify homogeneous subgroups of autism, while others treated
speech delay as an independent trait. Several regions have been
found to be associated with speech delay in autism. For instance,
the chromosome 7q31–q33 is one of the regions that have been
found to contain genetic polymorphisms linked to speech delay in
autism [43–47]. It has also been suggested that the 7q11–q12
duplication may be linked to speech delay in autism [48,49].
Additionally, the chromosome 2q is another region that might
contain genetic variants associated with speech delay in autism
[50,51]. Some of these candidate regions associated with speech
delay in sporadic case reports. However, the CNTNAP2
(contactin associated protein-like 2) gene on 7q, which has been
found to be linked to language impairment in some large-scale
studies [52,53], was not included in the ROH regions that were
significantly associated with speech delay in our sample. The
CNTNAP2 gene, as well as other candidate risk genes for autism,
Table 3. Case-only association test results for age of first phrase (only unadjusted p-value ,161025were shown).
RAB39, CUL5, ACAT1, NPAT, ATM, C11orf65, KDELC2, EXPH, DOX10
DPY19L2P4, STEAP1, STEAP2, C7orf63, GTPBP10, CLDN12, PFTK1,
GUCA2B, FOXJ3, ZMYND12, RIMKLA, PPCS, LOC728621,
DKFZp686K01114, PPIH, AF086102, YBX1, CLDN19, LEPRE1, CR623026,
C1orf50, CCDC23, ERMAP, ZNF691
CLN5, FBXL3, KIAA0916, MYCBP2, DKFZp586G0322, SEL, SLAIN1, EDNRB,
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might not be identified in a case-only analysis of our study. It
remains unclear if the molecular mechanisms of language
impairment in individuals without autism differ from those in
individuals with autism.
The current study has several limitations. First, the current
study might not have sufficient power to detect variants of small to
moderate effect on traits. This might at least partly explain the
failure of our case-control association tests to replicate findings of
previous studies. However, based on the parameters estimated in
our case-control study, we achieved the statistical power of 30%
given the a value=0.0001. Second, the psychosocial factors that
may influence language acquisition, such as parenting style and
previous intervention, are not available in our samples. However,
we did adjust for education levels of parents in the analysis and did
not detect remarkable impact of parental education level on the
genetic effect on clinical features. Nevertheless, parental education
level might not fully reflect the quality of parenting and preschool
education that may influence language acquisition. Third, the
ROH based on the Affymetrix SNP 6.0 data might not consist of
entirely homozygous SNPs, unless we have whole-genome
sequencing data to verify these findings. Therefore, such a
limitation might lead to the concern about the interpretation of
our findings. Additionally, since we could only perform the
analysis of clinical features in cases, our findings might not be
generalized to the genetic basis for speech delay. However, our
study has yielded some insight into the molecular basis for clinical
heterogeneity in autism.
In contrast to the continuous outcome, the analysis based on the
dichotomous outcome yielded relatively less significant results for
the same region on 11q22.3. This mild inconsistency might imply
that the variant on 11q associated with speech delay might lead to
a more extremely speech delay. Therefore, the comparison
between relatively extremely late age-of-first-phrase group and
extremely early age-of-first-phrase group might yield a more
remarkable difference in ROH distributions between the two
subgroups. However, in the replication study, we noticed that the
dichotomous outcome yielded a slightly stronger association signal
than the continuous outcome. These findings suggest that more
research is needed to investigate how to define ‘‘speech delay’’
based on the age of first phrase and genomic data.
To sum up, the current study suggests that novel candidate
genes may yield a greater impact on speech delay compared to
autism per se. Untangling the mechanisms of speech delay may
shed some light on molecular mechanisms underlying the
Figure 3. The analysis results based on the early-AFP group are shown. Panel A shows the scatter plot of REHH plotted against all core
haplotype frequency (circled dot indicates the selected core haplotype ‘‘rs1074014-rs1072877-rs1564582-rs11212724-rs11211725’’). Panel B shows
the haplotype bifurcation diagram, which visualizes the breakdown of LD at increasing distances from core haplotypes at the selected core region.
The root of each diagram is a core haplotype, identified by a dark blue circle. Panel C illustrates how the REHH value varies by the selected core
haplotype. Panel D shows the table of core haplotype, and the dot in the observed haplotype sequence represents the allele that matches the
ancestral. Panel E presents the theoretical phylogenetic tree of different core haplotypes. Gray squares represent haplotypes that are not present in
the observed data, but are missing links in the phylogeny. The area of the squares is proportional to the frequency of the haplotype.
Runs of Homozygosity Associated with Speech Delay
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development of autism. The extended homozygous haplotypes
associated with speech delay may be more likely to be attributable
to the recessive disease model than selection sweeps or consan-
guinity in our sample. Our findings also suggest that susceptibility
genes may not necessarily contribute to clinical heterogeneity in
autism. Taken together, these findings may lead to the evidence-
based classification algorithm for clinical subgroups. Finally, our
findings suggest that a few cerebellum-associated genes may play a
role in speech delay in autism. Multiple adjacent loci of these genes
may act in concert to cause speech delay in autism. More research
is warranted to investigate if any cerebellum-related pathological
changes could predispose to speech delay in autism.
Methods and Materials
The protocol entitled ‘‘Clinical and molecular genetic studies of
autism spectrum disorder’’, submitted by Principle Investigator
Dr. Susan Shur-Fen Gau, Department of Psychiatry, National
Taiwan University Hospital, Taiwan, has been approved by the
119th meeting of Research Ethics Committee of the National
Taiwan University Hospital on September 26, 2006 (NTUH-REC
ID: 9561709027) and the other two collaborating sites (Chang-
Gung Memorial Hospital in Taoyuan, CGMH ID: 93–6244 and
Taoyuan Mental Hospital in Taoyuan, TYMH ID: C20060905).
The committees of the three research sites were organized and
operated according to GCP and the applicable laws and
regulations. The Research Ethics Committee of three research
sites approvedthis study
NCT00494754]. Written informed consent was obtained from
majority of the probands if they were able to give their signature
after reading the informed consent and all their parents after the
purposes and procedures of the study were fully explained and
confidentiality was ensured. All subjects were Han Chinese. The
data-sharing plan has been approved by all key investigators (SSG,
YYW, and SKL) across three collaborating sites and approved by
the Research Ethics Committee of the three sites. SSG, the
principal investigator of this project, coordinated the research and
managed all the clinical and genetic data. We reached the
agreement that the de-identified data and key clinical variables will
be released to investigators upon the request with relevant
institutional approval documents.
The cases were selected from a sample
of totally 1,164 subjects from 393 families (probands aged
9.163.99 years, male 88.6%), recruited from the outpatient clinic
of Psychiatric Department of three institutes (i.e., National Taiwan
Figure 4. The analysis results based on the late age of first phrase (late-AFP) group are shown. Panel A shows the scatter plot of REHH
plotted against all core haplotype frequency (circled dot indicates the selected core haplotype ‘‘rs1074014-rs1072877-rs1564582-rs11212724-
rs11211725’’). Panel B shows the haplotype bifurcation diagram, which visualizes the breakdown of LD at increasing distances from core haplotypes
at the selected core region. The root of each diagram is a core haplotype, identified by a dark blue circle. Panel C illustrates how the REHH value varies
by the selected core haplotype. Panel D shows the table of core haplotype, and the dot in the observed haplotype sequence represents the allele that
matches the ancestral. Panel E presents the theoretical phylogenetic tree of different core haplotypes. Gray squares represent haplotypes that are not
present in the observed data, but are missing links in the phylogeny. The area of the squares is proportional to the frequency of the haplotype.
Runs of Homozygosity Associated with Speech Delay
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University Hospital in Taipei, Chang-Gung Memorial Hospital in
Taoyuan, and Taoyuan Mental Hospital in Taoyuan) in Northern
Taiwan. Probands diagnosed with fragile X and Rett’s disorder
based on DNA testing or clinical features were excluded
(unpublished data). Additionally, probands with previously iden-
tified chromosomal structural abnormality associated with autism,
or had any other major neurological or medical conditions were
also excluded. The initial diagnoses of probands were made by
senior board-certified child psychiatrists based on the DSM-IV
diagnostic criteria of autistic disorder or Asperger’s disorder, and
were further confirmed by interviewing the parents using the
Chinese version of the Autism Diagnostic Interview-Revised (ADI-
R) , adapted from the ADI-R . The algorithm focuses on
three domains based on the ICD-10 and DSM-IV diagnostic
criteria, including reciprocal social interaction, verbal and non-
verbal communication, as well as restricted, repetitive and
stereotyped patterns of behaviors. We retrieved age of first phrase
(AFP) to infer the presence of speech delay from the ADI-R
assessment. AFP was treated as a continuous variable in the linear
regression model that also controlled gender, SCQ, and parental
education level. Additionally, we used k-means clustering
algorithm with Euclidean distance to classify the sample into two
subgroups, which were denoted as early-AFP group and late-AFP
The recruitment of controls was documented in detail elsewhere
. Briefly, the Institute of Biomedical Sciences, Academia
Sinica and National Research Program for Genomic Medicine in
Taiwan initiated the efforts to collect data to establish Han
Chinese Cell and Genome Bank in Taiwan during 2002–2004. A
three-stage sampling was implemented and complete bio-specimen
and questionnaire data (with a focus on ethnicity and medical
history) were collected for 3,380 individuals (gender ratio, 1:1; age
range, 20–70 years). A total of 1,115 individuals with a Han
Chinese ancestry that were found to have no definite diagnosis of
major medical or mental illnesses were treated as the controls for
the current study.
All cases and controls were genotyped on
Affymetrix SNP array 6.0 platform that could generate a
maximum of 906,600 SNPs and 946,000 probes for the detection
of CNVs (Affymetrix Inc., Santa Clara, CA, USA). The DNA
samples were extracted and purified from the peripheral
lymphocytes according to the manufacture’s protocol. Genotype
calls for SNPs were made based on the Birdseed algorithm that
performs a multi-chip analysis to estimate a signal intensity for
each allele of each SNP . The average call rate was 99.86%.
We also performed the Hardy-Weinberg Equilibrium (HWE) test,
and excluded the SNPs with a HWE P,561025, so that the
analysis would be less likely to be affected from genotyping or
Figure 5. Linkage disequilibrium patterns in the 11q22.3 region are shown.
Runs of Homozygosity Associated with Speech Delay
PLOS ONE | www.plosone.org8 August 2013 | Volume 8 | Issue 8 | e72056
calling errors. A total of 546,080 SNPs were thereby analyzed in
the association tests.
We defined an ROH as a stretch of
DNA spanning at least 500 kb or 50 consecutive SNPs without
any heterozygous SNPs. Additionally, the maximum gap between
SNPs could not exceed 100 kb. The overlapped region of multiple
ROH regions shared by at least 10 individuals was regarded as a
core ROH region. Furthermore, the prevalence rate of each
common ROH marker should be at least 1% in the controls. We
performed a case-control analysis based on cases (n=315) and
controls (n=1,115) to identify risk ROHs. We also compared the
difference in the length of ROHs of case and controls by t-test. To
further clarify the role of ROHs in the heterogeneity of language
developmental function in autism, we also assessed the associations
between ROHs and the AFP. The continuous outcome variable
was regressed against each ROH marker using the linear
regression model. To adjust for the impacts of parenting and
other confounders, we controlled for educational levels of parents,
performance IQ, and gender in each linear regression model. To
alleviate the problem of over-fitting due to intra-collinearity, we
also performed step-wise regression analysis for the most
significant trait-associated ROH marker. To determine the
significance level, we took into account the number of ROH
markers and outcome variables and applied the conservative
Bonferroni method to correct inflated type-I errors due to multiple
tests (corrected genome-wide significance threshold=0.05/N, N is
the total number of OH regions). The ROH identification and
association tests were performed using the software Golden
HelixTMSNP and Variation Suite 7.6 (Golden Helix, Inc.,
Bozeman, MT, www.goldenhelix.com). Furthermore, we calcu-
lated the inbreeding coefficient F for sub-populations to assess if
any spurious association arose from the difference in relatedness.
Finally, we assessed if the size of gene might exert any impact on
the association between trait-associated ROHs and traits by
incorporating the gene size as a covariate in the regression model
for the most significant finding.
Selection sweep analysis.
the trait-associated ROH region were constructed using the
program of PHASE v 2.1 . We limited the search of core
haplotypes to the brain-expressed genes. We then calculated
extended EHH (i.e., the probability that two randomly chosen
chromosomes carrying the core haplotype of interest are identical
by descent) to evaluate the evidence for selection sweeps. We
further calculated the relative EHH value (REHH=core haplo-
type EHH divided by the decay of EHH on all other core
haplotypes combined) to detect the signature of recent positive
selection. We defined the evidence for selection sweep as REHH
values $2 with long-range markers, radiating to distances greater
than 200 kb from the core site, according to previous simulated
data sets . The phylogenic relationship among all possible core
haplotypes was inferred by ancestral alleles. All of the analyses
were performed using the software Sweep .
The phase of the haplotypes in
of the discovery population (Taiwan) and replication
population (AGRE) are shown.
The distributions of age of first phrase (AFP)
We greatly thank all the patients and families, who have made great
contributions to this study. We also appreciate all of the research staff,
especially Ms. Hui-Yi Huang and Ms. Mei-Hsin Su, for their efforts on
data management and research coordination.
Conceived and designed the experiments: PL SSG. Performed the
experiments: CC JW. Analyzed the data: PL. Contributed reagents/
materials/analysis tools: PK CC JW YW. Wrote the paper: PL SSG. SSG,
YW, and SL conducted clinical diagnosis and helped recruit the patients.
PK critically reviewed and revised the manuscript.
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