Association of common variants in the Joubert
syndrome gene (AHI1) with autism
Ana I. Alvarez Retuerto1, Rita M. Cantor2, Joseph G. Gleeson4, Anna Ustaszewska5,
Wendy S. Schackwitz5,6, Len A. Pennacchio5,6and Daniel H. Geschwind1,2,3,?
1Center for Autism Research and Treatment Semel Institute, David Geffen School of Medicine at UCLA, Los Angeles,
CA 90095, USA,2Department of Human Genetics and3Department of Neurology, David Geffen School of Medicine at
UCLA, Los Angeles, CA 90095, USA,4Department of Neurosciences, University of California, San Diego, La Jolla,
CA 92093-0691, USA,5US Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA and
6Genomics Division, MS 84–171, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Received March 18, 2008; Revised June 25, 2008; Accepted September 8, 2008
It has been suggested that autism, like other complex genetic disorders, may benefit from the study of rare or
Mendelian variants associated with syndromic or non-syndromic forms of the disease. However, there are
few examples in which common variation in genes causing a Mendelian neuropsychiatric disorder has
been shown to contribute to disease susceptibility in an allied common condition. Joubert syndrome (JS)
is a rare recessively inherited disorder, with mutations reported at several loci including the gene
Abelson’s Helper Integration 1 (AHI1). A significant proportion of patients with JS, in some studies up to
40%, have been diagnosed with autism spectrum disorder (ASD) and several linkage studies in ASD have
nominally implicated the region on 6q where AHI1 resides. To evaluate AHI1 in ASD, we performed a three-
stage analysis of AHI1 as an a priori candidate gene for autism. Re-sequencing was first used to screen
AHI1, followed by two subsequent association studies, one limited and one covering the gene more comple-
tely, in Autism Genetic Resource Exchange (AGRE) families. In stage 3, we found evidence of an associated
haplotype in AHI1 with ASD after correction for multiple comparisons, in a region of the gene that had been
previously associated with schizophrenia. These data suggest a role for AHI1 in common disorders affecting
human cognition and behavior.
Autism is a neurodevelopmental syndrome characterized by
impairments in social behavior, communication, language and
the presence of repetitive-restricted behaviors. It is best con-
sidered as the most severe form of a spectrum of symptom clus-
ters known as autism spectrum disorders (ASDs) under the
clinical diagnostic classification of pervasive developmental
disorders (1,2). There is strong evidence of genetic contri-
butions to ASD (3–5), with heritability estimated between 60
and 90% on the basis of twin studies (6). Rates of autism in sib-
lings of those affected are ?5–10%, which is 20–50 times
higher than the rate of autism in the general population (7,8).
Prevalence estimates for ASD are one in 166, and autistic dis-
order, which represents the narrowest diagnostic category,
has a prevalence of one to two in 1000 (9). Modeling suggests
multiple genes contributing to ASD genetic risk (10,11), which
is consistent with recent data from a variety of genetic
approaches that demonstrate significant genetic heterogeneity
(12–14), similar to that found in many other common diseases.
Studies of rare chromosomal or structural genomic altera-
tions, as well as rare Mendelian causes of more common dis-
orders, ranging from diabetes (15), hyperlipidemia (16) to
Alzheimer’s disease (17) and disorders of speech and language
(18), have played central roles in understanding disease patho-
physiology (19). However, the extent to which common vari-
ation in Mendelian disease genes contributes to common
diseases in general is not known, and few examples of such
contributions have been demonstrated (19). In this regard, it
is notable that autism has been described in more than 25
?To whom correspondence should be addressed at: Department of Neurology, UCLA School of Medicine, 695 Charles E. Young Drive South,
Los Angeles, CA 90095-1761, USA. Tel: þ1 3102066814; Fax: þ1 3102672401; Email: firstname.lastname@example.org
# The Author 2008. Published by Oxford University Press. All rights reserved.
For Permissions, please email: email@example.com
Human Molecular Genetics, 2008, Vol. 17, No. 24
Advance Access published on September 9, 2008
different genetic syndromes including Fragile X syndrome,
Rett syndrome, Down syndrome, tuberous sclerosis and
Joubert syndrome (JS) (20,21), further supporting the notion
that many different etiologies account for ASD. However,
common variants in any of the genes causing these syndromic
forms of autism have yet to be associated with ASD.
JS is an autosomal recessive disorder characterized by
partial or complete agenesis of the cerebellar vermis, and cog-
nitive and behavioral dysfunction. Features of ASD, such as
deficits in social behavior, language dysfunction and repetitive
behaviors, have been described in up to 40% of JS patients
(20–23), and ?25% of JS patients meet criteria for a
DSM-IV diagnosis of strict autistic disorder (20), making it
an important syndromic form of the disorder (5,20–23).
Recently, mutations were found in the Abelson’s helper
integration 1 gene (AHI1), encoding the ‘Jouberin’ protein
in a subset of families segregating JS (24,25). Mutations in
AHI1 are encountered in 7.3 (26) to 11% (27) of JS patients,
predominantly in those with the ‘pure’ form of the disease,
that is, in cases with signs and symptoms restricted to the
central nervous system (CNS) + retinal involvement (26).
The Jouberin protein (28) domain structure suggests that
it functions in signal transduction, perhaps as an adaptor
molecule, but little is known about AHI1 and how it might
be involved in the pathogenesis of JS.
We reasoned that in addition to the role that JS might have
as a rare cause of syndromic autism (20–22,29), the common
variation in the AHI1 gene may contribute to ASD risk. The
utility of studying genes first identified as causing rare, or syn-
dromic forms of common diseases, such as hyperlipidemia,
maternity onset diabetes of the young and type II diabetes,
in understanding the genetic basis and pathophysiology of
common disorders is becoming increasingly appreciated
(16–19). This approach is further supported by recent findings
in ASD, in which rare recessive mutations in a gene
(CNTNAP2) (30) have been shown to cause a syndromic
form of ASD, whereas common variation in the same gene
has been shown to contribute to genetic risk for the
common, non-syndromic forms of the disorder (31,32).
To investigate the AHI1 locus as a possible contributor to
non-syndromic ASD, we performed a three-stage study. To
detect any possibility of involvement, we first sequenced the
AHI1 gene in 48 independent ASD subjects from sibling
pairs ascertained for ASD and having the highest allele
sharing for markers in the genomic region around the AHI1
locus. This was done to identify common AHI1 variants in
families with autistic probands and identify candidates for
single-nucleotide polymorphism (SNP) association studies.
Common SNPs were compared with several publicly available
control group frequencies to identify whether there were any
SNPs with nominal case–control association in this first-stage
screening procedure. This was followed by a second stage,
where we performed SNP genotyping in an independent
sample of 326 ASD parent–child trios from the Autism
Genetic Resource Exchange (AGRE) cohort to test the two
variants that were nominally associated in stage-1 screening
analysis. Stage-2 family-based analyses yielded a significant
association of these two SNPs, suggesting that it would be
beneficial to more exhaustively study AHI1 with haplotype-
tagging SNPs to more completely cover the AHI1 gene.
This stage-3 association study covered 99% of the haplotype
diversity in AHI1 to more thoroughly assess AHI1 in 337
AGRE trios, 111 of which were independent of those analyzed
in stage 2. This family-based analysis identified an association
of ASD with an AHI1 haplotype, which was significant after
ASD-associated region overlaps with one previously identified
as significantly associated with schizophrenia in two indepen-
Sequencing and variant identification
We performed a first-stage evaluation by sequencing all the
exons and their flanking regions of the AHI1 gene in 48 unre-
lated ASD subjects (96 chromosomes) from the subsample of
the AGRE cohort ascertained for having the highest estimated
marker allele sharing by affected siblings in that region.
Thirty-six polymorphisms in both exonic and intronic regions
distributed throughout the AHI1 gene were identified and are
summarized in Table 1. Eleven changes within coding exons
were found, five of which were in non-translated exons. Out
of the six remaining exonic variants, three were non-
synonymous changes within the conserved WD40 5 repeat
and 7 codons downstream of WD40 7 repeat, domains highly
conserved in mammals. These polymorphisms were previously
reported in parents of JS-related syndrome patients, but did
not segregate in their affected offspring (26). An additional
maternally inherited insertion–deletion (indel) polymorphism,
located 6 bp downstream of exon 20, and predicted to alter
loop stability (see Materials and Methods), was also identified
in a patient (Supplementary Material, Fig. S1) and was not in
HapMap. On the basis of the potential functional consequence
of this polymorphism, we screened 189 independent autistic
probands and 320 unaffected sibling controls (see Materials
and Methods), but did not find a significant difference in its
frequency between patients and controls.
SNP genotyping and association analysis
In a stage-1 screen to identify SNPs potentially associated with
ASD, we analyzed the 13 coding variants that had a minor allele
frequency (MAF) . 1% in the autistic sample, and that had
been previously genotyped in control populations, such as
HapMap. We compared control allele frequencies HapMap
(n ¼ 120 chromosomes for CEU sample), Perlegen (n ¼ 48
chromosomes for EUR) and TSC panel (n ¼ 84 chromosomes),
with their frequencies in the AGRE probands (Table 2), identi-
fying four SNPs rs4896141, rs11970282, rs9494209 and
rs1052502 showing nominal association (P , 0.05, Table 2).
Two of these, rs9494209 and rs11970282, were in complete
LD with each other (r2¼ 1) in the AGRE sample and in
HapMap (http://www.hapmap.org), so SNP rs9494209 was
chosen to represent these two SNPs in subsequent analyses.
These three independent SNPs that showed significant
differences in frequency between patients and controls in the
initial screen, rs1052502, rs9494209 and rs4896141, were
then genotyped in 327 independent parent–child trios to
permit an independent family-based association analysis to
3888Human Molecular Genetics, 2008, Vol. 17, No. 24
confirm this preliminary, nominal level of association in stage
2. Out of the 327 trios, 326 were successfully genotyped
(99.7% successful call rate and 1 Mendelian error). One
SNP, rs1052502, was not in Hardy–Weinberg (H–W) equili-
brium in the parents (P ¼ 0.0002) and was excluded from
further analysis. Association testing, using the single-marker
transmission disequilibrium test (TDT) in the WHAP software
(33), was significant for SNPs rs9494209 and rs4896141
(Table 3) after Bonferroni correction (p_MAX ¼ 0.02 and
p_SUM ¼ 0.01).
These results suggested that a more thorough investigation
of AHI1 was warranted to more clearly define the associated
region(s) in AHI1. In this final third stage, 18 SNPs, represent-
ing four haplotype blocks defined using Haploview (see
Materials and Methods) were analyzed in 337 parent–child
trios, 226 of which had been typed for the three SNPs in
stage 2, and 111 of which were new. These four blocks
capture 99% of the haplotype diversity within the AHI1 gene
(Fig. 1A). Block 3, which extends over 79 kb, was signifi-
cantly associated after a Bonferroni correction (P ¼ 0.003)
(Table 4). Remarkably, this block contains two SNPs
(rs6914831, rs2246943) that serve as tags in this analysis for
five SNPs (rs9321501, rs6912933, rs2614258, rs2746429 and
rs6931735) that were previously shown to be associated with
schizophrenia (34) (Fig. 1B). For example, SNP rs2246943
typed here is in complete LD (r2¼ 1.0; Fig. 1B) with SNP
Table 2. Case–control association and AHI1 SNPs with ASD
SNP MAF autism sampleMAF controls
G ¼ 0.125
T ¼ 0.05
G ¼ 0.02
A ¼ 0.02
G ¼ 0.09
C ¼ 0.11
G ¼ 0.02
A ¼ 0.10
C ¼ 0.43
G ¼ 0.11
C ¼ 0.13
G ¼ 0.11
T ¼ 0.125
G ¼ 0.058 (Hmp)
T ¼ 0.025 (Hmp)
G ¼ 0.008 (Hmp)
G ¼ 0.020 (Per)
A ¼ 0.021 (Per)
G ¼ 0.05 (Hmp)
C ¼ 0.05 (Hmp)
C ¼ 0.06 (TSC)
G ¼ 0.008 (Hmp)
G ¼ 0.020 (Per)
A ¼ 0.05 (Hmp)
A ¼ 0.042 (Per)
C ¼ 0.405 (Hmp)
C ¼ 0.312 (Per)
G ¼ 0.05 (Hmp)
G ¼ 0 (Per)
C ¼ 0.042 (Hmp)
G ¼ 0 (Per)
T ¼ 0.05 (Hmp)
T ¼ 0 (Per)
Allele frequencies of each SNP in the AGRE-sequenced sample,
compared with HapMap (Hmp), Perlegen (Per) and SNP Consortium Ltd
(TSC) control panels. 91.7% AGRE sample is White, 81.8% of this sample
was White (not Hispanic or Latino) and the remaining 18.2% was White
Hispanic or Latino. The populations used in each panel were the CEU
(CEPH Utah residents with ancestry from northern and western Europe in
HapMap); the EUR (population of European American descent in
Perlegen) and the CSHL (Caucasian individuals in the TSC panel). The
number of chromosomes genotyped was 120 for Hmp CEU (except for
SNP rs11964449, where 118 chromosomes were typed, and SNP
rs6914831 with 116 chromosomes), 48 for EUR in Perlegen and 84 for the
TSC-CSHL. All genotypes were in H–W equilibrium. In the cases where
more than one panel genotyped the same SNP, the P-values are the result
of the combined panels compared with the 96 AGRE chromosome sample.
?Two-tailed P , 0.05 according to Fisher’s exact test.
Table 1. Re-sequencing results
SNP ID/bp positiona
SNPLocation Exon variants
SNP identity and location within the AHI1 gene are listed. The SNP NCBI
rs ID is shown in column 1. Where the SNP ID is not reported, position in
base pair is listed. Base pair position is according to Ensembl database
(http://www.ensembl.org). SNP ID is based on NCBI dbSNP Build 126
database (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=snp). The
exon/intronof theAHI1 gene where theSNPis found is reported incolumn
3. Amino acid changes are indicated where applicable in column 4.
aNon-synonymous variants located in the WD40 or downstream of
SH3-conserved domains. See Results section for exact locations in domains.
Table 3. Single SNP TDT analysis in stage 2
SNP ID Overtransmitted
Odds ratio (T:U)Empirical
rs9494209C 1.8; 95% CIE 1.34–2.26
1.64; 95% CIE 1.22–2.06
Empirical P-values are obtained by performing 1000 permutations with
transmitted versus untransmitted alleles using WHAP. The two individual
tagging SNPs are significant at P , 0.05. The global permutation tests
yielded significant P-values as well (p_max ¼ 0.019 and p_sum ¼ 0.015).
T:U, transmitted versus untransmitted.
Human Molecular Genetics, 2008, Vol. 17, No. 243889
Figure 1. Fine-scale depiction of the AHI1 ASD-associated haplotype. The genomic region is shown on top, followed by an exon–intron map and NCBI genome
release 35 SNP location. Shown below is an LD plot of the region based on r2(see Materials and Methods). SNPs genotyped in the autism sample in this study
are boxed, whereas those genotyped in the published schizophrenia associations (34,45) are circled. The single SNP P-values are shown adjacent to each SNP.
Schizophrenia SNPs are marked ‘Scza 1’ or ‘Scza 2’ depending on whether they were typed in the Arab-Israeli (34) or in the Icelandic population (45). Associ-
ated SNPs in our study are marked as ‘Auts’. The two schizophrenia haplotypes in the Arab-Israeli study are depicted with discontinuous lines. One can see the
?30 kb area of overlap of these two haplotypes in the center of this region, which occurs in a region of strong LD within the gene. The region also harbors the
two most highly associated SNPs in our current autism study (rs4896144 and rs17707754), which are in the center of this block of low LD.
3890 Human Molecular Genetics, 2008, Vol. 17, No. 24
rs9321501, which is at the center of the most significantly
associated schizophrenia haplotype identified in two indepen-
dent samples (1.9 ? 10210, ref 34).
The contributions to the association of individual alleles of
haplotype block 3 were further assessed in the stage 3 autism
sample (337 trios) using the WHAP software (Table 4), reveal-
ing that two major alleles of this haplotype, occurring at fre-
quencies of 6.7% and 5.6%, respectively, in ASD individuals,
account for a large proportion of the association (empiric
P-values of 0.003 and 0.02, respectively, after 100 000 permu-
tations) (Table 4). Both alleles are overtransmitted (Table 4),
themore common allele results inan odds ratio of1.9 [95%con-
fidence interval estimate (CIE) ¼ 1.5–2.4], and the other results
in an odds ratio of 1.7 (95% CIE 1.2–2.2). Although the region
spanned by this ASD-associated haplotype encompasses the two
haplotype blocks formed by three different SNPs associated
with schizophrenia in previous studies (34), the same SNPs
were not associated with ASD and schizophrenia (Fig. 1B),
suggesting that the same region, but not the same allele,
contributes to both disorders in the populations studied.
Autism, similar to other common diseases, is a complex genetic
disorder with contributions from both rare and common genetic
variants (13,31,32,35,36). In some genes, for example, neuroli-
gins 3 and 4 (37,38), only rare mutations have been identified.
For others, syndromic forms, as well as rare and common
variant contributions from the same gene, have been identified,
for example, CNTNAP2 (30,39). These published findings
emphasize the striking genetic heterogeneity of ASD and
support the potential utility of studying relatively rare genetic
syndromes to help provide insight into its etiologies (40).
Here, we took a relatively straightforward approach to
assess the role of common variants in AHI1, the first gene
identified as mutated in JS, within the ASD spectrum. As a sig-
nificant proportion of JS patients with mutations in AHI1 have
autism, this was a biologically focused candidate gene analysis
to test the hypothesis that this gene related to a syndromic
cause of autism, also harbored variation-mediating common
genetic susceptibility to ASD. Our analysis supports the
hypothesis that common genetic variants within a gene that
frequently results in a rare syndromic form of ASD can
contribute to more general ASD susceptibility. These findings
emphasize the importance of studying rare forms of neuropsy-
chiatric conditions as a means to inform our understanding of
common disease susceptibility and pathophysiology (19).
Here, we tooka stepwiseapproach toouranalysesof the gene.
identify any clear coding mutations. Sequencing a much larger
number of cases and controls, which is beyond the scope of the
current project, would be more likely to demonstrate a rare
variant contributing to ASD, as in Bakkaloglu et al. 2008 (41).
However, we were able to identify two common variants that
showed association with ASD in a large family-based cohort.
Although we chose ethnically matched cohorts for stage 1, we
acknowledge that ethnic stratification could be present, a factor
eliminated in stage 2 with the family-based approach. We
observed a significant association of two SNPs in the AHI1
gene, after correcting for multiple comparisons. In stage 3, we
lotypes in AHI1, demonstrating a significant association with
ASD in the same circumscribed region that had previously
been associated with schizophrenia. Although SNPs within the
ASD-associated haplotype are in high LD with some of the
schizophrenia-associated SNPs in HapMap, the specific alleles
associated with each condition are distinct (Fig. 1).
The AHI1 region in autism and other
The chromosome 6q region, where the AHI1 gene resides, is
not considered a major ASD linkage region. However, two
published studies have suggested an involvement of this
locus in ASD (40,42). Phillipe et al. (42) report an excess of
identity by descent (IBD) allele sharing on chromosome 6q
(MLS ¼ 2.23, IBD ¼ 68.6%) in a small cohort of multiplex
families from Sweden, France, Norway, the USA, Italy,
Austria and Belgium. McCauley et al. (40) used genome-wide
linkage and ordered-subset analysis (43), ranking families
according to family-specific LOD scores at peak sites with
LOD scores ?1.5. They used clusters of ADI indices such
as (i) to walk unaided; (ii) to sit unaided on flat surface; (iii)
age of first single words; (iv) age of first phrase; (v–vi) acqui-
sition of bladder control: daytime, night and (vii) acquisition
of bowel control and found support for a suggestive autism
Table 4. Haplotype TDT analysis in stage 3
Haplotype FrequencyT:UBlock empirical P-value
Allele empirical P-value
Block 3; markers: 10, 11, 12, 14, 15, 16
Allele frequencies, transmitted alleles, odds ratios with 95% CIE and empirical P-values after 100K permutations using WHAP are shown for block 3 in
all the trios analyzed in stage 3.
Markers: 10: rs6570000, 11: rs6914831, 12: rs4896144, 14: rs17707754, 15: rs2246943, 16: rs6570004.
Human Molecular Genetics, 2008, Vol. 17, No. 24 3891
locus on chromosome 6q23 in 158 combined Tufts, Vanderbilt
and AGRE multiplex families. Large deletions on 6q have also
been related to the autism phenotype (44), and a recent study
using array comparative genome hybridization reported chro-
mosomal re-arrangements in two autistic individuals from the
same family in the same 6q region highlighted by the linkage
studies cited earlier (39). However, no association between
autism and specific genes in this region has been identified
The current findings are notable within the context of the
recent association of the AHI1 gene with schizophrenia in an
inbred Arab population (34). Moreover, this association has
been replicated in a large, case–control study performed in
an independent European cohort (45). These association
studies were conducted in the context of prior evidence of
linkage, which has been described in the 6q AHI1 region for
schizophrenia (46–48) and for which fine mapping pointed
to 6q23 (49–51). The odds ratios for the risk variants in the
European schizophrenia cohort were small, but significant,
ranging from 1.15 to 1.29, whereas the odds ratio for the
associated haplotypes identified here in this autism sample
are larger, ranging from 1.7–1.9 (Table 4).
Remarkably, several of the SNPs that form the haplotype
block associated with ASD are in strong LD with those pre-
viously associated with schizophrenia (r2¼ 0.8–1.0, Fig. 1).
For example, one of the tagging SNPs in the associated
block reported here (rs6914831) is in strong LD and within
2.0 kb of rs9321501, which was one of those most strongly
associated with schizophrenia (34,45). Moreover, this region
of overlap within the region of the ASD-associated haplotype
contains the most associated single SNPs in ASD, and a
number of the most associated SNPs in both studies are
within several kilobases of each other. Despite this proximity,
the same SNPs and haplotype alleles are not associated with
both ASD and schizophrenia (29,49). But, the two associated
haplotype blocks identified in the previous schizophrenia
study in Arab-Israelis (34) delineate a 30 kb region that is con-
tained within the center of the larger haplotype block that was
most significantly associated with ASD here (Block 3, Fig. 1;
Table 4). This increases the evidence for a circumscribed
region of the gene in both disorders, but not a particular
shared allele. That different alleles are associated with each
disorder is not surprising and is likely due to the very different
ethnic backgrounds in the studies and also suggests that
different specific variants within this region of AHI1 contri-
bute to schizophrenia and ASD susceptibility.
Despite the differences between the two studies, which were
drawn from distinct populations, and have different ascertain-
ment schemes and diagnoses, the convergence on this locus
suggests a possible role for AHI1 in human cognition and
behavior that could be relevant to a wide body of psycho-
pathology. This observation is consistent with the notion of
disorders that were previously considered distinct (5). Over-
lapping symptom domains have been observed between
ASD and schizophrenia (52) and recent studies have even
identified mutations that are observed in both autism (53)
and childhood onset schizophrenia (54). These data eschew
using the categorical clinical diagnosis as a phenotype with
which to identify susceptibility genes for neuropsychiatric
conditions and support the exploration of endophenotypes
related to language, social cognition or brain development
that may reflect common susceptibility at a neurobiological
level. One can speculate that given their clustering within a
small region of the AHI1 gene, these alleles act through a
common biologic mechanism. Testing this hypothesis awaits
further replication and deep re-sequencing of this candidate
gene across allied disorders, an approach that is likely to be
productive in elucidating the full extent to which AHI1 con-
tributes to neuropsychiatric susceptibility. In addition, these
results support the idea that common variants in genes
known to cause rare, Mendelian forms of a neuropsychiatric
condition can contribute to common disease susceptibility,
as has been observed previously in metabolic disorders (55)
and suggested in ASD (41,44).
In this regard, it is interesting to speculate on the manner in
which AHI1 might influence ASD susceptibility. The gene is
expressed widely, and at high levels in the CNS. Patients
the cerebellum and brainstem, as well as abnormalities in the
cerebral cortex (56). Further study of the anatomical and bio-
logical basis of the cognitive and behavioral deficits in JS,
including comparison of patients with and without ASD, will
likely provide important clues as to the role of specific abnorm-
alities in the CNS and their relationship to autistic behaviors.
Parallel assessment of brain structure and function in those car-
rying AHI1 risk alleles, regardless of their diagnoses, may also
aid in defining the manner by which common variants of this
gene influence cognition and behavior.
MATERIALS AND METHODS
Forty-eight independent autistic individuals from the AGRE
sample were selected for sequencing. These individuals were
ascertained because they exhibited the highest likelihood of
marker allele sharing with their affected siblings in the
region harboring the AHI1 gene on the basis of the closest
(137.74 cM) used in our previous genome scans (57,58) The
individuals selected had the maximum IBD marker allele
sharing of both alleles (Genehunter v 2.1).
For association testing at stage 2, we selected 981 individ-
uals representing 327 trios (parents and their ASD-affected
proband), which were genotyped for three SNPs at the AHI1
locus. Affected individuals were ascertained for the diagnosis
of ASD under the categories of autism, broad spectrum and
not quite autism using the Autism Diagnostic Interview—
(3,59–61) (Supplementary Material, Table S1). Individuals
with non-idiopathic autism, including Fragile X syndrome,
chromosomal abnormalities and other medical conditions
(http://www.agre.org) were not included in this study.
For stage 3, we genotyped 1011 individuals from 337
AGRE parent–child trios, 111 of which were independent of
those analyzed in stage 2 (see Supplementary Material,
Table S1 for a list of all the individuals used in all stages).
We also screened for a complex indel downstream of exon
20 in 189 autistic probands and 319 unaffected siblings used
(128.93 cM)and D61009
3892Human Molecular Genetics, 2008, Vol. 17, No. 24
as controls from the AGRE population (Supplementary
Material, Table S2). More than half of these 189 autistic
patients (62%) were also genotyped in stage 2.
Both AGRE and UCLA have protocols that are approved by
respective Institutional Review Boards.
Sequencing was performed at DOE Joint Genome Institute
designed to give a maximum product size of 500 bp and a
minimum of 40 bp flanking the splice sites using the Exon
Locator & Extractor for Resequencing program (EXLR)
(http://mutation.swmed.edu/ex-lax/). For a complete list of
primers, see Appendix 1. An M13F tag (gttttcccagtcac-
gacgttgta) and an M13R tag (aggaaacagctatgaccat) were
added to forward and reverse primers, respectively. Ten nano-
grams of DNA from each sample was amplified in a 10 ml
PCR reaction using AmpliTaq Goldw(Applied Biosystems)
and purified using the PCR product pre-sequencing kit (USB
Corporation). Sequencing reactions were performed using
the M13 primers along with BigDye Terminator v3.1 Cycle
Sequencing Kit (Applied Biosystems) (http://www.jgi.doe.
purified with tetra-ethylene glycol before separation on a
3730xl DNA Analyzer (ABI). Base calling, quality assessment
and assembly were carried out using the Phred, Phrap, Poly-
phred, Consed software suite (www.phrap.org). All sequence
variants identified were verified by manual inspection of the
chromatograms (see Supplementary Material, Table S1 for a
list of all the individuals sequenced).
SNPs rs1052502, rs9494209 and rs4896141 were genotyped in
stage 2 by Illumina, Inc. in a set of 327 independent, parent–
child trios from the AGRE sample (http://www.illumina.com).
For stage 3, we initially selected 27 tagging SNPs having an
MAF ¼ 1% in the CEU population for genotyping using
HapMap data (NCBI build 35) and Tagger (two-point and
aggressive multimarker tagging options) to cover the entire
gene at an r2¼ 0.8. Twenty-five of these (93%) were success-
fully genotyped (call rate range 99.2–100%). For a subsequent
statistical analysis, only SNPs with an MAF ? 5% in our
sample of parents were used. After removing SNPs not in
H–W equilibrium in parents (n ¼ 1) and those with MAF ,
5% (n ¼ 6), 18 SNPs typed in 337 trios remained (see
Supplementary Material, Table S3 for a list of SNPs). These
included SNPs rs9494209 and rs4896141 for which we
found nominal association in stage 2 and four SNPs
(rs2246943, rs4526212, rs6914831 and rs7759971) that
tagged distinct regions of this gene that were previously
associated with schizophrenia.
Re-sequencing and digestion of a complex
We re-sequenced an indel polymorphism identified in two
individuals in our first round of rare variant identification in
48 subjects. We screened this indel in 189 independent autistic
probands from different families and 319 unaffected siblings by
PCR amplification and restriction enzyme digestion. Exon 20
(197 bp) þ 366 bp of intronic sequence (181þ185 bp on either
side of the exon) were amplified from genomic DNA using
primers AHI_20F and AHI_20R (see Appendix 1). The 563/
568 bp PCR product was digested with HpyCH4V. The digestion
pattern for the normal allele, five bands of 22, 49, 89, 147 and
256 bp, changed to four bands of 22, 89, 201 and 256 bp, respect-
ively, in the presence of the complex indel. This banding pattern
was resolved in a 4% NuSieve gel by electrophoresis. We then
used computational methods to predict possible alternative spli-
cing events [Berkeley Drosophila Genome Project (BDGP)
(http://www.fruitfly.org/seq_tools/splice.html) and SpliceView
ated by this polymorphism. We also checked potential alterations
in exonic splicing enhancer (ESE) sequences (http://rulai.cshl.
edu/tools/ESE/ESEbkgr.html) and in ACESCAN2 web server
(http://genes.mit.edu/acescan2/). Finally, hairpin loop stability
changes were predicted using the HairpinFetcher Institute of
Genomics & Interative Technology website, http://miracle.igib.
Genehunter v 2.1 was used to estimate IBD marker allele
sharing in the chromosomal region surrounding the gene
AHI1 to flag those individuals most likely to contain AHI1
mutations,in order to select
re-sequencing. In stage 1, we compared the frequencies of
the variants identified by re-sequencing to public ‘control’ fre-
quencies available in HapMap (Hmp) (http://www.hapmap.
org,), Perlegen (Per) (http://www.perlegen.com) and the SNP
Consortium panels (TSC) (http://snp.cshl.org) matching for
ethnicity. This allowed us to select for stage 2 those SNPs
showing frequency differences in ASD subjects when com-
pared with publicly available controls. To compare frequen-
cies, a two-tailed x2test of difference in proportions was
conducted. For alleles of low frequency, Fisher’s exact test
was conducted. SNPs chosen for stage 2 follow-up had a
P-value ,0.05. Stage 1 involved a small case–control popu-
lation that was subject to potential errors induced by popu-
lation stratification, whereas in stage 2, we used a relatively
large family-based design to control for stratification, and
the TDT analysis was conducted.
Haploview v 4.ORC2 (62) was used to detect Mendelian
errors, assess linkage disequilibrium, test for H–W equili-
brium, estimate the frequencies of transmitted and transmitted
alleles, form haplotype blocks and conduct the single SNP
TDT analyses. Haplotype TDT analyses were conducted
using the haplotype-based association analysis package
WHAP allows the estimation of empirical P-values through
permutations based on Monte Carlo simulations. Permutations
are performed by randomly swapping the within-family com-
ponents of association (the transmitted versus untransmitted
alleles). Significance was assessed by two statistics, p_MAX
(maximum) and p_SUM (summed). The p_Max P-value is
the number of times that a P-value less than or equal to the
maximum P-value for the observed data in the distribution
of P-values for all the permutations performed across all the
thesubjects for AHI1
Human Molecular Genetics, 2008, Vol. 17, No. 243893
SNPs. p_Sum is similar, but the statistic is the sum of the per-
mutated scores for all the SNPs tested. Both reflect region-wide
significance and correct for multiple testing in that region.
ELECTONIC DATABASE INFORMATION
Autism Genetic Resource Exchange (AGRE): http://www.
Applied Biosystems: http://www.jgi.doe.gov/sequencing/
Berkeley Drosophila Genome Project (BDGP): http://www.
DOE Joing Genome institute (JGI): http://www.jgi.doe.gov/
ESE finder: http://rulai.cshl.edu/tools/ESE/ESEbkgr.html.
Exon Locator & Extractor for Resequencing program
HapMap (Hmp): http://www.hapmap.org.
HairpinFetcher Institute of Genomics & Interative Technol-
NCBI dbSNP Build 126 database: http://www.ncbi.nlm.nih.
Phred, Phrap, Polyphred, Consed software suite: www.
Perlegen (Per): http://www.perlegen.com.
SNP Consortium (TSC): http://snp.cshl.org.
The low-density lipoprotein receptor (LDLR) gene in familial
WHAP software: http://pngu.mgh.harvard.edu/~purcell/whap/.
Retinal Degeneration Retnet Network Database: http://
Supplementary Material is available at HMG Online.
We gratefully acknowledge the resources provided by the
Autism Genetic Resource Exchange (AGRE) Consortium
given in Appendix 2 (http://www.agre.org) and the participat-
ing AGRE families. The AGRE is a program of Cure Autism
Now and is supported, in part, by grant MH64547 from the
National Institute of Mental Health to D.H.G. (PI). In addition,
we would like to thank Drs Jennifer Stone, Maricela Alarco ´n
and Rebecca Mar-Heyming for helpful discussions, as well as
Jackie Duvall for software assistance. We also thank the
reviewers for their helpful comments.
Conflict of Interest statement. None declared.
This project was supported by the National Institute of
Health research grants R01 (MH64547 to D.H.G. and ACE
Genetics Network grant MH081754 to D.H.G.); the National
Institute of Mental Health; the National Institute of Child
Health and Human Development; the National Institute of
Deafness and Other Communication Disorders; the National
MH068172 to D.H.G., M. Sigman, PI); Simons Foundation
to J.G.G.; Department of Energy Contract, University of
California, E.O. Lawrence Berkeley National Laboratory
(DE-AC02-05CH11231 to L.A.P.).
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APPENDIX 1: COMPLETE LIST OF PRIMERS FOR
APPENDIX 2: AGRE CONSORTIUM
Daniel H. Geschwind, University of California at Los Angeles,
Los Angeles; Maja Bucan, University of Pennsylvania,
Philadelphia; W. Ted Brown, New York State Institute for
Basic Research in Developmental Disabilities, Long Island;
New York; Rita M. Cantor, University of California, Los
School of Medicine, St Louis; T. Conrad Gilliam, Columbia
Genome Center, New York; Clara Lajonchere, Cure Autism
Now, Los Angeles; David H. Ledbetter, Emory University,
Atlanta; Christa Lese-Martin, Emory University, Atlanta;
Janet Miller, Cure Austism Now, Los Angeles; Stanley
F. Nelson, University of California at Los Angeles School of
Medicine, Los Angeles; Gerard D. Schellenberg, University
of Washington and Veterans Affairs Medical Center, Seattle;
Center, Baltimore; Sarah J. Spence, University of California,
Los Angeles; Rudolph E. Tanzi, Massachusetts General
SinaiSchool of Medicine,
3896 Human Molecular Genetics, 2008, Vol. 17, No. 24